Estimating the impact of quality adjustment on consumer price inflation.
Date: 2023-02-09
Institutions: European Central Bank
Subjects: EU statistics, European Economic Area, Germany, consumer price, data collection, economic statistics, harmonisation of prices, inflation, price index

Jan-Oliver Menz, Elisabeth Wieland, 
Jens Mehrhoﬀ 

Working Paper Series 

Estimating the impact of quality 
adjustment on consumer price 
inﬂation 

No 2773 / February 2023 

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 Micro Setting Analysis Network (PRISMA) 

This paper contains research conducted within the Price Micro Setting Analysis Network (PRISMA). PRISMA consists of economists 
from the ECB and the national central banks (NCBs) of the European System of Central Banks (ESCB). 

PRISMA is 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) act as external consultants. 

PRISMA collects and studies various kinds of price microdata, including data underlying official price indices such as the Consumer 
Price Index (CPI) and the Producer Price Index (PPI), scanner data and online prices to deepen the understanding of price-setting 
behaviour and inflation dynamics in the euro area and EU, with a view to gaining new insights into a key aspect of monetary policy 
transmission (for further information see https://www.ecb.europa.eu/pub/economic-research/research-
networks/html/researcher_prisma.en.html) 

The refereeing process of this paper has been co-ordinated by a team composed of Luca Dedola (ECB), Anton Nakov (ECB), Chiara 
Osbat (ECB), Elvira Prades (Banco d’Espana), Sergio Santoro (ECB), Henning Weber (Bundesbank). 

This paper is released in order to make the results of PRISMA research generally available, in preliminary form, to encourage comments 
and suggestions prior to final publication. The views expressed in the paper are the author’s own and do not necessarily reflect those of 
the ESCB. 

ECB Working Paper Series No 2773 / February 20231Abstract

How much does quality adjustment matter in measuring consumer price inﬂation? To address

this question, we use diﬀerent sources of micro and macro price data for Germany and the

euro area. For Germany, we ﬁnd that quality adjustment applies to a large range of goods

and services but, on average, price adjustments due to quality changes reduce headline

inﬂation only by 0.06 percentage points, which is balanced out by an increase due to quantity

adjustment (e.g. a smaller package size) of the same amount. For the euro area, we assess the

impact of heterogeneous quality adjustment methods by deriving the distribution of member

states’ cumulative inﬂation rates for typical quality-adjusted products. Our macro-based

estimate makes up to ± 0.2 percentage points for headline HICP inﬂation and ranges between

± 0.1 and 0.3 percentage points for core inﬂation, when controlling for income diﬀerentials

between member states. Finally, we illustrate the role of heterogeneous quality adjustment

methods in the euro area based on micro price data for washing machines. We show that

the price development of this product would have been lower by about 3.5 percentage points

during the ﬁrst years of the euro area and by about half a percentage point during recent

years, if prices in the member states had been quality-adjusted in exactly the same way.

JEL Classiﬁcation: E31, C43.

Keywords:

inﬂation measurement, quality adjustment, inﬂation diﬀerentials, micro price

data.

ECB Working Paper Series No 2773 / February 20232Non-technical summary

The measurement of consumer price inﬂation remains a key question for both statisticians, central bankers

and policy makers. One of the most important challenges consists of adequately accounting for changes

in product quantity and quality, not only in a single country but also between members of the euro area

if the adjustment practices are not harmonised suﬃciently. This issue has also been identiﬁed as a crucial

knowledge gap in the strategy review of the Eurosystem (ECB, 2021).

We provide estimates of the impact of quality and quantity adjustment on consumer price inﬂation in

three ways. First, we analyse the micro data underlying the German CPI and compare unadjusted

and adjusted inﬂation ﬁgures. Second, we approximate the eﬀect of heterogeneous quality adjustment

methods within the euro area by using disaggregate HICP data. And third, we oﬀer a more precise

measure of those inﬂation diﬀerentials with the help of transaction level data for washing machines.

For Germany, we ﬁnd that price adjustments due to quality changes have lowered the headline inﬂation

rate by about 0.06 percentage points between 2015 and 2020, which was oﬀset by an increase caused by

quantity adjustments of about the same size. However, the eﬀect of quality adjustment is substantially

larger for industrial goods and services, also because our data set excludes some electronic goods that are

typically adjusted for quality changes. Regarding the euro area, we ﬁnd that headline inﬂation ﬂuctuates

between ± 0.2 percentage points and core inﬂation between ± 0.1 and 0.3 percentage points according

to our estimate of heterogeneous quality adjustment methods across member states. Finally, using the

same adjustment method for all countries, we ﬁnd that the inﬂation rate for washing machines would

have been about 3.5 percentage points lower between 2000 and 2005, and about 0.5 percentage points

lower between 2017 and 2021 than the corresponding oﬃcial inﬂation rate, if prices in the member states

had been quality-adjusted in exactly the same way.

Overall, our estimate of the impact of heterogeneous QA methods on euro area inﬂation is quite substantial

and of similar magnitude in comparison to measurement biases in the HICP due to substitution or the

lack of owner-occupied housing (ECB, 2021). Hence, this would call for a further harmonisation of QA

methods across member states to reduce or eliminate their impact on euro area inﬂation. On top, more

eﬀorts should be made to quantify both the size and the direction of the impact of quality adjustment in

euro area inﬂation with greater accuracy. Using transaction data with a larger product coverage could

oﬀer valuable insights in this respect.

ECB Working Paper Series No 2773 / February 202331 Introduction

Measuring consumer price inﬂation properly poses several challenges. A measurement bias can

arise when new products, outlets and changes in consumption patterns are considered only

with a certain time lag.1 Moreover, in price statistics, the “pure” price change should be

measured by disentangling a price decrease or increase due to a product’s quality improvement

or deterioration. Hence, inﬂation will be overestimated if rising prices are not adjusted for

improved product quality or if products of diﬀerent quality are taken as close substitutes.

Concerning potential measurement bias in a CPI, quality adjustment was found to explain more

than half of the measurement error for US inﬂation (Boskin et al., 1996). For Germany, Hoﬀmann

(1998) argued that post-euro inﬂation could have been biased upwards by about 0.75 percentage

points mainly because of diﬃculties with respect to accounting for changes in product quality.

Yet, little is known on the impact of quality adjustment on consumer price inﬂation for a more

recent period, which might be due to the lack of more granular information about the underlying

methods and the size of the price adjustments at the product level.

In the euro area, an additional source of measurement bias might result not only from the lack

of quality adjustment (QA) itself, but from heterogeneous national QA practices. To this date,

various QA procedures are at hand for National Statistical Institutes (NSIs) in the euro area,

but without any binding rules, pointing out scope for further harmonisation (ECB, 2021). Het-

erogeneous QA practices might also contribute to the astonishingly large price diﬀerentials of

certain products in the euro area. For example, the average price change of mobile phones in

the HICP since 2016 ranges from +5% in Portugal to -17% in Ireland. Given the homogeneity

and tradability of this item, such huge price diﬀerentials are surprising; one candidate explana-

tion for diverging price trends – notably for industrial products with continuous technological

improvements – could stem from heterogeneous QA practices across euro area member states.

Likewise, a case study with Austrian and Italian Consumer Price Index (CPI) micro data by

Conﬂitti et al. (2022) suggests that the choice of QA methods can well explain divergent HICP

rates across countries. In the course of its strategy review, the Eurosystem has also stressed

the importance of gaining a better understanding of the various sources of measurement bias in

1Camba-Mendez (2003) oﬀers a discussion of four potential measurement biases in the Harmonised Index
of Consumer Prices (HICP) - substitution bias, quality bias, outlet bias and new good bias - and Beck and
Jaravel (2021) provide a comprehensive empirical assessment across more than 30 countries using scanner data
for fast-moving consumption goods.

ECB Working Paper Series No 2773 / February 20234euro area inﬂation, pointing out a knowledge gap concerning the bias due to quality adjustment

(ECB, 2021).

We contribute to ﬁlling this gap by estimating the impact of quality adjustment based on various

sources of micro and macro price data. First, we update the earlier ﬁndings of Hoﬀmann (1998)

and present some evidence regarding the scope and the size of quality and quantity adjustment

in the German inﬂation rate. Second, we try to approximate the impact of heterogeneous QA

practices across member states on euro area inﬂation.2 For this purpose, we follow a macro-

based approach which builds upon the oﬃcial inﬂation series published by Eurostat and selects

product categories whose prices are typically aﬀected by quality change. Based on the dispersion

across member states’ cumulative inﬂation rates, we derive a range for euro area headline and

core inﬂation that we interpret as an estimate for the impact of quality adjustment on the HICP.

Finally, we illustrate the impact of heterogeneous QA methods on euro area inﬂation by means

of micro price data; for this, we apply transaction prices for washing machines for ten member

states from the GfK.

According to our results, taking into account changes in quantity and quality has only a very

small impact on headline inﬂation in Germany. Since 2015, inﬂation has been increased on

average by +0.06 percentage points due to a smaller underlying quantity but has been lowered by

about the same amount due to quality improvements. Concerning the impact of heterogeneous

QA methods on euro area inﬂation, our macro-based estimate indicates a range between ± 0.2

and 0.6 percentage points for headline inﬂation and between ± 0.3 and 0.8 percentage points

for core inﬂation. Controlling for income diﬀerentials between member states, the impact is

reduced to up to ± 0.2 for headline inﬂation and ± 0.1 to 0.3 for core inﬂation. Regarding our

case study on washing machines, we ﬁnd that the corresponding euro area inﬂation rate would

have been lower by about 3.5 percentage points during the ﬁrst years of the euro area and by

about half a percentage point during recent years if the same QA method had been used across

countries.

The outline of this paper is as follows. Section 2 presents some stylised facts and a literature

overview on the impact of quality adjustment in consumer price statistics. Section 3 provides

2A precise estimate could only be derived by means of detailed micro price data. Although the Eurosystem’s
Price-setting Microdata Analysis Network (PRISMA) has moved somewhat in this direction, a direct comparison
of the HICP micro price data across countries is hampered by the lack of information on quality adjustment and
centrally collected prices such as electronics, which often undergo quality adjustment (see Gautier et al., 2022).

ECB Working Paper Series No 2773 / February 20235an estimate of the impact of quantity and quality adjustment on German inﬂation by using

CPI micro prices for the period 2010-2020. Section 4 discusses the role of quality adjustment

for euro area inﬂation. For this purpose, we estimate the impact of heterogeneous QA methods

on euro area headline and core inﬂation by using oﬃcial national inﬂation rates and a pre-

deﬁned list of typically quality-adjusted products (Section 4.1). Next, we illustrate the impact

of heterogeneous QA methods in the euro area based on micro price data for washing machines

(Section 4.2). Finally, Section 5 concludes.

2 Stylised facts and literature overview

The literature is relatively silent about the eﬀects from quality adjustment on inﬂation mea-

surement, in particular with respect to explaining price diﬀerentials between countries. As part

of the traditional debate on measurement errors, quality adjustment was found to explain more

than half of the measurement error for US inﬂation (Boskin et al., 1996). Based on this seminal

contribution, some euro area countries made similar eﬀorts to quantify the measurement bias in

domestic inﬂation, e.g. Hoﬀmann (1998) for Germany, Lequiller (1997) for France and Neves and

Sarmento (1997) for Portugal.3 Hoﬀmann (1998) argued that German inﬂation before the euro

introduction could have been biased upwards by about 0.75 percentage points mainly because of

diﬃculties with respect to accounting for changes in product quality. Based on a model of price

formation, the author states that – if inﬂation is moderate – the quality adjustment bias “might

be approximately 1/2 percentage point if the average advance in quality is 1% per annum”, with

non-linearities applying according to the level of inﬂation.4 In light of digitalisation and product

innovations, the question of the impact of quality adjustment on consumer prices has become

even more relevant nowadays (Reinsdorf and Schreyer, 2019). Nevertheless, to the best of our

knowledge, Statistics Sweden seems to be the only institute that regularly publishes the impact

of its quality adjustment on national inﬂation; according to its annual quality report, about

3Several studies also focus on the measurement bias in the HICP stemming from the underlying index formulae.
Herzberg et al. (2021) calculate the upper-level aggregation bias arising from product substitution and delayed
data availability in Germany and the euro area. According to their results, oﬃcial HICP inﬂation has been biased
upwards by about 0.1 percentage points. By contrast, Gabor-Toth and Vermeulen (2019) argue that the choice
of the index formula on the micro level, the elementary index bias, is quantitatively more important than the
upper-level substitution bias.

