1. Beyond test scores: the role of primary schools in improving multiple child outcomes Claire Crawford and Anna Vignoles Institute of Education, University of London

2. Motivation League tables measure school performance in terms of academic achievement e.g. test scores But Every Child Matters agenda suggests schools should have a wider remit than just increasing educational attainment –e.g. improving health, socio-emotional skills, etc

3. Research Questions Are schools at the top of the league tables also the best at improving child welfare? Or is there a trade-off? What methodological approaches are most appropriate when measuring multiple outcomes from education?

4. Previous research Small (but positive) impact of schools on educational attainment once variation in pupil characteristics is taken into account –e.g. Goldstein and Sammons 1997; Reynolds et al. 1996; Leckie, 2008; Hanushek 1997 Limited work on role of schools in promoting wider outcomes, such as well being –e.g Gibbons and Silva, 2009; Feinstein & Gutman 2008 But different disciplines have used different methods and comparisons of results is difficult –e.g. educationalists relied on multi level models/ economists tend to use fixed effect models

5. Outline of today’s talk Use simple value-added model of educational attainment to compare RE and FE models –Are assumptions underlying RE/multi level model satisfied? –Use results to inform later model development

6. Outline of today’s talk Document development of cognitive and non-cognitive skills in primary school –Can we think of these relationships as causal? What are our next steps? –How to consider joint determination of cognitive and non-cognitive skills

7. RE vs. FE Start with a model of the following form: Y ist = α + βY ist-1 + γX ist-1 + δ s + ε ist-1 Where: –Y: Key Stage maths points –X: vector of pupil characteristics (including family composition, parents’ education, parents’ socio-economic status, etc) RE model assumes: E[X ist-1 |δ s ] = 0 –i.e. pupil characteristics are unrelated to the school effects If E[X ist-1 |δ s ] ≠ 0, RE model produces biased estimates –Rely on FE model instead –Use Hausman test to judge whether condition has been met Future work will explore other tests as well

8. Determinants of KS2 maths score OLSFERE KS1 maths score0.113** [0.003] 0.114** [0.003] 0.113** [0.003] 2 nd SES quintile-0.015 [0.027] -0.009 [0.028] -0.015 [0.027] 3 rd SES quintile-0.013 [0.028] -0.010 [0.029] -0.004 [0.028] 4 th SES quintile-0.045 [0.029] -0.030 [0.030] -0.037 [0.029] Most deprived SES quintile-0.127** [0.032] -0.070** [0.033] -0.104** [0.032] Number of schools414 Hausman test rejects RE model

9. Modelling cognitive and non- cognitive skills Focus on value-added models between ages 7 and 11: KS ist = α + β 1 KS ist-1 + β 2 SDQ ist-1 + γX ist-1 + δ s + ε ist-1 SDQ ist = α + β 1 KS ist-1 + β 2 SDQ ist-1 + γX ist-1 + δ s + ε ist-1 KS: Key Stage test results –Standardised average of reading, writing, maths at age 7 –Standardised average of English, maths, science at age 11 SDQ: Strengths & Difficulties score –Teacher reports –5 questions on 5 topics: pro-social, hyperactivity, emotional symptoms, conduct problems, peer problems –Invert pro-social and combine to create total “difficulties” score –Standardise total “difficulties” score and invert so SDQ measures “strengths” rather than “difficulties”

10. Model controls X is a vector of pupil characteristics, including: –Month of birth –Ethnicity –Family composition (including twin status, number of older and younger siblings, whether parents are married or cohabiting) –Parents’ education –Parents’ socio-economic status –Birthweight and whether child was breastfed –Whether parents often read to the child at age 3 δ represent school fixed effects

11. Model estimation Estimate using seemingly unrelated regression analysis –Takes account of the fact that the error terms across the two equations may be correlated –Can extend to consider more outcomes e.g. may additionally want to consider health?

12. Development of cognitive and non- cognitive skills No school effectsSchool fixed effects Std KS2 APS Std KS1 APS0.546** [0.010] 0.542** [0.010] Std SDQ score (age 7)0.097** [0.009] 0.115** [0.009] Std SDQ score (age 11) Std KS1 APS0.221** [0.013] 0.219** [0.014] Std SDQ score (age 7)0.267** [0.011] 0.304** [0.013]

13. Causal effects? Instrument KS1 attainment using month of birth to identify causal effects of KS1 on KS2 and SDQ scores Std KS2 APSStd SDQ score Using std KS1 APS Std KS1 APS0.542** [0.010] 0.219** [0.014] Std SDQ score (age 7)0.115** [0.009] 0.304** [0.013] Using MOB as instrument for std KS1 APS Std KS1 APS (instrument using MOB) 0.559** [0.011] 0.075 [0.083] Std SDQ score (age 7)0.109** [0.010] 0.328** [0.018]

14. Summary Tested RE model with rich set of controls –Rejected RE model, so focus on FE models instead Investigated development of cognitive and non- cognitive skills between ages 7 and 11 –Found strong statistical relationships Inclusion of school fixed effects marginally increases the relationship between SDQ scores at 7 and 11 –Impact of attainment at age 7 on KS2 APS appears causal; but impact of attainment at age 7 on SDQ score at age 11 is not

15. Next steps Further robustness checks surrounding rejection of RE model, using other models and data Use other statistical software to run seemingly unrelated IV regressions Consider first difference models of KS and SDQ scores –Instrument change in SDQ score with death of a parent/close family member