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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?
Pre-1990
o Local Education Authorities (LEAs) determined the distribution and use of school funding
o LEAs determined allocation of pupils (except for church schools and grammar schools)
o LEAs appointed and employed teaching staff
o Limited role for head of school
o Limited role for parents and governors
Early 1990s: the creation of a quasi-market in secondary education
o Motivation: general dissatisfaction with educational outcomes
o Aim: to improve educational outcomes
o Method: creation of quasi-market + targeting of ‘disadvantaged’ pupils
Three main strands:
1. Establishment of a quasi-market: competition between schools
2. Specialist schools programme: diversity to improve pupil-school ‘match’
3. Urban education policy: Education Action Zones for ‘disadvantaged’
Current policy
The quasi-market reforms: post-1990
Pre-conditions for quasi-marketsPre-conditions for quasi-markets
Policy reformsPolicy reforms Decentralised Decentralised decision-decision-makingmaking
ChoiceChoice VoiceVoice IncentivesIncentives InformationInformation
Local management of schoolsLocal management of schools ++
Opting-out of government Opting-out of government controlcontrol
++ ++
Parents on governing bodyParents on governing body ++ ++
Funding based on enrolmentsFunding based on enrolments ++ ++
Parental choice of schoolParental choice of school ++
Specialist schoolsSpecialist schools ++
Attainment Tables + OFSTEDAttainment Tables + OFSTED ++ ++
Purpose of the quasi-market
o Improve performance through greater competition for pupils (diversity + choice + local management of schools)
o Increase transparency and accountability
o Improve efficiency through direct funding - schools now responsible for 90% of recurrent expenditure - more efficient allocation of resources - increase in total educational product
o Induce private funding into state education - private funders can contribute to creation of new schools (academies) or take over ‘failing’ schools to raise performance
But will the quasi-market improve educational outcomes for all pupils?
o 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
o 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
Constraints on the quasi-market
o ‘Comprehensive’ schools cannot (ostensibly) choose pupils
o Entry and exit severely limited
o Excess demand for places in popular schools
o 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)
o Choice severely limited in many school districts(non-metropolitan areas (20% of districts have 4 schools or less)
Number of specialist and non-specialist secondary schools in England
0
500
1,000
1,500
2,000
2,500
3,000
3,500
1992 1994 1996 1998 2000 2002 2004
Non-specialist schools
Specialist schools
Diversity: the Specialist Schools Programme
2006: 80% of schools now specialist
Year first Year first introducedintroduced
Total in Total in 20062006
TechnologyTechnology 19941994 585585
LanguagesLanguages 19951995 221221
Arts Arts 19971997 421421
SportSport 19971997 350350
BusinessBusiness 20022002 229229
EngineeringEngineering 20022002 5757
MathsMaths 20022002 225225
ScienceScience 20022002 303303
HumanitiesHumanities 20042004 7272
MusicMusic 20042004 2727
TotalTotal -- 24902490
Specialisms
Urban Education Programme
extra funding for schools in disadvantaged urban areas (28% of all schools) - 1999/05
(Education Action Zones)
o Support for gifted and talented pupils - learning mentors for individual pupils
o Support for the ‘hard to teach’ - learning support units (to improve attendance)
o Provision of high-tech equipment in poorly equipped schools
Estimating the impact of the educational reforms
o Have educational reforms been effective? (e.g. exam results, truancy)
o Have the reforms had any distributional consequences?
o Which policies have been the most effective?