4See Hoﬀmann (1998), p. 154: “Below this area, i.e. given falling prices, the bias increases rapidly. As a
maximum it could be in the region of one percentage point per annum. If inﬂation is higher, the bias might also
be over 1 percentage point p.a..”

ECB Working Paper Series No 2773 / February 2023627% of products of the Swedish consumption basket are adjusted for quality changes. Without

quality adjustment, the prices of these groups would be 1.2% higher resulting in an overall eﬀect

of +0.3 percentage points for headline inﬂation (Statistics Sweden, 2019).

For the euro area, on top of a bias caused by missing or insuﬃcient quality adjustment itself,

a measurement bias can result from country-speciﬁc quality adjustment; either because some

countries choose to adjust prices of speciﬁc goods for quality changes and others do not, or

because countries apply diﬀerent QA methods. As pointed out already by Ahnert and Kenny

(2004) in the early days of the currency union, this might contribute to divergent price trends

across member states.5 One illustration of the potential impact of heterogeneous QA methods

across euro area countries is shown in Figure (1), which plots the index and the cumulated

inﬂation rate of the HICP index for mobile telephone equipment (including smartphones) from

January 2016 onwards.6 Given that products in this category are assumed to be fairly homo-

geneous, we would expect prices to behave rather similarly across countries.7 Yet, we observe

remarkable price diﬀerentials ranging from a cumulative price decrease of more than 60% in

Estonia to an increase of about 2% in Portugal. A similar pattern emerges for prices of personal

computers, as shown in the lower panel of Figure (1). More generally, Byrne (2019) argues that

the problem of huge unexplainable deviations in oﬃcial prices for digital products becomes even

worse if one considers also countries outside of the euro area.

Most recently, in its strategy review, the ECB (2021) identiﬁed a knowledge gap concerning the

potential bias from quality adjustment in the euro area HICP. In a case study with Austrian

and Italian CPI micro prices, Conﬂitti et al. (2022) show that heterogeneous QA practices

might well explain divergent HICP rates and trends across countries. In this vein, Statistics

Austria predominantly uses explicit QA methods whereas Istat only uses implicit methods.8

For a selection of non-energy industrial goods, the study does not ﬁnd a strong measurement

bias driven by quality adjustment. However, between both countries, the results suggest that

5See Ahnert and Kenny (2004), p. 25: “At present, although only partial information is available on the
precise quality adjustment practices at the national level in EU countries, it would seem that for many items a
wide variety of diﬀerent approaches are used and this may have resulted in widely divergent price developments
for some individual goods and services.”

6Data at the lowest level of aggregation of the HICP, which refers to the 5-digit level of the European Clas-
siﬁcation of Individual Consumption according to Purpose (ECOICOP), only starts in 2015 for most euro area
countries.

7Note that the HICP index “08.2.0.2 - Mobile telephone equipment” only comprises mobile phone handsets,

whereas the mobile phone tariﬀ falls under ECOICOP “08.3.0.2 - Wireless telephone services”.

8Explicit methods infer quality changes by assumption or by direct computation with the help of product

characteristics. By contrast, implicit methods gauge the impact of quality changes from other information.

ECB Working Paper Series No 2773 / February 20237Figure 1: Price developments for selected products

HICP for Mobile Telephone Equipment

Index

Cumulated inﬂation rate

HICP for Personal Computers

Index

Cumulated inﬂation rate

Note: The ﬁgure shows the HICP indices “08.2.0.2 Mobile telephone equipment” and “09.1.3.1 Personal comput-
ers” indexed to January 2016=100 and as cumulated inﬂation rate between January 2016 and September 2021.
Data for Ireland and Finland are only available from December 2016 onwards. For Greece, HICP data on mobile
telephone equipment only start in December 2017.

the implicit adjustment used in Italy explains a larger part of price changes due to product

replacement with quality changes, in contrast to the explicit methods used in Austria. Overall,

to the best of our knowledge, there is no study that estimates the impact of quality adjustment

PTLVSKDEBEATLTLUGRFRSIMTESCYFIITNLIEEE40608010012014016171819202122-60-40-200PTLVSKDEBEATLTLUGRFRSIMTESCYFIITNLIEEESKATLVBELTITLUMTDESIFICYFRGRNLPTESEEIE40608010012016171819202122-60-40-20020SKATLVBELTITLUMTDESIFICYFRGRNLPTESEEIEECB Working Paper Series No 2773 / February 20238on euro area inﬂation.

3 The impact of quality adjustment on the German CPI

In the current section, we make use of the micro price data underlying the German CPI to

estimate the impact of quality adjustment on inﬂation. Moreover, this analysis also backs our

selection of products which typically undergo quality adjustment that we use later in Section 4.

3.1 Data and deﬁnitions

According to the Federal Statistical Oﬃce of Germany (Destatis), various methods of quality

adjustment are applied to the German CPI.9 These include option pricing (e.g.

for airbags

in new cars) and (supported) judgemental quality adjustment (e.g. refrigerators with changed

power consumption or washing machines with changed water and power consumption). Hedonic

methods are applied to about 1.4% of the German CPI basket (e.g. desktop PCs, tablet PCs,

notebooks, smart phones, printers and used cars). Finally, Destatis also accounts for changes in

the quantity (e.g. package size) of a given product.

The micro price data underlying the German CPI has recently been made available for research

purposes and has been applied by Adam et al. (2021) to analyse changes in relative prices over

time, and Gautier et al. (2022) to investigate price setting in the euro area.10 Prices are collected

each month at the product level. Our sample covers the period from 2010:01 until 2020:12. After

exclusion of imputed prices and aggregated price measures, the data set consists of about 50

million observations which represent roughly 85% of the HICP. The coverage varies somewhat

between components, from 77% for unprocessed food to 79% for non-energy industrial goods,

90% for services, 94% for processed food and 100% for energy. Overall, the data contains 716

diﬀerent product groups at the COICOP10 level.

Concerning quality-adjusted products, the data set lacks some centrally collected goods and

services that are deemed to be strongly aﬀected by quality changes, such as computers, smart

phones, and used cars. Hence, our analysis will present a lower benchmark to the impact of

9General information about the quality adjustment procedures used in the German CPI are provided by

Destatis online here and in Appendix (D).

10See Appendix (A) for a description of the data set.

ECB Working Paper Series No 2773 / February 20239quality changes on German inﬂation. Moreover, the data set contains a statistical break in

2015:01, since Destatis revises the price collection and underlying methods typically every ﬁve

years when a new consumption basked is introduced. Therefore, any analysis at the product

level needs to be split into the periods before and after 2015.

Based on the German CPI micro data, we estimate the impact of changes in quantity and quality

on consumer prices as follows. The data set contains two price variables: praw denotes the raw

price as observed by the price collector in store. padj is the quantity- and quality-adjusted

price which enters the oﬃcial CPI compilation.11 Among many other product information, the

data set provides information about the quantity and the measurement unit of a product. For

example, we know how many grams of rice are contained in one package or how many millilitres

of milk are within one bottle, but also, how many minutes are spent in a guitar lesson.12 The

quantity-adjusted price is then computed in two steps. First, we deﬁne the unit-value price punit

i,t

by dividing the raw price praw

i,t

of a given product i in month t by its corresponding quantity

quani,t:

punit
i,t =

praw
i,t
quani,t

(1)

Second, we follow the approach of Destatis that calculates the quantity-adjusted price relative

to the corresponding quantity of the base period, i.e., to the years 2010 and 2015. However,

since the reference quantity is not included in our data set, we use the ﬁrst available quantity of

each product spell instead. Hence, denoting t = 1 as the reference period, the quantity-adjusted
price pquan

is given as:

i,t

pquan
i,t =

punit
i,t
punit
i,t=1

praw
i,t=1

(2)

Regarding quality changes, our data set contains a variable quali,t that indicates the price

diﬀerential in EUR with respect to the previous month that is caused by a change in product

quality; this is the case whenever the price collector samples a replacement product which

diﬀers in terms of quality from the predecessor product. We deﬁne the quality variable such

11A detailed variable description can be found in the corresponding meta data report, see, for example,

Forschungsdatenzentren der Statistischen Aemter des Bundes und der Laender (2022).

12We cleaned these variables beforehand to avoid spikes in the data arising from re-deﬁnitions of measurement

units and the like.

ECB Working Paper Series No 2773 / February 202310that negative values denote quality improvements meaning that the raw price will be lowered

due to an increase in product quality. Likewise, a quality deterioration is captured by positive

values. Thus, the quality-adjusted price is deﬁned as:13

pqual
i,t = praw

i,t + quali,t

(3)

Finally, to assess the impact of changes in product quantity and quality on inﬂation, individual

product prices have to be aggregated to derive inﬂation measures. We closely follow the oﬃcial

CPI compilation in Germany at the lowest available product level, which is the COICOP 10-digit

level. Speciﬁcally, we apply a weighting by federal states; for higher-level aggregates, we apply

the expenditure share of a given product group. We end up with four inﬂation measures derived
from the micro data set: πadj

denotes inﬂation derived from the adjusted price as reported in the

t
denotes inﬂation derived from the raw price. πquan

and πqual

t

denote

t

micro data set, and πraw

t

inﬂation derived from the quantity-adjusted price and quality-adjusted price, respectively. As

reported in Table (A1), the resulting inﬂation rates move very closely with the oﬃcial inﬂation

rates, as reﬂected by a correlation coeﬃcient generally above 0.8.14

In Figure (A1) in the

Appendix, we plot the inﬂation rates over time showing that micro price inﬂation follows oﬃcial

inﬂation very closely.

3.2

Impact of quality and quantity adjustment on German inﬂation

Using these deﬁnitions, we ﬁrst take a closer look at the scope and the size of quantity and

quality adjustment on the product level. To this end, in Table (1) and (2), we present results

for 20 product groups in the German CPI with the largest quantity and quality adjustment in

absolute terms for the two sub-samples 2010-2014 and 2015-2020, sorted by the latter period.

Quantity adjustment in the German CPI mainly aﬀects food prices (e.g. apple juice, leek, lamb)

in addition to non-durable and semi-durable goods (e.g. bird food, blank CDs, clothing). The

share of adjustment varies across products, from about 80% for grapefruits, kiwis and cauliﬂower

to about 7% for fresh ﬁsh and apple juice, but also between sub-samples. For example, only

13Note that, if a product is replaced with a product of higher or lower quality, we count all price observations

of the follow-up product as being quality-adjusted.

14Although the correlation seems rather low with respect to non-energy industrial goods and services, this seems
to be driven by limited periods at the beginning of the sample and by the missing service component package
holidays, which is highly volatile in Germany.

