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GB - Great Britain Methodology
Updated over a week ago

Current Data Sources

Lightcast data is created from a collection of government sources that are combined to provide multi-layered cross-checking. These sources are collected and combined to create Lightcast's occupation data. Every year, Lightcast updates its data with more than 20 million data points describing labour market conditions across Great Britain. It is the most accurate and reliable source of labour market information available today.


Our sources include the following datasets:

• Annual Business Inquiry (ABI)

• Annual Population Survey (APS)

• Annual Survey of Hours and Earnings (ASHE)

• Business Register and Employment Survey (BRES)

• Department for Environment, Food and Rural Affairs (DEFRA)

• Labour Force Survey (LFS)

• Working Futures (WF)

• Workforce Job Series (WJS)

• Office for National Statistics (ONS)

• National Records of Scotland (NRS)

• National Statistics Wales (NSW)

In addition to the LMI data mentioned here Lightcast also offers insights through Global Postings and Global Worker profiles in Great Britain.

Data Process

To construct the occupation data, Lightcast uses three datasets:

• Lightcast Industry Data (outlined below)

• Lightcast Staffing Data (outlined below)

• Annual Survey of Hours and Earnings

Industry Data Process

Industry Data Lightcast industry data comprises 660 industries (at the lowest level) in 385 detailed geographies (Local Administrative Units). We provide job count data by industry from 2003 to 2026 and have information on earnings from 2017.

Historic Employment

Lightcast gathers historic industry employment data primarily from the Annual Business Inquiry (ABI) and the Business Register Employment Survey (BRES). These two are supplemented by other sources but together form the backbone of Lightcast's industry data. Both ABI and BRES are suppressed datasets, so the first step in creating Lightcast data is to fill in the gaps left by the suppressed data points. We use year-to-year and cross-set comparisons to come up with initial estimates, which we then adjust for rounding error. ABI does not cover employment numbers in agriculture, financial activities, or public-sector employment in health, education and public administration, so we supplement ABI with other government sets to make up for the lacking data. These sets include data from the Census, the former Department for Children, Schools and Families (DCSF), and DEFRA. Together with these supplemental datasets, ABI forms our historic industry data from 2003 to 2007.

The primary source of our historic data from 2008 to the present is BRES. While more complete in its coverage of employment sectors than ABI, BRES does require supplemental agricultural data, which is gathered from Workforce Job Series levels which have been expanded geographically. This expansion makes use of data from DEFRA, a Welsh agricultural dataset called Agricultural Small Area Statistics, and the Annual Population Survey. Together, these datasets fill in the agricultural gaps in the BRES data.

Because the results of BRES are not released until a year after the actual survey takes place, we report the current year data by projecting our historic data forward one year in full detail, using the projection methodology outlined in the next section. Workforce Job Series data is released three months after it is collected, as opposed to a year after collection for BRES. Because of this, we adjust our projection levels to match percent change in the WJS between the time BRES was released and the most recent WJS release. This completes our historic industry employment process.

Projected Employment

Lightcast projects industry employment totals using the average of three linear regressions. These linear regressions are based on three segments of data that correspond to the last ten years, five years, and three years, respectively. All of our industries are projected at their most granular level. We average the regressions, forming a single trend which we damp over time to curb excessive increases or decreases in employment totals.

Staffing Data

Lightcast staffing data comprises 660 industries and 367 occupations across 11 geographies, and contains percentages of occupation employment by industry from 2003 to 2026.

To construct staffing data, Lightcast uses three main datasets:

• Lightcast Industry Data (outlined above)

• Labour Force Survey (employment status by occupation)

• Quarterly Labour Force Survey microdata

Lightcast Staffing data is created in three steps:

• Primary and secondary job counts are derived from LFS microdata

• Occupation estimates for NUTS 1 (Local Authority) regions are created using LFS microdata combined with regional industry data

• Staffing by region is created using regional industry and occupation margins and national staffing seeds

We create occupation job counts for each NUTS 1 region by combining the LFS staffing data with Lightcast industry data. These occupation job counts are projected for all future periods. The occupation job count is scaled to our Lightcast industry job count for each region, and the two together form the margins of our staffing matrix. For each region, we create the regional staffing by adjusting our national industry by occupation percentages, derived LFS microdata, and Lightcast industry data to match the regional margin totals.

Occupation Data

Lightcast occupation data comprises 367 occupations across 385 detailed geographies (Local Administrative Units). The jobs data covers the years from 2003 to 2026, and the earnings data is from 2017.

To construct the occupation data, Lightcast uses three datasets:

• Lightcast Industry Data (outlined above)

• Lightcast Staffing Data (outlined above)

• Annual Survey of Hours and Earnings

To create occupation data, we first distribute the most granular regional industry data according to the staffing patterns described above. This distribution forms 2003-2026 employment levels across Great Britain. Next, we take NUTS 1 level earnings by occupation from the ASHE survey and apply them to all of the regional data. This completes the occupation data.

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