Some implications of micro price-setting evidence for inflation dynamics and monetary transmission.
Date: 2023-07-18
Institutions: European Central Bank
Subjects: economic analysis, economic model, fixing of prices, inflation, monetary policy

Luca Dedola, Erwan Gautier, Anton Nakov, 
Sergio Santoro, Emanuel De Veirman, 
Lukas Henkel, Bruno Fagandini 

Occasional Paper Series 

Some implications of micro price-
setting evidence for inflation 
dynamics and monetary 
transmission 

No 321 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). 
The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
 
 
Price-setting Microdata Analysis Network (PRISMA) 

This Occasional Paper contains research conducted within the Price-setting 
Microdata Analysis Network (PRISMA). PRISMA (2018-22) consisted of economists 
from the European Central Bank (ECB) and the national central banks (NCBs) of the 
European System of Centra Banks (ESCB). PRISMA was coordinated by a team 
chaired by Luca Dedola (ECB) and consisting of Chiara Osbat (ECB), Peter Karadi 
(ECB) and Georg Strasser (ECB). Fernando Alvarez (University of Chicago), Yuriy 
Gorodnichenko (University of California Berkeley), Raphael Schoenle (Federal 
Reserve Bank of Cleveland and Brandeis University) and Michael Weber (University 
of Chicago) acted as external consultants (for further information, see Price-setting 
Microdata Analysis Network (PRISMA). 

This work is part of a set of papers within the ECB’s Occasional Paper Series, 
resulting from the report of the ECB’s PRISMA network, which is summarised here. 

Set of Occasional Papers related to the ECB’s PRISMA network 

No 319, “Price adjustment in the euro area in the low-inflation period: evidence from 
consumer and producer micro price data” 

No 320, “E-commerce and price setting: evidence from Europe” 

No 321, “Some implications of micro price-setting evidence for inflation dynamics 
and monetary transmission” 

No 322, “Micro price heterogeneity and optimal inflation” 

No 323, “Measuring inflation with heterogeneous preferences, taste shifts and 
product innovation - methodological challenges and evidence from micro data” 

No 324, “Price setting during the coronavirus (COVID-19) pandemic” 

No 325, “Inflation heterogeneity at the household level” 

ECB Occasional Paper Series No 321 

1 

 
 
 
 
Contents 

Abstract 

1 

2 

3 

4 

Introduction 

Model-based analysis of euro area evidence on price setting 

2.1  Approach and models 

Box 1   A brief description of the theoretical models calibrated to euro 

area microdata 

2.2  Macroeconomic implications for small and large aggregate 

shocks: the Phillips curve and non-linearities 

Box 2   Evidence on time variation in estimates of the slope of the US 

Phillips curve 

Implications for monetary transmission of the responses of retail 
micro prices to macroeconomic shocks 

Box 3   Phillips multipliers and the structural slope of the Phillips curve 

Other micro-based evidence on the determinants of price 
adjustment 

4.1 

Further micro-based evidence on state dependence and 
selection 

4.2  Micro-based evidence on determinants of pass-through and 

specific channels of real rigidities 

5 

Conclusions and policy implications 

References 

3 

4 

6 

6 

6 

9 

11 

17 

19 

27 

27 

29 

31 

33 

ECB Occasional Paper Series No 321 

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Abstract 

This paper analyses the implications of the evidence on micro price setting gathered 
by Price-setting Microdata Analysis Network (PRISMA) for inflation dynamics and 
monetary policy, relying on calibrated models and direct empirical evidence. 
According to models calibrated to the euro area micro evidence in Gautier et al. 
(2022, 2023), infrequent price changes and moderate state dependence in price 
setting should result in a meaningful Phillips curve in the euro area. Empirical 
estimates of the Phillips curve during the low-inflation period confirm previous 
findings of a relatively flat but stable slope. This estimated flat slope reflects both 
infrequent and subdued price adjustment in response to aggregate shocks, i.e. the 
presence of nominal and real rigidities. Model-based simulations show that, due to 
non-linearities in price setting, changes in trend inflation above 5-6% would have 
significant effects on the euro area Phillips curve. Similarly, shocks to nominal costs 
larger than 15% would result in non-linear effects on inflation dynamics in calibrated 
models. In line with these simulations, recent micro evidence suggests that the 
return of higher and more volatile inflation seems to be associated with higher 
frequencies of price changes, mainly because the frequency of price increases rises 
with the level and volatility of inflation. 

Keywords: state-dependent price setting and non-linearities, heterogeneity, real and 
nominal rigidities, price Phillips curve. 

JEL codes: E3, E5. 

ECB Occasional Paper Series No 321 

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1 

Introduction 

Inflation is the sum of heterogeneous individual price-setting decisions, whose 
analysis requires microdata. Price adjustment is “lumpy”: prices change 
infrequently, and both price increases and decreases may be substantial. Infrequent 
price adjustment is critical for monetary transmission, as it shapes the speed at 
which aggregate shocks affect inflation. However, as well as this nominal rigidity, the 
size of the adjustment, i.e. how much various “reset” prices adjust, also affects 
inflation dynamics. An initial aspect is whether price setting is state-dependent, i.e. 
whether the prices that change are those most in need of adjustment, as otherwise 
they would be very misaligned with respect to their target values. As a result, price 
changes may be very large in response to small aggregate shocks, amplifying the 
reaction of aggregate inflation, even with a relatively low repricing rate. Non-
linearities in the size of aggregate shocks are also possible. A second aspect 
concerns “real rigidities”, i.e. the responsiveness of target prices to costs. If firms 
only partially pass through underlying changes in costs to price changes, “real 
rigidities” will slow inflation dynamics and amplify the effects of infrequent repricing. 
Obviously, both how often and how much prices change can only be gauged using 
microdata. Likewise, microdata are necessary to investigate how individual firms 
pass through their costs to prices. The ESCB Inflation Persistence Network (IPN) 
pioneered studies of micro prices as far back as the early 2000s.1  The Price-setting 
Microdata Analysis Network (PRISMA) updated and extended the IPN work in 
several areas (see Gautier et al., 2022). The microdata collected by PRISMA 
comprise 135 million price quotes underlying the Harmonised Index of Consumer 
Prices (HICP) in 11 countries from 2010 to 2019, encompassing 166 categories of 
the euro area HICP (60% of products), a much larger share than the 50 IPN 
categories. 

PRISMA has analysed the macroeconomic implications of the micro evidence 
on price setting by estimating the effects of structural demand and supply 
shocks, and by using simulations from models calibrated to match microdata. 
First, the micro price evidence can be used to derive implications for the monetary 
policy transmission mechanism through the lens of state-of-the-art price-setting 
models, which underscore state dependence in decisions to revise prices. Second, 
estimating the responses of micro prices to structural shocks may help to identify 
which margins of price adjustment matter for the inflationary consequences of 
aggregate shocks in the data. 

This paper reviews the implications of the evidence on micro price setting for 
inflation dynamics and monetary policy in the euro area. The next section uses 
cross-sectional evidence from micro price changes in the euro area to derive 
implications for the monetary policy transmission mechanism through the lens of 
state-of-the-art price-setting models, emphasising state dependence in firms’ price-
changing decisions. It shows that price setting in the euro area is consistent with a 

1    See the IPN webpage. 

ECB Occasional Paper Series No 321 

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limited degree of state dependence and a meaningful Phillips curve relationship. The 
latter, according to the calibrated models, was also broadly stable for the range of 
low inflation rates observed in the euro area until 2019. Nevertheless, model-based 
simulations show that, due to non-linearities in price setting, changes in trend 
inflation above 5-6% would have significant effects on the euro area Phillips curve. 
Similarly, shocks to nominal costs larger than 15% would result in non-linear effects 
on inflation dynamics in calibrated models. In addition to model-based analysis, 
estimates of the actual response of micro prices to demand and supply-side 
structural shocks can also help to shed light directly on key aspects of inflation 
dynamics and monetary transmission. Specifically, such analysis can document 
which price adjustment margins matter for the inflationary consequences of 
aggregate shocks in the data. Several PRISMA papers have studied these aspects. 
Section 3 focuses on the findings in the paper by Gautier et al. (2022), based on the 
large retail price dataset discussed in Gautier et al. (2023). Section 4 surveys 
findings from other PRISMA papers that use datasets limited to specific countries or 
sectors but that enable better identification of drivers of firms’ responses to cost 
shocks and their implications for inflation dynamics. 

ECB Occasional Paper Series No 321 

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2 

Model-based analysis of euro area 
evidence on price setting 

2.1 

Approach and models 

One way to infer the slope and possible non-linearities of the Phillips curve 
relationship of the euro area is to calibrate two structural price-setting models 
using micro price data. Specifically, this section relies on the following two price-
setting models (see Box 1 for more details): 

(a)  The “information-constrained state-dependent pricing” model of Woodford 

(2009). 

(b)  The “logit price dynamics” model of Costain and Nakov (2019). 

