Natalie Burr, Julian Reynolds and Mike Joyce

Monetary policymakers have a number of tools they can use to influence monetary conditions, in order to maintain price stability. While central banks typically favour short-term policy rates as their primary instrument, when policy rates remained constrained at near-zero levels following the global financial crisis (GFC), many central banks – including the Bank of England – turned to unconventional policies to further ease monetary conditions. How can the combined effect of these policies be measured? This post presents one possible metric – a Monetary Conditions Index – that uses a data-driven approach to summarise information from a range of variables related to the conduct of UK monetary policy. We discuss what this implies about how UK monetary conditions have evolved since the GFC.

What are monetary conditions?

The idea of constructing a Monetary Conditions Index (UK MCI) – a summary metric of variables related to the conduct of monetary policy – is not new.

Traditionally, monetary conditions were defined as a combination of information from short-term interest rates and exchange rates (eg Batini and Turnbull (2000)). Earlier literature on MCIs therefore typically focused on a small number of variables.

This approach has become less defensible as many central banks – including the Bank of England – extended their toolkit with a range of monetary tools. The key feature of more recent approaches to measuring monetary conditions, therefore, has been to examine a wider range of variables, in order to capture information about tools such as quantitative easing (QE) and forward guidance, which aim to influence longer-term interest rates.

Conceptually, monetary conditions don’t include risky assets or private credit. This is because they do not fall within the category of variables relating to the conduct of monetary policy, as they are likely to be affected by credit risk premia. These would be relevant for measures of broader financial conditions.

It is important to stress that monetary conditions do not provide a direct reading of a central bank’s monetary stance. The monetary stance describes the impact of policy rate today, in combination with expectations of future policy actions, on real economic activity (February 2024 Monetary Policy Report). Monetary conditions are related to, and influenced by changes in the monetary stance, but by other factors too (such as household preferences for holding bank deposits).

Methodology

Our approach for constructing the UK MCI is similar to the data-driven approaches of Kucharčuková et al (2016) and Choi et al (2022). We estimate a Dynamic Factor Model (DFM) from a combination of the policy rate – which was constrained for a prolonged period by the effective lower bound (ELB) on nominal interest rates post-GFC – with a wider range of monetary and financial variables. We extract common factors driving comovement of the variables in our data set and construct a weighted average of these factors. Weights are equal to the proportion of overall variance that each factor explains, divided by its standard deviation.

This data-driven approach avoids imposing priors on the weights (eg relating the weights to the impact of individual variables on macroeconomic outcomes), which seems a natural benchmark.

We use monthly data since 1993, after the UK adopted inflation targeting. Our data set combines both price and quantity variables and includes three main variable categories.

First, interest rates. More specifically, Bank Rate; short-term overnight index swap rates (up to three years); and long-dated gilt yields (up to 20 years). We motivate the inclusion of interest rates across the yield curve as these are directly affected by policy rates and QE purchases, and likely to contain useful information on forward guidance.

Second, we follow Lombardi and Zhu (2018) by including monetary aggregates and central bank balance sheet variables to provide further information about monetary policy operations. Following Kiley (2020), these variables enter the DFM twice, as (log) levels and as year-on-year changes, to account for stock and flow effects respectively. It is debatable whether monetary aggregates and balance sheet variables provide material additional information about the real economy effects of monetary policy, over and above their impact on interest rates (see Busetto et al (2022) and Broadbent (2023)). Though this may risk double-counting, to the extent that our modelling strategy aims to let the data speak for itself, incorporating monetary aggregates and balance sheet variables provides useful information about their comovement with interest rates.

A key question is how to treat the exchange rate. Some MCIs retain the exchange rate to account explicitly for policy transmission via this channel. While they are part of the transmission of monetary policy, exchange rates are not seen as a policy instrument by the Monetary Policy Committee (MPC), and, importantly, are influenced by many domestic and global factors which may not be informative about UK monetary conditions (Forbes et al (2018)). On those grounds, we exclude the exchange rate. Sensitivity analysis suggests its inclusion did not materially change the empirical results.

