Who am I?

  • Professor of Urban Analytics @ CASA, UCL
    • Data, Urban Policy, Quantitative Methods, Maps
  • Former State Secondary School Geography Teacher
  • Brighton Resident and Parent
  • Lifelong Labour voter and former party member

Why are we all here?

  • I made you come
  • An awareness of controversial changes to secondary admissions in the city over the last 2-years
  • An interest in schools and educational attainment more generally
  • An interest in supporting disadvantaged students to achieve
  • An interest in data and evidence-based policy making
  • You’ve read my report and want to ask some questions
    • Or took one look at the length of it and thought it easier to hear me talk about it
  • A good opportunity to throw something at me

First — today’s response from Class Divide

Class Divide have published a thoughtful response to this report today. I am grateful for it — they have spent six years working on exactly the barriers this talk is about, and we share the same goal.

  • We agree on far more than we differ on:
    • Poverty is foundational — the attainment gap has many tangled causes
    • Absence matters enormously — and much of it is poverty: bus fares, unmet special educational needs, ill health
    • Outcomes for our poorest children — in Whitehawk especially — are not good enough

Where we differ — and it is narrow

  • My question is deliberately narrow: in a city already among the most integrated in England, does moving children between schools deliver enough attainment gain to justify the certain cost to the children who move?
  • The disruption is certain, and now; the benefit is uncertain - looking at the data helps us reduce that uncertainty. The burden of proof sits with the intervention.
  • And it is not “almost no cost”: moving children further afield lengthens daily journeys — and longer journeys mean more absence (my own report shows this), borne hardest by the poorest. That is the very lever we all agree matters most.
  • Absence (which I will talk about a lot) is not an “unfixable excuse” - if all schools in the city had the same absence levels as the city’s best, it would make a huge dent in the disadvantage attainment gap.
  • This is, in the end, partly a question of values as well as evidence - but this talk is concerned principally with the evidence.

The Report

Headlines

  • Over the last 4-years, after accounting for structural factors, Brighton and Hove ranks (out of 152 LEAs in England):
    • 7th BEST for Disadvantaged GCSE Attainment
    • 5th BEST for Non-Disadvantaged GCSE Attainment
  • Last year, Brighton and Hove was 2nd WORST for absence.
  • GCSE Attainment is very predictable at School Level - 80% variation between schools explained by a handful of variables
  • The biggest factor affecting disadvantaged student attainment is absence
  • Data shows disadvantaged students in England do better in schools with more disadvantaged students in
  • Poor understanding of evidence → poor policy → poor parental choices

Motivation & Context

Why this report now?

  • The UK Government’s 2026 “Every child achieving and thriving” White Paper sets ambitious targets:

    • Halve the disadvantage attainment gap — ~15 Attainment 8 points today
    • Improve attendance
    • Reform admissions
    • Tackle place-based disadvantage

  • Achieving these requires LEAs to understand the national picture and local situation simultaneously

  • This paper is motivated by direct experience of policy development in Brighton and Hove in 2024–25

The problem: A crucial gap exists between the availability of open data and the availability of accessible, contextualised intelligence for decision makers.

The risk: Policy designed around an incomplete understanding of local situation can pull the wrong lever — doing little good and potentially causing harm in the process.

What is the report trying to do?

  1. Halve the local authority intelligence gap - following the report present a Policy Simulator tool that bridges the gap between data and intelligence for decision making

  2. Convince you that attainment levels are very predictable - show that DfE open data can explain ~80% of variation in school-level attainment with just a handful of variables

  3. Demonstrate the relative importance of different policy levers — and reveal which ones actually have mechanical advantage

  4. Re-calibrate how we view ‘good schools’ - and convince you that most of what many conflate with good schools (their outcomes), is out of their hands

  5. Use Brighton and Hove as a case study to illustrate all of these issues

We are not claiming the best possible model — we are showing that good enough models built from open data can transform the quality of local policy conversations.

