Signal versus noise - speech
by Ben Broadbent
Given at the London Business School
Published on 18 December 2023
In this speech Ben talks about setting monetary policy under uncertainty, about
both the behaviour of the supply side of the economy and incoming data.
Heightened uncertainty about underlying productivity growth and also the NAIRU
means policy has had to put some weight on domestic inflation and wage growth,
things that would normally be viewed as late-cycle indicators. There is also a
degree of uncertainty about the observable data themselves. In particular, official
estimates of average wages have been volatile and, in the spring and summer of
this year, exhibited somewhat faster growth than other indicators.
Speech
Introduction and summary
We are all aware that the future is unpredictable. When it comes to gauging the economy,
however, it's not just the future that's uncertain: so is the present. For all the time and effort put into
its forecasts the MPC also spends a great deal of it getting to understand the here and now.
This isn't straightforward. For one thing, it's not always possible to determine precisely what's
causing what - to trace the economy we observe back to the underlying forces that are driving it.
Is output growth being moved around by demand or supply? Ditto employment? To take an
example of particular relevance right now, has strong wage growth been the result of exceptional
tightness of the labour market, especially last year; or is it the "second-round effect" of very high
spot inflation in late 2022 and earlier this year?
These things aren't mutually exclusive: almost certainly, both have played a part. But the balance of
the two matters. As the direct effects of the pandemic and the war dissipate, wholesale prices of
energy and other traded goods have been declining. This is now feeding through to inflation rates
for retail goods prices and the aggregate CPI itself (Chart 1 plots core goods inflation against its
wholesale counterpart; there have been similar trends in food and energy markets). As this
happens, one might expect these second-round effects on wage growth and broader domestic
inflation to weaken as well, quite independently of the stance of monetary policy. To the extent the
tight labour market is the cause of strong domestic inflation, however, then the economy would
need a longer period of below-trend growth - possibly with corresponding consequences for
monetary policy - to bring it back into a more sustainable position. At any rate, the more general
point is that it's not always easy to infer the deeper, unobserved causes of economic fluctuations
from the directly observable information[1].
Chart 1: Core retail goods inflation likely to decline further
Sources: ONS and Bank calculations. Core output PPI is for manufactured products excluding non-core items. Core output
PPI annual inflation is advanced by two months, which maximises correlation with CPI core goods inflation.
Second, even what we do get to observe - GDP, employment, wages and the like - may not be
perfectly measured. For some things (notably GDP) the relevant information comes in only over
time and, as a result, the data are subject to revision. These changes can be sizeable. Recently,
for example, estimated growth during 2020 and 2021 was revised up by almost two percentage
points (Chart 2). As a result the economy is now thought to have reached its pre-pandemic size
nearly two years earlier than was previously thought.
Chart 2: Economy recovered its pre-pandemic size earlier than first thought
Sources: ONS and Bank calculations. ONS 2023 Blue Book revisions. See Box C in the November Monetary Policy Report
for more details.
Even in cases where revisions are less important, there's almost invariably a degree of
measurement error in economic series. Recently, a steep decline in the response rate led the
ONS temporarily to suspend publication of its Labour Force Survey, used to construct the official
estimates of employment and unemployment[2]. But, in the UK series, there was always some
sampling error in official employment data, probably at least part of the explanation for the
"zigzag" pattern in quarterly growth rates (Chart 3[3]).
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Chart 3: Surveys provide good real-time information about employment growth
Sources: Bank of England, HMRC, KPMG/REC/S&P Global UK Report on Jobs, Lloyds Business Barometer, ONS, S&P
Global/CIPS and Bank calculations. From July 2023 LFS employment growth was replaced by ONS experimental Labour
Market Statistics - see this ONS Labour Market Overview for more details. Latest data point is for October 2023. Bank
staff's indicator-based model of near-term employment growth uses mixed-data sampling (or 'MIDAS') techniques
(see Daniell and Moreira (2023) for more detail). A range of indicators inform the model, including series from the
Bank of England Agents, the Lloyds Business Barometer, ONS/HMRC PAYE payrolls, S&P Global/CIPS purchasing
managers' index and the KPMG/REC UK Report on Jobs. Indicators are weighted together according to their relative
forecast performance in the recent past.
This is why, in both cases, the MPC has always supplemented official estimates of GDP and
employment growth with additional sources of information, usually from business surveys. The
orange lines in Charts 3 and 4 are weighted averages of survey readings and other indicators, for
employment and output respectively, designed to tell one something about their "underlying" rates
of growth.