These two models provide different micro foundations for deviations from full state 
dependence in price setting with fixed costs for reviewing prices (see the seminal 
paper by Golosov and Lucas, 2007). Model (a) is a generalisation of the standard 
full-information model of state-dependent pricing (see, for example, Caballero and 
Engel, 2007). Specifically, decisions about when to review a firm’s existing prices are 
made based on imprecise awareness of current market conditions. Imperfect 
information is endogenised, using a variant of the “rational inattention” theory 
proposed by Sims (2003, 2006). Model (b) is also a generalised model of state-
dependent pricing. Like Woodford’s model, Golosov-Lucas (2007) is nested in this 
model, as is the benchmark time-dependent Calvo model, as two polar extremes. 
Within this framework, firms sometimes make mistakes in responding to aggregate 
and idiosyncratic shocks due to imperfect information processing. It, too, has rational 
inattention micro foundations, as shown by Matejka, Steiner and Stuart (2019). The 
main difference compared with Woodford’s model is that firms sometimes also err in 
their pricing decisions, conditional on adjustment, in addition to the “errors in timing” 
found in Woodford’s framework. 

Box 1 
A brief description of the theoretical models calibrated to euro area microdata 

Information-constrained state-dependent pricing 

The Woodford (2009) model of information-constrained pricing is a general state-dependent pricing 
model, similar to the “generalised Ss model” of Caballero and Engel (2007). The main difference 
compared with this model is that the adjustment hazard function is derived from optimising first 
principles, rather than exogenously imposed. Imperfect information is endogenised using a variant 
of the theory of “rational inattention” proposed by Sims (2003, 2006). In the model, the timing of 
price reviews is determined by optimisation, subject to an information constraint. It thus allows for 
the costs of obtaining and/or processing more precise information about the current state (both firm-
level and aggregate), between the intermittent occasions on which full reviews of pricing policy are 
undertaken. When a firm decides to pay the fixed cost required for a full review of its pricing policy, 

ECB Occasional Paper Series No 321 

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it obtains full information about the economy’s state at that moment. Hence, when price changes 
occur, they are based on full information, as in the standard fixed menu cost model. However, 
between such reviews, the firm’s information about current economic conditions is less accurate. In 
particular, the decision whether to conduct a full review must be made on the basis of much more 
imprecise information than will be available after the review is conducted. Therefore, prices do not 
necessarily adjust at precisely the moment at which they first become far enough out of line for the 
profit increase from a price review to justify the fixed cost. In other words, firms sometimes err in the 
“timing” of their price adjustments. 

Logit price dynamics 

In the Costain-Nakov (2019) model, firms set prices to maximise profits, subject to two types of 
“control” costs. The first cost is proportional to the level of precision in price setting. The more effort 
decision-makers devote to the pricing decision, the more accurately the newly set prices reflect 
fundamentals. The second cost penalises deviations from purely random timing of price adjustment. 
Since firms economise on this cost, sometimes they forgo profitable adjustment opportunities 
(generating stickiness), and sometimes adjust prices even though the realised gain from adjustment 
is relatively small. 

In equilibrium, the new prices firms set are distributed around their optimal levels, with a dispersion 
that is inversely related to the effort expended on decision-making. At the same time, prices are 
sometimes out of line with fundamentals, also because the resulting probability of adjustment is a 
smoothly increasing function of the price gap, rather than a zero-one step function, as in the 
benchmark “fixed menu cost” model. 

The parameters controlling the two price-setting frictions are the two costs in the pricing and in the 
timing decisions, which are assumed to take different values in principle. Both are expressed in 
terms of labour time and are multiplied by the wage and subtracted from the flow of profits. The 
model is otherwise akin to Golosov-Lucas (2007), with firms facing both idiosyncratic productivity 
and aggregate inflation risk. In fact, the Golosov-Lucas (2007) framework is nested in this model as 
a limit variant with perfectly accurate pricing and perfectly accurate timing decisions. The Calvo 
(1983) model is also nested in this model, as the limit with precise pricing but purely random timing. 

The Costain-Nakov model is quite similar in spirit to Woodford’s. The main difference is that, in the 
latter model, conditional on a review, prices are set optimally, while on adjustment they are 
distributed around the optimal price in the former model. Hence, Woodford’s model features “errors 
in timing” only, while the Costain-Nakov model has both errors in timing and “errors in pricing”. 

2.1.1 

Calibration to euro area micro price data 

To calibrate the two price-setting models, we use data on frequencies and the 
size distribution of price changes in the euro area. Specifically, to capture the 
heterogeneity in price setting in the euro area documented in Gautier et al. (2022), 
the two models are calibrated with data from the following three sectors: (1) food 
(combining processed and unprocessed), (2) non-energy industrial goods (NEIG), 
and (3) services. Energy prices are not included in the micro price dataset; this limits 

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sectoral heterogeneity, for example in comparison with Gautier and Le Bihan 
(2022). 2  To derive implications for aggregate inflation (excluding energy), the three 
sectors are aggregated using the following weights based on the euro area HICP: 
0.2405 (food), 0.2973 (NEIG) and 0.4622 (services). The monthly adjustment 
frequencies in the data are shown in Table 1, while Chart 1 shows the distribution of 
price changes for the three sectors (excluding sales prices). It is apparent that the 
sectors are quite heterogeneous in terms of price-setting features, even when 
excluding sales: for example, the repricing rate in the food sector is more than 
double that of NEIG and services (see also the discussion in Gautier et al., 2022). 

Table 1 
Sectoral monthly frequencies of price changes (excluding sales) 

(share of prices changing each month) 

Sector 

Frequency 

Processed and unprocessed food 

NEIG 

Services 

Overall 

Source: Gautier et al., 2022. 

Chart 1 
Empirical distribution of non-zero price changes by sector 

(percentage points) 

13.6% 

6.4% 

5.7% 

7.8% 

a) Processed and 
unprocessed food

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

b) NEIG

c) Services

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Source: Gautier et al., 2022. 

We calibrate the two models by minimising a distance criterion comprising the 
frequencies of price adjustment in each sector, as well as each sector’s histogram of 
price changes excluding sales.3  The sectoral repricing rates are matched perfectly, 
which is important, since they have a first-order effect on the simulation results. The 
food and NEIG sectors’ histograms are matched better than the services histogram, 

2    This paper calibrates a rich multi-sector model to French microdata, finding that accounting for sectoral 

heterogeneity is crucial to capture the monetary transmission mechanism. 

3    The histograms comprise 140 equally spaced bins between -0.70 and +0.70 log points of price 

changes. 

ECB Occasional Paper Series No 321 

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which shows the highest kurtosis (i.e. its probability mass is more concentrated 
around the mean, but with “fat” tails with much larger price changes). 

2.2 

Macroeconomic implications for small and large 
aggregate shocks: the Phillips curve and non-linearities 

Given the models and the calibration, it is possible to trace the implications of 
each framework for the Phillips curve and to identify potential non-linearities 
in its slope due to changes in trend inflation and large shocks. One way to 
characterise the Phillips curve is to compute the theoretical equivalent of the Phillips 
multiplier in Barnichon and Mesters (2021), defined as the ratio between the areas 
under the inflation and unemployment impulse responses to a monetary policy 
shock. The higher the ratio in absolute terms, the greater the effect on sectoral 
inflation of a 1% fall in sectoral unemployment over the horizon considered. At 2% 
trend inflation for a 24-month horizon, the ratios shown in Table 2 are obtained 
(Chart 2 shows that the absolute value of the multiplier does not vary much across 
horizons between six and 24 months, see also Box 3). 

Table 2 
Phillips multipliers by sector 

(ratios of the areas under the impulse responses of inflation and unemployment over a 24-month horizon) 

Model/Sector 

Food 

NEIG 

Services 

Overall 

Woodford 

Costain-Nakov 

-0.4353 

-0.4748   

-0.2126 

-0.2430 

-0.1437 

-0.1469 

-0.2343 

-0.2543 

Source: ECB calculations. 

Chart 2 
Phillips multipliers by sector and horizon at 2% annual trend inflation 

(percentage points) 

Food
Neig
Services

a) Woodford

0.6

0.5

0.4

0.3

0.2

0.1

0.0

b) Costain-Nakov

0.6

0.5

0.4

0.3

0.2

0.1

0.0

3

6

12

24

3

6

12

24

Source: ECB calculations. 

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The Phillips multipliers differ substantially across sectors, with inflation 
reacting less to unemployment in services than in food and NEIG, while the 
aggregate multiplier is close to the NEIG multiplier. The sectoral heterogeneity in 
price setting translates into heterogeneity in the Phillips multipliers. The lower 
multiplier in services reflects both the lower repricing rate and the lower degree of 
state dependence implied by the price-change distribution. Nevertheless, the 
aggregate Phillips multiplier for the overall economy is close to the multiplier for the 
NEIG sector, even though the 6.4% repricing rate in this sector is lower than the 
7.8% aggregate rate. At a value of -0.23 to -0.25 across models, the theoretical 
Phillips multiplier implies that, in order to increase aggregate inflation (excluding 
energy) by 1% on average over two years, it is necessary to decrease average 
unemployment by around 4 percentage points in the same period. The results in Box 
3 can also be used to derive the implied slope (κ) of the structural Phillips curve. 
Given a scale factor of around ten in both calibrated economies, the implied 
structural slopes are between -0.023 and -0.025. These values are larger than the 
implied slope, based purely on the calibrated 7.8% monthly frequency of price 
changes (abstracting from real rigidities). This stems from the implicit degree of state 
dependence, in line with the results of Auclert et al. (2021) on the Phillips curve in 
state-dependent models.4  The Costain-Nakov model has a slightly steeper Phillips 
curve slope than Woodford’s model, as on adjustment, prices are set around, rather 
than exactly at, their optimum level. This means that, in response to an inflationary 
shock, a larger mass of firms is pushed towards the steeper part of the adjustment 
hazard function, increasing the selection effect and the effective degree of price 
flexibility. 