Schools vs circumstances: a sixty-year argument

  • The Coleman Report (US, 1966): family background and peers explain far more of attainment than anything schools do — a finding that shocked policymakers (reinforced by Jencks, Inequality, 1972)

  • The British response: Rutter et al., Fifteen Thousand Hours (1979); Mortimore et al., School Matters (1988) — schools do differ, measurably → the birth of school effectiveness research

  • The settled consensus: school effects are real but a minority share — ~10% of achievement variance at school level in the landmark UK studies (Teddlie & Reynolds’s 2000 summary of Smith & Tomlinson, 1989), with early field estimates of 12–15% (Reynolds et al., 2014); intake and context carry the rest

“Context outweighs school” is a sixty-year-old finding — what has been missing is a way to make it usable. This work makes the decomposition open, reproducible and local — and later in this deck, puts today’s numbers on it.

What drives attainment at GCSE? The literature

  • Attendance: strongest predictor — persistently absent pupils: 36% pass rate vs 84% for full attenders (DfE, 2022, 2025)

  • Prior attainment at KS2: reflects accumulated earlier inequalities (Gorard and Siddiqui, 2019; Stopforth and Gayle, 2025)

  • Disadvantage (FSM): some residual effect remains after accounting for above. Gap narrowed post-2011, reversed post-COVID (Tuckett et al., 2023)

  • Workforce: leadership quality, teacher retention, and teacher sickness matter — especially for disadvantaged pupils (Gibbons et al., 2018; Menzies, 2023; Zuccollo et al., 2023)

  • Selectivity: creams-off rather than improves - grammar school premium largely disappears after SES controls (Anders et al., 2024; Gorard et al., 2022; Gorard and Siddiqui, 2019)

  • Individual Pupil characteristics (Houtepen et al., 2020; O’Connell and Marks, 2022)

The factors are multifaceted, complex and interrelated. What’s needed is an analysis combining them so their relative importance can be assessed concurrently.

The challenge of synthesis

  • The evidence base is vast — many factors, many studies, many interactions

  • Hard for anyone to judge which driver is most important in a particular context

  • Requires analysis that combines drivers simultaneously so relative influences can be assessed

This is exactly what was missing in Brighton and Hove in the 2024 consultation. A single strand of evidence (segregation) was treated as the whole story, to the exclusion of other — potentially more impactful — factors.

Brighton and Hove: Context

The city’s secondary school landscape

Figure 1

10 secondary schools — a unique admissions system

  • 6 community schools (LEA-controlled), 2 academies (BACA, PACA), 2 church schools

  • Catchment areas with lottery tie-break for oversubscribed schools (since 2008)

    • Replaced distance-based allocation following CoMArt closure
    • Very unusual in England

  • In 2023, FSM priority added — already affecting mixing without opposition

  • Brighton & Hove is already in the top 1/4 of LEAs nationally for integration of disadvantaged/non-disadvantaged pupils

The 2024 consultation: the proposals

What was proposed:

  • Reduce PANs at popular central schools (Blatchington Mill, Dorothy Stringer)
  • Reserve 20% of places for out-of-catchment children
  • Redraw catchment boundaries in the east

Council’s stated objectives (Dec 2024 Cabinet papers):

“reducing some schools’ barriers to success” for disadvantaged pupils

“a more mixed pupil intake creates better outcomes for disadvantaged pupils”

Stated premise: results in the city were “driven by economic advantage” — so redistribute → narrow the gap

An aside on economic advantage and house prices

  • Being close to a good secondary school has barely any effect on house prices in Brighton and Hove

  • We examined 81,000 residential property transactions over 20 years (2000-2019) in Brighton and Hove and looked at the factors influencing those prices

  • Being within walking distance of a mainline train station or a short bus ride to the city centre far more important for house prices than being close to a good secondary school

The 2024 consultation: the response

Public reaction:

  • “Strong preference for improving existing schools rather than redistributing students”
  • Concerns about community cohesion and student wellbeing
  • National media coverage of vocal objections

The institutional context:

  • Labour majority + Leader & Cabinet system
  • Compressed consultation timeline
  • Limited scope for deliberative engagement
  • Privileged access for some stakeholder groups over others in “policy formation”

The key questions this raises

  1. Is the premise correct? Is attainment in Brighton & Hove really “driven by economic advantage”?

  2. Is this the right lever? Even if concentrations of disadvantage matter, are they the most important factor?

  3. How is the city actually performing once structural factors are accounted for?

  4. What are the risks of pulling this particular lever — including second-order effects?

  5. Should you even be pulling it - or pushing it the other way?

We can answer all of these with DfE open data.