Chart 4: We also have fairly accurate indicator models for output growth
Sources: S&P Global/CIPS, CBI, BCC and Bank calculations. GDP growth is the ONS's first estimate. Bank staff's indicator-
based model of near-term GDP growth uses MIDAS techniques, like for employment growth. A range of indicators inform
the model, including from the S&P Global/CIPS Purchasing Managers' Index, CBI Growth Indicator, and BCC Quarterly
Economic Survey.
One shouldn't exaggerate the problem: in many instances these difficulties aren't that important.
The indicator models in Charts 3 and 4 are actually fairly good. We have more sources of
information than we once did and also better techniques for using it - including the "nowcasting"
methods pioneered by Lucrezia Reichlin here at the LBS[4].
Regarding GDP, errors in early estimates are in general smaller than they used to be back in the
1980s and 1990s. And long-dated revisions of the sort we saw in this year's Blue Book, relating
to economic growth well over a year ago, won't in any case have much bearing on monetary policy
as any effects the additional output might have had on employment and subsequently inflation
would already have come through. (As such, they're more likely to affect one's view of the level of
potential supply in the economy, rather than the output gap (or, therefore, future inflation)).
As for employment, there was a time when the MPC seemed to put very little weight on news from
the labour market, however well measured the data. Indeed in the pre-financial-crisis period it was
hard to find anything that mattered for interest-rate decisions, empirically speaking at least, other
than output growth. Chart 5, which I've used several times in the past, is one striking way to get the
point across. It plots a survey measure of GDP growth against the average vote for changes in
Bank Rate, in basis points, on the MPC[5]. The pre-2010 correlation is extremely tight and, once
one's allowed for it, nothing else - whether unemployment, wage growth or inflation itself - offers
any further statistical help in explaining how policy rates behaved.
Chart 5: Monetary policy was very sensitive to output growth before the financial
crisis, during the "great stability", much less so after it
Sources: Bank of England, S&P Global and Bank calculations. Six-month rolling averages.
During that pre-crisis period, when policymakers were confident about the stability of underlying
productivity growth, and of the supply side more generally, this made sense. An acceleration in
demand would lead to lower unemployment, albeit with some delay; as long as the NAIRU is also
stable, lower unemployment would necessarily, in time, indicate greater inflationary pressure,
including faster wage growth; and with inflation subject to few other disturbances, independently of
demand, why wait until the end of this chain to act? You may as well tighten policy as soon as you
see the economy strengthen and, symmetrically, loosen when output growth declines. In this happy
environment, sometimes known as the "divine coincidence", the policy that best stabilises
economic growth is also the policy that offers the best chance of controlling inflation.
The financial crisis marked the end of this "great stability" and with it this simple feedback rule for
monetary policy. Those days are long behind us. As Chart 5 reveals, policy decisions have since
been much less well correlated with GDP growth. Instead - or least in addition - policy then
started responding to more direct measures of the degree of slack in the economy, in particular
the rate of unemployment. Latterly, the MPC has also had to put weight on nominal indicators such
as wage growth and other late-cycle measures of domestic inflation.
Shortly I'll try and explain why this is. This will basically be a restatement of a point I made in a talk
in the summer (Broadbent, 2023) but, to flesh it about a bit, I'll chuck in the results of a simple
model. This is designed to get across how policy should behave when there's incomplete
information about the various shocks hitting the economy.
I'll then take a closer look at the unemployment and wage data. If the absence of the Labour Force
Survey (LFS) employment estimates isn't so serious, the temporary absence of the series for
unemployment is felt a little more keenly. It matters more in models of wage growth. And, during
the temporary suspension of the LFS, the ONS is in the meantime using a proxy - the claimant
count - that may not be quite as accurate as those we have for employment.
As for wages, one should say straightaway that the source of information for the official average
weekly earnings (AWE) series, a survey of businesses called the Monthly Wages and Salaries
Survey (MWSS), has not suffered from the same declining response rates as the LFS (Chart 6).
That said, it is a rather volatile series, particularly from quarter to quarter, and at times this year
there has been unusually high disparity between the AWE and other measures of wage growth
that the MPC routinely looks at.
Chart 6: The survey used for official estimates of wage growth hasn't suffered
from the same declining response rates as the LFS
Why unemployment and wage growth matter
As we saw a moment ago, policy in the pre-crisis "great stability" responded very sensitively - and
pretty much uniquely - to economic growth. This can be justified if the policymaker is confident
about the stability of the supply side of the economy.
