Our data is at the heart of what we do. Created from a collection of nine Government sources, which we combine to provide multi-layered cross-checking, our data is updated every year to give more than 20 million data points describing labour market conditions across Britain.
The following major data sources are included:
Business Register Employment Survey (BRES) • Workforce Jobs Series (WJS) • Working Futures (WF) • Annual Survey of Hours and Earnings (ASHE) • Labour Force Survey (LFS) • Annual Population Survey (APS) • Mid-Year Population Estimates • Subnational Population Projections • Annual Business Inquiry (ABI)
It is the most complete, accurate and reliable source of labour market information available today.
We give a full, contextualised view of the labour market, from the national level right down to the Local Authority level, blending historical and projected industry and occupation trends to give you a window onto any area you want to better understand. Highlights of our data include:
Our data scientists use algorithms to remove the suppressions that are inherent in Government data, lifting the lid on detail at the most granular industry, occupation and regional levels.
Our data includes all full-time and part-time employees throughout the country, along with proprietors (self-employed people who are VAT or PAYE registered).
Our complete dataset is housed in one online tool, Analyst, meaning that there is no need to jump between Government sites and other data providers.
Our data includes historic data going back as far as 2003, as well as forecast data out to 2022, giving users a complete picture of the direction of travel in the economy they are looking at.
Our industry data comprises all 563 industry and 369 occupation classifications across all regions of the country. This means that you can look at the most generic industries and occupations (SIC and SOC 1), or drill right down to the to the most specific (SIC and SOC 4), at national, Government Office Region, County/Unitary Authority and even Local Authority levels. In addition, staffing patterns allow users to see the occupational make up of any industry for any area, whilst inverse staffing patterns allow users to pick any occupation and identify the industries that employ people in this role.