Data & Methods

DfE open data

Data sources:

  • School performance (Attainment 8, Progress 8)
  • Pupil absence and attendance
  • Pupil characteristics (FSM, EAL, prior attainment)
  • School workforce (retention, sickness, pay)
  • Ofsted ratings
  • Admissions policies
  • School characteristics

Our panel:

  • 13,419 school-year observations
  • 3,523 academies and maintained schools
  • 152 Local Education Authorities
  • 4 academic years (2021–22 to 2024–25)
  • Post-COVID, post-imputed grades

All publicly available. All linkable. All underutilised.

What is the ‘model’?

  • Statistical explanation of how GCSE Attainment varies between schools after accounting for things we know affect attainment
  • Is the top boat better? Higher performing? Or experiencing more favourable conditions?
  • Raw league tables compare finishing positions, our model adjusts for the wind

What is the ‘model’? A fancy multidimensional scatter plot

Figure 2

Non-linear effects and local context matter

Figure 3

Where a school or a whole LEA sits on the curve matters enormously for policy.

Non-linear effects and local context matter

Figure 4

Logging both axes straightens the curve — so a single elasticity captures the relationship.

Model specification

Multilevel linear mixed effects model — schools nested within LEAs within regions:

\[\log(\text{ATT8}_{ij}) = \beta_0 + \sum_{k=1}^{9} \beta_k \, x_{kij} + u_{\text{year}} + u_{\text{Ofsted}} + u_{\text{region}} + u_{\text{LA|region}} + \varepsilon_{ij}\]

Fixed effects (predictors):

  • log(% FSM-eligible)
  • log(% Overall absence)
  • log(% English as an Additional Language - EAL)
  • % Low prior attainment (KS2)
  • Selective admissions (dummy)
  • Gorard Segregation Index (LA-level)
  • Teacher retention rate
  • Leadership pay proportion
  • log(Teacher sickness days)

Random effects (grouping):

  • Academic year
  • Ofsted rating (4-category)
  • Government Office Region
  • LEA nested within region

Three models:

  • All pupils
  • Disadvantaged pupils
  • Non-disadvantaged pupils

Model specification

Multilevel linear mixed effects model — schools nested within LEAs within regions:

\[\log(\text{ATT8}_{ij}) = \beta_0 + \sum_{k=1}^{9} \beta_k \, x_{kij} + u_{\text{year}} + u_{\text{Ofsted}} + u_{\text{region}} + u_{\text{LA|region}} + \varepsilon_{ij}\]

Fixed effects (predictors):

  • log(% FSM-eligible)
  • log(% Overall absence)
  • log(% English as an Additional Language - EAL)
  • % Low prior attainment (KS2)
  • Selective admissions (dummy)
  • Gorard Segregation Index (LA-level)
  • Teacher retention rate
  • Leadership pay proportion
  • log(Teacher sickness days)

Random effects (grouping):

  • Academic year
  • Ofsted rating (4-category)
  • Government Office Region
  • LEA nested within region

Three models:

  • All pupils
  • Disadvantaged pupils
  • Non-disadvantaged pupils

A Statistical Model

National Results

How good are the models?

Model Marginal R² Conditional R²
All pupils 0.63 0.77
Disadvantaged 0.56 0.73
Non-disadvantaged 0.62 0.79

\(R^2\) measures how much variation in Attainment 8 is explained by the model

Essentially - How close to the line of best-fit are the points in the scatter-plot?

Can be interpreted as a % - e.g. 0.8 = 80%

School-level attainment is remarkably predictable from a small number of structural factors.