ECB Working Paper Series No 2773 / February 202311Table 1: Quantity-adjusted products in the German CPI

COICOP10

% share

avg. size

1014

1520

1014

1520

CPI weight
1520

Bird food
Apple juice or similar fruit juice
Leek or celery
Lamb
Grapefruit
Blank CDs
Table cloth, table runner or the like
Children’s underwear
Oranges

934201200
122311100
117119200
112300100
116115100
914210100
520301100
312343100
116111100
1213105300 Wet shaving razor, razor blades or the like
312161200 Men’s underwear
116170200
121201100
117121100
113500100
113100100
116165100
113200100
114501100
1213211100 Hair shampoo

Kiwis, ananas or mangos
Black tea or green tea
Cauliﬂower
Smoked ﬁsh
Fresh ﬁsh
Grapes
Frozen ﬁsh
Hard cheese

13.2
8.7
7.7
16.3
78.2
22.7
0.8
18.6
40.3
18.8
10.7
5.2
13.4
81.2
13.8
2.8
16.1
21.8
25.7
15.0

19.0
7.8
15.9
15.0
79.4
14.7
4.7
27.7
38.3
18.4
18.9
78.8
13.1
83.4
13.2
6.5
15.4
36.6
15.5
28.9

2.8
0.2
1.9
8.1
27.8
3.4
1.0
3.1
10.4
14.5
-2.0
6.9
5.0
4.7
18.4
5.1
6.4
4.7
10.7
4.8

19.5
14.6
14.4
13.1
12.4
12.2
12.2
-11.2
10.9
10.7
-9.8
9.6
9.0
8.1
8.1
8.1
8.0
7.9
7.9
-7.6

0.05
0.11
0.01
0.02
0.01
0.00
0.02
0.02
0.05
0.03
0.02
0.05
0.02
0.01
0.05
0.05
0.08
0.04
0.09
0.06

Note: The table shows the 20 COICOP10 groups with the largest absolute quantity adjustment from 2015 to 2020.
% share denotes the fraction of products adjusted for quantity changes, and avg. size gives the average absolute
size of the adjustment in percentage terms. 1014 and 1520 refer to the sub-samples 2010-2014 and 2015-2020.
CPI weights gives the COICOP share in the CPI.

Table 2: Quality-adjusted products in the German CPI

COICOP10

% share

avg. size

1014

1520

1014

1520

CPI weight
1520

Impact drill
Camper

1255000200 Premium for legal protection insurance
1255000100 Premium for personal liability insurance
551102100
921101100
1120201100 Campsite fee
531201200 Washing machine
New passenger car
711100100
Freezer or chest freezer
531102100
Football or other sports ball
932111100
Satellite kit
911250100
914920100
USB ﬂash drive
712004100 Moped
551102200
1111112100
1270402100 Classiﬁed advertisement in a newspaper
532900200
911210200
531101100
531103100
531203100

Electric kettle, egg boiler or the like
Television
Refrigerator
Fridge-freezer
Tumble dryer

Cordless screwdriver or cordless drill
Soups, hotel

5.5
0.0
8.3
67.4
0.0
53.2
66.1
51.4
5.1
17.3
11.7
0.0
10.0
5.4
3.1
8.7
40.5
50.8
54.5
46.2

47.2
10.4
19.9
79.2
28.4
60.1
67.8
44.4
7.1
11.0
9.0
17.6
8.9
5.8
1.9
7.1
60.8
39.6
55.2
51.7

-0.8
0.0
-0.7
-1.1
0.0
-1.0
-2.2
-1.6
-0.3
-0.6
-0.2
0.0
-0.0
0.1
0.4
0.6
-0.3
-1.9
-2.6
0.0

5.5
5.1
-2.5
-1.8
-1.7
-1.6
-1.6
-1.5
1.4
1.3
-1.2
-1.1
-1.1
1.0
1.0
0.9
-0.9
-0.9
-0.8
-0.8

0.17
0.24
0.04
0.11
0.04
0.13
2.39
0.03
0.01
0.03
0.04
0.03
0.05
0.00
0.05
0.02
0.34
0.06
0.05
0.04

Note: The table shows the 20 COICOP10 groups with the largest absolute quality adjustment from 2015 to 2020.
% share denotes the fraction of products adjusted for quality changes, and avg. size gives the average absolute
size of the adjustment in percentage terms. 1014 and 1520 refer to the sub-samples 2010-2014 and 2015-2020.
CPI weights gives the COICOP share in the CPI.

ECB Working Paper Series No 2773 / February 2023125% of the prices of kiwis, ananas and mangos have been quantity-adjusted before 2015, compared

to about 80% thereafter. The size of the quantity adjustment is typically positive meaning that

the raw price of a given product is adjusted upwards because it is sold with a lower quantity.

Exceptions apply to some clothing products (children’s and men’s underwear) and hair shampoo.

Also note that the size varies markedly between the two sub-samples under consideration.

Quality adjustment mainly aﬀects prices of durable goods, in addition to some services. This is

especially true for insurance premia where the price adjustment has been the largest among all

products. Interestingly, the quality of these insurances has deteriorated which is suggested by

the positive price adjustment. By contrast, quality adjustment of the remaining products has

mainly led to a price decrease, in particular for cars, tools, washing machines and the like.

Finally, in Table (3), we compute the impact of quantity and quality adjustment on German

headline inﬂation, as well as on the ﬁve main aggregates unprocessed food, processed food,

energy, non-energy industrial goods (NEIG), and services. Two ﬁndings are worth to stress.

First, the share of both quantity and quality adjustments has risen over time. About 3.5%

of headline inﬂation has been quantity-adjusted from 2010 until 2014, compared to 6.1% after

2015. As suggested earlier, accounting for changes in the package size mainly aﬀects food prices,

and also, to a lesser extent, prices of services and industrial goods. Quality adjustment is

somewhat less important (keeping in mind that we lack prices for some electronic products that

are largely adjusted for quality changes), amounting to 2.8% and 4.4% for headline inﬂation

mainly stemming from industrial goods and services. Second, we ﬁnd that taking into account

changes in quantity and quality has only a very low impact on headline inﬂation. Between

2010 until 2014, inﬂation has been quantity-adjusted downwards by -.02 percentage points and

quality-adjusted by -0.06 percentage points, whereas since 2015, inﬂation has been increased by

+0.06 percentage points due to a smaller underlying quantity but has been lowered by about

the same amount due to quality improvements. Nevertheless, taking a more disaggregate view,

these eﬀects are more pronounced. Food prices have been adjusted upwards by about +0.3

percentage points during both sub-samples due to quantity changes, wheres prices of industrial

goods and services have been lowered by about -0.1 percentage points due to higher quality.

Altogether, we ﬁnd a negative, but quantitatively small impact of quality adjustment on the

German inﬂation rate. Thus, without quality adjustment (and abstracting from quantity ad-

justment), the average inﬂation rate during the period 2010-2020 would have been higher by

ECB Working Paper Series No 2773 / February 202313Table 3: Impact of quantity and quality adjustment on German inﬂation

% quan % qual

πt

(πquan
πraw
t
t
2010-2014

− πraw
t

)

(πqual
t

− πraw
t

)

Total
Unprocessed food
Processed food
Energy
NEIG
Services

Total
Unprocessed food
Processed food
Energy
NEIG
Services

3.5
14.0
11.0
0.1
2.8
3.1

6.1
13.0
11.1
0.4
2.6
6.6

2.8
0.9
1.6
0.5
7.6
2.4

4.4
0.7
0.9
0.2
5.1
4.7

1.75
2.54
2.68
3.83
0.86
1.41

1.07
2.14
1.81
-1.22
0.80
1.54

2.20
0.49
1.56
3.69
1.56
2.48

2015-2020

1.35
1.08
1.29
-1.95
1.39
2.23

-0.02
0.36
-0.14
0.00
0.02
-0.03

0.06
0.33
0.23
0.00
0.04
0.00

-0.06
-0.01
-0.01
0.01
-0.16
-0.06

-0.06
-0.01
0.00
0.00
-0.11
-0.08

Note: The table reports the impact of quantity and quality adjustment on German inﬂation. The columns % quan
and % qual give the fraction of price observations that have been adjusted for changes in quantity and quality.
πt lists the oﬃcial inﬂation rate published by Destatis, and πraw
the inﬂation rate derived from the raw price as
reported in the micro price data. (πquan
) report the diﬀerence between adjusted and
unadjusted micro price inﬂation.

) and (πqual

t
− πraw
t

− πraw
t

t

t

about +0.1 percentage points only. This is well below other estimates for consumer price in-

ﬂation, as for Germany during the pre-Euro period (Hoﬀmann, 1998: +0.5 percentage points

during a moderate inﬂation regime) and more recently for Sweden (Statistics Sweden, 2019:

+0.3 percentage points). Nevertheless, as stated before, our data set misses some centrally col-

lected prices of products which typically undergo quality adjustment. Thus, our results can be

considered as a lower bound of the impact of quality adjustment on German inﬂation.

4 The impact of quality adjustment on euro area inﬂation

Measuring the impact of quality adjustment on consumer price inﬂation in the euro area, which

currently consists of 19 member countries, is challenging because of the lack of detailed and

harmonised micro price information. We try to tackle this by means of two approaches. First, we

follow a macro-based approach which builds upon the oﬃcial inﬂation series and selects product

categories whose prices are typically aﬀected by quality change. Based on the dispersion across

member states’ cumulative inﬂation rates, we derive a range for euro area headline and core

inﬂation that we interpret as an estimate for the impact of quality adjustment on the HICP

ECB Working Paper Series No 2773 / February 202314(Section 4.1). Second, we illustrate the role of heterogeneous QA methods across euro area

countries by means of micro price data for washing machines (Section 4.2).

4.1 Estimating the impact of quality adjustment in euro area inﬂation based

on typical quality-adjusted products

Whereas there is a rich documentation of available methods and recommendations on quality

adjustment for the euro area HICP,15 eventually, little is known on the detailed share of and

applied methods at the product level. Section (A) in the Appendix reproduces all public infor-

mation from Eurostat’s HICP Monitoring Reports about QA practices across euro area member

states. Based on this, Table (4) summarises the list of product groups whose prices are typically

adjusted for quality changes in euro area member states.

Overall, almost all member states adjust prices of cars, clothes and footwear as well as electronic

products. In some cases, also prices of food (France, Latvia, Lithuania, Germany) or package

holidays (Estonia, Slovakia) are quality-adjusted. Concerning the share of quality-adjusted prod-

ucts, three countries report detailed ﬁgures: In its Monitoring Report 2015, Germany indicates

to adjust 5-10% of its HICP, followed by Austria with 4.6% in 2016 and Slovenia with 0.4% in

2019. In addition to the heterogeneous selection of product groups, the applied QA methods

vary strongly between countries. Whereas a detailed discussion of the pros and cons of these

methods is beyond the scope of this paper and has a strand of literature on its own (see, for

example, Groshen et al., 2017), it is important to note that NSIs also consider adjusting prices

for a change in package size as a QA method. Hence, quality adjustment should not only matter

if an existing product is replaced with a new one but should also apply to the same product if

only its quantity (e.g. package size) has been changed.

Based on the list of quality-adjusted products in Table (4), we deﬁne two sets of products that

we believe to be rather homogeneous and hence, whose price trends should not diﬀer too much

between euro area countries. In a narrow sense, this set consists of telephones, radio and televi-

sion sets, photographic instruments, information processing equipment and data storage media.

More broadly, we add major household appliances, small electric household appliances, phar-

maceutical products, medical products, therapeutic appliances, cars and bicycles and durable

15See, for example, Eurostat (2018).

ECB Working Paper Series No 2773 / February 202315Table 4: Quality-adjusted product groups in the HICP

Country

Austria

Belgium
Cyprus
Estonia
Finland
France
Germany

Greece
Ireland
Italy
Latvia
Lithuania

Products

Clothing and footwear, recreation and culture (books, DVDs, CDs), telecommunica-
tion, durable goods and cars.
Cars, video games, CDs, DVDs, books, clothing and footwear.
Electronics, cars.
Cars, mobile phones, clothing and footwear, restaurants and caf´es, package holidays.
Cars.
Durable goods, clothes, cars, newspapers, books.
Clothing and footwear, technical products, books, CDs, downloads, computer games,
software, cars, electronics, residential property.
No information available.
Clothing and footwear, cars, electronics, CDs, DVDs.
Clothing and footwear, processed or fresh food, electronics, DVDs, fuels, cars.
Cars, electronics, fruit, vegetables, clothing and footwear, books.
Food and beverages, clothing and footwear, furnishings, household equipment, cars,
electronics, books.

