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Authors

Charles Wilson, Sam Beardsworth, Ekaterina Khriakova, Lovisa Moller Vallgarda, Benjamin Ruisch

Partners
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National Centre for Social Research

Evidence and Research on Youth Employment  · 

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National Centre for Social Research
Evaluation panel, Research & evaluation partner

ProgrammeEvidence and Research on Youth Employment

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About

NatCen is Britain’s largest independent not-for-profit social research organisation, working collaboratively to generate evidence that uncovers the reality of people’s lives in the UK.
Driven by the power of understanding, NatCen delivers exceptional value for money by pairing steadfast independence with the ability to ask the right questions that unlock truly rigorous evaluation evidence. Its non-profit commitment ensures every resource is dedicated to providing the bold clarity that enables clients to act with certainty and make life better

Working with Youth Futures

NatCen collaborates with us on research and evaluation activity as a member of our evaluation panel.

Overview

We commissioned the National Centre for Social Research to develop a short-term forecasting tool that gives four-quarter-ahead signals about the likely direction of the UK NEET rate.

This short report presents a summary of how the tool was built, what it currently forecasts, and how it should be used, focusing on headline results and implications for decision-making.

For a more in-depth details on the data sources, modelling approach, diagnostics, and limitations as well as the series of analysis scripts for the statistical software R that enable the full forecasting process to be repeated in the future, see the technical annexe report.

The research shows that short-term NEET forecasting is achievable and useful for policy. The tool can be updated every quarter, giving decision-makers an early, evidence-based view of likely NEET trends.

Methodology

The tool uses a time-series forecasting model (SARIMAX). It learns from past patterns in NEET rates, including seasonal ones, and adjusts its projections using economic and labour market indicators.

Core data: The model is built on the Labour Force Survey (LFS), the most widely used and officially recognised source of UK NEET statistics. It can be re-run each quarter as new LFS data are released.

Triangulation: To test the robustness of findings and guide which factors to include, models were also built using Understanding Society (USoc) survey data and Longitudinal Educational Outcomes (LEO) administrative data. Factors that improved accuracy consistently across datasets were prioritised.

Factors in the final model: the unemployment rate, GDP, the disability rate, and the gap between USoc and LFS NEET estimates. The last of these helps the model correct for periods when LFS estimates may be less reliable, given the survey’s recent data quality issues.

Development: Exogenous variables were proposed by NatCen and Youth Futures Foundation, and peer-reviewed by two independent experts, before being tested in more than 500 model combinations. Their selection was shaped by consultations held throughout the project, including model design workshops and midpoint calibration meetings.

Key insights

  • NEET rates are forecasted to stay high. The model forecasts that the seasonally adjusted NEET rate will remain elevated from 2026 Q3 to 2027 Q2, at between 12.9% and 13.2% each quarter, compared with 13.0% in 2026 Q2. This points to sustained pressure on services supporting young people.
  • Uncertainty grows further ahead. By 2027 Q2, the 95% forecast interval ranges from 11.2% to 15.2%, so forecasts should always be read alongside their intervals.
  • Unemployment is the strongest signal. Of the factors tested, the unemployment rate made the largest contribution to forecast accuracy, followed by the disability rate and GDP.
  • The forecasts are reasonably accurate. On average, the final model’s forecasts were within about 5% of the reported NEET rate. Drawing on three datasets improved accuracy compared with an LFS-only model (6.2%).
  • Individual-level risk factors don’t always predict short-term trends. Educational attainment is strongly linked to an individual’s NEET risk, but it did not improve short-term forecasts, likely because it changes too slowly to explain quarter-to-quarter movements.

How to use these insights

The forecasts should be used as one source of intelligence among others, not as a definitive prediction:

  • When using these forecasts, always report and interpret forecast intervals alongside point estimates.
  • Interpret forecasts alongside current economic conditions and expert judgement.
  • Treat forecasts with caution in the event of major economic shocks or policy changes, which the model cannot anticipate.
  • Avoid extending forecasts beyond four quarters without re-running the validation, as uncertainty builds over time.

To get the most recent version of the forecasts, please get in touch.

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