The calibrated state-dependent models allow the degree of non-linearities in 
the inflation dynamics implicit in the micro price data of the euro area to be 
inferred. We distinguish two types of non-linearities in the Phillips curve: (1) for 
increasing trend inflation rates; and (2) in response to nominal shocks of increasing 
size. 

2.2.1 

Model-based non-linearities under increasing trend inflation rates 

The repricing rate is predicted to increase with higher trend inflation, resulting 
in a steeper Phillips curve and a more rapid inflation response to demand and 
supply-side shocks. One feature of state-dependent pricing models is that price 
setting depends on the level of expected and actual trend inflation (see the recent 
paper by Alexandrov, 2021). The higher the trend inflation, the higher the repricing 
rate, as unchanged prices are eroded over time, deviating from their desired level 
even absent other cost shocks. Moreover, effective price flexibility also increases, 
since firms respond more to shocks that raise their costs, as they factor in the higher 
rate of trend inflation expected to prevail in the future. Table 3 reports the 24-months 
Phillips multiplier for annual trend inflation rates of between 0% and 10%, together 
with the associated repricing rates. As trend inflation rises, the Phillips curve 

4    The value of κ in the standard Calvo model based on a 7.8% monthly repricing rate would be around -

0.007. 

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becomes steeper, as shown by the Phillips multiplier, which almost doubles in 
absolute terms, from -0.23 to -0.25, to -0.37 to -0.40. Demand shocks, such as 
monetary policy impulses, are transmitted more rapidly to inflation, and their real 
effects diminish. Note that the relatively rapid increase in the Phillips multipliers is 
accompanied by only a limited rise in the overall adjustment frequency, shown in the 
last two rows of Table 3. Not only is this due to the repricing rate entering the Phillips 
multiplier non-linearly (see Box 3), but it also suggests higher effective price 
flexibility, since selection effects are also intensified as trend inflation rises, as 
discussed above. Support for the mechanism underlying this non-linearity is found in 
US data by De Veirman (2022), who finds an empirical link between changes in the 
repricing rate and the slope of the Phillips curve (see Box 2 below).5 

Table 3 
Phillips multipliers and frequencies at increasing trend inflation rates 

(ratios of the areas under the impulse responses of inflation and unemployment over a 24-month horizon) 

Model/Trend 
inflation 

Phillips multiplier 

0% 

2% 

4% 

6% 

8% 

10% 

Woodford 

-0.2240 

-0.2343 

-0.2665 

-0.2989 

-0.3344 

Costain-Nakov 

-0.2445   

-0.2543 

-0.2864 

-0.3201 

-0.3569 

Predicted frequencies 

Woodford 

Costain-Nakov 

Source: ECB calculations. 

7.8 

7.6 

7.9 

7.7 

8.1 

7.9 

8.3 

8.1 

8.5 

8.3 

-0.3666 

-0.3964 

8.7 

8.5 

Box 2 
Evidence on time variation in estimates of the slope of the US Phillips curve 

In sticky price models, inflation is more sensitive to aggregate demand fluctuations when firms 
change their prices more frequently. These models imply that the slope of the short-run Phillips 
curve depends positively on the frequency of price adjustment. 

De Veirman (2022) empirically examines how the Phillips curve slope depends on the frequency of 
price adjustment in the United States between the first quarter of 1979 and the fourth quarter of 
2016. The paper detects a statistically significant positive relationship between the slope and the 
frequency when using trimmed mean consumer price inflation, while the relationship is insignificant 
for other standard inflation measures. This is reminiscent of the finding of Ball and Mazumder 
(2019, 2021) that, in the United States and in the euro area, the Phillips curve slope is more 
precisely estimated when they measure core inflation by median inflation, rather than by inflation 
excluding food and energy. They argue that median and trimmed mean inflation are better at 
filtering out transitory fluctuations in relative prices than measures of inflation excluding food and 
energy. This may explain why, in De Veirman (2022), the Phillips curve with trimmed mean inflation 
has an excellent fit and relatively tight confidence bands around the coefficient estimates, which 

5    This is different from the non-linearities in recessions versus expansions at low inflation rates found by 

Forbes et al. (2021) for reduced-form Phillips curves in the United States and other advanced 
economies. Hazell et al. (2021) also find that post-1990s point estimates of the slope of the Phillips 
curve are lower than in previous periods. 

ECB Occasional Paper Series No 321 

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makes it easier to detect a relationship between the Phillips curve slope and the price adjustment 
frequency. 

An extensive body of literature finds that, in several countries, the Phillips curve has flattened 
relative to the 1970s and 1980s. See, for example, De Veirman (2009), Ball and Mazumder (2011) 
and Blanchard, Cerutti and Summers (2015). The results from De Veirman (2022) are consistent 
with the interpretation that the US Phillips curve has flattened endogenously, as a result of declining 
repricing rates.6  His findings suggest that the US Phillips curve slope has mostly been close to zero 
since the late 1990s, due to relatively infrequent price adjustment at that time. For the period from 
the Great Recession onwards, this is consistent with the fact that inflation has remained stable, 
notwithstanding a long sequence of negative output gaps. 

When allowing for a non-linear relationship between the Phillips curve slope and the frequency of 
price adjustment, De Veirman (2022) finds that, empirically, this relationship is convex. In other 
words, the slope is more sensitive to price adjustment frequency when many firms adjust their 
prices. In this respect, the data are in line with the New Keynesian Phillips Curve (NKPC), which is 
derived from a standard New Keynesian model with nominal rigidities, as in Calvo (1983). 

However, the slope of the empirical Phillips curve is much flatter than that of the theory-based 
NKPC, which assumes the same empirical price adjustment frequencies, while other structural 
parameters are set to standard values. This finding suggests that relying only on empirical repricing 
rates to gauge the short-run response of inflation to aggregate demand fluctuations would result in 
overestimation (see also Section 3). 

2.2.2 

Non-linearities under large nominal shocks 

According to the models calibrated to the euro area, nominal shocks need to 
be relatively large to have non-linear effects on inflation dynamics. One feature 
of state-dependent pricing models is that the greater the shocks to nominal costs, 
the more misaligned the unchanged prices, resulting in more frequent price 
adjustment and larger price changes. Therefore, in a more volatile macroeconomic 
environment, effective price flexibility should increase under state-dependent price 
setting. Chart 3 below shows how inflationary effects and adjustment frequencies 
change in response to once-and-for-all nominal shocks ranging in size from 0 to 30 
log points (0 to 35%) (as a reminder, see the finding of Gautier et al., 2023, that the 
median price increase in the euro area, excluding sales, is around 7%). For all three 
sectors, the first-year inflationary effects (calculated as the average increase in the 
price level over this period) increase approximately linearly with the size of the shock 
for sizes of 0 to 15 log points (around 15%). Just over this threshold, the price 
response starts rising at a somewhat faster rate, as larger shocks to nominal costs 
have increasing inflationary consequences. The frequency of repricing is initially 
rather flat, but then rises steadily. Nevertheless, the limited effects on repricing rates 

6    Other papers documenting a flattening Phillips curve include Blanchard (2016), Del Negro, Lenza, 

Primiceri and Tambalotti (2020) and Okuda, Tsuruga and Zanetti (2021). See Costain, Nakov and Petit 
(2021) for an explanation of this flattening based on a state-dependent pricing model. 

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for shocks below 5% are consistent with the evidence in Gautier et al. (2022) of 
small cyclical fluctuations in this statistic in the euro area in the period 2005-19. 

Chart 3 
Effects of nominal shocks of increasing size across sectors 

a) Processed and unprocessed food 

(size of shock (log points), monthly average price response (%)) 

(size of shock (log points), monthly freq. of price changes (%)) 

25%

20%

15%

10%

5%

0%

45%

40%

35%

30%

25%

20%

15%

10%

5%

0%

0

5

10

15

20

25

30

0

5

10

15

20

25

30

b) NEIG 

(size of shock (log points), monthly average price response (%)) 

(size of shock (log points), monthly freq. of price changes (%)) 

20%

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

35%

30%

25%

20%

15%

10%

5%

0%

0

5

10

15

20

25

30

0

5

10

15

20

25

30

c) Services 

(size of shock (log points), monthly average price response (%)) 

(size of shock (log points), monthly freq. of price changes (%)) 

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

35%

30%

25%

20%

15%

10%

5%

0%

0

5

10

15

20

25

30

0

5

10

15

20

25

30

Source: ECB staff calculations. 