Luxembourg Cars.
Malta

Netherlands
Portugal
Slovakia
Slovenia

Spain

Cars, laptops, mobile phones, cameras, clothing and footwear, books, recording media,
computer games.
Clothing and footwear, tobacco, cars, electronics, boats.
Cars, clothing and footwear, mobile phones.
Package holidays, cars, clothing and footwear, books, CDs, computer games.
Electronics, household appliances, cars, clothing and footwear, books, DVDs, com-
puter games, medicaments, audio-video equipment, PCs.
Cars, food, medicines, personal care, fresh food, clothing and footwear, furniture,
household appliances, restaurants.

Note: List of product groups whose prices are adjusted for quality changes by NSIs. Information is collected from
the individual HICP Monitoring Reports published at Eurostat’s web page https://ec.europa.eu/eurostat/
web/hicp/methodology, also reproduced in section (A) in the appendix.

consumer goods.16 In terms of the euro area HICP, the narrowly deﬁned set of products makes

up 1.7% of the total basket and the more broadly deﬁned set 9.0%. Compared to the practice

followed by NSIs, our choice might well miss-classify some products in some countries. Nev-

ertheless, Reinsdorf and Schreyer (2019) argue that digitalisation should also aﬀect prices of

products in categories such as restaurants, accommodation and other services which would call

for an even larger set of products whose prices are likely to depend on quality changes. In this

respect, our choice will be rather cautious instead of overstating the eﬀect of quality adjustment

on inﬂation. To be able to classify quality-adjusted products as exactly as possible, we have used

the lowest index level in the HICP, the so-called COICOP5. This has the drawback that, for

most countries, inﬂation series at this level of aggregation are only available from 2015 onwards

or even later, but we have repeated the analysis at the higher level of COICOP4 leaving the

16Table (B1) in the appendix reports the selected products in detail.

ECB Working Paper Series No 2773 / February 202316results broadly unchanged.

We then compute a band for the impact of quality adjustment on euro area inﬂation along the

following lines:

1. For all products according to the narrow or broad deﬁnition of quality adjustment, we com-

pute the minimum and maximum cumulated inﬂation rates across countries from January

2016 until present, i.e. the rate of change between the ﬁrst and last index period.

2. Next, we replace the countries’ price indices of the selected products with the price index

of those countries with the lowest and highest cumulated inﬂation rate. Returning to the

mobile phone example, we ﬁnd that Estonia has the lowest cumulated rate from 2016 to

present and Portugal the highest. Hence, we replace the price index of mobile phones in all

countries with the Estonian one when computing the lower range and with the Portuguese

one when computing the upper range.

3. Using product and country weights, we aggregate the adjusted price series to obtain an

upper and a lower range for quality-adjusted euro area headline and core inﬂation; we

interpret this range as an estimate for the impact of quality adjustment on the euro area

HICP.

The resulting quality-adjusted inﬂation rates by country are shown in Figure (2). The picture

for the narrowly deﬁned set of quality-adjusted products broadly mirrors the one for mobile

phones. In contrast, the pattern looks quite diﬀerent for the broad deﬁnition of products for

which we observe as many negative inﬂation rates as positive ones.

Obviously, heterogeneous QA practices are not the only driver of inﬂation diﬀerentials between

euro area countries. In a perfect world, their impact would be zero and homogeneous goods

should only be priced diﬀerently according to the local individual preferences, market structure,

local distribution costs and living conditions. Since the ﬁrst two aspects are hard to measure, we

focus on the latter aiming to explain euro area price diﬀerentials that are driven by heterogeneous

ECB Working Paper Series No 2773 / February 202317Figure 2: Cumulated inﬂation rates of quality-adjusted products by countries

Note: The ﬁgure shows the weighted average of the cumulated inﬂation rates of products aﬀected by quality
adjustment deﬁned narrowly and broadly from January 2016 onwards.

living standards and business cycle conditions across member states.17 Thereby, we follow

Crucini et al. (2005) and regress the monthly country- and product-speciﬁc inﬂation rates πc,i,t

on national GDP per capita:

πc,i,t = α + βi,cGDP Cc,t + εc,i,t,

(4)

where GDP Cc,t is the year-on-year growth rate of national GDP per capita (linearly interpolated

from quarterly to monthly ﬁgures), c represents a euro area country and i a quality-adjusted

product according to the narrow or broad deﬁnition. In this regression, we allow for the possi-

bility that income growth aﬀects prices of each product group in each country diﬀerently. Note

17The related literature can be distinguished into studies that explain price diﬀerentials within the euro area
by Phillips curve type regressions (Honohan and Lane, 2003, Angeloni and Ehrmann, 2007, Lagoa, 2017) and into
papers that analyse deviations from the law of one price using micro price data (Crucini et al., 2005, Lipsey and
Swedenborg, 2010, Fischer, 2012, Crucini and Yilmazkuday, 2014). An earlier overview of the topic is provided
by Deutsche Bundesbank (2009). Our approach is inspired by these studies, although it is not our goal to fully
explain price diﬀerentials by testing and adding diﬀerent explanatory variables. By contrast, we seek to estimate
the (unobservable) impact of the diﬀerent QA procedures conditional on income diﬀerentials between euro area
countries.

-60-40-20020LVSKBEATDELTSINLLUCYFRMTPTESITGREEFIIEnarrow definitionbroad definitionECB Working Paper Series No 2773 / February 202318that we refrain from including country- and time-ﬁxed eﬀects since these would basically take

out the unobserved impact of country-speciﬁc QA practices. We interpret the residuals of this

regression as the annual inﬂation rates net of income diﬀerentials.18 In order to rule out that

our estimates are aﬀected by the Covid-19 pandemic, we split the estimation sample into a

pre-Covid period from 2016:01 to 2020:02 and a Covid period from 2020:03 to 2021:09.

The resulting estimates for the impact of quality adjustment on the euro area HICP are sum-

marised in Table (5). Over the period 2017 to 2020, we observe an average increase of 1.5%

for headline inﬂation and 1.0% for core inﬂation using the aggregate series as published by

Eurostat. Note that aggregating these rates ourselves by combining the available disaggregate

inﬂation series at the COICOP5 level results in some rounding diﬀerences with oﬃcial ﬁgures,

since the disaggregate series are published only with one decimal or, in a very few cases, are not

published due to conﬁdentiality reasons.19

Concerning the potential impact of heterogeneous QA practices, the second and third rows of

Table (5) report the upper and lower bound of the inﬂation rates adjusting the price index of

the narrowly deﬁned quality-adjusted products. Likewise, the fourth and ﬁfth rows give the

limits of the products deﬁned more broadly. Computing the diﬀerence between these limits

provides us with a range that we interpret as an estimate for the impact of quality adjustment

on euro area inﬂation. According to our approximation, this estimate varies between ± 0.2 and

0.6 percentage points for headline inﬂation and between ± 0.3 and 0.8 percentage points for

core inﬂation. Controlling for the impact of income diﬀerentials between countries, the impact

of quality adjustment is reduced by up to ± 0.2 percentage points for headline inﬂation and ±

0.1 to 0.3 percentage points for core inﬂation.

Regarding the Covid period, the results shown in the lower panel of Table (5) yield a fairly

similar estimate of the impact of heterogeneous QA methods on inﬂation without controlling

for income diﬀerentials. However, accounting for income changes actually does not lower this

estimate, which suggests that our simple regression approach is not able to capture all the diﬀer-

ent economic and statistical eﬀects of the pandemic such as lock-down measures and estimated

missing prices (“imputations”) in an adequate way.

18Alternatively, we could have allowed for country-speciﬁc income eﬀects and ﬁxed eﬀects interpreting the

contribution of the latter as the impact of quality adjustment net of income eﬀects.

19Since the HICP is a chain-linked price index, simply averaging the disaggregate inﬂation series would be

incorrect; therefore, we ﬁrst unchain the disaggregate price indices, compute the average and rechain again.

ECB Working Paper Series No 2773 / February 202319Table 5: The impact of quality adjustment on the euro area HICP, 2017-2021

Inﬂation:

Oﬃcial rates
Own agggregation

Narrowly deﬁned products:

Minimum rate
Maximum rate

Broadly deﬁned products:

Minimum rate
Maximum rate

Range:

Narrow deﬁnition
Broad deﬁnition

Inﬂation:

Oﬃcial rates
Own agggregation

Narrowly deﬁned products:

Minimum rate
Maximum rate

Broadly deﬁned products:

Minimum rate
Maximum rate

Range:

Narrow deﬁnition
Broad deﬁnition

2017:01-2020:02

unadjusted for income
Headline

Core

adjusted for income
Headline

Core

1.5
1.5

1.3
1.5

1.1
1.7

0.2
0.6

1.0
1.0

0.8
1.1

0.5
1.3

0.3
0.8

1.5
1.5

1.4
1.6

0.0
0.2

1.0
1.1

0.9
1.2

0.1
0.3

2020:03-2021:09

unadjusted for income
Headline

Core

adjusted for income
Headline

Core

0.9
0.9

0.8
1.0

0.6
1.2

0.2
0.6

0.9
0.8

0.7
1.0

0.4
1.3

0.3
0.9

0.8
1.0

0.6
1.2

0.2
0.6

0.7
1.0

0.4
1.3

0.3
0.9

Note: Oﬃcial rates refers to the euro area HICP published by Eurostat, own aggregation gives the euro area
inﬂation rates aggregated from disaggregate national inﬂation rates. The minimum rates and maximum rates
denote the lowest and highest inﬂation rates of adjusting products aﬀected by quality adjustment narrowly and
broadly. Range gives the diﬀerence between the maximum and minimum rates.

Finally, it is important to note that our regression results can themselves be distorted by the

impact of quality adjustment in diﬀerent member states. If the link between income growth

and “true” inﬂation is actually positive, but if the QA practice introduces a bias, the observed

correlation will be lowered or estimated with the wrong sign. Keeping this in mind, plotting our

estimate for the impact of quality adjustment over time in Figures (B1) and (B2) in the appendix

suggests that for the pre-Covid period, the impact of quality adjustment tends to be negative for

the inﬂation rates without controlling for income diﬀerentials, meaning that inﬂation would have

ECB Working Paper Series No 2773 / February 202320been lower if QA practices had been more harmonised across countries. However, controlling

for income diﬀerentials yields a small positive impact of quality adjustment. These conﬂicting

results point to the limits of this simple approximation of the impact of quality adjustment,

which is why we turn to a more precise measure based on micro price data in the next section.20

4.2 Estimating the impact of quality adjustment based on transaction data

for washing machines

To illustrate the potential impact of diﬀerent QA methods on euro area inﬂation, we apply a

micro data set from the GfK for washing machines. The data set stems from the GfK Point-

of-Sales (POS) panel, which covers about 60 countries worldwide. The POS panel is a regular,

comprehensive survey to monitor sales of so-called Slow-Moving Consumer Goods (SMCG) such

as consumer electronics, major and small domestic appliances, and automotive. The purchases

recorded include both in-store and online purchases. Our data set comprises ten euro area

countries for two separate periods, 2000:01-2005:12 and 2017:01-2021:05:21 Austria, Belgium,

Finland (from 2003 onwards), France, Germany, Greece (until 2005), Italy, the Netherlands,

Portugal and Spain. The frequency of the ﬁrst sample runs at a two-monthly basis, whereas the

second sample covers monthly data. In addition to prices and volumes (sales), several physical

features of the speciﬁc washing machine model are included, such as load capacity, spinning

speed and construction type. For the more recent period, the data set also covers information

on energy eﬃciency, the noise level, spin eﬃciency and whether the washing machine is equipped

with any smart connect functions.22

To set the scene for the analysis, imagine the following scenario. The GfK POS panel covers

the population of washing machines available to consumers along with their prices, volumes and

20Note that, within this macro-based approach, we also cannot take into account several other diﬀerences
in inﬂation measurement across NSIs in the euro area that might aﬀect inﬂation and are unrelated to quality
adjustment, such as diﬀerences in the sampling of products, in the deﬁnition of elementary products, in the
treatment of sales, in the use of auxiliary data sources like scanner or webscraped data, as well as in the index
formula used for aggregation.