Results

To give a sense of what is driving changes in the UK MCI, Table A summarises the estimated factor loadings from the DFM, as well as the weight of each factor in the UK MCI. The factor loadings reflect how the variables are weighted together within each factor, as well as the correlation between the variables and each factor. We assign a positive sign to Bank Rate across all factors, so that increases imply tighter monetary conditions; we expect a negative sign on monetary aggregates and central bank balance sheet variables, as an expansion in these quantities implies looser conditions.

Table A: Factor loadings

Notes: Factor loadings are averaged across different subcategories of variables.

Source: Authors’ calculations.

The factor loadings suggest that all blocks of variables have a significant bearing on the UK MCI. The first factor – which explains the largest share of common variance between the variables – is mainly driven by interest rates, the stock of monetary aggregates and balance sheet variables. By contrast, the rate of change of the quantity variables is the main driver of the second factor. We retain the first three factors, which explain almost 90% of overall variance in our data set.

Chart 1 plots the UK MCI in the bottom panel and some key input variables that feed into it. To interpret the UK MCI, note that it is normalised by subtracting its mean and dividing by its sample standard deviation. As such, we place less weight on the level of the UK MCI, and more on changes. As Batini and Turnbull (2000) highlight, you cannot make a statement about degrees of tightness, but you can make relative statements, such as whether monetary conditions are tightening or easing.

Chart 1: UK MCI and selected input variables

Notes: The index is expressed in standard deviations from average. Stalks denote: (I) GFC; (II) EU Referendum; (III) Covid-19; and (IV) start of tightening cycle. Latest observation: November 2023.

Sources: Bank of England, Bloomberg Finance L.P, Tradeweb and Bank calculations.

Our index points to a loosening in UK monetary conditions during previous stimulus episodes. The UK MCI drops significantly during the GFC (Chart 1, Stalk I), consistent with the MPC’s conventional and unconventional monetary policy actions. The UK MCI also suggests monetary conditions eased as a result of monetary policy actions following the EU Referendum (Stalk II) and Covid-19 (Stalk III), however less so than during the GFC.

During the recent tightening cycle (Stalk IV), the UK MCI increased slightly earlier than Bank Rate, reflecting the slowing pace of QE purchases in 2021. The tightening over 2021–23 was driven first by reduced balance sheet flows, and then moves in the yield curve, first at the short end, and then also at the longer end. The UK MCI also suggests that monetary conditions have loosened slightly since peaking in September 2023.

It is important to keep in mind that the UK MCI presented here is a statistical construct and reflects only one approach to measuring monetary conditions. Our modelling strategy is designed to weight together variables based on their historic comovement with each other, not their correlation with GDP or inflation. Due to our use of fixed weights, any state-contingent effects of policies are only indirectly captured in our index, to the extent that it is reflected in interest rates. That said, to the extent that monetary conditions transmit changes in the monetary stance to the real economy, it is plausible that our UK MCI provides some information about future macroeconomic outturns. Preliminary analysis is consistent with this view, though further research is required to substantiate the relationship between monetary conditions and the macroeconomy.

Conclusion

The UK MCI presented in this post provides a comprehensive new measure of UK monetary conditions, which synthesises information about both conventional and unconventional policies. Crucially, our measure shows material variation in the post-GFC period, when Bank Rate was constrained by the ELB. Indeed, it highlights that unconventional policy tools supported significant loosening in UK monetary conditions in response to the GFC and subsequent stimulus episodes. Even at times when the ELB is not binding, including the recent tightening cycle, the UK MCI provides more information about the evolution of monetary conditions, faced by economic agents, than a sole focus on Bank Rate would suggest.

Given that unconventional tools are now an established part of the monetary toolkit, further research into monetary conditions, and what they imply for macroeconomic outcomes, remains important.

Natalie Burr and Julian Reynolds work in the Bank‘s External MPC Unit, and Mike Joyce works in the Bank’s Monetary and Financial Conditions Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Tim Willems and Rick van der Ploeg

Since the post-Covid rise in inflation has been accompanied by strong wage growth, interactions between wage and price-setters, each wishing to attain a certain markup, have regained prominence. In our recently published Staff Working Paper, we ask how monetary policy should be conducted amid, what has been referred to as, a ‘battle of the markups’. We find that countercyclicality in aspired price markups (‘sellers’ inflation’) calls for more dovish monetary policy. Empirically, we however find markups to be procyclical for most countries, in which case tighter monetary policy is the appropriate response to above-target inflation.