~80% of variation explained for all pupils; ~70–80% for subgroups.

  • Disadvantaged pupils: more variation from unmeasured factors (individual resilience, specific interventions)
  • Non-disadvantaged: tighter, more predictable

Mediating Variables

Mediating variables sit on the causal pathway, not including can inflate apparent relationships: Disadvantage(FSM) → Absence → Attainment

The mediation story: building the model step by step

Table 1
M1: FSM only M2: + Absence M3: + Prior att.
FSM coefficient -0.201 -0.122 -0.073
Absence coefficient — -0.364 -0.287
Prior att. coefficient — — -0.006
R² 0.393 0.612 0.669

The coefficient is a number that represents how steep the line through the scatter plot is. Bigger number (positive or negative) → steeper the line → stronger the effect. 0 = flat line = no effect

Adding absence halves the FSM coefficient. Adding prior attainment reduces both further. With just 3 variables → two-thirds of variation in GCSE scores between schools explained.

Seeing the mediation: the disadvantage line flattens

Figure 5

Same schools, same axes — but once absence and prior attainment are held constant (right), the disadvantage line flattens dramatically: most of the apparent effect ran through attendance and prior attainment.

What about segregation?

  • The Gorard Segregation Index is not statistically significant in the full model

  • Once a school’s own FSM levels, attendance, prior attainment and geographic location are accounted for → no additional predictive power

  • Any harm segregation causes is fully mediated by the other variables

  • This doesn’t mean segregation is harmless — it means its effects operate through absence, prior attainment, and other measured factors

  • We include it because it formed much of the justification for the Brighton & Hove proposals

  • Also remember - Brighton and Hove is in the top 1/4 of LEAs for least segregation / most integration of disadvantaged / non-disadvantaged students already

Relative variable importance

Figure 6

Key finding: absence dominates

Absence is the most powerful predictor of school-level attainment by a considerable margin — its coefficient is roughly 2.6 times larger than concentrations of disadvantage.

  • For disadvantaged pupils, nearly half of school-level performance variation is explained by attendance alone

  • Absence matters ~1.8× more (≈77%) for disadvantaged than non-disadvantaged pupils’ attainment

  • Disadvantaged pupils lack safety nets (tutors, engaged parents, revision guides) — they are more reliant on classroom instruction

  • Attendance is the ultimate equity lever

  • On the council’s own framing — “reducing some schools’ barriers to success” for disadvantaged pupils — attendance is the largest, best-evidenced barrier the model can quantify

The contentious finding: concentrations of disadvantage (1)

For non-disadvantaged pupils:

  • Higher FSM → lower attainment

For disadvantaged pupils:

  • Higher FSM → slightly higher attainment (opposite BHCC consultation statement)
  • After controlling for absence & prior attainment
  • Robust across years and alternative specifications - echos 2015 DfE evidence

Why? Schools with higher concentrations likely develop specialised support systems — targeted use of Pupil Premium, vocational pathways, specialist staff.

Effect shrinks in the full multilevel model → operates through LEA/Ofsted factors, not concentration per se.

Impact Even before negative impacts of redistribution policy (longer journeys etc.), BHCC policy likely to negatively impact disadvantaged attainment at city level.

The contentious finding: concentrations of disadvantage (2)

  • To be clear: We do not advocate for active concentration of disadvantaged pupils.

  • But deconcentration policies premised on the assumption disadvantaged students inevitably fare worse in higher-disadvantage schools are not supported by this evidence.

  • Where deconcentration policies create other negative externalities, classic example of pulling the wrong lever

Brighton & Hove: The Evidence

How is Brighton & Hove actually performing?

Figure 7

Each dot is one LEA’s average boost (above the dashed line) or penalty (below) to disadvantaged attainment, after accounting for intake, absence and prior attainment — i.e. how much better or worse its schools do than the national picture predicts.

Brighton & Hove: a story of success, not failure

LEA rankings (after structural adjustment):

Pupil group Rank / 152
Disadvantaged 7th
Non-disadvantaged 5th
All pupils 4th

The positive random intercept ≈ +2 GCSE points above what structural factors predict.