21The GfK data set of the ﬁrst period has been also used by Fischer (2012) who studied prices for washing
machines in euro area countries to test for price convergence in the aftermath of the euro introduction. Likewise,
several studies have investigated price convergence in single markets in the euro area, see for example Goldberg
and Verboven (2001, 2005) and Brenkers and Verboven (2006) who take a detailed look at the European car
market. Interestingly, these papers ﬁnd a clear tendency towards price convergence until the euro introduction
whereas Dvir and Strasser (2018) show that car prices do not converge further after 2003. More recently, Duch-
Brown et al. (2020) also use a GfK data set (for portable computers) to analyse the impact of online market
integration on consumer prices.

22See Table (C1) in the appendix for a description of variables.

ECB Working Paper Series No 2773 / February 202321features. Each NSI then somehow samples from this population to represent washing machines

in their national CPI. Based on the samples, price indices are calculated using the nationally

available data (e.g. sales information or just price quotes) and speciﬁc methodological choices, in

particular on QA procedures. At all steps of the process, diﬀerences in the compilation practices

between statistical oﬃces can and will occur, be it because of diﬀerences in the available data

or in the methodological choices. What we are limiting our analysis here to is the narrow ﬁeld

of quality adjustment.

To this end, we compute for each of the ten euro area countries in our sample diﬀerent transaction-

based price indices. For this purpose, we rely on three prominent approaches which are used in

oﬃcial price statistics to varying degrees and that can be considered to cover the range from

“best practices procedures” to “no quality adjustment”.23 These three approaches are then

compared to oﬃcial HICP data.

First, we estimate a Time-Dummy Hedonics (TDH) regression, which represents “explicit” qual-

ity adjustment based on observable product characteristics. Thereby, we run a hedonic price

regression to obtain a quality-adjusted washing machine price per time period. The below semi-

logarithmic regression equation is estimated for any given country based on pooled data over all

periods t = 0, . . . , T :

ln pk = β0 +

T
(cid:88)

t=1

δtdt

k +

p
(cid:88)

j=1

βjzkj + εk,

(5)

where pk denotes the price of washing machine model k in a given country, the time-dummy

variable dt

k has the value 1 if the observation of washing machine k comes from period t and
0 otherwise, and zkj is the j-th product characteristic of model k. The vector of product

characteristics for the ﬁrst sample 2000-2005 closely follows Fischer (2012) and consists of ﬁve

variables, namely the load capacity in kg, the spinning speed, the degree of automation and the

presence of a drying function, the loading direction, the construction type, as well as brand-

speciﬁc dummies.24 For the second sample, four additional variables (energy eﬃciency, noise

level, smart connect functions, and spin eﬃciency) are added. Equation (5) is estimated by

23See International Monetary Fund (2020), Chapter 6 on quality adjustment, and Chapter 10 on price indices

in the context of transaction (scanner) data.

24We diﬀer from Fischer (2012) by estimating the hedonic regression for each country separately, allowing the
shadow prices to vary by country, and weighting each observation by its expenditure share rather than the number
of sales, as is common practice in index compilation. Moreover, we focus on gross prices including the value-added
tax (VAT) rate, since the HICP also comprises VAT charges.

ECB Working Paper Series No 2773 / February 202322weighted least squares, with observations weighted by their corresponding expenditure share

to properly represent the local market structure. The quality-adjusted price index, It, can be

derived directly from the exponential of the coeﬃcient on the time dummy:

T DH = 100 × exp( ˆδt).
I 0:t

(6)

Second, we perform what is known as an “implicit” quality adjustment by means of a Time-

Product Dummy (TPD) regression. Here, quality adjustment is performed by controlling for

diﬀerences in the levels of the prices of washing machine models k as identiﬁed by the combination

of brand and the speciﬁc model. The hedonic regression equation simpliﬁes to:

ln pk = β0 +

T
(cid:88)

t=1

δtdt

k +

K−1
(cid:88)

k=1

γkDk + εk,

(7)

where Dk represents a dummy variable which equals 1 if the price relates to model k and 0

otherwise. Again, a quality-adjusted price index can be derived from the exponential of the

coeﬃcient on the time dummy, such that:

T P D = 100 × exp( ˆδt),
I 0:t

(8)

Finally, we also consider a price index method which does not comprise any quality adjustment

at all. For this purpose, we compute a Unit Value (UV) price index such that:

I 0:t
U V =

(cid:80)Kt

k=1 pt
(cid:80)Kt

k × qt
k

k=1 qt
k

(cid:44) (cid:80)K0

k × q0
k

k=1 p0
(cid:80)K0

k=1 q0
k

,

(9)

where qt

k (q0

k) denotes the sales of the k-th model in period t (0).

Comparing these three transaction-based price index methods with oﬃcial HICP data faces

some caveats. First, the comparison is limited due to the unavailability of oﬃcial data at a more

disaggregate level. The best candidate for comparison, “05.3.1.2 Clothes washing machines,

clothes drying machines and dish washing machines”, is only available from December 2016

onwards. The closest match for the period before can be found for the HICP subindex “05.3

ECB Working Paper Series No 2773 / February 202323Household appliances”, which captures mainly major household appliances.25 However, given

that the subindex “05.3.1.2” makes up about one-fourth of the higher-level index “05.3 Household

appliances”, we argue that the overall trend of the latter should also reﬂect the price development

of washing machines.26 Second, even for the more recent sample, there is no oﬃcial price index

solely for washing machines, which are grouped together with drying and dish washer machines.

However, as stated by Fischer (2012), cross-country price deviations of washing machines should

expected to be similar for drying and dish washing machines. Third, the meta data on the

sampling and quality adjustment at the national level are limited. Going back to the ideal

scenario, it should be possible – with the right choice of subset of observations and using diﬀerent

methodologies – to replicate the oﬃcial HICP data.

Figure (3) shows the transaction-based price indices and the cumulative rate of change from

2001 to 2005, together with the HICP counterpart “05.3 Household appliances”. The resulting

cumulative rates of change are consistently negative when applying the same hedonic quality

adjustment across countries. In comparison to the unadjusted case of the Unit Value approach,

the range of hedonic measures is also smaller, with a range between -3% and -29% vs. +7%

and -24% in the unadjusted case. In comparison to the quality-adjusted transaction-based price

indices, the cumulated rates of the HICP subindex “Household appliances” deviate less from

each other in terms of the range (from 6% to -8%), but more in terms of the sign of the overall

price trend, with ﬁve out of ten countries indicating a positive increase over time. Also note

that all three methods do not provide a seasonal pattern for prices of washing machines as in

the case of the oﬃcial HICP ﬁgures for some countries (Belgium and Greece).

Figure (4) compares our transaction-based price indices and the cumulative rate of change for

the later period from 2017 onwards, together with the more disaggregate HICP series “05.3.1.2

Clothes washing machines, clothes drying machines and dish washing machines”. Unlike during

the ﬁrst period, the unadjusted case of a Unit Value price index signals now a strong price

increase over time for all countries under consideration. This could be related to the fact that

this index method is unable to control for compositional changes in the basket of washing

machines, with consumers switching to more sophisticated, but also more expensive products

25According to the euro area HICP weighting scheme in 2017, the HICP “05.3 Household appliances” consists
of three sub-indices: “05.3.1 Major household appliances whether electric or not” (71%), “05.3.2 Small electric
household appliances” (21%), and “05.3.3 Repair of household appliances” (8%).

26A strong comovement between both series can be observed with the start of the more disaggregate series in

December 2016, as reﬂected in Figure (C3) in the appendix.

ECB Working Paper Series No 2773 / February 202324Figure 3: Transaction-based price indices for washing machines vs. HICP, 2000-2005

Index

Cumulated Inﬂation Rate

Time-Dummy Hedonics: Baseline

Time-Product Dummy

Unit Value

HICP “Household Appliances”

Note: The ﬁgure shows the three transaction-based price indices (weighted by turnover) and the HICP series
“05.3 - Household Appliances” for the years 2000-2005, indexed to January 2003=100 as well as cumulated
inﬂation rates between 2001:01 and 2005:12. Data for Finland are only available from January 2003 onwards.

GRFRESFINLITATBEDEPT809010011012000010203040506-30-25-20-15-10-50510GRFIITNLESFRPTATBEDEGRATESFIITNLPTDEBEFR809010011012000010203040506-30-25-20-15-10-50510GRFIITESPTATNLFRBEDEESGRBEITFRFINLPTATDE809010011012000010203040506-30-25-20-15-10-50510GRESITPTNLBEFRFIATDEBEITGRFIPTNLESATDEFR94969810010210400010203040506-30-25-20-15-10-50510BEGRPTITFIATNLESDEFRECB Working Paper Series No 2773 / February 202325Figure 4: Transaction-based price indices for washing machines vs. HICP, 2017-2021

Index

Cumulated Inﬂation Rate

Time-Dummy Hedonics: Baseline + energy + smart + noise + spin

Time-Product Dummy

Unit Value

HICP “Clothes washing machines, clothes drying machines and dish washing machine”

Note: The ﬁgure shows the three transaction-based price indices (weighted by turnover) and the HICP series
“05.3.1.2 Clothes washing machines, clothes drying machines and dish washing machines” for the years 2017-2021,
indexed to January 2017=100 as well as cumulated inﬂation rates between 2017:01 and 2021:05.

FRBEDEESITNLPTATFI8590951001051718192021-15-10-50510FRBEDEESITNLPTATFIBEDEESFRATNLPTITFI8590951001051718192021-15-10-50510BEDEESFRATNLPTITFINLATFRPTITBEESDEFI90951001051101151718192021-15-10-50510NLATFRPTITBEESDEFIBEATNLESDEFRITFIPT80901001101201718192021-15-10-50510BEATNLESDEFRITFIPTECB Working Paper Series No 2773 / February 202326over time, known as the Unit Value Bias. In contrast, the resulting cumulative rates of change

are consistently negative when applying the same hedonic quality adjustment across countries,

as derived from the Time-Dummy Hedonics and Time-Product Dummy method. In comparison

to the HICP subindex, the latter again signals mixed price trends across countries during the

years 2017-2021. Figures (C1) and (C2) in the appendix also reﬂect various speciﬁcations of

the Time-Dummy Hedonics method for the period 2017-2021. It reveals the sensitivity of the

regression speciﬁcation, with diverging price trends according to the baseline estimation (i.e.

without controlling for more recent model features such as energy eﬃciency and smart control).

Thus, the resulting price trend is not only depending on the chosen method, but also on the

variable selection.

Finally, aggregating all transaction-based country indices using HICP country weights yields

an estimate for euro area inﬂation of washing machines. For this purpose, we only use those

eight euro countries with transaction data consistently available in both periods (i.e. excluding

Finland and Greece). The results shown in Figure (5) again point to the fact that our quality-

adjusted price indices yield a much stronger price decline compared to the oﬃcial price index.

In terms of monthly annual rates, during the ﬁrst period under consideration, prices of washing

machines declined on average by -0.5% as measured by the HICP, compared to rates between

-4.1% using Time-Dummy Hedonics and -3.8% using Time Product Dummy. For the second

period starting in 2017, diﬀerences become smaller, with prices of washing machines declining

on average by -0.9% as measured by the HICP, compared to rates of -1.4% using Time Product

Dummy and -1.3% using Time-Dummy Hedonics. Hence, ceteris paribus, this would result in a

positive impact of quality adjustment on euro area inﬂation for washing machines of about 3.5

percentage points during the early years of the euro and of about half a percentage point in the

more recent sample, which is quite substantial.

Concerning the ideal scenario described above, we could not perfectly reproduce the price

changes as shown by the HICP subindex, notably due to limited information on QA methods as

well as the unavailability of a more disaggregate HICP benchmark for washing machines. Hence,

deviations between both series should not be interpreted as the impact of quality adjustment

only.27 Nonetheless, a pronounced pattern appears when comparing our transaction-based price

indices and the oﬃcial HICP subindex, namely consistently declining price trends across coun-

27As shown by Henn et al. (2019) for German package holidays, diﬀerent data sources (transaction prices vs.

oﬀer prices) can also play a role.

