Modelling school assignment with administrative data

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Modelling school assignment with administrative
data

• Simon Burgess
• Based on School Assignment, School Choiceand
  Social Mobility by Simon Burgess and Adam
  Briggs, CMPO DP 06/157

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Introduction

• Not all schools are good schools
• Which pupils go to the good schools?
• To the extent that children from poor families
  are allocated to worse schools, this perpetuates
  disadvantage, reducing social mobility
• Questions
• What is the extent (if any) of a differential
  chance of going to a good school?
• How does it happen?

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What we do

• We estimate the chances of poor and of non-poor
  children getting places in good schools
• One of the key factors is location distance
  between school and home.
• Our dataset allows us to measure distance
  precisely and characterise the pupils very local
  area.
• We compare pupils living in the same place.
  Exploit within-street variation and also control
  for other personal characteristics including
  prior test scores.

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Results

• Poor children half as likely to go to good
  schools.
• Much of that, but not all, comes through
  location. That is, accounting fully for location,
  the gap is much smaller, but not zero.
• Children from poor children tend not to go to a
  good school, even if it is their nearest.
• (more . See paper)

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Plan

• Modelling Framework
• Data
• Results
• Conclusions

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Modelling Framework

• We model the assignment of children to schools,
  as a function of the characteristics of the
  school and of the children. Its a matching
  problem.
• The observed data on the outcome of this
  assignment are realisations of an underlying
  process, composed of two decisions
• applications by parents and children for places
  in particular schools (demand),
• the administrative procedures that allocate
  children to schools given their choices
  (assignment rule)

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Allocation

• Write a general model of the outcome of the
  allocation as
• where

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Reverse causation?

• We interpret the estimated relationship between
  the schools quality score qa(i, t), t-6 and a
  students personal characteristic, fit, as
  representing the outcome of the assignment
  process.
• Alternative from student characteristics to the
  outcome score.

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• Timing the quality score derives from the
  performance of a group of children 6 years older
  than the current intake.
• But persistence in school attendance. Two
  interpretations
• Islands story Schools located on islands,
  with no mobility between them. All students from
  succeeding generations therefore go to the school
  on their island.
• Correlation from one generations poverty to the
  next.
• But this is not what Englands schools look like
• Half of children do not go to local school
• See map of Birmingham

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Figure 1 School Distance Contours in Birmingham
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• Dynasties pupils living in particular
  locations always go to the same school. And with
  persistence in area poverty, particular locations
  always house poor families.
• poverty of succeeding generations is correlated,
  score of one generation of pupils drawn from that
  area is correlated with the poverty of the next.
• Econometrically, estimating
• Will be biased because omitted variable of the
  nature of is location is correlated with fi, and
  with the nature of the previous cohort of pupils
  who generated the school quality score.
• Response control for location to remove omitted
  variable bias within postcode variation.

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Data

• Data on pupils
• Data on schools
• Data on location
• Our sample

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Pupils

• PLASC/NPD Census of all children in state
  schools in England, taken each year in January.
• First in 2002 use three PLASCs
• Gender, within-year age, ethnicity, SEN,..
• Key-stage 2 test taken at age 11 as the pupils
  finish primary school. This is a nationally set
  group of tests (in English, Maths and Science),
  marked outside the school
• Key variable for our purposes is an indicator of
  family poverty, the eligibility for Free School
  Meals (FSM).

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Schools

• Quality of the secondary school that each child
  attends.
• Use the publicly available and widely quoted
  measure of the proportion of a schools pupils
  which passes at least 5 GCSE exams at age 16.
• These exams are important, are nationally set and
  come at the end of compulsory schooling.
• Define a good school as a school in the top
  third nationally of the distribution of 5A-C
  scores.
• Dating we use the score for each school from
  the time that the cohorts made their decisions on
  school applications, so deriving from the results
  of a cohort of pupils 6 years older.

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Location

• We have access to each pupils full postcode.
  This locates them quite precisely.
• Also the coordinates of the school, which locates
  it exactly.
• We rely on the postal geography of the UK for
  this analysis. Overall, there are about 1.78m
  unit postcodes covering 27.5m addresses. On
  average, there are 15 addresses in a unit
  postcode, but this varies.
• Using pupils postcodes, we match in data on
  neighbourhoods, on two scales postcode, and area
  (ward).

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Location (2)

• Mosaic classification, a postcode level dataset
  that categorises each postcode in the UK into one
  of 61 different types.
• Over 400 variables used. This is commercial
  geo-demographic data, kindly provided to us by
  Experian.
• Index of Multiple Deprivation (IMD) produced by
  the Office of the Deputy Prime Minister. Ward
  level dataset that ranks every ward in England on
  a range of criteria.
• Distance can be measured in a number of different
  ways. It is only computationally feasible to use
  straight-line distances.

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Sample

• We take the cohort of new entrants into secondary
  school from each PLASC, so pupils in their first
  year of secondary school. Roughly 0.5m pupils in
  each cohort, so our full sample is 1.57m pupils.
• State schools in England non-selective LEAs
  (this cuts out 13.4 of the pupil total) omit
  pupils from some special schools, and pupils are
  omitted if they have missing values for data.
• Sample for the overall regressions in Table 2 is
  1.24m some 91 of the available total in
  non-selective LEAs.

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Table 2 Probit of whether pupil goes to a good
school
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Results

• Controlling fully for Location
• How much of the difference in probability of
  attending a good school is due to location?
• Need to control completely for location.
• Interpretation location not exogenous
  estimating how important choice of location is
  for parents strategy.

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Table 5 Statistics on numbers of pupils per
postcode
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Figure 5 Differences in school quality by
differences in FSM status
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Table 6 Postcode-cohort FE regressions of school
quality
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Figure 6 Probability of pupils attending their
nearest school
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Conclusions

• Poor children half as likely to go to good
  schools.
• Much of that, but not all, comes through
  location. That is, accounting fully for location,
  the gap is much smaller.
• Controlling for location, this gap doesnt vary
  much by degree of choice.
• Children from poor children tend not to go to a
  good school, even if it is their nearest.
• Our econometric strategy is not to identify
  causal relationships in this paper (future work).