This was entirely unknown at the time of the 2024 consultation and completely absent from the public narrative.

The starting point was a narrative of failure. The evidence says the opposite: this is one of the highest-performing LEAs in England.

Rather than “what is the city doing wrong?”, the question should be “what is it doing right?”

But there is a problem — and it isn’t segregation

FSM/Disadvantaged Mixing (the lever chosen):

  • City mean: 28.8%
  • National mean: 28.3%
  • National percentile: 52th
  • Completely unremarkable

Absence (the lever totally ignored):

  • City mean: 10.8%
  • National mean: 8.2%
  • National percentile: 99th
  • Among the very worst in England

Brighton & Hove has near-average disadvantage but near-worst absence. The city’s strong underlying performance is being dragged down by an absence problem at the extreme of the national distribution.

Absence league table - Worst LEAs in England (ranked 2024-25)

Table 2
Local Authority 2024-25 2023-24 2022-23 2021-22
Knowsley 10.9% (152/152) 11.9% (151/152) 11.8% (149/152) 12.6% (152/152)
Brighton and Hove 10.8% (151/152) 11.0% (143/152) 10.5% (137/152) 10.8% (144/152)
Newcastle upon Tyne 10.6% (150/152) 11.4% (150/152) 12.9% (152/152) 12.2% (151/152)
Southampton 10.5% (149/152) 11.0% (145/152) 10.6% (139/152) 10.1% (119/152)
Bradford 10.3% (148/152) 11.2% (147/152) 11.9% (150/152) 11.5% (149/152)
Plymouth 10.2% (147/152) 11.3% (149/152) 10.8% (144/152) 11.1% (147/152)
Middlesbrough 10.1% (146/152) 12.2% (152/152) 12.9% (151/152) 11.8% (150/152)
Sefton 10.0% (145/152) 10.5% (133/152) 10.3% (129/152) 10.1% (122/152)
Devon 9.9% (144/152) 10.8% (139/152) 10.9% (145/152) 10.8% (145/152)
Dorset 9.9% (143/152) 10.7% (137/152) 10.2% (127/152) 10.3% (134/152)
Halton 9.8% (142/152) 11.0% (144/152) 10.3% (131/152) 10.1% (118/152)
Blackpool 9.8% (141/152) 11.3% (148/152) 11.1% (147/152) 9.8% (103/152)
Hartlepool 9.8% (140/152) 10.5% (134/152) 11.1% (148/152) 10.2% (130/152)
Bristol, City of 9.6% (135/152) 11.0% (142/152) 11.0% (146/152) 10.6% (141/152)
Gateshead 9.5% (130/152) 11.2% (146/152) 10.8% (143/152) 11.4% (148/152)
St. Helens 9.1% (121/152) 10.0% (122/152) 9.6% (103/152) 10.7% (143/152)
Torbay 8.2% (75/152) 10.3% (126/152) 10.2% (125/152) 10.9% (146/152)

Accelerating returns: comparing the two levers

Figure 8

What would closing the absence gap deliver?

Table 3
School Current Absence % National Avg (Target) Reduction (pp) ATT8 gain (all) ATT8 gain (disadv.)
Longhill High School 16.7 8.2 8.4 5.3 6.1
Brighton Aldridge Community Academy 14.4 8.2 6.2 4.6 6.2
Hove Park School and Sixth Form Centre 12.9 8.2 4.6 4.1 4.7
Portslade Aldridge Community Academy 11.3 8.2 3.1 3.0 3.2
Blatchington Mill School 10.8 8.2 2.6 3.0 3.3
Dorothy Stringer School 9.8 8.2 1.5 2.0 1.9
Varndean School 9.0 8.2 0.8 1.0 1.0
Cardinal Newman Catholic School 8.5 8.2 0.3 0.3 0.4

Bringing absence to the national average → predicted gains of 3–5 GCSE points (even more for disadvantaged) at the worst-affected schools. FSM reductions → closer to 1–2 points (and not for disadvantaged students).