ECB Working Paper Series No 2773 / February 202327tries. This also calls into question the QA procedures applied for the speciﬁc product “washing

machines” in some of the euro area countries under consideration. Moreover, the crucial role of

the choice of QA methods for the resulting price trend is also in line with micro-price ﬁndings

by Conﬂitti et al. (2022) for Austria and Italy.

Figure 5: Transaction-based euro area price index for washing machines

Index

Annual inﬂation rate

Note: The ﬁgure shows the euro area aggregate price indices and annual inﬂation rates of washing machines
derived from GfK data and from the oﬃcial country-speciﬁc HICP subcomponents “05.3 - Household Appliances”
(2000:01-2005:12) and “05.3.1.2 Clothes washing machines, clothes drying machines and dish washing machines”
(2017:01-2021:05). Aggregation uses country weights of Austria, Belgium, Germany, Spain, France, Italy, the
Netherlands, and Portugal.

80859095100000102030405Time-Dummy HedonicsTime-Product DummyUnit ValueHICP -8-6-4-202000102030405Time-Dummy HedonicsTime-Product DummyUnit ValueHICP 951001051101718192021Time-Dummy HedonicsTime-Product DummyUnit ValueHICP -505101718192021Time-Dummy HedonicsTime-Product DummyUnit ValueHICP ECB Working Paper Series No 2773 / February 2023285 Conclusion

In this paper, we have tried to shed some light on the impact of quality adjustment on consumer

price inﬂation in Germany and the euro area. Based on micro and macro price data, we have

documented several stylized facts. First, for Germany, we ﬁnd that quality adjustment applies

to a large range of goods and services but, on average, price adjustments due to quality changes

reduces headline inﬂation only by 0.06 percentage points, which is balanced out by an increase

due to quantity adjustment (e.g. a smaller package size) of the same amount. Second, we have

provided an approximation of the range of euro area headline and core inﬂation that could be

spanned by heterogeneous QA practices across euro area countries. According to our estimate,

the impact of quality adjustment varies between ± 0.2 and 0.6 percentage points for headline

inﬂation and between ± 0.3 and 0.8 percentage points for core inﬂation, depending on the deﬁni-

tion of narrowly or broadly deﬁned products aﬀected by QA. Controlling for income diﬀerentials

between countries, the impact of quality adjustment is reduced up to ± 0.2 percentage points

for headline inﬂation and ± 0.1 to 0.3 percentage points for core inﬂation. Finally, we have

illustrated the impact of heterogeneous QA methods in the euro area at the product level based

on GfK transaction data for washing machines for a early-Euro period (2000 to 2005) and a

more recent period (2017-2021). We ﬁnd that euro area inﬂation of washing machines would

have been lower by about 3.5 percentage points during the ﬁrst years of the euro area and by

about half a percentage point during recent years if prices had been quality-adjusted in the same

way across countries.

Overall, our estimate of the impact of heterogeneous QA methods on euro area inﬂation is quite

substantial and of similar magnitude in comparison to measurement biases in the HICP due to

substitution or the lack of owner-occupied housing (ECB, 2021). Hence, this would call for a

further harmonisation of QA methods across member states to reduce or eliminate their impact

on euro area inﬂation. On top, more eﬀorts should be made to quantify both the size and the

direction of the impact of quality adjustment in euro area inﬂation with greater accuracy, such

as it is regularly done, for instance, by Statistics Sweden (2019). Concerning future research,

it would be worth to repeat our exercise with more recent micro price data, importantly by

covering more euro area countries and a larger set of products and even services.

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ECB Working Paper Series No 2773 / February 202332Appendix A The impact of quality adjustment on the German

CPI

German CPI micro data

The German CPI micro data set contains more than 77 million of observations for the period

2010:01-2020:12. The database is provided by the Research Data Centres (RDC) of the Federal

Statistical Oﬃce and Statistical Oﬃces of the Federal States and publicly available for research

purposes.28 Most prices are decentrally collected by the Federal States. Concerning individual

price information, the database contains ﬂag indicators on sales, replacements and imputation

of the individual price (e.g. carry-forward in case of a missing price) as well as information on

quality and quantity adjustment. The lowest level of product category with weight information

is the COICOP10 level (e.g. “01.1.1.1.01100 - Rice”); after excluding imputed prices and aggre-

gated price measures, our underlying data set contains 716 product categories at the COICOP10

level. The product id is constructed based on a combination of ﬁve variables (region, store id,

COICOP10 number, survey id and product variant). Due to the regular revision of the survey id

with every new base year, the data set contains a statistical break in 2015:01; thus, all statistics

are computed on each subsample separately (base year 2010: 2010:01-2014:12 and base year

2015: 2015:01-2020:12).

Inﬂation measures derived from German CPI micro data

Table A1: Oﬃcial CPI inﬂation vs. micro price inﬂation

HICP component

total
unprocessed food
processed food
energy
NEIG
services

πadj
t
0.84
0.84
0.64
0.99
0.28
0.22

πraw
t
0.84
0.81
0.63
0.99
0.35
0.18

πquan
t
0.83
0.83
0.56
0.99
0.29
0.18

πqual
t
0.84
0.81
0.62
0.99
0.36
0.18

Note: The table reports the correlation coeﬃcients of the oﬃcial inﬂation rates as reported by the Federal
Statistical Oﬃce of Germany compared with the four diﬀerent rates computed from the micro price data set from
2015:01 to 2020:12. πadj
inﬂation adjusted for quality and quantity changes; πraw
: raw inﬂation without any
adjustments; πquan
: inﬂation adjusted for quality changes. NEIG:
t
non-energy industrial goods.

: inﬂation adjusted for quantity changes; πqual

:

t

t

t

28See “Verbraucherpreisindex f¨ur Deutschland”, EVAS 61111, 2010 - 2019, DOI: https://doi.org/10.21242/

61111.2010.00.00.3.1.0 to https://doi.org/10.21242/61111.2019.00.00.3.1.0.

ECB Working Paper Series No 2773 / February 202333Figure A1: Oﬃcial inﬂation rates and micro price inﬂation in Germany

Headline Inﬂation

Unprocessed Food

Processed Food

Energy

NEIG

Services

Note: The ﬁgure shows year-on-year inﬂation rates for Germany for both headline inﬂation and ﬁve subcompo-
nents. πadj
: micro price inﬂation adjusted for quality and quantity changes (based on “adjusted price” variable);
πraw
: oﬃcial CPI
t
inﬂation.

: micro price inﬂation without any adjustments (based on “raw price” variable); πHICP

t

t

-202461112131415161718192021ptadjptrawptHICP-10-505101112131415161718192021ptadjptrawptHICP-202461112131415161718192021ptadjptrawptHICP-15-10-505101112131415161718192021ptadjptrawptHICP-20241112131415161718192021ptadjptrawptHICP024681112131415161718192021ptadjptrawptHICPECB Working Paper Series No 2773 / February 202334Figure A2: Unadjusted and quality-adjusted micro price inﬂation in Germany

Headline Inﬂation

Unprocessed Food

Processed Food

Energy

NEIG

Services

0123451112131415161718192021quality-adjusted priceraw price-10-505101112131415161718192021quality-adjusted priceraw price-202461112131415161718192021quality-adjusted priceraw price-15-10-505101112131415161718192021quality-adjusted priceraw price012341112131415161718192021quality-adjusted priceraw price024681112131415161718192021quality-adjusted priceraw priceECB Working Paper Series No 2773 / February 202335Figure A3: Unadjusted and quantity-adjusted micro price inﬂation in Germany

Headline Inﬂation

Unprocessed Food

Processed Food

Energy

NEIG

Services

0123451112131415161718192021quantity-adjusted priceraw price-10-505101112131415161718192021quantity-adjusted priceraw price-202461112131415161718192021quantity-adjusted priceraw price-15-10-505101112131415161718192021quantity-adjusted priceraw price012341112131415161718192021quantity-adjusted priceraw price024681112131415161718192021quantity-adjusted priceraw priceECB Working Paper Series No 2773 / February 202336Appendix B Estimating the impact of quality adjustment in

euro area inﬂation based on typical quality-adjusted

products

Table B1: Deﬁning a list of typical quality-adjusted products

EA Weight

DEFINITION

in ‰

NARROW BROAD

COICOP-5

08201

08202

09111

Landline telephones

Mobile phone without contract

Equip.

for the reception, recording & reproduc-

tion of sound

0.2

3.48

0.59

09112

Equip.

for the reception, recording & reproduc-

2.71

tion of sound & vision

Portable TV sets, sound and vision devices

Other equip. for the reception, recording and re-

production of sound & picture

Cameras

Accessories and parts for photographic and cine-

matographic equip.

Optical equipment

Personal computers

Accessories for information processing equip.

Pre-recorded recording media

Unrecorded recording media

Other recording media

Refrigerators, freezers and fridge-freezers

Washing machines, dishwashers or the like

Cookers

Room heaters and air conditioners

Vacuum cleaners and other cleaning equip.

Other major household appliances nec

Food processing appliances

Coﬀee machines, tea makers and similar appli-

ances

Irons

Toasters and grills

09113

09119

09121

09122

09123

09131

09132

09141

09142

09149

05311

05312

05313

05314

05315

05319

05321

05322

05323

05324

0.07

0.35

0.64

0.09

0.04

3.45

0.76

1.24

0.02

0.48

1.49

2.22

1.06

1.11

0.64

0.03

0.81

0.58

0.29

0.11

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

ECB Working Paper Series No 2773 / February 202337. . . continued

COICOP-5

EA Weight

DEFINITION

in ‰

NARROW BROAD

05329

06110

06121

06129

06131

06132

06139

07111

07112

07120

07130

09211

09213

09221

Total

Total

Other small electric household appliances

Pharmaceutical products

Pregnancy tests, condoms or the like

Other medical products nec

Glasses and contact lenses

Hearing aids

Other therapeutic appliances and equip.

New passenger cars

Used passenger cars

Motorcycles

Bicycles

Camper, caravans or other trailers

Boats, outboard motors and equip. for boats

Musical instruments

NARROW

BROAD

0.36

11.5

0.34

0.66

4.78

0.87

1.33

27.41

10.57

1.99

1.09

1.52

0.77

0.53

14.12

86.18

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

1

ECB Working Paper Series No 2773 / February 202338Figure B1: The impact of quality adjustment over time: pre-Covid

HICP

HICP excluding energy & food

Without income adjustment

With income adjustment

Note: The ﬁgure shows the annual inﬂation rate of euro area headline and core inﬂation as published by Eurostat
(“oﬃcial rate”), and aggregate from the disaggregate COICOP5 series (“own aggregation”). “Narrow” and
“broad” denote the inﬂation rates using the lowest and highest inﬂation rates by country and product group
assumed to be aﬀected from quality changes.

012317181920official rateown aggregationnarrowbroad01217181920official rateown aggregationnarrowbroad012317181920official rateown aggregationnarrowbroad01217181920official rateown aggregationnarrowbroadECB Working Paper Series No 2773 / February 202339Figure B2: The impact of quality adjustment over time: Covid period

HICP

HICP excluding energy & food

Without income adjustment

With income adjustment

Note: The ﬁgure shows the annual inﬂation rate of euro area headline and core inﬂation as published by Eurostat
(“oﬃcial rate”), and aggregate from the disaggregate COICOP5 series (“own aggregation”). “Narrow” and
“broad” denote the inﬂation rates using the lowest and highest inﬂation rates by country and product group
assumed to be aﬀected from quality changes.

