(University of Manchester) ,
(Lancaster University)

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Performance of girls is superior to boys and getting wider
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Concern about low achieving boys
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Girls do better in ‘language’ based subjects, boys do better in Maths & Science
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Even if girls outperform boys, does it matter if they are discriminated against in the labour market?

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Objectives
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Use biannual YCS (1985-2001) & NPD (2002-03)
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1. Define & measure the gender gap and document how it changes through time
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2. Explain how the gap changes when we control for
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Observable effects – individual, family, school, neighbourhood
Unobservable effects
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School-level (e.g. discipline, tiering, streaming
Individual-level (e.g. attitudes, motivation)
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3. Repeat 1 & 2 for subject groups
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4. Measure & explain how the gap changes during the educational process
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Age 11-16 (at KS2, KS3, KS4)

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Educational
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Descriptive studies e.g. Gorard et al (1999)
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School effectiveness e.g. Wong et al (2002)
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Qualitative / case studies e.g. OFSTED (2003)
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Organisation, teaching & learning, curriculum & assessment
School organisation
Culture of laddishness
Idiosyncratic school effects
Home background
Economics e.g. Dolton et al (1999), Burgess et al (2004)

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Estimate education production functions
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Outcome = function of:
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Girl (gap)
Individual characteristics
School characteristics
Neighbourhood characteristics
Unobserved individual-level effects
Unobserved school-level effects
Are there correlations between girl and (observable & unobservable) effects?
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Zero – gap is the published figure
Girl & personal (zero?)
Girl & school (sorting?)
Girl & unobserved individual effects (motivation)
Girl & unobserved school effects (sorting?)
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Unobserved individual-level & unobserved school-level effects

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Pooled cross-section (YCS) data (1985-2001)
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YCS2-3 – GCE/CSE
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YCS4+ -- GCSE
Observed variables
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Individual – gender, ethnicity, age
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Family – parental occupation, single parent, housing tenure
School – Pupil-teacher ratio, pupil composition, size, competition
Neighbourhood – unemployment rate, occupational mix
YCS6-11 observe the same school up to 6 times – school level unobservables

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NPD 2002 & 2003
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Observe KS2, KS3 & GCSE results
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Population
Advantages:
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Control for (estimate?) unobserved individual effects
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41,000 pupils move schools
Identify individual & school level unobservables
But … few individual-level covariates

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Pass/fail for each subject (grade C +)
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Number A*-C GCSEs – all subjects
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5 + A*-C GCSEs – headline figure
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Points score – distribution (A*=7, etc.)

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Debate
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Educationalists label the absolute gap as the
‘politicians error’
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Absolute gap increases as relative gap falls
Absolute gap is correct
Note the increase in the gap from the introduction of GCSE

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Selective schools have a very large effect on attainment
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Single sex schools have a large, but smaller, effect
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Neither of these effects contribute much to the gender gap
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Other observable differences between girls and boys (e.g. family background, poverty) do not explain the gap
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Are the findings genuine? Biased sample for YCS but we observe similar effects for NPD (population)

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Observable differences between girls & boys do not explain the gap
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Girls must therefore behave differently prior to GCSEs
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1. Choice of secondary school
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2. Subject level gaps at GCSE
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3. Differences in exam performance between KS2 & KS4

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Control for school-level unobservables
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YCS6-11 & NPD1-2 (panels)
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Controlling for school level unobservables is important
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 level not trend
Discipline, tiering, streaming
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Between 1991-2001 the gender gap is halved
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E.g. YCS10 = 0.04 versus 0.10
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Implication: Has the quasi-market (ERA, 1988) meant that girls are marginally more attractive to better schools?
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Un-testable because of lack of linked school data prior to 1991

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Data shows that girls outperform boys in languages, English & vocational subjects
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‘One-off’ GCE-GCSE effect disadvantaging boys – languages, science, maths
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Since 1988 the gap has increased at the same rate – girls catch-up in maths & science
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Controlling for observable & unobservable differences lowers the gap by one-tenth of a GCSE grade
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Girls ahead in English, languages & vocational, level in humanities & behind in Maths and Science

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Maths, English, Science at KS2, KS3 & KS4 (population)
See Table on KS2-4
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Gaps at GCSE: English (0.63), Maths (0.03) and Science
(0.06)
At KS2: Girls better in English (0.23), behind in Maths
(-0.07) & Science (-0.04)
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Girls improve between KS3 & KS4 in all subjects, but only in
English between KS2 & KS3

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Controlling for school & pupil-level unobservables
1. Correlation between ‘Girl’ & individual-level = 0!
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But, disaggregating we find that girls are unobservably better in English and worse in Maths & Science
Note that KS2 & KS3 do not test other ‘girl-good’ subjects – see YCS results
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2. The correlation between unobserved-school level & unobserved individual-level effects is greater than zero
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Unobservably good pupils go to unobservable good schools (i.e. middle class parents, catchment areas)
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3. The correlation between ‘Girl’ & unobserved school-level effects is greater than zero (see YCS results)
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Girls go to unobservably better schools
Girls are observably better at KS2 – schools therefore select them

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1. Gender gap emerges once the GCSE system is introduced
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Learning & assessment methods favour girls
2. Girls are better than boys
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A) English
B) Selected into unobservably better schools
3. No effect of single sex schooling
4. Selective schools & poverty have a small effect on the gap
5. Gap is greatest in English & languages and has closed in
Maths & Science
6. Unobserved differences between schools (e.g. discipline, tiering, streaming) are important – YCS only

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A) Introduction of GCSE system created the gap
B) Quasi-market exacerbated the gap
 changed incentives facing schools
 select the best – girls
Cumulative & self-perpetuating
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Girls go to good schools
But the gap stabilises
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Shocks A & B eventually burn out (equilibrium)
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The introduction of KS2 helps boys (fewer ‘girl-good’ tests), which means they also sort into ‘good’ schools