In a simplified setup where wages are firms’ only input cost, while consumers only buy domestically produced goods, the ‘battle of the markups’ takes an intuitive form (Rowthorn (1977)): 

• Workers aspire to have their nominal wage feature a certain excess, typically referred to as the ‘wage markup’ () over the cost of their consumption basket . This is equivalent to saying that their aspired real wage equals .
• Firms aspire to set their price to secure a price markup ‘’ over their marginal cost of production, which is the nominal wage rate . That is: they aspire for , implying that the real wage desired by firms equal to .

By itself, there is nothing guaranteeing that real-wage aspirations held by workers and firms are mutually consistent in this framework – ie, there is nothing to ensure that = (Blanchard (1986); Lorenzoni and Werning (2023)). Every time that workers get to reset their wage, they may consider the prevailing real wage too low, upping the nominal wage. When firms next get to reset prices, they may consider the current real wage too high, upping prices. This could give rise to unstable wage-price dynamics.

Unemployment as an equilibrating device

Layard and Nickell (1986) argued that the moderating effect from the presence of unemployment acts like a clearing mechanism. They posed that aspired markups and are likely cyclically sensitive. Workers might feel that they have less bargaining power when unemployment ‘’ is higher, making them settle for a lower wage markup. Unemployment can thus act to tame unrealistic aspirations. Formally, this can be captured by modelling the aspired wage markup  as consisting of a structural component (‘’) alongside a cyclically sensitive one (‘’):

                                    (1)

Here, the structural component ‘’ captures workers’ aspirations based on ‘exogenous’ factors, eg what they have gotten used to given their past consumption patterns. If , the cyclical term ‘’ captures the notion that workers’ aspired markups are procyclical, so that workers are likely to ‘settle for less’ when the threat of unemployment is greater.

Similarly, price markups aspired by firms also consist of a structural component alongside a cyclically sensitive one:

                                      (2)

When it comes to the cyclicality of price markups, it is debated whether they are pro or countercyclical. On the one hand, a slowdown makes firms afraid of having to carry large inventories or suffer from capacity underutilisation. This would imply that aspired price markups are procyclical (). On the other hand, other theories imply that firms’ aspired markups move countercyclically (). For example, by pushing some firms out of business, a recession may increase the market power of surviving firms – implying that firms’ aspired markups rise in downturns.

In general, and irrespective of the sign of , it is possible to find an equilibrium rate of unemployment, ensuring consistency between the real wage aspired by workers and that aspired by firms. At this point the wage-price cycle is put to rest – enabling inflation to land at target.

It can be shown that the equilibrium level of unemployment increases in structural aspirations held by workers and firms (): when workers and/or firms aspire to obtain a greater size of the pie, without the pie having grown in size, something will have to give. Here, that is unemployment which has the effect of moderating the elevated aspirations, to re-establish consistency. If unemployment does not rise to tame aspirations, there will be pressure on inflation in the short run. This is what has been called conflict inflation.

The role of the central bank

The story so far assumes that, somehow, the unemployment rate ‘agrees’ to clear any conflict between firms and workers. In reality, it won’t automatically. There are many reasons for unemployment to exist, eg search frictions (Pissarides (2000)) or providing incentives to limit shirking (Shapiro and Stiglitz (1984)). This implies that the level of unemployment is not ‘free’ to clear any conflict and further action is required.

This is where the central bank comes in. Through its mandate, the central bank is tasked with setting policy to keep inflation at target. In our framework, this implies that the central bank will attempt to set its policy to ensure that cyclical conditions are such that markup aspirations are consistent with the size of national income. And if aspired markups are cyclically sensitive, there is an ‘aspirational channel’ of monetary policy transmission.

If aspired markups of both firms and workers are procyclical (), the policy prescription for the central bank is conventional: it should tighten in response to inflationary pressures, as doing so will lower aggregate markup aspirations – eventually re-establishing consistency, which brings inflation back to target.

There is however debate over the sign of , with many studies arguing that firms’ aspired markups are, in fact, countercyclical (), for example because more bankruptcies in recessions increase market power of surviving firms. Any resulting price increases can then be seen as a form of ‘sellers’ inflation’ (Weber and Wasner (2023)). In that case, policy prescriptions are less clear: even if a monetary tightening reduces workers’ aspired markups, it may not be successful in lowering inflation if the ensuing recession ends up increasing markups aspired by firms. On balance, inflation might thus increase following tighter monetary policy, and a more ‘dovish’ monetary policy would be called for – particularly if the channel via the Phillips curve (a monetary tightening lowering firms’ marginal costs) is weak. 