But if the financial crisis marked the end of a long period of healthy productivity growth, it also
made the supply side more uncertain. And this in turn necessitated a change of approach for
monetary policy.
When underlying productivity growth can vary, and swings in output might be associated as much
with supply as demand, they may no longer carry the same implications for the degree of slack in
the economy. In that case it makes sense to put at least some weight on more direct indicators of
spare capacity, including unemployment, even if one loses some time in doing so. That time is lost
because of the delay between shifts in demand and their consequences for unemployment (in UK
data the figure seems to be around 4-6 months). Following a genuine demand-driven rise in
output, waiting to see what happens in the labour market would prevent you from responding to it
as promptly as you otherwise would. On the other hand it might also save you from mistakenly
tightening policy if it turns out to have been driven (or at least accompanied) by better supply. As
long there's a reasonable degree of stability in the NAIRU - the rate of unemployment consistent
with stable wage growth and domestic inflation - then changes in unemployment can give you a
cleaner (if later) read than GDP growth alone on how spare capacity is developing. This, at least,
is how I understood the MPC's conditional guidance back in 2013 (which singled out the rate of
unemployment) and, more generally, the weight the Committee began to put on the labour market
following the financial crisis (Broadbent, 2014).
However, if even the NAIRU can shift around - or, more generally, wages and domestic inflation
are subject to important influences other than spare capacity - then GDP and unemployment may
still be insufficient. You also need to consider the behaviour of these nominal variables more
directly. As I sought to explain in a talk this past summer, this is one way to understand why the
MPC has had to pay more attention to wage growth and services inflation than in the past - more,
in fact, than to economic growth.
Judging by the huge expansion in the number of vacancies in 2022, and steep declines too in
surveys of labour availability, the labour market seemed to be significantly tighter than the rate of
unemployment alone would have suggested (Chart 7).
Chart 7: The labour market was exceptionally tight in 2022
Sources: Bank of England, ONS and Bank calculations. Average of Agents' individual company visit scores.
This indicated that that the NAIRU had risen, perhaps because, amidst all the shifts created by the
pandemic, the labour market was having a hard time reabsorbing the significant numbers coming
back to work and reallocating them in line with demand[6]. The very pronounced second-round
effects of higher import prices on domestic prices and wages can be thought of in a similar way -
they too can boost inflation even for a given rate of unemployment. Taken together these
developments suggest that, on top of output growth and unemployment, policy should put some
weight on these nominal variables, even if that means waiting that much longer to respond to (what
subsequently turns out to have been) true shocks to demand. Trading off timeliness for a better
understanding of what's going on in the economy, uncertainty about the supply side pushes one
further to the right of the schematic picture in Chart 8.
Chart 8: Lags in the inflation process mean that, when the policymaker is
uncertain about supply, there's a trade-off between a better understanding of the
economy and the timeliness of the policy response
The lag between movements in spare capacity and inflation is longer and the price one pays for
this extra information, in time, is therefore higher. There are clearly risks in moving only when you
see "the whites of inflation's eyes". But it may still be a price worth paying. The two graphs below
(Chart 9), derived from a simple model of inflation in which there's incomplete information about
the shocks hitting the economy[7], help to cement the point. In this representation inflation can be
moved around by four underlying drivers - demand, productivity, firms' mark-ups and movements
in the NAIRU - but the policymaker has only three observable variables from which to deduce
them (output, unemployment and inflation itself). Importantly, he or she is assumed to understand
how variable these underlying disturbances are (estimates that can be built up over time). But
there is nonetheless what is sometimes called a "signal extraction" problem, and one can
estimate only imprecisely, in real time, what is driving what. And, in doing so, the policymaker will
lean on a greater range of information.
Chart 9: More weight put on unemployment when there's uncertainty about
underlying productivity growth; uncertainty about the NAIRU means wage and
price inflation matter too
Sources: ONS and Bank calculations. First panel plots the contribution of activity and unemployment data to the estimated
variance of the output gap, where along the x-axis we increase the standard deviation of the supply shock relative to the
others.Second panel charts the contributions of unemployment and inflation to the estimated variance of the output gap,
where along the x-axis we increase the standard deviation of the shock to u*.
The graphs plot two pairwise comparisons of the relative importance of the observable series for
estimates of the output gap, depending on the variance of (and therefore uncertainty about) the
underlying shocks. As uncertainty about underlying productivity growth rises, the policymaker puts
increasing weight on developments in unemployment rather than GDP growth. When the NAIRU is
more uncertain, inflation becomes more significant. As that happens, the variability of inflation
itself rises, because policy has a less precise understanding (at least in real time) of what's
driving the economy and there's a greater chance of a misdiagnosis[8].