ECB Occasional Paper Series No 321 

13 

 
 
 
 
 
 
 
 
 
 
 
2.2.3 

The return of inflation and non-linearities in price adjustment: some 
preliminary evidence for 2022 

Since 2022, due to the supply chain disruptions caused by the coronavirus 
(COVID-19) pandemic and the major shocks caused by the Ukraine war, 
inflation has risen sharply in all euro area countries. This period of more volatile 
and higher inflation allows us to investigate further the presence of non-linearities in 
repricing rates and to understand better the transmission of such large shocks to 
inflation. 

However, the micro price data underlying HICPs and producer price indices 
(PPI) in the euro area countries are not yet available for 2022. To investigate 
how features of price adjustments change with higher inflation, we rely on various 
data sources and previous findings from the academic literature documenting pricing 
patterns in a period of high and more volatile inflation.7 

A few papers have documented the features of price adjustment in the mid-
1970s and 1980s, when inflation was high and more volatile. Wulfsberg (2016) 
and Nakamura et al. (2018) have provided several results on price adjustment 
patterns using CPI micro price data in Norway and in the United States, contrasting 
the mid-1970s and 1980s with more recent periods, when inflation was lower and 
less volatile. They both show that the frequency of price adjustment correlates much 
more strongly with inflation when the inflation rate is high and more volatile than 
when inflation is low and relatively stable. In contrast, the absolute size of price 
increases did not rise with inflation in the United States, while Wulfsberg (2016) even 
found that the absolute size of price changes decreases with inflation. Results from 
other countries during periods of hyperinflation, such as Argentina (Alvarez et al., 
2018) and Mexico (Gagnon, 2009), corroborate these findings. Overall, when trend 
inflation is higher than 5%, the correlation between frequency and inflation becomes 
stronger. 

Firm-level business surveys could provide timely information on the frequency 
of price adjustment in 2022. For example, every month, Banque de France collects 
qualitative information from business leaders about their price decisions. This survey 
is conducted among several thousands of firms of all sizes, in the manufacturing 
sector and also in business-to-business services.8  Chart 4 below shows the 
evolution of the frequency of firms reporting price increases and decreases in the 
period 2012-22. In the low-inflation period, the overall frequency of firms reporting 
price changes was broadly constant, while the frequencies of price increases and 
price decreases moved in opposite directions, consistent with the findings of Gautier 
et al. (2022, 2023) for both CPI and PPI microdata. Since inflation was higher and 
more volatile in late 2021 and early 2022, the share of firms reporting price changes 
has been steadily rising across all sectors, including services. The peak frequency of 
firms changing prices in April 2022 was close to 50% in the manufacturing sector 

7    For the period 2020-21, Henkel et al. (2023) also provide detailed evidence on how large economic 

shocks induced by the COVID-19 period have affected pricing patterns in several euro area countries. 

8    Loupias and Sevestre (2013) and Harris et al. (2020) have documented several facts on price 

stickiness using these survey data. 

ECB Occasional Paper Series No 321 

14 

 
 
 
and 30% in business services, ranging between 10% and 15% in the period 2012-21 
(compared with an average monthly frequency of price changes in the French PPI of 
around 27%). This increase in the frequency of price changes by firms is mainly 
driven by a sharp rise in price increase frequency. Overall, in the manufacturing 
sector, the frequency of price changes moves more closely in line with inflation when 
PPI inflation is higher than 4% to 5%. 

Chart 4 
Share of firms changing their prices in French business survey and PPI inflation 

a) Share of firms reporting price changes 

b) Share of firms and PPI inflation (yoy) 

(percentage points) 

(percentage points) 

Share of firms increasing prices (Industry)
Share of firms increasing prices (Services)
Share of firms cutting prices (Industry)
Share of firms cutting prices (Services)

March 2021 - October 2022
January 2012 - February 2021

60%

50%

40%

30%

20%

10%

0%

2012

2014

2016

2018

2020

2022

60%

50%

40%

30%

20%

10%

0%

-5

0

5

10

15

Source: Banque de France monthly economic survey (Enquête Mensuelle de Conjoncture – EMC), manufacturing sector and business 
services, all firm sizes. Answers to the qualitative question about past evolution of firms’ own prices over the last month. 
Note: Share of firms changing prices on vertical axis; months and 12-month French PPI inflation on left-hand and right-hand horizontal 
axes, respectively. 

In the United Kingdom, CPI microdata document the frequency and size of 
price changes in the recent period. The UK microdata underlying the CPI are 
released monthly with the publication of price indices, allowing for the timely 
computation of standard price-setting statistics. Chart 5 plots the frequency of price 
changes in the United Kingdom before and after 2022 against CPI inflation. In line 
with evidence for the euro area, when inflation is low, the frequency of price changes 
is only weakly correlated with inflation. When inflation exceeds 4% in 2022, the 
frequency of price adjustments is much more strongly correlated with inflation. As 
regards the absolute size of the price changes, we do not find a strong correlation in 
2022 or in prior periods. This evidence suggests that retailers update their prices 
more frequently when inflation is higher, which should accelerate the response of 
inflation to macro shocks, other things equal. 

ECB Occasional Paper Series No 321 

15 

 
 
 
 
Chart 5 
Frequency and size of price adjustment in the United Kingdom versus CPI inflation 
(2017-22) 

b) Absolute price changes (mean)

(percentage points) 

October 2021 - October 2022
April 2017 - September 2021

a) Frequency of price changes

20%

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

25

20

15

10

5

0

0

2

4

6

8

10

12

0

2

4

6

8

10

12

Source: ECB staff calculations based on Office for National Statistics (ONS) data. 

Overall, the return of higher and more volatile inflation seems to be associated 
with higher price-change frequencies, mainly because the frequency of price 
increases rises with inflation. Conversely, there is no consistent evidence that the 
absolute size or dispersion of absolute price changes moves with inflation. In higher 
inflation periods, either due to larger shocks or higher trend inflation, aggregate 
factors become an important motive for price changes, as predicted by state-
dependent price-setting models. This provides some empirical support that large 
shocks or higher trend inflation would help to accelerate inflation dynamics and the 
transmission of nominal shocks to inflation in line with the above simulation results. 

ECB Occasional Paper Series No 321 

16 

 
 
 
3 

Implications for monetary transmission 
of the responses of retail micro prices to 
macroeconomic shocks 

Evidence on the actual response of micro prices to aggregate shocks can also 
help to shed light directly on key aspects of inflation dynamics and monetary 
transmission. The previous section used cross-sectional evidence from micro price 
setting to derive key implications for the monetary policy transmission mechanism, 
through the lens of state-of-the-art price-setting models, focusing on the Phillips 
multiplier. Nevertheless, studying the actual response of micro prices to structural 
shocks can help to document which price adjustment mechanisms are key for 
inflation dynamics. Several PRISMA papers have studied these issues. This section 
focuses on findings based on the large retail price dataset in Gautier et al. (2023). 
The next section will survey results from other PRISMA papers, using datasets that 
are limited to specific countries but enable better identification of how firm-level costs 
affect prices. 

Building on the evidence on conditional price adjustment to structural shocks, 
including monetary policy shocks, provides empirical estimates of the Phillips 
multiplier. The Phillips multiplier introduced in the previous section operationalises 
empirically the link between economic slack and the aggregate price level and 
inflation underpinning the theoretical Phillips curve, and the monetary transmission 
mechanism in sticky price models (see Box 3). Moreover, using estimates of the 
impulse responses of average reset prices conditional on structural shocks, it is 
possible to disentangle the role of nominal and real rigidities in shaping inflation 
dynamics. It is well known that, in the workhorse Calvo model, the dynamics of the 
aggregate price level h periods after a shock occurring is given by the following 
equation: 

=

, 

∗

(1 − θ)𝑝𝑝𝑡𝑡+ℎ−1 + θ𝑝𝑝𝑡𝑡+ℎ

𝑝𝑝𝑡𝑡+ℎ
where p* is the average “reset” price conditional on the shock (defined as the cross-
sectional average of all non-zero price changes in response to the shock in each 
period t+h), and θ is the repricing rate in each period. Clearly, the lower the repricing 
rate θ, and thus the more pervasive the nominal rigidities due to price stickiness, the 
smaller and slower the response of the aggregate price level to shocks buffeting 
reset prices. For the typical aggregate shock, substantially smaller than idiosyncratic 
shocks affecting micro prices, this equation provides a good approximation of the 
effects of aggregate shocks independently of the degree of state dependence, since 
movements in the repricing rate do not respond significantly (see, for example, 
Costain and Nakov 2012). Nevertheless, the response of the reset price p* can still 
provide important clues on the relevance of state dependence since it reflects the 
strength of the selection effect. In other words, when state dependence results in a 
strong selection effect, firms that revise their prices should react very sharply in the 
short run, as the main drivers of price changes are relatively large idiosyncratic 

ECB Occasional Paper Series No 321 

17 

 
 
shocks of the same sign as the aggregate shock. For example, in the first few 
periods after an expansionary monetary policy shock, price changes will mostly 
reflect large and positive idiosyncratic shocks to firms’ costs, which are the key 
determinant of repricing decisions. These “selected” price changes will show sharp 
increases on average, much greater than the marginal effect on firms’ costs of the 
monetary easing itself. 