Exam results at age 16: % with 5+A*-C grades
30
35
40
45
50
55
60
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Truancy rate (%)
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Days lost through unauthorised absence
30.0
35.0
40.0
45.0
50.0
55.0
60.0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Specialist schools
Non-specialist schools
Proportion of pupils with ‘good’ exam results (5 or more A*-C grades)
Gap widened from 7 (2001) to 14 (2005)
Metropolitan v non-metropolitan schools
% 5 or more A*-C grades
30.0
35.0
40.0
45.0
50.0
55.0
60.0
19931994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
2005
%
Metropolitan
Non-metropolitan
Gap narrowed from 7 (2001) to 3 (2005)
Truancy rate (%)
0.6
0.8
1.0
1.2
1.4
1.6
1.8
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Non-metropolitan schools
Metropolitan schools
Truancy rate = % of half days unauthorised absence
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 districtSPEC = specialist schools policyURBPROG = Education Action Zone policy (low income areas)
Endogeneity problems with the OLS production function
o 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)
o Hence: - school quality variables (e.g. pup/teach): underestimated - policy effects (SPEC and URBPROG): overestimated
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 policyX = 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
Estimated coefficientEstimated coefficient
Competition Competition 0.20***0.20***
Urban programmeUrban programme 1.8***1.8***
Specialist schools programmeSpecialist schools programme 0.9***0.9***
Pupil-teacher ratio Pupil-teacher ratio -0.001***-0.001***
Part-time / full-time teachersPart-time / full-time teachers 0.0080.008
Number of pupilsNumber of pupils 0.010***0.010***
Number of pupils squaredNumber of pupils squared 0.0000.000
% eligible for free school meals% eligible for free school meals -0.260***-0.260***
Y94 (Some year dummies)Y94 (Some year dummies) 1.81.8
Y95Y95 2.02.0
Y97Y97 3.43.4
y00y00 5.55.5
y02y02 6.66.6
y04y04 8.38.3
y05y05 10.610.6
ConstantConstant 0.2980.298
R-squaredR-squared 0.410.41
nn 4025140251
Fixed effects model: dependent variable = exam performance
Single-year OLS v fixed effects results
Controls = year dummies, pupil-teacher ratio, % pupils eligible for free school meals, etc.
No. of schools in districtNo. of schools in district Competition Competition Urban Urban education education programmeprogramme
Specialist Specialist schools schools programmeprogramme
OLS model for 2005OLS model for 2005 0.13***0.13*** 8.5***8.5*** 6.5***6.5***
Fixed effects model for 1992-2005Fixed effects model for 1992-2005 0.20***0.20*** 1.8***1.8*** 0.9***0.9***
Explanatory variableExplanatory variable With policy With policy effectseffects
Without policy Without policy effectseffects
CompetitionCompetition 0.200.20 --
Urban programmeUrban programme 1.81.8 --
Specialist schools programmeSpecialist schools programme 0.90.9 --
y94y94 1.81.8 2.12.1
y95y95 2.02.0 2.72.7
y96y96 3.33.3 4.04.0
y97y97 3.43.4 4.54.5
y98y98 4.14.1 5.65.6
y99y99 5.45.4 7.47.4
y00y00 5.55.5 8.48.4
y01y01 5.85.8 9.59.5
y02y02 6.66.6 11.111.1
y03y03 7.77.7 12.712.7
y04y04 8.38.3 13.913.9
y05y05 10.610.6 16.616.6
Note: Controls not shown
Effect of including policy variables on time trend of exam performance
Explanatory variablesExplanatory variables Estimated coefficientEstimated coefficient
CompetitionCompetition 0.20***0.20***
Urban programme: phase 2000 2.3***
Urban programme: phase 2001 1.4***
Urban programme: phase 2002 1.1***
ArtArt 1.1***1.1***
Business studiesBusiness studies 2.5***2.5***
EngineeringEngineering -0.7-0.7
LanguagesLanguages 0.00.0
MathsMaths 0.00.0
ScienceScience 0.7*0.7*
SportSport -0.2-0.2
TechnologyTechnology 1.6***1.6***
HumanitiesHumanities -0.3-0.3
MusicMusic 0.80.8
More detailed policy effects
Aggregate effect of education policies on exam results, 1992-2005
Main findings:o 10pp improvement in competitor schools is associated with a 2pp improvement for individual schools
– small (but significant) effect: overall effect around 3pp
o Specialist schools effect in arts, business studies, science and technology: but only 1pp overall
o 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?
Distributional consequences of the quasi-market reforms
Have the reforms benefited some groups more than others?