Consequently, it is important for central banks to know whether firms’ aspired markups are pro or countercyclical. We have estimated the cyclicality of the price markup () for 61 countries (details are in our Staff Working Paper), and find that price markups are procyclical in most, including the UK and the US, but countercyclical in various other countries (see Chart 1).

Chart 1: Estimated degree of cyclicality in price markups () in various countries

Paying for imports

Recent UK experiences have been more involved than the stylised situation described thus far. Next to domestic workers and firms, foreign exporters also lay a claim on UK output – as output is partly produced with imports, like energy. As energy prices rose around Russia’s 2022 invasion of Ukraine, the UK’s terms-of-trade worsened and the share of national income flowing abroad suddenly went up – leaving less pie to be distributed domestically.

Absent any reduction in the structural components of markups aspired by firms and workers ( and ), a larger share of national income flowing abroad implies distributional conflict domestically – pushing inflation away from target. Since price markups are estimated to be procyclical in the UK (Chart 1), while the same is thought to apply to workers’ aspired wage markups, a rise in inflation may require the central bank to tighten. This is needed to moderate markup aspirations, ultimately clearing any conflict, enabling inflation to return to target.

Indeed, central bankers appear to have an ‘aspirational’ transmission mechanism in mind as can be seen from Christine Lagarde (2023):

We need to ensure that firms absorb rising labour costs in margins (…) The economy can achieve disinflation overall while real wages recover some of their losses. But this hinges on our policy dampening demand for some time so that firms cannot continue to display the pricing behaviour we have recently seen (emphasis added).

Conclusions and policy implications

A monetary tightening is not the only way via which markup aspirations could be moderated. Faced with an adverse terms-of-trade shock, it is also possible that workers and/or firms internalise the implications (that there is less income to be divided domestically), inducing them to lower the structural components of their aspired markups ( and ). In this regard, it would be interesting to obtain a better understanding as to whether communication (by central banks or governments) can ‘endogenise’ aspirations of workers and firms (making them directly sensitive to the terms-of-trade), as it is ultimately costly for a central bank to have to step in and tame aspired markups by affecting the business cycle.

Absent such a co-ordinated response, bringing inflation back to target following an adverse terms-of-trade shock may require a cyclical slowdown to moderate markups aspired by workers and firms. An important caveat is that this strategy might not work if firms’ aspired price markups are countercyclical, but we find no evidence for this in the UK. As a result, the monetary tightening implemented in recent years is likely to aid the disinflation process via our ‘aspirational channel’ (not present in most standard models, featuring acyclical desired markups), which facilitates inflation returning to target.

Tim Willems works in the Bank’s Structural Economics Division and Rick van der Ploeg is a Professor at the University of Oxford.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Dario Bonciani and Johannes Fischer

The UK economy has been hit by significant terms-of-trade shocks, most notably the rise in energy prices following the Russian invasion of Ukraine. These shocks have created substantial and persistent inflationary pressure in many countries. Such upheavals bring increased uncertainty about the future, making macroeconomic forecasting more challenging. In this post, we assess the forecasting performance of a state of the art empirical model, of the type commonly employed in academic research and policy institutions. This model is not used to produce the Monetary Policy Committee’s (MPC’s) forecast but has been used periodically within the Bank of England including as a cross-check to the main forecast. Specifically, we assess its performance in predicting UK inflation out of-sample at key dates around the start of the war in Ukraine. The model performs well in forecasting short-term inflation, but it struggles to fully capture inflation persistence over the longer term.

Methodology

To conduct our forecasting analysis, we have employed a Bayesian Vector Autoregressive (BVAR) model. These types of models have gained widespread popularity in academia and central banks for their flexibility and strong forecasting abilities, as evidenced by studies like Bańbura et al (2010) and Angelini et al (2019).