Analysing reset prices is useful in understanding the transmission mechanism 
since they provide information on real rigidities. This is particularly true when 
selection effects are small, as in this case idiosyncratic shocks do not have much 
effect on the average response of reset prices to macroeconomic shocks.9  Since 
reset prices depend on firms’ current and expected marginal costs and mark-ups, 
their conditional responses may provide crucial evidence on the role of real rigidities 
in shaping the propagation of monetary policy and other structural factors, beyond 
nominal rigidities and price stickiness. Real rigidities broadly indicate all the channels 
through which measures of slack, such as unemployment or the output gap, affect 
marginal costs, mark-ups and thus reset prices. As argued by Bils et al. (2012), real 
rigidities play a crucial role in making the Phillips curve very flat in most estimated 
dynamic stochastic general equilibrium (DSGE) models used for policy analysis. 
Specifically, in the case of an expansionary monetary policy shock, a reset prices 
response that builds up gradually over time could be due to the slow propagation of 
the monetary stance into nominal cost pressures, owing to the sluggish reaction of 
wages to the monetary stimulus or of other input costs, working through the supply 
chain and via second-round effects (a channel dubbed “pipeline pressures”: see, for 
example, Rubbo, 2020, and Smets et al., 2019). Alternatively, it could be due to 
reluctance on the part of repricing firms to set their new prices above those of 
competitors that have not yet decided to change their prices, temporarily accepting 
lower mark-ups (a channel dubbed “firms’ strategic complementarities”: see, for 
example, Nakamura and Steinsson, 2010). Finally, a slow reset price response could 
also be due to information frictions that cause firms to act “myopically”, for example, 
by preventing repricing firms from accurately forecasting the effects of the monetary 
policy (or aggregate demand) shock on their own future costs and demand 
conditions (see, for example, Sims, 2008).10  By slowing down the response of reset 
prices to cost changes, real rigidities are crucial in accounting for a relatively flat 
Phillips curve, in addition to nominal rigidities, even under state-dependent pricing 
(see, for example, Gertler and Leahy, 2010). However, nominal rigidities are often 
necessary for real rigidities to matter. This is particularly true in the cases of pipeline 
pressures and mark-up adjustment above (due to firms’ strategic complementarities). 
Under both mechanisms, repricing firms delay full adjustment of their new prices as 
they “wait” for adjustment by suppliers or competitors that have not yet changed their 
prices. Moreover, the role of real rigidities may differ across shocks and thus account 
for different prices and inflation dynamics (see, for example, Mackowiak and Smets, 
2008). For example, strategic complementarities at the firm level may imply a 

9   

In general, state dependence implies that it is necessary to correct for selection bias in reset prices: for 
example, following the approach of Dedola, Kristoffersen and Zuellig (2023). 

10    Real rigidities may thus affect the link between measures of slack, such as unemployment and 

marginal costs (e.g. sticky wages), and how marginal costs translate into price changes (e.g. mark-up 
adjustment). 

ECB Occasional Paper Series No 321 

18 

 
 
 
different response to idiosyncratic and macro shocks, whereas the latter affect all 
competitors. Otherwise, very large shocks to which most firms adjust can attenuate 
the desire to “wait” for adjustments by others, thus reducing the degree of strategic 
complementarity and increasing the size and speed of price responses. 

Nominal and real rigidities in the euro area can be assessed empirically on the 
basis of estimates of the responses of both aggregate inflation and reset 
prices inflation, conditional on the structural demand shocks used in Gautier 
et al. (2022). Specifically, Gautier et al. (2022) estimate the response of cumulative 
inflation and of some decompositions of cumulative inflation11  to the following 
structural shocks affecting changes in aggregate demand: a) monetary policy shocks 
obtained from Jarocinski and Karadi (2020); and b) global demand shocks obtained 
from Baumeister and Hamilton (2019). Following Barnichon and Mesters (2021), 
these two shocks can also be used to estimate Phillips multipliers for aggregate 
inflation and, extending the methodology of these authors, for reset price inflation as 
well. As discussed in Box 3, the Phillips multiplier for reset price inflation can provide 
evidence on the contribution of real rigidities to a relatively flat Phillips curve in the 
euro area. Nevertheless, the Phillips multiplier for reset price inflation would not allow 
us to disentangle the role of the specific channels underlying real rigidities, such as 
firms’ strategic complementarities or information frictions. As discussed in the next 
section, the identification of these channels separately requires detailed information 
about firm-level costs or expectations, which is not readily available in matched 
datasets of firm-level prices. 

Box 3 
Phillips multipliers and the structural slope of the Phillips curve 

The Phillips multiplier was introduced in Barnichon and Mesters (2021). As already observed by 
these authors, there is a close link between the slope of the Phillips curve in sticky price models 
and the Phillips multiplier of aggregate inflation. Specifically, the Phillips multiplier is proportional to 
the structural slope of the Phillips curve (κ) relating inflation to unemployment. This proportionality 
factor (φ) depends on other structural parameters determining how monetary (or other demand) 
shocks affect unemployment, under the further identifying assumptions that these structural shocks 
do not affect natural unemployment or have permanent effects on (expected) inflation. For example, 
positing that, in response to a monetary shock, unemployment follows a simple autoregressive 
process (AR(1)) with parameter 
𝑢𝑢
h is equal to the following expression in a standard Calvo model: 
ρ

, it is possible to show that the Phillips multiplier over any horizon 

1
𝑢𝑢
1 − βρ

𝑃𝑃𝑀𝑀ℎ = κφ = κ
where β is the relevant firm-level discount factor. Since Auclert et al. (2021) show that a very broad 
class of sticky price models, under both time-dependent and state-dependent price setting, yields a 
Phillips curve with such a structural slope κ for small aggregate shocks, the above result implies 

11    Cumulative inflation is constructed at COICOP-5 country level, using the statistics on frequency and 
size of price adjustment computed from microdata: in particular, inflation from time t-1 to time t for a 
given COICOP-5 sector in a given country is obtained by multiplying the corresponding frequency of 
price adjustment by the corresponding average size of (non-zero) price changes. A similar approach is 
also taken for the other price variables. For more details, see Gautier at al. (2022). 

ECB Occasional Paper Series No 321 

19 

 
 
 
 
 
that this crucial parameter can be recovered from estimates of the Phillips multiplier and the 
proportionality factor, and related to its theoretical counterparts in state-dependent models. An 
important caveat is that, since the empirical identification of κ relies on using demand shocks, its 
estimates cannot be used to gauge the effects of supply-side shocks. 

κ

  can be estimated by regressing average inflation on average 

Moreover, building on the results of Hazell et al. (2021), either the proportionality factor φ or the 
slope 
  can be estimated directly, without assuming that unemployment conditional on the 
monetary policy (or other demand) shock necessarily follows an AR (1) or other specific processes. 
Specifically, the slope 
unemployment over a relatively long time interval t, t+T (and appropriately discounted with an 
assumed discount factor β), instrumented with monetary policy or any other structural demand 
shocks (see eq. 15 in Hazell et al., 2021). While micro price data are not strictly necessary to obtain 
these estimates, their use to compute aggregate inflation ensures a degree of consistency with 
models calibrated to match their features. Moreover, microdata can be useful to further disentangle 
the determinants of the slope of the Phillips curve, as we discuss below. 

κ

It is well known that the slope κ is, in turn, a function of parameters reflecting both nominal and real 
rigidities, where the latter are broadly defined as the elasticity of marginal costs (and possibly also 
mark-ups) to changes in unemployment due to a demand shock (which does not affect natural 
unemployment). For instance, in the benchmark Calvo model, the following expression holds for κ 
in terms of deep structural parameters: 

�1 − β(1 − θ)�θ
1 − θ

ω,

κ =
where ω (<0) indicates the elasticity of marginal cost to unemployment, while θ is the Calvo 
repricing rate. A lower ω in absolute value implies a lower value of κ and a flatter Phillips curve. 
Importantly, the parameter ω could be a function of frictions affecting the mapping from 
unemployment into marginal costs, such as sticky wages, and of frictions affecting the mapping 
from marginal costs into reset prices, such as strategic complementarities. One way to infer the 
strength of real rigidities through the value of ω would be to use the above expression for κ, setting 
θ as equal to the observed repricing rate. However, in the presence of state dependence, the 
observed repricing rate would underestimate the impact of nominal rigidities, resulting in an 
overestimation of the elasticity ω for a given estimate of the slope parameter κ. An alternative way 
to infer the elasticity ω relies on estimating the Phillips multiplier of reset price inflation. It is possible 
to show that this multiplier can help to identify ω separately and thus provide independent evidence 
on real rigidities relative to estimates of the Phillips multiplier of aggregate inflation. Intuitively, reset 
prices depend on current and expected marginal costs (and possibly mark-ups), so the empirical 
link between the latter and unemployment conditional on a demand shock can be inferred from the 
data. 

