Pre-1990 Local Education Authorities (LEAs) determined the distribution and use of school funding

1. Diversity, choice and the quasi-market: an empirical analysis of England’s secondary education policy, 1992-2005 Steve Bradley and Jim Taylor Department of Economics Lancaster University Management School How has education policy changed? What have been the consequences of the policy reforms? How can the impact on outcomes be estimated?

2. Pre-1990 • Local Education Authorities (LEAs) determined the distribution and use of school funding • LEAs determined allocation of pupils (except for church schools and grammar schools) • LEAs appointed and employed teaching staff • Limited role for head of school • Limited role for parents and governors

3. Early 1990s: the creation of a quasi-market in secondary education • Motivation: general dissatisfaction with educational outcomes • Aim: to improve educational outcomes • Method: creation of quasi-market + targeting of ‘disadvantaged’ pupils

4. Current policy • Three main strands: • Establishment of a quasi-market: competition between schools • Specialist schools programme: diversity to improve pupil-school ‘match’ • Urban education policy: Education Action Zones for ‘disadvantaged’

5. The quasi-market reforms: post-1990

6. Purpose of the quasi-market • Improve performance through greater competition for pupils • (diversity + choice + local management of schools) • Increase transparency and accountability • Improve efficiency through direct funding • - schools now responsible for 90% of recurrent expenditure • - more efficient allocation of resources - increase in total educational product • Induce private funding into state education • - private funders can contribute to creation of new schools • (academies) or take over ‘failing’ schools to raise performance

7. But will the quasi-market improve educational outcomes for all pupils? • Choice may lead to more sorting/segregation: • - ‘poorly educated’ parents less able to utilise information flows • - better-off parents move to live within a ‘good’ school’s • catchment area (allocation - lottery?) • - also better-off parents can afford travel costs leading to • cream-skimming by popular schools • Why is sorting harmful? • - may lead to loss of peer effects for lower ability pupils; efficiency • losses if peer effects are non-linear • - long term - reinforces persistence of income disparities

8. Constraints on the quasi-market • ‘Comprehensive’ schools cannot (ostensibly) choose pupils • Entry and exit severely limited • Excess demand for places in popular schools • Accurate information needed for choice • (5-yearly inspection reports, annual assessment tables, open-days, annual school reports). But information can be misleading (e.g. raw scores and value added) • Choice severely limited in many school districts • (non-metropolitan areas (20% of districts have 4 schools or less)

9. Number of specialist and non-specialist secondary schools in England 3,500 3,000 2,500 Non-specialist schools 2,000 1,500 1,000 Specialist schools 500 0 1992 1994 1996 1998 2000 2002 2004 Diversity: the Specialist Schools Programme 2006: 80% of schools now specialist

10. Specialisms

11. Urban Education Programme • extra funding for schools in disadvantaged urban areas • (28% of all schools) - 1999/05 • (Education Action Zones) • Support for gifted and talented pupils • - learning mentors for individual pupils • Support for the ‘hard to teach’ • - learning support units (to improve attendance) • Provision of high-tech equipment in poorly equipped schools

12. Estimating the impact of the educational reforms • Have educational reforms been effective? (e.g. exam results, truancy) • Have the reforms had any distributional consequences? • Which policies have been the most effective?

14. Days lost through unauthorised absence

15. 60.0 Specialist schools 55.0 50.0 45.0 Non-specialist schools 40.0 35.0 30.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Proportion of pupils with ‘good’ exam results (5 or more A*-C grades) Gap widened from 7 (2001) to 14 (2005)

16. % 5 or more A*-C grades 60.0 55.0 50.0 Non-metropolitan % 45.0 40.0 Metropolitan 35.0 30.0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Metropolitan v non-metropolitan schools Gap narrowed from 7 (2001) to 3 (2005)

