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School leaders know when a role is hard to fill – they live it. But quantifying that difficulty, tracking it over time, or comparing it across schools has historically been next to impossible. The Pressure Index gives leaders a standardised frame of reference for the first time.

In parts of Scotland, local authorities have digitised their education supply staffing process end-to-end. The whole journey (jobs posted, who’s in the workforce pool, who was placed, and for how long) now lives in one system.

The nature of this closed system gives us a unique opportunity to analyse supply trends where 100% of both supply and demand data are captured. We analysed anonymised datasets from these closed-pool systems (workforces confined to a single local authority) and derived a simple insight:

We track PI in two ways:

PI(Jobs):

Number of unique postings ÷ active workers. “How many distinct jobs were chasing each person, on average?”

PI(Jobs) signals demand intensity (the volume of staffing needs actively posted into the supply market). Think of it as a demand-per-capacity ratio: how many times the system ‘calls’ for staff compared with how many workers are available to answer.

A high PI(Jobs) doesn’t mean each worker personally received that many requests. It means that, collectively, demand was several times larger than the active workforce.

PI(Days):

Requested working days ÷ active workers. “How many days of work were chasing each person, on average?”

PI(Days) captures workload intensity (not just how many jobs, but how much work those jobs represent).

In both cases, ‘active workers’ means those available in the specific role/subject during the same time window being measured.

Why both? PI(Jobs) is useful for spotting volatility and short-notice pressure (a surge of brief postings will push it higher). PI(Days) is better for capacity planning and recruitment because it accounts for job length: ten single-day jobs and one ten-day job both represent ten days of demand, and PI(Days) captures that workload equivalence.

All figures below are anonymised and cover the last 12 months of one Scottish LA’s supply data, focusing specifically on Early Years Practitioners.

Example: Early Years Practitioner (EYP), Council X

• Active EYP workers in that 12-month window: 161
• Unique jobs posted: 1,180 → PI(Jobs) = 7.3
• Requested days: 4,066 → PI(Days) = 25.3

What this means:

Over the year, there were roughly seven distinct job postings for every available EYP in the system and around 25 days of requested work per worker

That doesn’t mean each worker received seven requests. It means the total demand for EYP cover was about seven times larger than the available workforce during the period.

It’s a market-level indicator showing that the same small pool of practitioners was being drawn on repeatedly across many short-term posts.

Across the closed-system datasets we examined, higher PI consistently sat alongside:

• A higher share of posts with no applicants
• Slower response times at short lead times
• Demand concentrated in a handful of schools or centres

That aligns with intuition: when many requests compete for a limited workforce, pressure rises. But PI makes that pressure visible, comparable, and trackable.

How to use PI: It’s a tracking signal, not a judgment. There’s no magic number separating ‘healthy’ from ‘crisis’. What matters is movement: Is your Maths PI rising or falling? How does this term compare to last year? Is your Science PI double your English PI and if so, why? Calculate PI by time window (week / month / term) and you see pressure patterns emerge.

Scotland’s closed-pool systems let us calculate PI accurately and observe how it moves with operational pressure. England’s market is different because it is fragmented on both sides. Supply is split across hundreds of agencies. Demand is split across hundreds of MATs, thousands of standalone schools, and multiple local authorities. No single system sees both demand and workforce together.

PI is meaningful at every level of the education system, from individual schools to national strategy. Its power lies in tracking and comparison, revealing patterns that would otherwise stay invisible.

Individual schools

What PI reveals:

• Which of their roles are hardest to fill
• How their pressure compares to their own historical baseline (“our PE PI is usually 5; it’s now 9”)
• Whether their staffing difficulty is unique or part of a wider pattern, i.e. benchmarking against regional and national averages

Why it matters: Headteachers can prioritise energy and budget on the highest-PI roles; communicate objectively with governors (“here’s why we’re struggling with Maths cover, PI shows it. We should allocate a budget for specific recruitment campaigns or retention programmes”); and have more informed conversations with their agency partners (“our Maths PI is 11. What’s yours across your network?”).