Aggregate inflation is very persistent in response to monetary policy and 
demand shocks, reflecting both the infrequent repricing rate and the slow 
reaction of reset prices. The rows in Chart 6 present, for the monetary policy 
shock and the global demand shock in Gautier et al. (2022), the estimated 
responses over 36 months of the following variables: i) the aggregate price level 
(including unchanged prices); and ii) reset prices (including only non-zero price 
changes). Both variables are based on the micro prices of “core” sectors in the 

ECB Occasional Paper Series No 321 

20 

 
 
 
 
dataset of Gautier et al. (2022) for 11 euro area countries. The above two shocks are 
normalised so that the monetary policy shock is contractionary, while the demand 
shock brings about inflationary pressures (shocks in the opposite direction will have 
symmetric effects by construction). Starting with the first column, depicting the 
response of the aggregate price level, consistent with the pervasive price stickiness 
documented in Gautier et al. (2023), the effects of all shocks appear very gradual, 
building up over time. The effect of the monetary policy shock is less precisely 
estimated, while the response to the global demand shock is statistically significant 
at all conventional confidence levels after 12 months.12  The price-level response is 
very persistent, peaking after around 20-24 months, and staying broadly constant, 
with little sign of further acceleration. This is consistent with these shocks having 
transitory effects on inflation, suggesting no sustained dynamics in wages and other 
nominal costs in their aftermath. The responses of reset prices in the second column 
of Chart 6 help to shed light on the transmission mechanism underlying the inflation 
dynamics in the first column. Consistent with the micro price evidence discussed in 
Section 1, when idiosyncratic factors are the main driver of the decision to change 
individual prices, the typically smaller macro shocks have no material impact on the 
repricing rate, and affect aggregate inflation mainly by shifting the average of those 
prices that are reset according to the “normal” repricing rate (indeed, Gautier et al., 
2022, show that repricing frequency is not affected by the above monetary and 
global demand shocks). However, since the latter is low, but positive, in each period, 
the very limited short-run effects of both shocks on the aggregate price level must 
then be mainly due to a very subdued average response of reset prices. This 
implication is borne out by the response of reset prices whose dynamics in both rows 
are, indeed, very similar to those of aggregate inflation (which, however, also 
includes zero price changes). On average, reset prices that change after each shock 
react very little in the short run, and converge rather slowly to their long-run level, 
only responding in a statistically significant way after 10-12 months. Moreover, the 
fact that aggregate and reset price inflation stabilise around similar values after 
roughly two years implies that the medium-term (dis)inflationary consequences of 
these aggregate shocks are mostly driven by the dynamics in reset prices, and thus 
by how much firms decide to adjust their prices in the medium term. 

12    Nevertheless, the likelihood that inflation will fall in the 18-24 months after a monetary contraction is 

around 80%. 

ECB Occasional Paper Series No 321 

21 

 
 
 
Chart 6 
Impulse responses of aggregate (

) and reset prices (

) to a monetary policy 

and a global demand shock 

𝑚𝑚,𝑑𝑑
βℎ

𝑚𝑚,𝑑𝑑
γℎ

a) Monetary policy shock 

(percentage points) 

𝒎𝒎
𝜷𝜷𝒉𝒉
0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

-0.4

-0.5

𝒎𝒎
𝜸𝜸𝒉𝒉
0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

-0.4

-0.5

0

10

20

30

0

10

20

30

b) Global demand shock 

(percentage points) 

𝒅𝒅
𝛃𝛃𝒉𝒉
0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

-0.1

-0.2

-0.3

𝒅𝒅
𝜸𝜸𝒉𝒉
0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

-0.1

-0.2

-0.3

0

10

20

30

0

10

20

30

Source: ECB calculations based on the data and methodology presented in Gautier et al. (2022). 
Note: The light and dark grey areas correspond to one and two standard error bands, assuming calendar-based clusters. 

The following counterfactual exercise is useful to gauge the effect of the 
sluggish short-run response of reset prices on aggregate inflation dynamics. 
We can assume that reset prices jump on impact to 
they converge and stabilise around after the shock, and compute the impulse 
response of aggregate prices according to a Calvo model for each period 
shock: 

, the estimated value at which 

after the 

𝑝𝑝

∗

h 

=

ℎ+1

∗

�𝑝𝑝

�1 − (1 − θ)

𝑝𝑝𝑡𝑡+ℎ
  to 0.12, the average frequency of price changes in 
We calibrate the repricing rate 
the euro area as estimated in Gautier et al. (2022, 2023), including the sales prices 
that are used in the impulse responses in the chart, and set 

  to the average of the 

θ

ECB Occasional Paper Series No 321 

22 

∗

𝑝𝑝

 
 
 
 
 
 
 
 
 
 
 
reset prices impulse response after it stabilises (after roughly 18 months). Chart 7 
shows the results of this counterfactual, together with the point estimates of the 
impulse response of the aggregate price level already presented in Chart 6, where, 
for ease of comparison, the monetary policy shock (in the left-hand graph) is now 
normalised to increase prices like the demand shock (in the right-hand graph). The 
counterfactual tracks the estimated aggregate price dynamics closely after around 
18 months, when it amounts to almost 90% of the long-run level of reset prices 
. 
However, it entails a much faster dynamic in the short run: after 12 months, 
counterfactual inflation is larger than actual inflation. This exercise suggests that the 
estimated subdued response of reset prices contributes to the sluggish reaction of 
inflation in the short run, in addition to the low repricing rate. 

𝑝𝑝

∗

Chart 7 
Impulse response functions of aggregate prices, and counterfactual assuming that 
reset prices jump on impact to their new long-run value 

(percentage points) 

Estimated IRF
Counterfactual IRF

a) Monetary policy shock

b) Demand shock

0.12

0.10

0.08

0.06

0.04

0.02

0.00

-0.02

-0.04

0.6

0.5

0.4

0.3

0.2

0.1

0.0

-0.1

0

6

12

18

24

30

36

0

6

12

18

24

30

36

Source: ECB calculations based on the results in Gautier et al. (2022). 

The evidence of a subdued average response of reset prices in the short run is 
consistent with the model-based results in Section 1 on the limited degree of 
state dependence and selection in monetary transmission, but also suggests a 
significant role for real rigidities. A strong selection effect would imply 
counterfactually large price changes on average in the short run, which would offset, 
rather than strengthen, the impact of infrequent price changes on aggregate inflation. 
Moreover, the subdued response of reset prices also suggests that real rigidities, in 
addition to nominal rigidities, play a significant role in slowing down the propagation 
of demand shocks into inflation. This sluggish response by reset prices to aggregate 
shocks stands in stark contrast with the evidence of typically large individual price 
changes shown in Gautier et al. (2023), as well as the volatile unconditional 
behaviour of reset prices, documented, for example, by Bils et al. (2012) for the US 
economy. This finding thus provides new direct evidence in support of a key role for 
real rigidities in the monetary policy transmission mechanism, in line with standard 
DSGE models used for policy analysis, including at the ECB. 

ECB Occasional Paper Series No 321 

23 

 
 
 
Chart 8 
Phillips multipliers computed for aggregate and reset prices, with cumulated 
unemployment instrumented with a monetary policy (
demand shock (
) 

  and 

  and 

) and a global 

𝑚𝑚
𝛽𝛽ℎ

𝑚𝑚
𝛾𝛾ℎ

a) Monetary policy shock 

𝑑𝑑
𝛽𝛽ℎ

𝑑𝑑
𝛾𝛾ℎ

(percentage points) 

𝒎𝒎
𝜷𝜷𝒉𝒉
0.50

0.25

0.00

-0.25

𝒎𝒎
𝜸𝜸𝒉𝒉
0.50

0.25

0.00

-0.25

-0.50

9

19

29

-0.50

9

19

29

b) Global demand shock 

(percentage points) 

𝒅𝒅
𝜷𝜷𝒉𝒉
0.10

0.00

-0.10

-0.20

-0.30

𝒅𝒅
𝜸𝜸𝒉𝒉
0.10

0.00

-0.10

-0.20

-0.30

-0.40

9

19

29

-0.40

9

19

29

Source: ECB calculations, based on the data and methodology presented in Gautier et al. (2023). 
Note: The light and dark grey areas correspond to one and two standard error bands, assuming calendar-based clusters. 

Estimates of the Phillips multipliers enable assessment of the role of nominal 
and real rigidities in the propagation of monetary policy and global demand 
shocks in the euro area. Chart 8 shows estimates of the Phillips multiplier for 
aggregate inflation and reset price inflation13  from the nine-month horizon onwards, 
instrumenting the cumulated unemployment with the monetary policy (first row) and 
the global demand shock (second row). Starting first with monetary policy shocks, 
the aggregate inflation Phillips multiplier in panel a) of Chart 8 is estimated to be 
around -0.1 from the nine-month horizon onwards, similar to estimates in Eser et al. 

