Two puzzles: recent UK labour market dynamics − speech by Megan Greene

Speech

Many thanks for hosting me at Make UK. Given the interests of this group and the challenges I’m told some of you have faced with recruitment, I thought I would spend some time taking a deep-dive into recent UK labour market developments. Over the last 2-3 years, we have seen a historically tight labour market, driven by recruitment difficulties as the pandemic likely caused structural shifts. I aim to focus on two puzzles that have been a feature of this period. First, unemployment has remained at historically low levels despite subdued economic growth. And second, wages have remained high amid an easing labour market and inflation expectations. Labour hoarding may partially explain both.

Before I dive into these puzzles, it’s important to define what we mean by labour hoarding. Firms are said to hoard labour when they choose not to adjust employment in line with short-term fluctuations in demand^{footnote [1]}. This happens all the time, and can save firms costs on things like recruitment and training. Today I want to look at whether there has been evidence of excess labour hoarding, beyond what can be considered normal.

This can manifest in two ways – employers might hang on to workers in the face of subdued demand, and/or they might decrease the average hours worked per head. I will analyse both measures through different lenses, but with a focus on whether or not recent developments match historical trends or represent excess labour hoarding. How much excess labour hoarding there is and whether firms will continue to do it has a direct impact on inflation dynamics and therefore the Monetary Policy Committee’s policy stance. Persistent hoarding may slow the return of inflation to target by preventing the labour market from loosening. But if firms suddenly capitulate and stop hoarding labour, unemployment could jump and inflation could undershoot the target.

Puzzle 1 – Unemployment remains low by historical standards despite subdued growth

I’ll begin by setting out the first puzzle: unemployment is not rising much despite weak economic growth. The unemployment rate was low by historical standards prior to the pandemic at around 4%. Following Covid, unemployment has returned to these historically low levels despite massive shocks from the pandemic, supply chain disruptions, Russia’s invasion of Ukraine and a cost of living crisis. This is especially curious given the latest models indicate the medium-term equilibrium rate of unemployment, u*, has risen since the pandemic (Greene, 2023) , and the MPC has revised up its estimate of this to 4.5%^{footnote [2]}.

One way to think about the relationship between GDP growth and unemployment is through the lens of Okun’s law, which suggests that for a 1% fall in GDP, there’s less than a 0.5% rise in the unemployment rate. You can see this negative correlation between GDP growth and the unemployment rate in Chart 1. The relationship isn’t 1:1 because there is always a degree of fluctuation in labour supply changes and there are market frictions like labour hoarding^{footnote [3]}. So the question we should focus on is whether these frictions are greater than usual.

Chart 1: GDP vs Unemployment ^(a)

Footnotes

• Sources: ONS and author’s calculations.
• (a) Dashed lines denote line of best fit through data sample. 2020Q2 and 2021Q2 data points are omitted. Latest data to Q1 2024.

Usually when we see growth below potential and/or a recession, as has been the case recently, unemployment rises significantly. But unemployment currently remains historically low at 4.3% as of March 2024, below our estimate of medium-term u* at 4.5%. During the pandemic, we saw structural changes in the labour market due to government policies like the furlough scheme, which allowed people to stay in work during Covid lockdowns. As a result, the relationship between unemployment and growth weakened considerably between 2020 and 2022, while the furlough scheme was in place. Since the end of the furlough scheme in September 2021, the classic Okun relationship has remained a bit weaker than the historical trend, as shown by the purple dots, suggesting that the labour market is not completely back to normal. Some of the purple dots may have lasting distortions from the furlough scheme, as they plot out four quarter changes. Excluding these data points, the classic Okun relationship looks stronger in the post-furlough period.

The Sahm rule^{footnote [4]} is another lens through which to examine the relationship between unemployment and activity. Typically applied in the US, the Sahm Rule suggests that a 0.5ppt rise in unemployment^{footnote [5]} relative to its lowest point over the previous 12 months has been the best indicator of recession. In the UK, historical data suggests this threshold is a 0.75ppt rise. As shown in Chart 2, the Sahm Rule has been triggered in every recession the UK has had since the 1970’s, except for the one at the end of last year. In fact, unemployment was falling in the second half of 2023 as the economy went into recession.

Chart 2: Sahm Rule Recession Indicator ^(a)

Footnotes

• Sources: ONS and author’s calculations.
• (a) Sahm Rule Recession Indicator equals three-month moving average of unemployment rate, minus the minimum value of the unemployment rate during the preceding 12 months. Shaded areas denote periods of technical recession (consecutive quarters of negative GDP growth). Orange line indicates Sahm recession threshold (0.75). Latest data to March 2024.

This is somewhat puzzling. If GDP growth remains weak (though strengthening), as in the latest MPC forecast for this year, we might expect to see greater rises in unemployment than currently forecast.

At this point, I want to acknowledge that any analysis using recent unemployment data needs to be taken with a pinch of salt. The Labour Force Survey continues to experience low response rates and sample sizes (Chart 3), increasing the uncertainty of underlying unemployment and complicating our view of slack in the economy. Our main cross-check for the LFS unemployment rate is the claimant count – the numbers of people receiving unemployment benefits. But as my colleague Ben Broadbent explained in a speech last year, the typical correlation between the two measures has broken down over the last few years.

We have better cross-checks for employment figures, from HRMC’s Pay As You Earn (PAYE) dataset and Workforce Jobs (WFJ), which compiles estimates from a number of other surveys^{footnote [6]}. As Chart 3 shows, these two metrics have diverged from the LFS employment measure since the start of the pandemic, coinciding with the sharp drop in LFS response rates. Both alternative metrics point to higher employment than the official figures. Stronger employment growth in the face of subdued growth and even economic contraction last year only adds to this first puzzle.