What is a good school?

Schools about much more than raw attainment:

  • Centres of pastoral support and places for children to grow as individuals
  • Community hubs (where they are focal points for local communities)
  • Places for extra-curricular opportunities

But in purely attainment terms:

  • Is a good school one that simply has high levels of attainment?
  • Is a good school one where pupils exceed the levels we would expect, given their circumstances?

Observed vs predicted: disadvantaged pupils nationally (2024–25)

Figure 9

Alternative league tables: value-added

Disadvantaged Pupils: Value-Added Rankings (ATT8 points vs prediction)
Rank School 2021-22 2022-23 2023-24 2024-25 Mean
1 Brighton Aldridge Community Academy +3.2 -1.3 +4.1 +5.2 +2.8
2 Varndean School +3.2 +3.6 +4.5 -1.9 +2.3
3 Dorothy Stringer School +2.9 +3.2 +0.3 +1.7 +2.0
4 Cardinal Newman Catholic School +2.4 -5.0 +6.5 +2.5 +1.6
5 King's School +5.8 +0.9 +4.4 -5.4 +1.5
6 Blatchington Mill School -5.4 +1.3 +5.2 +3.4 +1.1
7 Hove Park School and Sixth Form Centre +2.4 -1.7 +1.3 +0.5 +0.6
8 Portslade Aldridge Community Academy +2.6 -4.0 +4.7 -1.3 +0.5
9 Patcham High School +3.8 +1.6 -11.9 -3.7 -2.6
10 Longhill High School -4.6 -6.2 +0.8 -1.4 -2.9

How this compares to Progress 8

Progress 8 — England’s official value-added measure — adjusts each pupil’s Attainment 8 for one thing only: KS2 prior attainment.

Known criticisms (Leckie & Goldstein; Perry; FFT Datalab)

  • Ignores intake context → penalises disadvantaged intakes at equal prior attainment
  • Confounds the school effect with catchment sorting
  • Volatile year-to-year, worst for small schools
  • Sensitive to outliers / off-rolling

What this measure adds

  • Adjusts for FSM, EAL, absence, admissions & neighbourhood context — not prior attainment alone
  • Decomposes and bounds the true school effect (~16% of variance)
  • Multilevel shrinkage + persistent vs transient split → stabler, honest about uncertainty
  • Two-stage absence split: separates exogenous from school-controllable

The trade-off: Progress 8 is pupil-level; this is school-level open data — richer context, coarser resolution.

School Effects vs Everything Else

How much does the school really matter? New, exploratory material — not in the accompanying paper

Where variation in Attainment 8 comes from

Workforce — the part schools directly control — is a sliver (3%) next to the hand they are dealt (62%). Even the persistent school effect (16%) is an upper bound bundling genuine school ethos/management etc. with other things they don’t control in their catchment, not otherwise measured e.g. cultural capital, parental motivation.

…and for disadvantaged pupils — the group the policy targets

Even for disadvantaged pupils, workforce (school-controllable) is just 1.1% vs 56.8% inherited — the persistent school effect (10.5%) is still a dwarfed upper bound.

What causes Attainment 8 to vary between schools?

Figure 10

Bar height = each group’s average school-level score; each block = that factor’s share of the variation in school outcomes. The pink block — what leadership & teaching add — is real and visible for all three groups, and it is a floor, not a ceiling: a school like BACA adds ~1 SD more than the typical school for its disadvantaged pupils. Figures are unweighted school-level averages (each school counts once); the pupil-weighted national gap is ~15 points.

So what does a better school add?

That pink slice was the school’s share of the variation — so what does a school one step better than average actually add to a disadvantaged pupil?

Figure 11

The school effect is additive — a good school adds its ~2.7 points to the pink block, a very good school ~5.5. A genuinely good school closes a good part of the gap to the typical non-disadvantaged pupil (~46% for the best) — the lever is real, but even the best schools cannot close it alone. BACA does exactly this: +2.8 over four years.