13    We do not consider the impact period when we cumulate reset price inflation and unemployment. 

ECB Occasional Paper Series No 321 

24 

 
 
 
 
 
 
 
 
 
 
 
(2020) based on euro area aggregate data.14  This implies that, to bring about an 
average inflation increase of 1% over 12 (or 24) months, average unemployment 
over the same period needs to be around 10% lower. Clearly, the Phillips multiplier 
estimated in the data is less than half the model-based multiplier in Section 1, 
calibrated to the micro evidence on price setting. This suggests that real rigidities, 
which are not accounted for in the calibrated model in the previous section but are 
instead a key driver of the actual effects of monetary policy, as discussed above, act 
to dampen by more than half the effects of slack on inflation. The relevance of real 
rigidities is also evident in the second column: the Phillips multiplier estimated for 
reset prices is negative and relatively small, suggesting that the size of price 
changes also reacts quite sluggishly to the monetary policy shock. In the second 
row, we report on the same exercise for the global demand shock. These estimates 
are more precise (they are significantly negative at the 64% confidence level for 
aggregate inflation and as much as 95% for reset price inflation) and in the same 
ballpark as the first row. 

Chart 9 
Impulse responses of inflation and reset prices to inflationary oil supply shocks 

(percentage points) 

a) Inflation

b) Reset prices

c) Inflation asymmetric effect

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

1.2

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

0

5

10 15 20 25 30 35

0

5

10 15 20 25 30 35

0

5

10 15 20 25 30 35

Source: ECB calculations based on the data and methodology presented in Gautier et al. (2023). 
Note: The light and dark grey areas correspond to one and two standard error bands, assuming calendar-based clusters. 

Reset prices accounted for a more persistent dynamic in response to 
inflationary oil supply shocks than disinflationary ones, suggesting that their 
underlying drivers may be a source of asymmetries and non-linearities, in 
addition to state dependence. Chart 9 shows the estimated responses over 36 
months to the oil supply shock in Gautier et al. (2022) of the following variables: i) 
the aggregate price level (including unchanged prices) in the first column; ii) reset 
prices (including only non-zero price changes) in the second column; and iii) the 
asymmetric effects between the response of inflation to inflationary and 
disinflationary shocks. Specifically, the estimates in the first two columns are 
obtained for negative oil supply shocks which are inflationary, while the third column 
shows the difference of the (absolute value of the) inflation response between 

14    As in the case of the impulse response function to a monetary policy shock, the estimates are not 

significant at conventional confidence levels. Estimates are significant at the 64% level for the global 
demand shock. 

ECB Occasional Paper Series No 321 

25 

 
 
 
 
negative (inflationary) and positive (disinflationary) oil supply shocks. In line with the 
demand shocks discussed above, the first two columns show that inflationary 
pressures in response to oil supply shocks also build up slowly, reflecting both 
nominal rigidities and the sluggish response in reset prices. The response of reset 
prices converges rather slowly to its higher long-run level, even though it is already 
statistically significant after the first few months. Moreover, the third column shows 
that the difference in the (absolute value of) price responses to an inflationary and 
disinflationary oil supply shock is positive and increasing, becoming statistically 
significant after 24 months.15  This implies that inflationary oil supply shocks have 
much more persistent effects than disinflationary ones, and that this asymmetric 
response is entirely due to the more persistent responses of reset prices (as they 
drive inflation beyond 24 months). This suggests that the underlying drivers of the 
latter, reflecting how much firms decide to adjust their prices, may be a source of 
asymmetries and even non-linearities, in addition to state dependence, in firms’ 
decisions on whether to change prices. 

15    Using daily gasoline price data collected in all gas stations in France, Gautier, Marx and Vertier (2022) 

show that the reaction of gasoline retail prices to a wholesale price shock does not show any 
asymmetry. 

ECB Occasional Paper Series No 321 

26 

 
 
 
4 

Other micro-based evidence on the 
determinants of price adjustment 

This section reviews other micro-based evidence on the determinants of price 
adjustment obtained in PRISMA for specific countries and sectors in Europe. 
These contributions are grouped into the following two strands: a) papers studying 
the role of state dependence and selection; and b) papers investigating the 
determinants of adjustment in reset prices and cost pass-through, focusing on 
specific channels of real rigidities such as firm-level strategic complementarities. 

4.1 

Further micro-based evidence on state dependence and 
selection 

The evidence of an overall limited degree of state dependence and small 
selection effects in euro area price setting is directly confirmed by a number of 
PRISMA studies. Karadi et al. (2022) use supermarket scanner data in the euro 
area and the United States to estimate the distribution of price misalignments and 
their relationship with the probability of price adjustments. It uses these objects to 
assess the contribution of state dependence to the flexibility of the aggregate price 
level, according to the flexible accounting framework of Caballero and Engel (2007). 
The estimates imply that price setting is state-dependent in both the euro area and in 
the United States, but the extent of state dependence is small. The level of monetary 
non-neutrality predicted by the framework is only 33% higher than in a time-
dependent model, which is calibrated to match the frequencies of reference price 
changes in each region. In another contribution to PRISMA, Dedola, Kristofferson 
and Zuellig (2023) model state dependence and pass-through jointly using microdata 
on Danish manufacturers’ prices and costs to test for and estimate the strength of 
selection effects. In support of state dependence, these authors find that energy and 
import cost shocks affect the probability of price changes. However, they also find 
that selection contributes little to the aggregate inflation response to these cost 
shocks. 

One set of papers found support for the proposition that not only the repricing 
rate, but also the kurtosis of price changes, directly related to selection, is 
important to understand the heterogeneity of monetary policy effects across 
sectors. A standard prediction of sticky price models is that the repricing rate is a 
key predictor of the real effects of monetary policy. For instance, Henkel (2020) 
shows that the output reaction of US manufacturing industries to monetary policy 
shocks is systematically related to an industry degree of price stickiness. For the 
euro area, Gautier et al. (2022) show that inflation in sectors with a higher repricing 
rate reacts more strongly to a monetary policy shock. Recent theoretical literature 
has shown that, in a broad class of sticky price models under low inflation, the 
cumulative response of output to a once-and-for-all small monetary shock – 

ECB Occasional Paper Series No 321 

27 

 
 
essentially the area under the output impulse response – is proportional to the ratio 
of the frequency of price changes and the kurtosis of the distribution of (non-zero) 
price changes (a measure of “fat tails” in this distribution).16  This ratio is called a 
sufficient statistic for the real effects of monetary policy. One suggestion as to why 
these two moments of the price-change distribution do capture the effects of 
monetary shocks is as follows. If price changes are infrequent, prices will respond 
slowly to a shock, inducing larger real effects. Thus, the inverse of the frequency of 
price changes captures the overall degree of price rigidity, a feature which is 
standard in most sticky price models. For a given frequency of price changes, the 
patterns of the size of price adjustments also matter. Specifically, if the firms that 
adjust their prices are those whose prices are very far from their desired value, the 
price response will be larger and quicker, and the output effect smaller. The kurtosis 
of price changes turns out to capture this “price selection” effect. Weaker price 
selection is associated with a higher kurtosis, since the mass of price changes is 
more concentrated around the mean, with occasional values far from it. For a given 
repricing rate, this results in a slower response of the aggregate price to a shock and 
larger real effects of monetary shocks. This price selection effect is maximal in a 
standard menu cost model, such as Golosov and Lucas (2007), and minimal in a 
standard Calvo model. In two distinct empirical contributions, Alvarez et al. (2021) 
and Gautier, Marx and Vertier (2022) test this theoretical proposition using micro 
price data and find strong supporting evidence for it. Using both consumer and 
producer price data for France, Alvarez et al. (2021) find that the sectoral price 
response to a monetary policy shock is proportional to the ratio of kurtosis over 
frequency of price changes and that both kurtosis and frequency contribute to this 
result. They also show that the relationship is more robust for producer prices than 
consumer prices. In the latter case, seasonal sales may explain why the theoretical 
prediction is less strong than for producer prices. 

Frequency and kurtosis of price changes are also closely correlated with the 
price reaction to cost shocks across gas stations. In Gautier, Marx and Vertier 
(2022), the empirical test focuses on gasoline retail prices, for which we can observe 
high-frequency price data and can precisely measure a marginal cost shock. In this 
case study, where more precise information is available on the kurtosis of price 
changes and on the reaction of prices to an observed shock, the authors find very 
robust correlations, not only between the cumulated price response of gas stations 
and the ratio of kurtosis over the frequency of price changes, but also with both the 
kurtosis and the frequency of price changes taken separately (Chart 10). These 
correlations are very much in line with the theoretical predictions, and also show that 
none of the other moments of the price-change distribution correlate as strongly as 
the frequency and the kurtosis with the cumulated price response. Overall, these two 
papers show the importance, not only of the repricing rate in understanding the 
response of prices to a shock to marginal costs across sectors, but also of the 
kurtosis of price changes, which captures the potential selection effects. This 

16    Specifically, kurtosis is the average of the standardised data raised to the fourth power. High values of 
kurtosis thus arise in two circumstances: a) where the probability mass is concentrated around the 
mean and the data-generating process produces occasional values far from the mean; or b) where the 
probability mass is concentrated in the tails of the distribution. 