-101232021official rateown aggregationnarrowbroad0122021official rateown aggregationnarrowbroad-101232021official rateown aggregationnarrowbroad0122021official rateown aggregationnarrowbroadECB Working Paper Series No 2773 / February 202340Appendix C Estimating the impact of quality adjustment based

on transaction data for washing machines

Table C1: Variable description of GfK data set

Variable

Type

Variable description

Sample
2000-05

Sample
2017-21

model
country
brand

categorical
categorical
categorical

lnprice

numeric

turnover

numeric

construction categorical

revpermin
loadingkg
loadingdir

numeric
numeric
categorical

autoxdry

categorical

energy

categorical

smart

categorical

noise
spin

numeric
categorical

X
X
X

X

X

X

X
X
X

X

Identiﬁer of washing machine model
Country (local market)
Brand’s name of a given washing machine
model
(Log) average price of a given model (incl.
value-added tax)
Transaction value (average price × quantity)
of a given model
Construction type (base: freestanding / built
in or under / unknown)
Spinning speed (revolutions per minute)
Load capacity in kg
Loading direction (base:
toploading / unknown)
Degree of automation and presence of drying
function (base:
fully automatic, no dryer /
semi-automatic, no dryer / washdryer / un-
known)
Energy eﬃciency according to the EU energy
label from A+ + + (best) to G (worst)
Equipment with any smart connect functions,
e.g. smart check/diagnosis, smart app control,
voice control
Noise level in decibel
Spin eﬃciency from A (best) to G (worst)

frontloading /

Source: GfK Point-of-Sales (POS).

X
X
X

X

X

X

X
X
X

X

X

X

X
X

ECB Working Paper Series No 2773 / February 202341Figure C1: Alternative hedonic price indices for washing machines vs. HICP, 2017-2021 (I)

Index

Cumulated Inﬂation Rate

Time-Dummy Hedonics (Baseline)

Time-Dummy Hedonics: Baseline + energy

Time-Dummy Hedonics: Baseline + energy + smart

Note: The ﬁgure shows various speciﬁcations of the Time-Dummy Hedonics regression of washing machine prices
for the years 2017-2021, indexed to January 2017=100 as well as cumulated inﬂation rates between 2017:01 and
2021:05.

BEFRNLDEESITATPTFI90951001051718192021-15-10-50510BEFRNLDEESITATPTFIBEFRNLDEESITPTATFI90951001051718192021-15-10-50510BEFRNLDEESITPTATFIFRBEDEESNLITPTATFI8590951001051718192021-15-10-50510FRBEDEESNLITPTATFIECB Working Paper Series No 2773 / February 202342Figure C2: Alternative hedonic price indices for washing machines vs. HICP, 2017-2021 (II)

Index
Time-Dummy Hedonics: Baseline + energy + smart + noise

Cumulated Inﬂation Rate

Time-Dummy Hedonics: Baseline + energy + smart + noise + spin

Note: The ﬁgure shows various speciﬁcations of the Time-Dummy Hedonics regression of washing machine prices
for the years 2017-2021, indexed to January 2017=100 as well as cumulated inﬂation rates between 2017:01 and
2021:05.

FRBEDEESITNLPTATFI8590951001051718192021-15-10-50510FRBEDEESITNLPTATFIFRBEDEESITNLPTATFI8590951001051718192021-15-10-50510FRBEDEESITNLPTATFIECB Working Paper Series No 2773 / February 202343Figure C3: HICP on Household Appliances

Index

Annual inﬂation rate

Note: The ﬁgure shows the HICP subindices “05.3 - Household Appliances” and “05.3.1.2 Clothes washing
machines, clothes drying machines and dish washing machines”. Annual inﬂation rates are correlated with .8.

94969810010216182022Household appliancesClothes washing machines, clothes drying machinesand dish washing machines-2-10121718192021Household appliancesClothes washing machines, clothes drying machinesand dish washing machinesECB Working Paper Series No 2773 / February 202344Appendix D Treatment of quality changes in the HICP by na-

tional statistical institutes in the euro area

This section reproduces the NSI information on quality adjustment as reported in the individual HICP Compliance

Monitoring reports, published at Eurostat’s website: https://ec.europa.eu/eurostat/web/hicp/methodology

(section “Compliance Monitoring”).

Austria:

Monitoring Report 2019:

Price collectors work with relatively broad item descriptions and choose a well-sold product that falls under the

item description. If the product is not available anymore or not well-sold the price collector choses a replacement

and notes all relevant information. The quality adjustments, if needed, are made later by the central oﬃce staﬀ.

An elaborate methodology is used to make quality adjustments. In 2016 quality adjustments were made in 4.6 %

of the cases.

Reference Metadata:

In the year 2016 4.6 percent of all prices were subject to a quality adjustment (QA). Most QAs were applied in

COICOP 03 (”Clothing and footwear”) due to the rapid change of product models and the domination of seasonal

products in this division. COICOP 09 (”Recreation and Culture”) where the supply of best-selling books, DVDs

and CDs change on a monthly basis also requires many QA. Also COICOP 08 (”Telecommunication”) faces fast

changing product oﬀers, mostly in the segment of electronic devices for telecommunication, which make QAs

necessary. Most used QA methods are quantity adjustment (e.g. for food), expert judgment adjustment method

(e.g. for clothing), option pricing method (e.g. for durables and cars), and hedonic repricing method (for books

and memory cards/sticks).

Quality adjustments in percentage of all prices in the relevant COICOP-group for the year 2019:

ECB Working Paper Series No 2773 / February 202345COICOP % QA

01

02

03

04

05

06

07

08

09

10

11

12

1.9

1.4

14.1

1.0

5.2

0.3

1.1

8.5

5.7

1.6

1.7

1.5

Belgium:

Monitoring Report 2014:

Mostly implicit quality adjustment is performed, using the bridged overlap method. Option pricing is used for

cars, while direct comparison is used for products such as video games, CDs, DVDs, books and clothing and

footwear.

Reference Metadata:

Explicit quality adjustment tends to be avoided. This means that quality adjustments are mostly implicit and

done using the bridged overlap method. Option pricing is used for cars, while direct comparison is normally

used for products such as video games, CDs, DVDs, books and clothing and footwear. The number of quality

adjustments carried out is not kept in a database, so no ﬁgures can be given.

ECB Working Paper Series No 2773 / February 202346Cyprus:

Monitoring Report 2020:

In case a product is not available and no price can be collected for two consecutive months, the product will be

replaced by the most popular variety of the same product. In case it is known earlier that the product will not be

available again, the replacement is carried out immediately. Price collectors are responsible for identifying quality

changes. The method of the quality adjustment is decided by the central oﬃce on a case-by-case basis. The

prices of replacement products are directly compared to the last price of the replaced product in case the quality

diﬀerence is not signiﬁcant. This method is used in most of the cases. Overlap replacement is only applied for

products for which the price in the previous period is known. In the case of products that show frequent changes

and therefore require replacements on a regular basis such as consumer electronics, prices of potential replacement

products are being collected on reserve so as to enable an immediate replacement. Explicit quality adjustment

methods used are quantity adjustment, option pricing and expert judgement. In case of option pricing, the added

option’s price is taken into account to 50% in most of the cases. In the case of new cars, however, the added

option’s price is taken into account to 100%.

Reference Metadata:

Generally, in the case of a replacement is important to apply quality adjustment procedures in order to determine

pure price change.

Explicit quality adjustment procedures are used to estimate directly the price change of a product whose char-

acteristics change over time. The diﬀerences in the physical characteristics of two products are evaluated based

either on quantity adjustment (when the only diﬀerence is in the quantity included) or on expert judgment (when

the quality change is estimated by experts) and no hedonic methods are used. Also, in cases where the replace-

ment incorporates features formerly available as option at additional cost then the option costs method is applied,

whereby the additional characteristics are priced separately as optional features and then a percentage of this

value (usually 50 per cent) is used.

Implicit quality adjustment procedures are applied in the cases of an overlap replacement (bridging) when the

value of the quality change is assessed to be equal to the price diﬀerence in the overlap period, where the two

products were available.

Also in the cases where the quality does not change signiﬁcantly (the change is assessed as zero) the prices are

directly compared (direct comparison).

Initially, the quality control is made by the price collectors, who are instructed to carefully monitor all the prod-

uct characteristics and to identify any quality changes. Then the central oﬃce decides which quality adjustment

method to apply in each case. The incidence of quality changes and adjustments is monitored but there no

available suﬃcient statistics about this.

CYSTAT cannot provide an overview of the quality adjustment methods applied.

There is not any documentation that is readily available on the quality adjustments methods applied. Even

though quality adjustment methods are carried out, those are not clearly marked thus is not easy to prepare

corresponding documentation. As of January 2019 and onwards the quality adjustments methods will be clearly

identiﬁed and recorded in the developed excel ﬁles in order to be able to provide respective documentation in the

future.

ECB Working Paper Series No 2773 / February 202347Estonia

Monitoring Report 2020:

When a product oﬀer cannot be observed because the item is said to be temporarily out of stock, no replacement

takes place. If a product-oﬀer continues to be out of stock for two consecutive months, the price collector selects

a replacement product-oﬀer which matches the product description. Replacements for new cars typically do not

happen in the middle of the year, although they are possible if a certain model disappears from the market. When

a model changes, SE uses direct comparison for minor changes. Otherwise, expert judgement and option pricing

may be used; SE calculates a factor to account for the most important characteristics that may have inﬂuenced

the price, such as horse power and fuel consumption. For used cars, SE scrapes data from the websites of the most

popular used car dealers, controlling for model, vintage, engine size, and mileage. The sample includes 20 models,

stratiﬁed by large, medium, and small. The average age of sampled cars is kept ﬁxed. The sample is updated on

a rolling basis, based on the year of registration of the car. As regards mobile phones, SE carries forward for two

months the price of a model that has disappeared from the market. If a model disappears from one out of four

outlets, SE will use the average price for the model from the other three outlets. When a model disappears from

the market permanently, SE ﬁnds the nearest model and considers the parameters that have changed between

the two models and how much they would inﬂuence the price up or down. SE relies on expert judgement to carry

out the comparison. Direct comparison can be used for any good or service taken into account in the Estonian

HICP. Only direct comparison is used for clothing and footwear. The bridge overlap method is most commonly

used for restaurants and cafes and in the treatment of package holidays.

Reference Metadata:

Restricted from publication.

Finland:

Monitoring Report 2012:

No information provided.

Reference Metadata:

The Finnish price statistics uses the following quality adjustment methods:

• Quantity adjustment (=Package Size Adjustment) can be used for any good where a change in size/ weight

is relevant to the quality of the product and where we do not pre-specify a weight/volume - e.g. a kilo of

apples. Adjusting the package size quantity is necessary when the quantity of a packaging unit has changed

while holding its quality constant. It is crucial for the application of this method that the quantity directly

and proportionally aﬀects the monetary value of the product. Thereby, also ”hidden” price increases

(through reduced quantities at the same price) are captured.

• Hedonic regression is currently applied only to second hand cars. Hedonics are particularly applied to

calculate the price increase of products which features and properties (=characteristics) has strong eﬀect

on the price. The hedonic quality adjustment is a statistical procedure which calculates by means of a

ECB Working Paper Series No 2773 / February 202348regression the inﬂuence of individual product characteristics such as age, driven miles, etc.

• Direct price comparison may be used for virtually any good or service. The direct price comparison can be

applied if a replacement model of equivalent quality is available. It is an extreme of a quality adjustment

method as the prices of both products are directly compared, hence the share of quality is zero percent

and the price change fully enters the index calculation. Particularly for technical products it is precisely

deﬁned, for which characteristics of models explicit quality adjustment should not be applied.

• Class mean imputation may be used for any good or service. Although the number of times this method

is used varies year on year.

• Judgmental quality adjustment: For some product changes, none of the previously mentioned methods is

able to identify in which dimension the price diﬀerence between the replaced model and the new model

is aﬀected by quality diﬀerences. In this case, experts (CPI staﬀ) are asked for a subjective judgment.

Based on their experience (and specialist knowledge), they determine the share of the price diﬀerence due

to quality diﬀerences.

Importance by prevalence of the method in use are in Finland: a) quantity adjustment B) direct comparison C)

class mean imputation. Option costing and overlap pricing are used only occasionally.

