When you are born matters: the impact of date of birth on child cognitive outcomes in England

© Institute for Fiscal Studies, 2008

When you are born matters: the impact of date of birth on child

cognitive outcomes in England

Claire Crawford, Lorraine Dearden & Costas Meghir

Institute for Fiscal Studies


Background

• Children must have started school by the beginning of the term after they turn five

• Local Education Authorities (LEAs) are free to set admissions policies within this framework– Single entry point, 2 entry points or 3 entry points

• Academic year: 1st September to 31st August– Expect children born at the end of the academic

year to perform more poorly than children born at the start of the academic year


Raw differences (example).3

.4.5

.6.7

.8.9

1

KS

2 -

Pro

port

ion a

t ex

pec

ted l

evel

Aug88 May88 Feb88 Nov87 Aug87 May87 Feb87 Nov86 Aug86 May86 Feb86 Nov85

Day of Birth

Mean of Outcome

.3.4

.5.6

.7.8

.91

KS

3 -

Pro

port

ion a

t ex

pec

ted l

evel


Day of Birth

Mean of Outcome

.3.4

.5.6

.7.8

.91

KS

4 -

Pro

port

ion a

t ex

pec

ted l

evel


Day of Birth

Mean of Outcome

.3.4

.5.6

.7.8

.91

KS

5 -

Pro

port

ion a

t ex

pec

ted l

evel


Day of Birth

Mean of Outcome

Males Females


Background

• Why might this be?– Age of sitting the test (absolute age) effect

• They are younger when they sit the tests

– Age of starting school effect• They start school at a younger age

– Length of schooling effect• They receive less schooling prior to the test

– Age position effect• They are the youngest relative to others in their class


Previous research

• Children born at the end of the academic year do perform worse:– Puhani & Weber (2005), Bedard & Dhuey (2006)

• Some studies attempt to disentangle the effects of these four factors:– But only for post-compulsory schooling outcomes

• Fredriksson & Ockert (2005)• Black, Devereux & Salvanes (2008)

– More difficult for compulsory schooling outcomes• Age at test = age at starting school + length of schooling


Our contribution

• Regional variation in admissions policies allows us to break this linear relationship for compulsory schooling outcomes– Children born on the same day (who sit tests at

the same age) start school at different ages

• We can separately identify:– Absolute age effect– Age of starting school/length of schooling effect– Age position effect


Modelling strategy (1)

• How large is the August birth penalty?– Regression discontinuity design

– Compare boys and girls born in August with boys and girls born in September • In the same school (and school year)• No need to worry about observable characteristics


Modelling strategy (2)

• What drives the August birth penalty?– Compare children born on the same day across

admissions policy areas

– Controlling for observable characteristics now very important

– OLS regression framework:'

1 2 3. ( ) . ( ) . ( )is s i i i i iy f age f agess f agepos x


Data

• Administrative data on all children attending state school in England– National Pupil Database

• Key Stage test results from age 5 (Foundation Stage) to age 18 (A-levels and equivalent)

• Limited background characteristics from PLASC (e.g. ethnicity, FSM, SEN)

• Home postcode used to link in local area data from 2001 Census and 2004 Index of Multiple Deprivation

– HESA data– Admissions policy data


Our samples

• Not possible to follow the same individuals all the way through, so consider 2 groups here:– Group 1:

• 2 cohorts (born 1990-91 or 1991-92)• Test results at ages 7, 11 and 14

– Group 2:• 2 cohorts (born 1985-86 or 1986-87)• Test results at ages 11, 14, 16 and 18• HE participation at age 18


How large is the August birth penalty?