This is a pretty simple representation but it does, I think, capture something about the environment
we've been in for the past two to three years.
Greater uncertainty about movements in unemployment
Having tried to get across why indicators like unemployment and wage growth have been
important for policy, I now want to take a closer look at the data themselves.
I mentioned earlier that business surveys can give one a reasonable read on what's happening to
employment. To that extent the temporary suspension of the LFS isn't that costly.
I don't think the same is true of unemployment. If the active workforce were reasonably stable it
wouldn't be too much of a problem. In that case one could infer changes in unemployment directly
from those in employment.[9] In practice, however, estimates of labour market activity move
around quite a bit, even over relatively short periods of time (Chart 10). The most recent Labour
Force Survey, in quarterly data, was for 2023Q2. Over the previous three quarters, from its trough
in the third quarter of 2022, the rate of unemployment was estimated to have risen by a little over
half a percentage point. That happened not because employment declined - it actually grew
reasonably strongly - but because of a recovery in participation. According to the LFS, significant
numbers of people decided to return to work and not all of them were able to find jobs
straightaway. The more general point is this: we care more about unemployment than employment
and movements in the second aren't necessarily enough to tell us about the first.
Chart 10: Change in unemployment depends on the difference between the
growth rates in the active workforce and employment; both can vary
Sources: ONS and Bank calculations.
So, during this temporary suspension of the LFS, the ONS is instead basing its estimates of
unemployment on developments in the claimant count - the numbers of people receiving
unemployment-related benefit.
However - and even if there's nothing better - it's not clear this is a perfect proxy. People can and
often do look for work without claiming benefit. And although the two measures are reasonably
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well correlated in the past, and over longer periods of time, that's less true from quarter to quarter,
particularly in the past couple of years (Charts 11 and 12). The rise in LFS 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 roll-
out 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 for more details. Unemployment data available to October,
claimant count data to November.
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 as
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unemployed. This makes intuitive sense. And it means that one wouldn't expect any material rise
in the claimant count unless or until there were significant job losses.
Chart 13: LFS data suggest both rising unemployment and flat claimant count
Sources: ONS and Bank calculations. In the LFS unemployment-related benefits are defined as claims for Job Seekers
Allowance or Universal Credit for the reason of being 'unemployed and searching for work'. This is a slightly narrower
definition than for the headline claimant count measure. Latest data point is Q2 2023.
Chart 14: People who've lost jobs much more likely to claim benefit than those (re-
)entering the labour market from inactivity
Sources: ONS and Bank calculations. First panel plots quarterly labour market flows data for people aged 16-64. Flows
relate to activity status three months prior. Right panel plots share that report claiming unemployment-related benefits.
Activity status relates to 12 months prior. Latest data point is Q2 2023.
I should say that there's very little sign of that. The redundancy rate ticked up a little during the first
half of the year. So-called "HR1 notifications" - advance notice of redundancies that larger
employers are obliged to give to the government's Insolvency Service - provide somewhat
timelier information and those too have edged up. But they're still very low by historical standards.
More generally, I do not mean to imply that the true rate of unemployment is definitely still rising.
Our indicator model suggests employment is broadly flat so, at least in the near term, any rise in
unemployment would arise only because of continuing growth in the active workforce - and the
participation rate is simply too volatile to count on that in the near term.
The point I want to make is more that, during the temporary suspension of the LFS, the loss of the
unemployment series matters a little more than that for employment. It matters more in basic
models of wage growth (which is why it's one of the variables stressed by the MPC). And proxies
for the change in unemployment - whether just flipping round the figure for employment growth or
seeing what's happened to the claimant count - do not, over short periods of time, perform that
well.
Wage growth: how high did it go and is it now declining?
As we saw earlier, the survey used to construct the official wage series has none of the
responserate problems that have afflicted the LFS (Chart 6). A high and stable proportion of businesses
asked to do so complete the MWSS. It covers only those businesses that pay VAT or PAYE (about
120,000 of the 5-million-plus registered firms in the UK); of those "only" 9,000 complete the survey.
But the skew - in both the eligible and surveyed populations - is very much towards the larger
businesses. So the AWE actually captures 14 million employees, almost half of their total number.