In essence, this empirical framework encompasses a series of linear equations designed to model the interdependencies and dynamics of macroeconomic variables. Further details on BVARs can be found here. Our specification includes 20 variables. Among these, 15 are specific to the UK economy, including the consumer prices index (CPI), real gross domestic product (GDP), the Bank Rate, and specific components of CPI, such as energy and food. Additionally, we incorporate global variables in the model, including world real GDP, global trade, and world CPI. Our model specification was used in a recent speech by Catherine L Mann, an external member of the Bank of England’s MPC. In her speech, she highlights how including periods of high inflation, such as 2022 Q1–2023 Q2, in the estimation sample affects the inflation forecasts of the BVAR.

The BVAR model relies on historical regularities between the included variables to produce forecasts. To capture these historical regularities, we estimate the model parameters using quarterly data from 1992 to 2019. To produce our BVAR forecasts, we make the additional assumption that the energy and food CPI components in the model are expected to follow exactly the same path as implied by real-time market futures curves (which will be influenced by financial market participants’ expectations about future prices). This assumption enables our model to factor in information about the latest events affecting food and energy prices. This assumption is necessary as we want the model to have all the information available at each point in time. Using the estimated historical regularities along with real-time information on the futures curves for energy and food prices, we then generate out-of-sample inflation forecasts at various points in time. In this post, we focus on the forecasts implied by the model before and after the onset of the Ukrainian conflict.

Empirical model estimated on pre-pandemic data

Chart 1 presents three panels illustrating inflation forecasts based on real-time data at two distinct time points: 31 January 2022 and 30 April 2022. The purple lines represent the BVAR forecasts, while the dashed lines depict the evolution of actual inflation. For comparison, we also include the median inflation forecast from the Market Participants Survey results as green dots. Finally, the shaded areas denote the statistical uncertainty surrounding the BVAR forecasts.

Chart 1: Comparing inflation forecasts at different points in time

In January 2022, as the threat of the Russian invasion became more likely, the BVAR forecast (upper Chart 1 panel) projected inflation to peak at 8% in November 2022. In comparison, professional forecasters expected inflation to peak at 6%, 2 percentage points lower than the BVAR.

Two months after the beginning of the Russian invasion, in April 2022 (lower Chart 1 panel), both the BVAR and professional forecasters had adjusted their forecasts upwards to reflect the increase in energy prices. In the short term (the first two quarters), the BVAR model closely tracked realised inflation. However, inflation proved more persistent than the model’s historical regularities and futures curves about food and energy prices could predict. The gap between the forecast of the BVAR and that of professional forecasters that existed in January disappeared almost completely by the end of April. One potential explanation for the initial difference in forecasts (and its disappearance) could be that professional forecasters had not considered the Russian invasion of Ukraine to be as likely as financial market participants had. Finally, professional forecasters also did not anticipate inflation remaining high for an extended period.

Overall, the BVAR model’s forecasts implied high rates of inflation before the Russian invasion of Ukraine but missed realised inflation by several percentage points. Once the Russian invasion had begun, the inflation peak of the BVAR forecast is close to the eventual peak.

Including post-pandemic information

Lastly, we examined whether the persistent rates of inflation seen over the past two years may significantly affect future BVAR forecasts, as argued in the above-mentioned speech by Catherine L Mann. To do so, we re-estimated the model with data that includes the run-up in inflation up until 30 April 2023, excluding the outlier data during the pandemic years (2020–21), as per the methodology in Cascaldi-Garcia (2022). The out-of-sample forecast with the data available at this point in time slightly increased the inflation persistence. Interestingly, over the full forecast horizon, the predictions from the BVAR model and professional forecasters aligned very closely.

Chart 2: Does post-pandemic data affect the inflation forecast?

Conclusion

Returning to our initial question, to what extent a linear model can predict inflation in the face of large terms-of-trade shocks. Prior to the war in Ukraine the model forecasted inflation significantly below its eventual realisation. This is not surprising because the model could not have foreseen the extent of the energy price increase associated with the war. Following the start of the war, when the energy price increase was realised, the BVAR model performed well in forecasting inflation in the nearer term despite its relative parsimony. However, it struggled to fully capture the inflation’s persistence over the longer term. Using data realisations from 2020 onwards to estimate the BVAR parameters can potentially help better capture the persistence of inflation in the future. Our analysis suggests that a linear model such as the BVAR can still prove to be robust for forecasting even in a turbulent macroeconomic environment.

