Headlines

  • Over the last 4-years, after accounting for structural factors, Brighton and Hove ranks (out of 152 LEAs):
    • 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 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

The problem

  • The UK Government’s 2026 Schools White Paper targets halving the disadvantage attainment gap

  • Achieving this requires LEAs to understand local drivers of attainment

  • A crucial gap exists between open data availability and accessible, contextualised intelligence

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

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

Our aim: Show that DfE open data + multilevel models can explain ~80% of attainment variation — enough to transform local policy conversations.

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.

Data and Model

What is the ‘model’?

  • Statistical explanation of how GCSE Attainment varies between schools after accounting for things we know affect attainment
  • Raw league tables compare finishing positions, our model adjusts for the wind

Model specification

4-year panel (2021–22 to 2024–25): ~12,200 school-year observations from 3,523 schools across 152 LEAs.

Multilevel LME with log-transformed Attainment 8:

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

Fixed effects: log(%FSM), log(%Absence), log(%EAL), %Low prior attainment, Selective admissions, Gorard Segregation Index, Teacher retention, Leadership pay %, log(Teacher sickness days)

Random intercepts: Year, Ofsted rating, Region, LA nested within region

Separate models fitted for all pupils, disadvantaged, and non-disadvantaged Attainment 8.

Results: Relative Explanatory Variable Importance for Attainment 8

Figure 1

Key finding: absence dominates

Absence is the most powerful predictor of school-level attainment — its standardised 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

  • Conditional R² ≈ 0.73 for disadvantaged pupils

  • The Gorard Segregation Index is not significant once other factors are controlled for

  • For disadvantaged pupils, the FSM concentration effect runs in the opposite direction to common assumption — they do slightly better in schools with higher proportions of disadvantaged students

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

Attendance is the ultimate equity lever — and the most accessible one for local policy.

Brighton and Hove Case Study

The city’s secondary school landscape

Figure 2

The 2024 consultation (1)

What was proposed:

  • Reduce Published Admission Numbers at popular central schools
  • Redraw catchment boundaries
  • Reserve places for out-of-catchment children

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”
  • Stated premise: attainment “driven by economic advantage”

The 2024 consultation (2)

The response:

  • Overwhelmingly negative from parents and schools
  • “Strong preference for improving existing schools rather than redistributing students”
  • Proposals would force significant numbers of children into 2+ hour daily commutes

Detailed local data and analytics were not available at the time of the consultation. The city was painted as a failure — but was it?

How is Brighton & Hove actually performing?

Figure 3

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 (the lever they chose):

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

Absence (the lever they 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.

Accelerating returns: comparing the two levers

Figure 4

What would closing the absence gap deliver?

Table 1
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 at the worst-affected schools. FSM reductions → closer to 1–2 points.

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

Figure 5

The BACA paradox

What people thought:

  • ‘Requires Improvement’ Ofsted
  • Low raw attainment scores
  • → narrative of “Avoid this school”
  • Equity in Education campaign: parents 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, may have been choosing the school with lower value-added for their children — if that is the most important factor. They were operating in a knowledge vacuum.

Absence isn’t just one number

  • Absence is the single biggest predictor of attainment — but it sits in two roles at once
    • Part is structural (intake, area health, deprivation, family circumstances)
    • Part is school-managed (pastoral systems, attendance practice)
  • A two-stage decomposition1 separates them, giving each school a value-added and a residual absence indicator
  • Most absence variance nationally is structural; school-controllable share is the smaller part — but it’s the one local policy can actually move

Closing a city’s absence problem needs both school-level attention and cross-departmental work — public health, children’s services, area deprivation policy. Schools cannot fix the structural component alone.

Brighton and Hove on the joint-signal plane

Figure 6

BACA: Q2 — adding value despite worse attendance. Patcham: Q4 — underperforming despite better attendance management. Two very different policy stories the headline league table conceals.

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

Discussion

The danger of single-lever policy thinking

  1. The council’s proposals were built on a single causal narrative: redistribute disadvantaged pupils to narrow the gap

  2. Concentrations of disadvantage are one of the weaker available levers

  3. For disadvantaged pupils specifically, the effect runs counter to the policy assumptions

  4. Longer journeys from redistribution → higher absence — worsening the factor that most drags on attainment

  5. The claim that social mixing can be achieved at ‘almost no cost’ overlooks second-order effects

  6. Brighton & Hove’s schools are already in the top third for integration — yet policy sought to mix them further

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

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

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

  6. Better analytical tools applied to existing open data could transform local policy conversations

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
  • 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 what drives attainment in any local context — and where the most productive interventions lie.

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

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