Multi-Academy Trusts

What PI reveals:

• Which schools within the MAT face the most acute pressure
• Which roles or subjects are strained Trust-wide
• How pressure varies between schools (for example urban vs. rural settings, or over capacity or under capacity in pupil numbers)

Why it matters:

• Resource allocation: “Three schools have Maths PI >10; deploy our trust-wide pool there first”
• Recruitment prioritisation: “Science PI has climbed for two years; we need a targeted recruitment push or enhanced retention offerings”
• Negotiating with agencies: “Our Teaching Assistant PI is 13; we need you to expand the pool or adjust distribution”

This is where PI becomes most immediately actionable in England today. MATs have direct oversight of multiple schools (can aggregate demand), established agency partnerships (can request workforce data), and the autonomy to act on insights.

When MATs and agencies both see PI, the partnership shifts from reactive to strategic. A MAT sees its PI for Teaching Assistants climbing from 8 to 14 between January and February. Its agency partner sees the same rise in their workforce utilisation data. Instead of the MAT posting increasingly urgent requests and the agency scrambling reactively, both are looking at the same signal. They can plan capacity adjustments together, recruit ahead of pressure, adjust rates proactively, or redistribute roles across the trust.

Local Authorities

What PI reveals:

• Pressure across all schools in the LA
• Which schools are competing for the same scarce workers
• Whether LA-employed supply pools are adequately sized for high-pressure roles

Why it matters: LAs could justify expanding their own supply workforce in roles where PI is consistently high, identify schools needing targeted support (e.g., isolated rural schools with PI of 15+ because they’re far from workforce pools), and support fair allocation across their area.

National level

What PI reveals:

• Which subjects or roles are under systemic pressure nationwide (“Physics PI has risen 40% in three years”)
• Regional variation (North West vs. South East PI patterns)
• Seasonal trends across the whole system

Why it matters: Informs national workforce strategy and teacher / support staff recruitment campaigns, provides an evidence base for policy decisions (retention incentives for high-PI roles), and creates benchmarks: schools and MATs can see “our Geography PI is 12; the national average is 8.”

This requires data aggregation across MATs, LAs, and agencies, which doesn’t exist in England today. National-level PI is aspirational without coordinated data-sharing, but it holds the highest strategic value.

The real strength emerges when PI is visible at multiple levels simultaneously. Consider three scenarios:

Mismatched PI across levels

• National Maths PI: 7 | MAT Maths PI: 12 | School Maths PI: 15
• Diagnosis: This school’s pressure is unusually high even within a stretched MAT. Is the cause location, reputation, pay rates, lead times? PI flags the outlier; investigation follows.

Systemic pressure

• National, LA, MAT, and school-level PI for Science all climbing in parallel
• Diagnosis: Systemic shortage. This is a recruitment and retention problem at the sector level which individual school optimisation can’t fix.

Concentrated pressure within a MAT

• MAT-wide Maths PI: 8 (moderate), but three schools have PI >12
• Diagnosis: Demand is unevenly distributed. The MAT might redistribute requests, stagger timings, or target recruitment to those three schools.

A school with PI of 15 in a region where the average is 7 knows its problem is local. A school with PI of 15 in a region where the average is 14 knows it’s facing a systemic shortage. That context, the macro and micro together, turns PI from a number into a decision-making tool.

The Pressure Index is a metric that can have a meaningful impact on education staffing strategy at all levels. It is the unique nature of the Scottish supply system that gives us the opportunity to actually obtain and interrogate the information needed to uncover this sort of insight.

There is a growing interest in capturing ‘data’ from the English education supply market, but often this interest is without a clear notion of what this ‘data’ is or what insights could be gleaned by capturing and processing it.

The information from Scotland, however, can guide us in this process and begs the question: What would you want to investigate if you had access to perfect data?

Our ongoing Data Insights series will continue to offer valuable perspectives and practical guidance to help schools master absence management. Keep an eye on our blog to stay informed!