Dario Bonciani and Johannes Fischer both work in the Bank’s Monetary Policy Outlook Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Lennart Brandt, Natalie Burr and Krisztian Gado

The Bank of England has a 2% annual inflation rate target in the ONS’ consumer prices index. But looking at its 700 item categories, we find that very few prices ever change by 2%. In fact, on a month-on-month basis, only about one fifth of prices change at all. Instead, we observe what economists call ‘sticky prices’: the price of an item will remain fixed for an extended amount of time and then adjust in one large step. We document the time-varying nature of stickiness by looking at the share of price changes and their distribution in the UK microdata. We find a visible discontinuity in price-setting in the first quarter of 2022, which has only partially unwound.

Theory of sticky prices and related literature

Understanding price-setting dynamics is essential for central banks. Most structural models in the literature use a form of time-dependent pricing, under which firms keep prices the same for fixed amounts of time (Taylor (1980)), or for random amounts of time such that there is uncertainty about the precise length of the price spell (Calvo (1983)). Another way of modelling sticky prices emphasises that firms will not just look at the time that has passed since they last adjusted its price, but also at how far their price is from some desired price level. This is called state-dependent pricing. Macroeconomic models don’t typically allow for time-variation in the degree of stickiness or switching between pricing strategies. Recently, however, firms in the Decision Maker Panel tell us that they have moved increasingly away from time-dependent towards state-dependent pricing. In this case, when there is a large shock affecting many firms, the shock leads to an increased frequency of price changes and so more immediate pass-through to overall inflation.

In order to better understand the pricing behaviour of firms in times of large inflationary shocks, we explore the pricing dynamics at the micro level using CPI microdata published by the ONS. We are of course not the only ones who have been interested in this type of data. Bank authors have been using this data set for a number of years. For example, Bunn and Ellis (2011) document stylised facts about pricing behaviour from the UK microdata and the August 2020 Monetary Policy Report used CPI microdata to inform policy. Elsewhere, Karadi et al (2020) use US microdata to analyse firms’ price-setting in response to changes in credit conditions and monetary policy. Nakamura et al (2018) analyse the societal cost of high inflation using microdata from the 1970s and 1980s, and Montag and Villar (2023) analyse the effect of more frequent price-changes on aggregate inflation during Covid. Relatedly, Davies (2021) finds that the difference between the share of price rises and price cuts in the UK microdata is related to aggregate inflation, focusing on price-setting during the pandemic. And finally, authors of the FT’s Alphaville blog have also been looking into these data (see here and here).

The data

The microdata spanning from 1996 until September 2023 is publicly available and updated monthly after each CPI release. It contains the monthly price quote data underpinning the ONS’ CPI series for over 700 items with identifiers at the shop, shop type, and region levels. We clean the data which works out to about 30 million observations. When identifying a price change in the data, which is ultimately what matters for inflation, we try to be as precise as possible with regards to the product and the timing of the change. To that end we only count the change if we find the same item, in the same region, in the same shop, in two exactly neighbouring months. For example, if a bag of potatoes cost £2 in January and £3 in March but was not recorded in February, rather than imputing a price we discard the observation since we cannot be sure in which month the change actually happened.

Stylised facts from the microdata

A brief look at the data lets us establish some stylised facts. Chart 1 shows a decomposition of these month-on-month price movements over all items in the data set. Four key observations stand out:

1. Prices rise and fall all the time, but the vast majority of prices do not change between months. In any given month, on average since 1996, around 80% of prices remain unchanged relative to the previous month (blue line).
2. The share of prices rising (in green) has increased notably since 2021 to an extent that has not happened in previous inflationary episodes in the sample (excluding VAT changes).
3. The share of prices falling (in red) has fallen somewhat but remains stable since 2021, relative to historical average. The main margin of adjustment has been in the share of price increases.
4. But, in recent months, while the share of prices rising has tapered off, it remains elevated relative to its historical average. 

Chart 1: Decomposition of price movements month-on-month

Notes: The share of prices rising and falling reflect month-on-month changes. Shares are seasonally adjusted using the R package seasonal. Spikes in 2008, 2010 and 2011 are a consequence of UK VAT changes (17.5% to 15% in 2008, increase to 17.5% in 2010 and increase to 20% in 2011). The grey shaded area covers the time between March 2020 and July 2021 when the economy (and data collection) was most affected by the Covid pandemic. Dashed lines show the 2011–19 averages. Latest observation: September 2023.