KS1 KS2 KS3 KS4 KS5 HE

Group 1 Boys -0.263**

[0.003]

-0.128**

[0.003]

-0.085**

[0.003]

Base 0.612 0.719 0.688

Girls -0.257**

[0.003]

-0.133**

[0.003]

-0.077**

[0.003]

Base 0.703 0.760 0.729

Group 2 Boys -0.152**

[0.003]

-0.100**

[0.003]

-0.066**

[0.003]

-0.014**

[0.003]

-0.015**

[0.003]

Base 0.578 0.609 0.422 0.341 0.207

Girls -0.150**

[0.004]

-0.086**

[0.003]

-0.052**

[0.003]

-0.012**

[0.003]

-0.015**

[0.003]

Base 0.623 0.651 0.470 0.431 0.281


Does the August birth penalty vary by subgroup?

• Made comparisons across several groups: – FSM vs. non-FSM– Black Caribbean vs. White British

• Most noteworthy finding is lack of significant differences across subgroups– August birth penalty is the same for all individuals


What drives the August birth penalty?

Raw gap Estimated difference (i)+(ii)+(iii)

Absolute age effect

(i)

Length of schooling effect (ii)

Age position

effect (iii)

Boys KS1 -0.594** [0.010]

-0.577** [0.007]

-0.683** [0.058]

0.101** [0.013]

0.005 [0.057]

KS2 -0.339** [0.010]

-0.321** [0.007]

-0.296** [0.055]

0.021 [0.011]

-0.046 [0.054]

KS3 -0.229** [0.010]

-0.199** [0.006]

-0.127 [0.081]

-0.004 [0.010]

-0.067 [0.080]

Girls KS1 -0.561** [0.010]

-0.545** [0.007]

-0.576** [0.055]

0.125** [0.012]

-0.094 [0.054]

KS2 -0.325** [0.010]

-0.320** [0.007]

-0.254** [0.053]

0.045** [0.011]

-0.111* [0.052]

KS3 -0.200** [0.010]

-0.187** [0.007]

-0.238** [0.088]

0.014 [0.010]

0.036 [0.088]


Which admissions policy is best?

Raw gap Estimated difference (i)+(ii)+(iii)

Absolute age effect

(i)

Length of schooling effect (ii)

Difference in (ii) from

1 entry point

Age position

effect (iii)

KS1 2 -0.611** [0.022]

-0.598** [0.008]

-0.576** [0.055]

0.072** [0.009]

-0.053** [0.009]

-0.094 [0.054]

3 -0.645** [0.016]

-0.629** [0.009]

-0.576** [0.055]

0.040** [0.006]

-0.085** [0.010]

-0.094 [0.054]

KS2 2 -0.377** [0.022]

-0.349** [0.008]

-0.254** [0.053]

0.017* [0.008]

-0.028** [0.009]

-0.111* [0.052]

3 -0.389** [0.016]

-0.357** [0.008]

-0.254** [0.053]

0.008 [0.005]

-0.037** [0.009]

-0.111* [0.052]

KS3 2 -0.236** [0.022]

-0.204** [0.008]

-0.238** [0.088]

-0.002 [0.007]

-0.016** [0.008]

0.036 [0.088]

3 -0.239** [0.016]

-0.201** [0.008]

-0.238** [0.088]

0.000 [0.005]

-0.014 [0.009]

0.036 [0.088]


Summary

• August-born children experience significantly poorer educational outcomes than September-born children

• Almost entirely due to differences in the age at which they sit the tests

• Starting school earlier is marginally better for August born children– They benefit from having more time in school


The policy dilemma

• Results presented emphasise August birth penalty, but findings also apply more generally– On average, the younger you are the worse you do

• Ideally need to create a level playing field for all children regardless of date of birth– But also need to have school years, so someone will

always be the youngest


Possible policy options?

• Flexibility in school starting age not enough!• Age adjustment of tests/testing when ready

– Could use principle that proportion reaching expected level should not vary by month of birth

• We show a simple linear adjustment could be appropriate

– Alternatively could set expected level by age (rather than school year)

• e.g. reach Level 4 by age 11½ rather than end of Year 6

• But requires more testing opportunities (“testing when ready”)

When you are born matters: the impact of date of birth on child cognitive outcomes in England

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