Diversity, choice and the quasi-market: an empirical analysis of England

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Diversity, choice and the quasi-market an
empirical analysis of Englands 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?
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• 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

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• 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

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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

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The quasi-market reforms post-1990
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• 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

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• 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 schools
• 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

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• 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)

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Diversity the Specialist Schools Programme
2006 80 of schools now specialist
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Specialisms
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• 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

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• 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?

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(No Transcript)
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Days lost through unauthorised absence
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Proportion of pupils with good exam results (5
or more A-C grades)
Gap widened from 7 (2001) to 14 (2005)
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Metropolitan v non-metropolitan schools
Gap narrowed from 7 (2001) to 3 (2005)
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Truancy rate of half days unauthorised absence
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Estimating the effect of the policy reforms on
educational outcomes Following Hanushek (1979,
1986), a schools 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)
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• 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

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An alternative approach fixed effects model with
panel data Endogeneity problems less severe -
control for unobservables Model to be
estimated Yst as ?COMPst ?SPECst
dURBPROGst Xstß Tt? est 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 as
school fixed effects (time invariant) - FE
model estimates effect of policy variables on
within-school variation in Y over time
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Fixed effects model dependent variable exam
performance
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Single-year OLS v fixed effects results
Controls year dummies, pupil-teacher ratio,
pupils eligible for free school meals, etc.
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Effect of including policy variables on time
trend of exam performance
Note Controls not shown
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More detailed policy effects
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• 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?

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• 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

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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

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Do policy effects vary over the family income
range?
Answer Schools with highest poverty levels have
benefited the most from education policy
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Do policy effects vary according to a schools
ethnicity?
Answer Biggest policy effects for schools with
high of ethnic minority pupils
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Distributional consequences of the specialist
schools programme by specialism
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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)
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Impact of competition, urban programme and
specialist schools programme metropolitan v
non-metropolitan

• Much stronger policy effects in metropolitan
  areas

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Impact of policy on truancy rate metropolitan v
non-metropolitan
Policy effects much stronger in metropolitan areas
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• 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)