Sources: ONS and authors’ calculations.

To be clear, this chart is not saying that 80% of products never change prices. If the price of an item remained constant between December and January, and rose between January and February, it would move from the blue into the green category during this period. Similarly, it would fall out of the green, into the blue or red, if from February to March the price again remained constant, or fell, respectively.

So, perhaps surprisingly, this chart shows that monthly price dynamics in the economy are driven by only a relatively small fraction of roughly 20% of all goods and services in the consumption basket. Also, we see that in the most recent episode, the shift into rising prices has been mostly out of the ‘no change’ category. Hence, fewer prices are staying fixed, and more are rising. It is worth noting that the recent up-tick in the shares of prices rising is only matched historically by those caused by VAT changes in 2008, 2010 and 2011, which however appear as one-off price spikes rather than a persistently higher share of price rises, as in 2022.

If it is a minority of total products whose price changes, it is important to take a closer look. Chart 2 shows the distribution of prices changes from 2019 by quarter (truncated at zero to exclude no-change observations). In line with the rise in the green line in Chart 1, we observe that over 2021 and 2022 a lot of mass moved into the right side of the distribution, that is the share of price increases, with the share of price decreases being relatively stable.

Chart 2: Evolution of the distribution of price changes by quarter 2019–23

Notes: The share of prices that did not change is excluded from these densities. The truncated densities are estimated in R via the Bounded Density Estimation package using the boundary kernel estimator. Darker colours correspond to quarters in which year-on-year CPI inflation was relatively high, lighter colours to quarters in which it was low. Each distribution represents month-on-month changes within the same quarter. Latest observation: 2023 Q3.

Sources: ONS and authors’ calculations.

A note on the chart: the distribution of price changes, when aggregate inflation is at or close to target, is roughly symmetric in logarithms. On this scale, a doubling (+100%) is equally far away from zero as a halving of the price (-50%). Due to sales, the doubling and halving of prices actually happens regularly in the data, which explains the bunching around these points. While these may be a source of seasonality in the data, which may obscure the underlying dynamics, we do not believe they are important for the overall shape of the distribution which we show here.

In Chart 3, we zoom in on a couple of these densities to better see differences in their shape. They are the densities corresponding to price changes in the third quarter of 2022 and 2023 alongside an average density over the pre-Covid period.

Chart 3: Comparison of densities from 2022 and 2023 against a pre-Covid average

Notes: The share of prices that did not change is excluded from these densities. The truncated densities are estimated in R via the Bounded Density Estimation package using the boundary kernel estimator. To compare densities across time, they are normalised to sum to the average share of prices falling and rising respectively within the quarter. The yellow line shows the pointwise average density over the third quarters of the years 2011–19.

Sources: ONS and authors’ calculations.

We can see how, compared to this historical average – which we use as a stand-in for pricing behaviour when inflation was close to the 2% inflation target – 2022 saw a huge number of prices increase while there was little change in the behaviour of the lower part of the distribution. In the latest data, this mass of increases has begun to subside, and, at the same time, there is a growing number of prices outright falling on the month. However, the modal price increase (that is, the most probable) is still elevated at about 6%, compared to roughly 3% on average during 2011–19).

Conclusion

To summarise, looking at the micro level of price changes, we find a visible discontinuity in price-setting in the first quarter of 2022. A variety of factors, such as the large rise in energy prices in early 2022, as well as supply-chain issues following Covid lockdowns, likely contributed to this significant change in price-setting dynamics in the UK (relative to any recent historical precedent at least). At the micro level, firms’ pricing decisions led to the emergence of a large rebalancing in the distribution of price changes. Suddenly, more prices for many different products were rising at the same time. Compared to the available history for these data, the recent period is unique. More research will be needed on the causes of this marked shift in the distribution of price changes, both at a micro and at a macro level.

In the very latest data, there is some evidence that the distribution of price changes has indeed begun to return in the direction of its historical average, though it is too soon to establish a trend.

Lennart Brandt and Natalie Burr work in the Bank’s External MPC Unit, and Krisztian Gado is a PhD candidate at Brandeis University.

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