ONS Labour Market Data: January 2024 Quarterly Overview
- Tuesday, January 16, 2024
Today, the ONS published its latest quarterly overview of the UK labour market. The report included estimates of employment, unemployment, economic inactivity, wage growth, and other employment-related statistics.[i]
Aside from the number of vacancies declining once again, much of the data was unchanged from the previous period, with unemployment flat and pay growth still above average.
Quarterly Employment Data
The national employment rate stood at 75.8 percent between September and November 2023 – a 0.1 percent increase on the previous valid three-month period (August to October 2023). The unemployment rate flatlined at 4.2 percent. Meanwhile, the economic inactivity rate decreased by 0.1 percent to 20.8 percent.
Quarterly Job Vacancies
From October to December 2023, the number of job vacancies stood at 934,000 – a reduction of 49,000, or 5.0 percent, on July to September, and the 18th consecutive decline.
Quarterly Wage Growth
During September to November, average total pay growth, which includes bonuses, was 6.5 percent. Average total regular pay growth, which excludes bonuses, was 6.6 percent – a drop on the previous period.
According to the ONS, “[i]n real terms (adjusted for inflation using Consumer Prices Index including owner occupier's housing costs (CPIH)), annual growth for total pay rose on the year by 1.3 percent in September to November 2023, and for regular pay rose on the year by 1.4 percent.”
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[i] October’s report was delayed due to concerns over the accuracy of Labour Force Survey (LFS) estimates. To address these concerns, the ONS changed its methodology. Instead of using LFS data, the statistics body referred to Pay as You Earn Real-Time Information and the Claimant Count for the periods from May to July 2023 onwards. Their latest overview explained that this approach aims to provide “a more holistic view … while the LFS estimates are uncertain”. The newest figures are therefore described as “experimental estimates”, complicating, though not invalidating, like-for-like comparisons with previous datasets.