Anareli

Acosta

Fiona Kelmendi

Nora Kokaj

Anna Schuster

Close Neighbours matter neighbourhood

effects

on early

performance at

school

Dominique GOUX, eric maurinThe economic Journal, 117 (October),1193-1215

“Children’s outcomes are influenced by the characteristics and outcomes of their neighbours.”

Is there a causal impact?

Strategies:

Using the Date of Birth as an Instrumental variable (part 1)

Using standard regression in the context of social housing (part 2)

Lack of consensus on the importance of the influence of peers on own educational achievement

Shared characteristics

Reflection effect (Manski, 1993)

Introduction (I)

Solution to measurement issues:

French Labour Force (1991-2002) survey uses a representative sample based on sampling units of 20-30 adjacent households, 1/3 of it being renewed each year

Formal and main research question:

“Does the variation in an adolescent’s educational advancement between the age of 15 and 16 depend on the educational advancement of his/her close neighbours of the same age?”

Introduction (II)

Is a 15 years-old probability of repeating a grade (redoubler) influenced by the proportion of other 15 years-old in his/her neighbourhood who have repeated a grade already?

Use of Peers’ Date of Birth (early or late in the year) as an IV

A proven determinant of French children’s early performance

Correlated to students outcomes

Uncorrelated to the individual outcome

Using

Instrumental Variables (I)

Peers’

probability

to repeat

a gradeIndividual

probability

to repeat

a grade

Peers’

date of birth

CorrelationCausality

Results

Is the performance of a individual influenced by the performance of other pupils in the same class?

Use of Date of Birth again as an IV

Use of a different dataset

Performance measured with test scores after two years of school

Using Instrumental Variables (II)

Peers’

test score in 3rd grade

Individual test score in 3rd grade

Peers’date of birth

CorrelationCausality

Results

Whether a child’s performance at school is influenced by the level of human capital of families living in he neighborhood.

Identification using

information on families living in public housing

HLM: About 20% of the population

Eligibility:

Allowed to live in France

Income: threshold 30,000 euros for a family of 4 in 2002

Process:

Eligible families apply simultaneously

1.1 million households for 400,000 available dwellings

Waiting lists: 2-3 years

Lower rents: -40% on average

Methodology

The assignment

of families

across

HLM appears

to be

random with

respect to children

performance

An adolescent’s advancement at school is negatively affected by the proportion of non-educated families in the

neighborhood

Strategy 1 –

Date of birth

Probability of repeating grade increases if other adolescents in

neighborhood

repeated grade

Strategy 2 –

HLM

Educational advancement negatively affected by uneducated families in neighborhood

Conclusions

discussion

Methodology

Applicability outside France context

Validity: HLM are not so randomly assigned

Instrumental variable

Implications of the research conclusions

Causality?

Channels of influence

Effects of neighborhood on other outcomes

Policy implications and questions

Contribution?

Need for a qualitative approach?