The BACA paradox

What people thought:

  • ‘Requires Improvement’ Ofsted
  • Low raw attainment scores
  • Equity in Education campaign from BACA Catchment:
    • “We want choice” to not attend catchment school
    • “A system which pays particular attention to the needs of children from disadvantaged areas”
  • many parents (~50 families last year) opt for Patcham instead

What the data shows:

  • Best performing school in the city for disadvantaged pupils
  • +5 GCSE points above national peers (2024–25)
  • April 2025: Ofsted upgraded to ‘Good’ in all areas

Patcham — “the safer choice”:

  • Rated ‘Good’ by Ofsted
  • Comfortable mid-table in raw terms
  • But negative value-added for disadvantaged pupils
  • 4-year average: –2.6 GCSE points

Parents, acting rationally on available information and vocal campaigning, may have been choosing the school with lower value-added for their children — if that is the most important factor.

Well intentioned but poorly informed campaign could have nudged parents into poor decisions.

Attendance as a school-level lever

  • Patcham is one of only two schools in the city with attendance better than the national average

  • This is an actionable lever at the school level

  • BACA exemplifies the national finding: schools with higher disadvantage can develop specialised, effective support

  • Every school in the city could learn from Patcham on attendance — and from BACA on supporting disadvantaged pupils

The city needs to disaggregate the attendance problem — different causes require different strategies: family holidays, SEND challenges, transport barriers, caring responsibilities.

Absence isn’t one thing — it’s two

  • Absence in our headline model is a single number, but conceptually it sits in two roles at once

  • Part of it is structural — driven by intake, family circumstances, area health, deprivation, SEN, EAL…

  • Part of it is school-managed — pastoral systems, attendance officers, parental engagement, ethos around getting children into class

  • A two-stage decomposition1 separates these:

    • Stage 1: model expected absence from intake variables only
    • Stage 2: refit attainment using expected absence; what the school adds (including its attendance management) flows into the value-added residual

Most of school-level absence variance nationally is structural. The school-controllable share is the smaller part — but it is the share local policy can actually move.

Where do schools sit on the joint-signal plane?

Figure 12

What this view changes

  • BACA sits firmly in Q2 — adding value despite worse-than-predicted attendance. Closing its residual-absence gap would compound an already-strong pedagogical signal.

  • Patcham sits in Q4 — underperforming despite better-than-predicted attendance. The simple “fix attendance” story isn’t available here.

  • Schools in Q3 (underperforming + attendance worse) are the cleanest single-lever targets — both signals point the same way.

  • Schools in Q1 (adding value + attendance helping) are where the city should look for practice worth sharing.

Reading value-added alongside residual absence separates “this school adds points to attainment” from “this school’s pupils attend more than their intake predicts” — two distinct signals that single-residual league tables fold together.

Discussion & Reflections

The danger of single-lever policy thinking

  • The council’s proposals were built on the premise that attainment was “driven by economic advantage”

  • This premise is false in the context of the DfE’s own research (Macleod et al. 2015) — our modelling confirms this was at best a flawed understanding

  • Concentrations of disadvantage are one of the weaker levers available

  • For disadvantaged pupils specifically, the direction of effect runs contrary to the policy assumptions - across England over 4-years of data, disadvantaged pupils get better results in schools with higher concentrations of disadvantaged pupils

  • The policy was pulling a lever with relatively little mechanical advantage while ignoring one with considerably more

Second-order effects: the risks of the chosen lever

  • The chosen policy if implemented as intended requires longer journeys for many children

  • Thomson (2023): pupils who travel further to school are absent more often

  • Given absence is the city’s most acute problem and the strongest predictor of attainment…

A policy that even marginally increases absence could be counterproductive in attainment terms. The claim of “almost no cost” for social mixing overlooks these second-order effects.

Where we are now?

  • Following opposition and appeals: PAN reductions rejected, 20% out-of-catchment → 5%.

  • But the policy reverberations continue through:

    • parental choices already made
    • 2025/26 Proposed Sibling Link changes

  • Low levels of pupil displacement on 2026 offer day luck not judgement!