17. Truancy rate = % of half days unauthorised absence

18. Estimating the effect of the policy reforms on educational outcomes Following Hanushek (1979, 1986), a school’s production function can be written as follows: Yst = f(PUPst, FAMst, SCH,t) + errorst Y = outcome (e.g. exam results, attendance) PUP = pupil characteristics (e.g. ability, gender, ethnicity) FAM = family background variables (e.g. household income, parental education) SCH = school inputs (e.g. school & teacher quality) Extending this to include three separate measures of education policy: Yst = f(PUPst, FAMst, SCHst, COMPst, SPECst, URBPROGst) + errorst COMP = competition from other schools in the same district SPEC = specialist schools policy URBPROG = Education Action Zone policy (low income areas)

19. Endogeneity problems with the OLS production function • Single equation production function likely to produce biased results: • Error term includes unobservables (e.g. parental attitudes towards education & innate ability of pupils) • FAM and SCH are correlated (e.g. schools with a high proportion of rich children find it easier to recruit ‘good’ teachers) • SCH is endogeneous (e.g. schools with ‘good’ exam results find it easier to recruit ‘good’ teachers) • Hence: • - school quality variables (e.g. pup/teach): underestimated • - policy effects (SPEC and URBPROG): overestimated

20. An alternative approach: fixed effects model with panel data Endogeneity problems less severe - control for unobservables Model to be estimated: Yst = αs + λCOMPst + ηSPECst + δURBPROGst + Xstβ + Ttλ + εst Y = exam outcome COMP = exam outcome of other schools in district (lagged) SPEC = a specialist school dummy (policy-off / policy-on) URBPROG = inner city schools policy X = time-varying controls (e.g. pup/teach, % poor) T = year dummies αs = school fixed effects (time invariant) - FE model estimates effect of policy variables on within-school variation in Y over time

21. Fixed effects model: dependent variable = exam performance

22. Single-year OLS v fixed effects results Controls = year dummies, pupil-teacher ratio, % pupils eligible for free school meals, etc.

23. Effect of including policy variables on time trend of exam performance Note: Controls not shown

24. More detailed policy effects

25. Aggregate effect of education policies on exam results, 1992-2005 • Main findings: • 10pp improvement in competitor schools is associated with a 2pp improvement for individual schools • – small (but significant) effect: overall effect around 3pp • Specialist schools effect in arts, business studies, science and technology: but only 1pp overall • Urban programme raised exam score by 1.8pp • Total policy impact:6pp of the 16pp improvement in exam results (1993-2005) is ‘explained’ by the three policies. • What about the other 10pp? Grade inflation?

26. Distributional consequences of the quasi-market reforms • Have the reforms benefited some groups more than others? • Three tests: • Effect on different ability groups • Effect on different income groups • Effect on different ethnic groups

27. Do policy effects vary over the ability range? • Answer: • competition: effect is very small at top end of ability range • urban programme: effect is weakest at bottom end of ability range • specialist schools programme: effect is greatest at bottom end of ability range

28. Do policy effects vary over the family income range? Answer: Schools with highest poverty levels have benefited the most from education policy

29. Do policy effects vary according to a school’s ethnicity? Answer: Biggest policy effects for schools with high % of ethnic minority pupils

30. Distributional consequences of the specialist schools programme: by specialism

31. Metropolitan v non-metropolitan schools Why might the policy effect differ between metropolitan and non-metropolitan schools? (i) Parental choice is greater in metropolitan areas (ii) Greater competition for pupils in metropolitan areas (iii) Extra resources for deprived urban areas since 1999 - Education Action Zones (virtually all schools in metropolitan areas + some other deprived areas)

32. Impact of competition, urban programme and specialist schools programme: metropolitan v non-metropolitan • Much stronger policy effects in metropolitan areas

33. Impact of policy on truancy rate: metropolitan v non-metropolitan Policy effects much stronger in metropolitan areas

34. Some conclusions • 1. Effect of increased competition • - Only around 3pp of the increase of 20pp can be attributed to • the increased competition for pupils • - But impact bigger in metropolitan schools • 2. Specialist schools programme • - accounted for only an extra 1pp in exam results • - but variation between specialisms (up to 3pp in business studies/ enterprise) • 3. Inner cities programme has accounted for an extra 2pp in GCSE results • 4. Hence only one-third of the total improvement is accounted for by • the three major policy initiatives • 5. Estimated impact of policy has had important distributional benefits • (biggest effects for low ability and low income groups)