Three tests:
1. Effect on different ability groups
2. Effect on different income groups
3. Effect on different ethnic groups
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
Exam score quintileExam score quintile Competition Competition Urban Urban education education programmeprogramme
Specialist Specialist schools schools programmeprogramme
Schools with lowest exam scoresSchools with lowest exam scores 0.23***0.23*** 0.9***0.9*** 1.9***1.9***
Second quintileSecond quintile 0.24***0.24*** 1.7***1.7*** 1.5***1.5***
Third quintileThird quintile 0.26***0.26*** 2.9***2.9*** 1.2***1.2***
Fourth quintileFourth quintile 0.18***0.18*** 2.3***2.3*** 0.10.1
Schools with highest exam scoresSchools with highest exam scores 0.04*0.04* 2.4***2.4*** 0.5*0.5*
Do policy effects vary over the family income range?
Answer: Schools with highest poverty levels have benefited the most from education policy
Free school meals quintileFree school meals quintile Competition Competition Urban Urban education education programmeprogramme
Specialist Specialist schools schools programmeprogramme
Lowest % eligible for free meals (rich kids)Lowest % eligible for free meals (rich kids) -0.1-0.1 -1.1*-1.1* 0.20.2
Second quintileSecond quintile 0.13***0.13*** 0.90.9 0.8*0.8*
Third quintileThird quintile 0.25***0.25*** 1.5***1.5*** 1.1***1.1***
Fourth quintileFourth quintile 0.24***0.24*** 1.4***1.4*** 1.2***1.2***
Highest % eligible for free meals (poor kids)Highest % eligible for free meals (poor kids) 0.23***0.23*** 1.4***1.4*** 2.9***2.9***
Do policy effects vary according to a school’s ethnicity?
Answer: Biggest policy effects for schools with high % of ethnic minority pupils
EthnicityEthnicity Competition Competition Urban Urban education education programmeprogramme
Specialist Specialist schools schools programmeprogramme
Under 10% ethnic minority Under 10% ethnic minority pupilspupils
0.16***0.16*** 0.7***0.7*** 0.9***0.9***
10% to 50% ethnic minority 10% to 50% ethnic minority pupilspupils
0.15***0.15*** 1.7***1.7*** 0.50.5
Over 50% ethnic minority Over 50% ethnic minority pupilspupils
0.27***0.27*** 2.8***2.8*** 2.4***2.4***
% eligible for free school meals % eligible for free school meals (average 1992-2005)(average 1992-2005)
Lowest Lowest quintile quintile
Middle Middle quintilesquintiles
Highest Highest quintilequintile
ArtsArts 0.10.1 1.3**1.3** 2.3***2.3***
Business studies Business studies 1.11.1 2.3**2.3** 6.0***6.0***
EngineeringEngineering -2.7**-2.7** 1.51.5 -3.9*-3.9*
LanguagesLanguages -0.5-0.5 0.00.0 5.6***5.6***
Mathematics Mathematics -0.6-0.6 0.9*0.9* 2.22.2
ScienceScience 0.10.1 1.6***1.6*** 2.7***2.7***
SportSport 0.00.0 -0.1-0.1 -0.2-0.2
TechnologyTechnology 1.1***1.1*** 1.5***1.5*** 4.3***4.3***
Controls included?Controls included? YesYes YesYes YesYes
R-squared (within)R-squared (within) 0.420.42 0.390.39 0.500.50
nn 80918091 2414324143 80178017
Distributional consequences of the specialist schools programme: by specialism
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)
Impact of competition, urban programme and specialist schools programme: metropolitan v non-metropolitan
Competition Competition Urban Urban education education programmeprogramme
Specialist schools Specialist schools programmeprogramme
Non-metropolitan Non-metropolitan areasareas
0.11***0.11*** 0.9**0.9** 0.5***0.5***
Metropolitan areasMetropolitan areas 0.39***0.39*** 1.1***1.1*** 1.7***1.7***
o Much stronger policy effects in metropolitan areas
Competition Competition Urban Urban education education programmeprogramme
Specialist schools Specialist schools programmeprogramme
Non-metropolitan areasNon-metropolitan areas -0.42**-0.42** -0.13***-0.13*** -0.05**-0.05**
Metropolitan areasMetropolitan areas -3.35***-3.35*** -0.22***-0.22*** -0.10**-0.10**
Impact of policy on truancy rate: metropolitan v non-metropolitan
Policy effects much stronger in metropolitan areas
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)