  • Underlying belief that current policy direction will benefit disadvantaged pupils in the city

What should Brighton and Hove do?

The council has recently agreed to a cross-party working group on schools — a positive step. Our strongest recommendation:

Priority one: a deep dive into absence. The city has the 2nd worst absence rate in England but near-average disadvantage. This is not inevitable — it is, in principle, solvable.

The absence problem needs disaggregating — very different situations exist within the headline statistics:

  • A few days missed for a family holiday by an otherwise well-attending pupil
  • A SEND student who struggles with the school environment but has strong family support
  • A student with caring responsibilities living far from school, struggling with bus journeys
  • Post-pandemic disengagement and mental health challenges

Each requires a different strategy. Schools like Patcham — one of only two with attendance below the national average — offer lessons in what works locally.

  • Be prepared for counter-intuitive outcomes (does increased absence in B&H current improve outcomes for those who do attend school through smaller class sizes and less disruptive behaviour?)

Lessons for other LEAs

  • Every LEA has a different profile of challenges — different positions along non-linear curves

  • What works in one context may be irrelevant or counterproductive in another

  • The DfE open data provides the raw materials for local understanding

  • But there’s a crucial gap between raw data and actionable intelligence

  • National ambitions will founder if pursued locally without adequate analytical infrastructure

The Policy Simulator Tool

Bridging the gap

What it does:

  • Search for any school in England
  • See key statistics and contextual benchmarks
  • Compare observed vs predicted attainment
  • Simulate the impact of changes to absence, FSM, workforce etc.
  • Explore LA-level patterns and typologies
  • Find a school’s contextual “twin”

Built with:

  • R Shiny
  • Same multilevel models as this analysis
  • DfE open data (updated annually)
  • Development accelerated by Claude (Anthropic) — AI4CI funded

Not the finished article — but a demonstration of what’s possible when open data + appropriate methods + accessible tools come together.

The simulator in action

School Finder → select any school → view contextual statistics → send to the Policy Simulator

adam-dennett.shinyapps.io/School_Attainment_Policy_Simulator

Adjusting policy levers

Non-linear effects mean the same adjustment produces different impacts depending on the school’s starting position

Conclusions & Recommendations

Key takeaways

  1. School-level attainment is remarkably predictable (~80% variance explained) from a handful of open data variables

  2. Absence is the dominant predictor — 2.6x more important than concentrations of disadvantage

  3. For disadvantaged pupils, the concentration effect runs in the opposite direction to common assumption

  4. For disadvantaged pupils the most important factor impacting their attainment is attendance

    • nearly 1/2 of school level performance is explained by attendance alone
    • and it matters almost twice as much for disadvantaged pupils than non-disadvantaged pupils.

  5. Brighton & Hove is one of the highest-performing LEAs — but has the 2nd worst absence in England

  6. The 2024 policy was pulling the wrong lever — targeting the weaker factor while ignoring the stronger one

  7. Crude metrics (raw league tables, single-word Ofsted) mislead parents and policy makers

Recommendations

For the DfE:

  • Invest in analytical infrastructure at the local level
  • Fund LA analytical capacity or develop nationally maintained benchmarking tools
  • Go beyond raw league tables

For LEAs:

  • Resist single causal narratives
  • The factors are multiple, interacting, non-linear, context-dependent
  • Create institutional space for genuine deliberation before irreversible decisions
  • Prioritise attendance as a policy lever

For schools, governors & parents:

  • Contextualised benchmarking offers a fairer basis than raw scores
  • Demand better information before making choices
  • School ‘quality’ ≠ raw attainment

The final word

The caution: Policy made in haste, anchored to a single causal narrative and implemented without adequate regard for evidence, risks doing more harm than good — particularly when it disrupts a system that is working well.

The encouragement: The raw materials for better policy already exist in DfE open data. With the right analytical tools and a commitment to evidence over ideology, it is possible to understand with considerable precision what drives attainment in any local context — and where the most productive interventions lie.

The challenge: Ensuring this understanding reaches decision makers in a usable form before decisions are made — not after.

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