is apparent enough in chart 15. it can be seen more directly in chart 17, which plots the standard deviations of quarterly private - sector awe growth over rolling four - year periods ( so that the very last data point covers the period since the onset of the pandemic ). the sharp pick - up in wage growth has been accompanied by a similar rise in its volatility. it ’ s also clear in chart 18. this plots the output of what ’ s called a “ dynamic factor model ”, a statistical filter that uses the sectoral information within the awe to decompose headline wage growth into “ trend ” and “ transitory ” components [ 10 ]. quite a bit of the marked acceleration in awe growth over the spring and summer is interpreted, at least by this model, to have been transitory. chart 17 : short - term awe growth has become more volatile ; unlike during the financial crisis this has been driven more by regular than bonus pay sources : ons and bank calculations. chart 18 : an estimate of trend awe growth derived from sectoral information has been lower than the headline rate in recent months sources : ons and bank calculations. model of trend wage growth based on the approach of stock and watson. shaded area shows 68 % confidence intervals around underlying trend estimate. unsurprisingly, in this environment, the mpc has been keen to supplement these official data with other indicators of wage growth. none of the three in chart 15 has a long history [ 11 ] ; the dmp and the two surveys of recruitment agents ( the indeed wage tracker and the rec index in chart 16 ) all have much smaller coverage than the official mwss. so they should certainly be treated with a degree of caution. taken together, however, it ’ s noticeable that, like the “ trend ” estimate in chart 18, none exhibited quite the same strength as ( or pick - up in ) awe growth in mid - 2023. and i do think it ’ s worth paying attention to two in particular, the rec index and the tax - derived “ real time information ” ( rti ) pay series. as i said, the coverage of the rec survey must be significantly smaller than that of the awe ( most people don ’ t find jobs via recruitment agents ). but it ’ s also much longer - lived than the other indicators ( it started in 1997 ). and for nearly all that period it has had a pretty good – and, indeed, a leading – statistical

unemployment through the first half of 2023 wasn ’ t matched by any sort of increase in the claimant count. chart 11 : unemployment and claimant count closely related in the past … sources : ons and bank calculations. the correlation between unemployment rate and claimant count rate fell with the rollout of universal credit, which increased the number of people eligible to claim benefits. as part of the uk government ’ s response to covid - 19, more people became eligible for unemployment - related benefits, although still employed. as a result, changes in claimant count were not due wholly to changes in the number of unemployed people. chart 12 :... but less so recently sources : ons and bank calculations. unemployment rate published as an experimental statistics ( shown as a dotted line ) starting from july – see this ons labour market overview claimant count data to november. for more details. unemployment data available to october, it ’ s possible that the problem exists not in the benefit numbers but the lfs. after all, if there ’ s measurement error in the level of employment, presumably the same must be true of unemployment. so maybe the benefit numbers are telling us that the rise in lfs unemployment, even as moderate as it ’ s been, has been overstated. but for several reasons i suspect it ’ s the other way around – that the claimant count understates the rise in unemployment over that period. first, for what it ’ s worth, lfs unemployment is considerably less variable over short periods of time than employment growth. this suggests that its measurement error may be less acute. second, the picture of rising unemployment but flat claimant numbers is one that ’ s actually confirmed within the lfs itself. one of the more detailed questions in the survey is whether or not the respondent is receiving unemployment - related benefit. and that series too has been flat ( chart 13 ). third, there are good reasons, in periods of rising participation, why one might expect the claimant count to understate any rise in actual unemployment. the detailed numbers in the lfs reveal that people looking for work are much more likely to claim benefit if they ’ d had a job immediately beforehand. those moving from inactivity – people coming back into the labour market after taking time off, for example, or students leaving full - time education – are much less likely to do so ( chart 14 ), even if they ’ re unable immediately to find work and therefore count