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The educational gender gap, catch up and labour market performance

The educational gender gap, catch up and labour market performance. Martyn Andrews (University of Manchester) , Steve Bradley, Dave Stott & Jim Taylor (Lancaster University). The educational gender gap. Issues Performance of girls is superior to boys and getting wider

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The educational gender gap, catch up and labour market performance

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  1. The educational gender gap, catch up and labour market performance Martyn Andrews(University of Manchester), Steve Bradley, Dave Stott & Jim Taylor(Lancaster University)

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

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

  4. Previous research • Educational • Descriptive studies e.g. Gorard et al (1999) • School effectiveness e.g. Wong et al (2002) • Qualitative / case studies e.g. OFSTED (2003) • 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)

  5. Data & methodology • Estimate education production functions • Outcome = function of: • 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? • Zero – gap is the published figure • Girl & personal (zero?) • Girl & school (sorting?) • Girl & unobserved individual effects (motivation) • Girl & unobserved school effects (sorting?) • Unobserved individual-level & unobserved school-level effects

  6. Data • Pooled cross-section (YCS) data (1985-2001) • YCS2-3 – GCE/CSE • YCS4+ -- GCSE • Observed variables • Individual – gender, ethnicity, age • 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

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

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

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

  10. Econometric findings - observables

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

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

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

  14. 2. Subject level gaps at GCSE

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

  16. 3. Differences in exam performance between KS2 & KS4 • Maths, English, Science at KS2, KS3 & KS4 (population) • See Table on KS2-4 • 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) • Girls improve between KS3 & KS4 in all subjects, but only in English between KS2 & KS3

  17. Differences in exam performance • Controlling for school & pupil-level unobservables • 1. Correlation between ‘Girl’ & individual-level = 0! • 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 • 2. The correlation between unobserved-school level & unobserved individual-level effects is greater than zero • Unobservably good pupils go to unobservable good schools (i.e. middle class parents, catchment areas) • 3. The correlation between ‘Girl’ & unobserved school-level effects is greater than zero (see YCS results) • Girls go to unobservably better schools • Girls are observably better at KS2 – schools therefore select them

  18. Conclusions & implications for policy • 1. Gender gap emerges once the GCSE system is introduced • Learning & assessment methods favour girls • 2. Girls are better than boys • 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

  19. Speculation • 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 • Girls go to good schools • But the gap stabilises • Shocks A & B eventually burn out (equilibrium) • The introduction of KS2 helps boys (fewer ‘girl-good’ tests), which means they also sort into ‘good’ schools

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