All that said, the official wage data are nonetheless subject to revision, thanks to late returns; even
though it's adjusted for outliers (the ONS tops and tails the raw numbers from the MWSS) AWE
growth can be pretty volatile, particularly over short periods of time; and, for much of this year, the
headline figure has been higher than many other indicators of wage growth (Charts 15 and 16).
Chart 15: Acceleration in AWE in the spring and summer not matched by other
indicators of wage growth
Sources: DMP Survey, Indeed Hiring Lab, ONS and Bank calculations. All measures are three-month average over three-
month average a year earlier. RTI 'private sector' pay is a Bank staff estimate that strips out sectors with a high share of
public workers, such as public administration and defence, social security, education, health and social work.
Chart 16: The REC has been a good leading indicator of AWE growth for much of
its history but not in 2023
Sources: ONS, REC/KPMG/S&P Global Report on Jobs and Bank calculations. Underlying private sector regular pay growth
is Bank staff's estimate of underlying pay growth between January 2020 and November 2022 and ONS AWE private sector
regular pay growth otherwise. REC measure is an index of permanent staff salaries, mean-variance adjusted to quarterly
ONS private sector regular pay growth over March 2001-19. REC is advanced by four months, which maximises correlation
with AWE quarterly private sector regular pay growth.
It may be that we're now seeing a degree of re-convergence - the official data have softened a bit
in the last couple of months. But the slightly muddy picture of the recent past, coupled with the
general volatility of the data, means the MPC would probably want to see more evidence, across
several indicators, before concluding things are on a clear downward trend.
The recent volatility of the AWE 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 (2016) .
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 relationship with short-term changes in the official wage index. Movements
in the REC have tended to foreshadow those in quarterly AWE growth around four months later,
with a reasonable degree of reliability.
This was clearly not the case earlier this year: the REC index peaked over a year ago and then fell
back steeply over the following six months. On past form one might then have expected a decline
in private-sector AWE growth starting early in the New Year. In fact, between January and July, the
index accelerated significantly, rising at an annualised rate of over 9% over that six-month period.
It's not clear why the REC proved so misleading. One possibility is that the "second-round" effects
of high inflation were skewed towards existing rather than new employees (coming from a survey
of recruitment agents the REC picks up only the latter). We know that firms were accepting
significant increases in wages early this year in order to help compensate employees for the
steep rises in the cost of living. If these were aimed more at retaining than attracting people, and
therefore paid disproportionately to people already at the firm, this would help to explain the
divergence in Chart 16. It would also suggest that, as these effects fade, the relationship between
the two should re-establish itself (certainly it's hard to think of something that would permanently
have ruptured the link).
The second is the RTI series derived from tax data. This is much younger than the REC survey (it
started in 2014). On the other hand, its sample size is even bigger than the MWSS - more than
twice as big, in fact. It covers everybody subject to PAYE income tax (around 30 million
people[12]). Helpfully, it also has a wealth of detail. One can distinguish between changes in the
median and the mean (for various reasons economists at the Bank tend to favour the former[13]). It
also offers a sectoral breakdown, allowing us to construct a reasonably good proxy for private-
sector pay alone[14]. One drawback is that, unlike the AWE, it's not possible to strip out bonus
payments (the MPC generally cares more about regular, non-bonus pay growth[15]).
Given these huge samples, for both series, and what must therefore be a big overlap between the
two populations, it's perhaps surprising that the AWE and RTI series aren't routinely closer to each
other. At least in 2023, some of the gap in Chart 15 may relate to this question of bonuses.
According to the AWE, bonus growth was relatively subdued this year. So growth of the aggregate
- with which the RTI series is probably more fairly compared - was slightly weaker than that of
regular pay, and therefore closer to the RTI figure (Chart 19). But there's still a gap, although that
too has narrowed a bit in the past couple of releases. In the three months to October,
privatesector pay including bonuses was 7.2% higher than in the same period of 2022. This compares
with 6.8% for the median of the RTI series, a figure that in the three months to November fell to
6.2%.
Chart 19: For much of this year tax-based estimates of private-sector wage growth
have been lower than that in the AWE
Sources: ONS and Bank calculations. RTI 'private sector' measures are Bank staff estimates - see footnote to Chart 15.
I should be clear that the MPC still puts most weight on the AWE series. And it's evident from all of
them that pay growth - and with it other indicators of domestic inflation like the services
component of the CPI - has been significantly faster than the sorts of rates that, over time, one
might think of as consistent with the 2% inflation target.
But, taking all this together, there is a question about whether rapid pick-up in wage growth in the
spring and summer was quite as marked as in the official data. AWE growth has been particularly
volatile in the past