1. © 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

2. © Institute for Fiscal Studies, 2008 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: 1 st September to 31 st 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

3. © Institute for Fiscal Studies, 2008 Raw differences (example)

4. © Institute for Fiscal Studies, 2008 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

5. © Institute for Fiscal Studies, 2008 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

6. © Institute for Fiscal Studies, 2008 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

7. © Institute for Fiscal Studies, 2008 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

8. © Institute for Fiscal Studies, 2008 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:

9. © Institute for Fiscal Studies, 2008 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

10. © Institute for Fiscal Studies, 2008 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

11. © Institute for Fiscal Studies, 2008 How large is the August birth penalty? KS1KS2KS3KS4KS5HE Group 1Boys-0.263** [0.003] -0.128** [0.003] -0.085** [0.003] Base0.6120.7190.688 Girls-0.257** [0.003] -0.133** [0.003] -0.077** [0.003] Base0.7030.7600.729 Group 2Boys-0.152** [0.003] -0.100** [0.003] -0.066** [0.003] -0.014** [0.003] -0.015** [0.003] Base0.5780.6090.4220.3410.207 Girls-0.150** [0.004] -0.086** [0.003] -0.052** [0.003] -0.012** [0.003] -0.015** [0.003] Base0.6230.6510.4700.4310.281

12. © Institute for Fiscal Studies, 2008 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

13. © Institute for Fiscal Studies, 2008 What drives the August birth penalty? Raw gapEstimated difference (i)+(ii)+(iii) Absolute age effect (i) Length of schooling effect (ii) Age position effect (iii) BoysKS1-0.594** [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] GirlsKS1-0.561** [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]

14. © Institute for Fiscal Studies, 2008 Which admissions policy is best? Raw gapEstimated difference (i)+(ii)+(iii) Absolute age effect (i) Length of schooling effect (ii) Difference in (ii) from 1 entry point Age position effect (iii) KS12-0.611** [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] KS22-0.377** [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] KS32-0.236** [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]

15. © Institute for Fiscal Studies, 2008 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

16. © Institute for Fiscal Studies, 2008 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

17. © Institute for Fiscal Studies, 2008 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”)