ECB Occasional Paper Series No 321 

28 

 
 
 
evidence is consistent with the result in Section 2 that state dependence, though 
limited in the aggregate, is quite heterogenous across sectors in the euro area. 

Chart 10 
Scatter plots of the cumulative impulse response of prices of gasoline to a cost 
shock and frequency and kurtosis 

(percentage points) 

a) Kurtosis/Frequency

b) Frequency

c) Kurtosis

27

26

25

24

23

27

26

25

24

27

26

25

24

5

10

15

20

25

23

0.1

0.3

0.5

0.7

23

1.5

3.5

5.5

7.5

Source: Gautier, Marx and Vertier (2022). 
Note: Correlations are calculated across gas stations; statistics are measured for every gas station in France, based on a dataset of 
daily gasoline prices between 2007 and 2018, collected by the French Ministry of the Economy. 

4.2 

Micro-based evidence on determinants of pass-through 
and specific channels of real rigidities 

Several PRISMA papers have used microdata to investigate the determinants 
of cost pass-through and real rigidities. Microdata are key to estimate how firms 
pass through changes in their costs to prices. Therefore, a few PRISMA papers have 
merged firm-level price and cost data available for some countries and sectors to 
obtain precise estimates of pass-through. 

A first set of papers has looked at the determinants of pass-through of energy 
costs, finding evidence in support of pipeline effects. Dedola, Kristofferson and 
Zuellig (2023) estimate energy costs pass-through due to oil supply shocks by 
Danish manufacturing firms. As in Gautier et al. (2022), the estimated response of 
micro prices is slow and builds up over time; this reflects the propagation of the 
shock through the supply chain from firms more exposed to oil to other firms 
(“pipeline” effects). 

A second set of papers has investigated strategic complementarities at the 
firm level, finding mixed evidence. Santoro and Viviano (2022) estimate the pass-
through of labour costs to prices in Italian microdata. This empirical exercise uses a 
dataset obtained by merging administrative employer-employee data, firms’ balance 
sheets and a survey on industrial and non-financial service firms conducted every 
year by Banca d’Italia, which reports information on firms’ changes to their prices. To 
identify the causal relationship, this paper relies on a quasi-natural experiment based 
on a policy change that occurred in Italy in late 2014, when a substantial, three-year 

ECB Occasional Paper Series No 321 

29 

 
 
 
social security contribution cut was announced by the Government for recruitments 
in the following year. This policy change is interpreted as an exogenous variation in 
labour costs and related to firms’ price changes. The results show that the prices of 
small firms and firms paying lower wages react to a change in labour costs to a 
much greater degree than the prices of large firms, supporting the hypothesis that 
strategic complementarities at the firm level might be at play. Meanwhile, two other 
papers find opposing evidence of a differential cost pass-through for large versus 
small firms. The above-mentioned paper by Dedola, Kristofferson and Zuellig (2023) 
finds that the pass-through of firm-level import costs is very similar across small and 
large firms (proxied with employment or sales). Dedola, Osbat and Reinelt (2022) 
find that changes in the corporate tax rates of German manufacturers of supermarket 
products result in substantial pass-through to retail prices, consistent with producers’ 
market power. Moreover, there is evidence that corporate tax pass-through is 
significantly larger for producers and retailers with larger market shares 

ECB Occasional Paper Series No 321 

30 

 
 
5 

Conclusions and policy implications 

This paper has analysed the implications for inflation dynamics and monetary 
policy of the evidence on micro price setting gathered by PRISMA, focusing on 
model-based and empirical estimates of the structural Phillips curve. In this 
paper, we have used the cross-sectional evidence on price setting presented in 
Gautier et al. (2022, 2023) to derive implications for the monetary policy transmission 
mechanism through the lens of state-of-the-art price-setting models, emphasising 
state dependence in firms’ decisions to change prices. Estimates of the actual 
response of micro prices to structural shocks, including shocks to the supply side, 
also help to shed light directly on key aspects of inflation dynamics and monetary 
transmission, in particular by documenting which margins of price adjustment are 
key for the inflationary consequences of aggregate shocks in the data. 

According to models calibrated to the euro area micro evidence in Gautier et 
al. (2022, 2023), infrequent price changes and moderate state dependence in 
price setting should result in a meaningful Phillips curve in the euro area. The 
model-based analysis shows that price setting in the euro area is consistent with a 
limited degree of state dependence and a meaningful Phillips curve relationship. The 
latter has been broadly stable for the range of inflation rates observed in the euro 
area in the last 15 years. 

Sectoral heterogeneity in price setting results in heterogeneous monetary 
transmission across sectors both in calibrated models and in the data. 
Consistent with the evidence in Gautier et al. (2022), state dependence in euro area 
price setting is heterogeneous across sectors, but overall played a limited role in 
aggregate inflation determination during the low-inflation period. Nevertheless, 
heterogeneity in state dependence is crucial to account for the heterogeneity in the 
effects of monetary policy across sectors. 

Model-based simulations show that due to non-linearities in price setting trend 
inflation above 5-6% may have significant effects on the euro area Phillips 
curve. Non-linearities in price setting associated with state dependence imply that 
increases in trend inflation above 5-6% would affect the repricing rate and generally 
price setting across sectors differently, with significant aggregate effects, as 
summarised by a steeper theoretical Phillips curve. 

Similarly, shocks to nominal costs larger than 15% have non-linear effects on 
inflation dynamics in calibrated models. Non-linearities in price setting due to 
state dependence can explain the effects of larger business cycle shocks on the 
repricing rate in the data documented in Gautier et al. (2022), although these shocks 
have to clear a relatively high threshold for non-linearities to materially affect the 
transmission mechanism in the calibrated models. Given the relatively stable 
environment in the period for which microdata were available, however, we have little 
evidence on the value of this threshold in the euro area data. 

ECB Occasional Paper Series No 321 

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Overall, the return of higher and more volatile inflation seems to be associated 
with higher frequencies of price changes, mainly because the frequency of 
price increases rises with inflation. In higher inflation periods, either due to larger 
shocks or higher trend inflation, aggregate factors become an important motive for 
price changes, as predicted by state-dependent models of price setting. This 
provides empirical support that large shocks or a higher trend inflation would help to 
accelerate inflation dynamics and the effects of nominal shocks on inflation, in line 
with model simulation results. 

Empirical estimates of the Phillips curve during the low-inflation period 
confirm previous findings of a relatively flat but stable slope. We have used 
demand shocks to estimate the structural slope of the Phillips curve in the euro area, 
linking core inflation to unemployment as a measure of economic slack. The inflation 
measure was based on micro price data, thus reducing the incidence of 
administrative prices or imputations. The structural slope is estimated at -0.013, 
similar to other estimates for the euro area. Despite being low, such values are still 
larger than those recently estimated for the United States with similar methodologies. 

Such an estimated flat slope reflects both infrequent and subdued price 
adjustment in response to aggregate shocks, i.e. the presence of nominal and 
real rigidities. Empirical estimates of the slope of the Phillips curve reflect not only 
infrequent price adjustment, but also the estimated slow response of reset prices to 
the small aggregate shocks prevailing in the low inflation period. It is also important 
to stress that, given the methodology used, linking inflation to unemployment, these 
low estimates mainly apply to demand shocks and monetary transmission. Supply 
shocks directly affecting firms’ costs may be transmitted at a different speed. 

A few PRISMA studies have investigated specific channels through which real 
rigidities may affect price adjustment, finding mixed evidence. Specifically, the 
evidence is mixed as to whether firm-level strategic complementarities matter 
empirically and are stronger for larger firms. This is an important question for future 
work, since strategic complementarities feature prominently as a driver of real 
rigidities in the structural models used for policy analysis at the ECB and ESCB. A 
further important question concerns whether the strength of real rigidities may 
depend on types of shocks, both at the firm level and in the aggregate. 

ECB Occasional Paper Series No 321 

32 

 
 
 
 
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Acknowledgements 
The authors would like to thank Peter Karadi and Chiara Osbat for valuable comments and suggestions, and Marta Oliva Riera and 
Stavros Konstantios for outstanding editorial support. 

Luca Dedola 
European Central Bank, Frankfurt am Main, Germany; email: luca.dedola@ecb.europa.eu 

Erwan Gautier 
Banque de France, Paris, France; email: Erwan.GAUTIER@banque-france.fr 

Anton Nakov 
European Central Bank, Frankfurt am Main, Germany; email: anton.nakov@ecb.europa.eu 

Sergio Santoro 
European Central Bank, Frankfurt am Main, Germany; email: Sergio.Santoro@ecb.europa.eu 

Emanuel De Veirman 
De Nederlandsche Bank, Amsterdam, The Netherlands; email: manu.veirman@dnb.nl 

Lukas Henkel 
European Central Bank, Frankfurt am Main, Germany; email: Lukas.Henkel@ecb.europa.eu 

Bruno Fagandini 
European Central Bank, Frankfurt am Main, Germany; email: Bruno.Fagandini@ecb.europa.eu 

© European Central Bank, 2023 

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