France:

Monitoring Report 2015::

Quality adjustment is done for prices of products from all COICOP divisions. Where there are agreed European

standards, methods that can be classiﬁed as B-methods (in accordance with Article 5 of Regulation no 1334/2007)

are applied. Decisions about quality adjustment are made on a case by case basis. The most used method is

direct comparison which applies to around 90% of the replacements. For household durables bridged overlap (at

regional or national level) or hedonic adjustment is applied. For clothes mainly direct comparison is applied.

Option pricing is used for new cars. In the case of newspapers and periodicals there is an adjustment of the price

according to the number of pages.

Reference Metadata:

Quality adjustments are carried out primarily in the framework of the replacement of products. For ﬁeld record-

ings, several methods are used according to the expertise of the collector, manager or sector expert:

• If the replacement product is considered to be a perfect substitute for the replaced product, then the

replacement product will have the same base price as the old product. There is no ”quality eﬀect” and

the entire price diﬀerence is likened to a ”pure” price variation (direct price comparison method). In 2015,

this method was applied to 44.0% of replaced products.

• If the replacement product is not considered to be a perfect substitute, then its base price is estimated

according to the base price of the replaced product monitored in month m-1 and the change in prices

between m-1 and m of products belonging to the same consumption segment as the replaced product for

which observations are available (bridged overlap method). In addition, if the collector knows the price of

ECB Working Paper Series No 2773 / February 202349the replacement product in the month m-1 or in the month of December, the estimate will take account of

this price. The quality adjustment is applied to all products but especially to durable goods.

• A base price can also be estimated by hedonic models using the speciﬁcations of the replacement and the

replaced product. This method requires a suﬃcient number of products in order to estimate the eﬀects of

each technical speciﬁcation that contributes to the model. It is used for several durable goods (washing

machines, dishwashers, refrigerators, televisions, computer) and also for ”best sellers”.

• The Option pricing model has been adopted for replacements relating to new motor vehicles.

Germany:

Monitoring Report 2015:

The application of quality adjustment methods is kept to a minimum. Around 5 to 10 percent of price obser-

vations are aﬀected. Where there are agreed European standards, methods that can be classiﬁed as B-methods

(in accordance with Article 5 of Regulation no 1334/2007) are applied. The product sample is stratiﬁed into

consumption segments. Replacements within a consumption segment will lead either to direct price comparison

(e.g.

for clothing and footwear) or to explicit quality adjustment (e.g.

for technical products). Replacements

outside the consumption segments, if unavoidable, are seen as sample refreshment and lead to bridged overlap.

Quality adjustments for diﬀerences resulting from latest fashion trends are not carried out. The bestseller ap-

proach is pursued for books, CDs, downloads, computer games and software. Otherwise, quantity adjustment,

option pricing (cars), hedonic regression and bridged overlap are applied.

Reference Metadata:

The German price statistics uses the following quality adjustment methods:

• Direct Price Comparison: The direct price comparison can be applied if a replacement model of equivalent

quality is available. It is an extreme of a quality adjustment method as the prices of both products are

directly compared, hence the share of quality is zero percent and the price change fully enters the index

calculation. Particularly for technical products it is precisely deﬁned, for which characteristics of models

explicit quality adjustment should not be applied. Usually these are characteristics such as design, fashion

or other, very subjective assessment of quality. Typical examples for the consumer price index are amended

designs of durable consumer goods as toasters or fashion for clothing.

• Price change taken as quality change: As opposed to the direct price comparison a price change between

replaced and replacement model is completely attributed to a quality change, i.e. the share of quality thus

amounts to 100 percent of the price change. Hence, no price change is measured between the two periods

in which the replacement takes place. This method may be suitable if no similar replacement model can

be found and the price collector has to choose for example a device of another type of construction or a

diﬀerent product variety as replacement model.

• Overlap: Sometimes it may occur that a producer oﬀers various product variants, which diﬀer by a certain

characteristic or a combination of characteristics. If both product variants are simultaneously available on

the market (overlap period), the price diﬀerence can be used as an estimate for the quality change, as the

ECB Working Paper Series No 2773 / February 202350buyer has the choice between the alternatives. The changeover from a replaced to a replacement model

has no impact on the price index, hence the price diﬀerence between predecessor and successor will not be

considered.

• Bridged Overlap: This procedure can be used when the new model and the replaced model are not si-

multaneously available on the market. The method of bridged overlap uses the average price development

within the same product group or the average price development of appropriately equivalent models as a

reference. Especially in cases of product changes in which it is reasonable to assume that also a price change

has been taken in addition to changed material or personal costs, the price development of an appropriate

comparison group (of models) is used as a substitute.

• Package Size Adjustment: Adjusting the package size quantity is necessary when the quantity of a packaging

unit has changed while holding its quality constant. It is crucial for the application of this method that

the quantity directly and proportionally aﬀects the monetary value of the product. Thereby, also “hidden”

price increases (through reduced quantities at the same price) are captured. In the consumer price index

for Germany, package size adjustments are typically conducted in the group of foods and the groups of

other consumer goods.

• Option Pricing: Using option prices, the estimation of the monetary value of a quality change between a

new and its replaced model is conducted by applying list prices of individual product characteristics. This

procedure is usually used for more complex products when a certain characteristic turned in the meantime

from being an additional option to being part of the standard equipment. In such cases, a part of the

amount which had to be paid for the optional equipment can be set as the monetary value of the quality

diﬀerence. Even optional equipment of other manufacturers can be implemented into the calculation. New

cars with additional equipment like special airbags in the consumer price index are an example where this

procedure is carried out. However, in most cases it is not known whether the customer really requires a

product characteristic which turned from optional to standard (such that s/he also would have bought it

earlier when it was only an additional option). Then, according to international conventions, a share of

50% of the purchase price is usually considered as the monetary value of the quality change.

• Supported judgmental quality adjustment: For some products, there exist transparent procedures involving

additional information sources which are able to determine concretely the consumer’s added value of a re-

cently launched model. Typically, this concerns the consumption value of technical products (fuels, energy)

or other follow-up costs (maintenance). Examples are refrigerators with amended power consumption or

washing machines with amended water and power consumption. Calculating the monetary value of such a

quality change, assumptions are needed, e.g. regarding frequency of use and useful life of the new model.

Actual market prices are used for the calculation of follow-up costs due to energy sources. This implicitly

presumes that also the consumer takes actual prices of energy sources etc. into consideration in order to

evaluate the quality of a product.

• Hedonics: Hedonics are particularly applied to calculate the price increase of products which strongly

change in a short period of time.

In the oﬃcial price statistics, products which are currently quality

adjusted by hedonics are desktop PC, printers, hard drives, notebooks, processors, RAM, servers, used

ECB Working Paper Series No 2773 / February 202351cars, tablet PC and residential properties. The hedonic quality adjustment is a statistical procedure which

calculates by means of a regression the inﬂuence of individual product characteristics, e.g. the size of hard

drives in desktop PC, on the price of the product. Thereby, the monetary value of the quality diﬀerence

between a new model and the replaced model can be determined and taken out of the price change.

Currently there is a labelling in the processing program to indicate prices which are subject to quality adjustment

but the applied method of quality adjustment is not recorded. Consequently we cannot provide the percentage or

the number of the diﬀerent methods for quality adjustment. Furthermore the pure percentage/number of quality

adjusted prices (independent of the method used) can currently not be provided without considerable eﬀort. Due

to the organisation of the consumer price statistics in Germany, only the respective Laender oﬃce has access to

the price data collected by itself. The FSO does not have permanent access to the price data. In the medium

term, it is planned to adjust the processing program to enable the provision of information about the application

of quality adjustment.

Greece:

Monitoring Report 2013:

No information provided.

Reference Metadata:

No information provided.

Ireland:

Monitoring Report 2017:

Quality adjustments are made by price collectors and checked by the central oﬃce staﬀ. Price collectors have

a choice, when making a replacement, between direct comparison (no quality change) and bridged overlap, i.e.

implicit quality adjustment. Bridged overlap is applied to approximately 7% of 48 000 collected prices. The

bridged overlap approach might not be the most appropriate approach to quality adjustment of products if it is

applied by default. The CSO is running a project in 2017 aiming at improving the quality adjustment methods

and bringing these more in line with HICP recommendations. The project focusses on clothing and footwear,

cars and electronic goods. The treatment of second hand cars is now consistent with HICP recommendations and

takes depreciation into account.

Reference Metadata:

Whenever a product has to be replaced by another product, some statistical adjustment has to be made in order

to link the price indices and create continuous price series. In principle, this situation may happen for any product

covered in the HICP.

In most cases, we apply the “Bridged-overlap” method (implicit QA). This approach consists in estimating a price

development between the replaced product and the replacement product which is equal to the actual average price

development (geometric mean) observed for the products belonging to the same consumption segment. In duly

motivated circumstances, we may also apply direct comparison (if the two products are deemed to be almost

ECB Working Paper Series No 2773 / February 202352identical) or link-to-show-no-price-change (if no reliable quality adjustment method can be applied).

In all COICOP divisions Bridged-overlap is the dominant method of implicit QA. Explicit quality adjustment

tends to be avoided. Direct Comparison normally occurs on items such as CD’s and DVD’s.

Italy:

Monitoring Report 2014:

Decision on the need of quality adjustment is taken on a case-by-case basis. In these cases where there are agreed

European standards, methods classiﬁed as B-methods (in accordance with Article 5 of Regulation no 1334/2007)

are applied. In particular:

• Direct comparison is carried out in some cases for clothing and footwear, in some cases for processed or

fresh food, for products for which the best seller approach is adopted such as electronic games, DVD movies

and fuels.

• For most of the products for which prices are centrally collected by ISTAT, direct comparison within the

speciﬁc stratum is conducted.

• A combination of bridged overlap and class mean imputation is adopted in some cases for clothing and

footwear. Quantity adjustment is adopted only when the diﬀerence between a product oﬀer in a month

and a product oﬀer in the following one, is due exclusively to a diﬀerence in the package size.

• Direct comparison in combination with monthly deletion and replenishment is used for tablet PCs.

• For new cars, direct comparison or reconstructed overlap is applied in the case of minor changes and overlap

is applied in the case of major changes. Eurostat welcomes ISTAT’s eﬀorts to use quality adjustment

methods classiﬁed as A or at least B methods in more product groups.

Reference Metadata:

For quality adjustment, as it is required by EC Regulation No 1334/2007, a case-by-case approach is adopted

and therefore group of products by group of product. In details, methods adopted to manage quality adjustment

issues are:

• Direct comparison:

– for clothing and footwear, when the product oﬀers in two consecutive months are evaluated com-

parable on the basis of criteria deﬁned by ISTAT. In the month when the replacement is done, the

elementary price is ﬂagged, the reason of the replacement is ticked and the direct comparison is

carried out directly by automatic procedures;

– in some cases for processed or fresh food (for which prices are monthly collected) when, for a list of

product deﬁned by ISTAT, the change of the product oﬀer is due exclusively to a change of brand,

and therefore the product oﬀers in two consecutive months are evaluated comparable. In the month

when the replacement is done, the elementary price is ﬂagged, the reason of the replacement is ticked

and the data collector has to register the price of the product oﬀer available in the previous month

as the price of the previous month of the new product oﬀer;

ECB Working Paper Series No 2773 / February 202353– for products for which best seller approach is adopted such as electronic games, DVD movies;

– for fresh food and ﬁsh, for which prices are collected twice a month, in the month when the replace-

ment is done, the elementary price is ﬂagged and the reason of the replacement is ticked (quantity

collected or collection unit; brand and variety are not take into account). In the following ﬁgure there

is an example of direct comparison for fresh fruits as it is showed in the web application available to

ISTAT and Municipal Statistical Oﬃces.

– for most of the products for which prices are centrally collected by ISTAT, it allows direct comparison

inside each speciﬁc stratum.

• A combination of bridged overlap and class mean imputation:

– in some cases for clothing and footwear, when the product oﬀers in two consecutive months are eval-

uated not comparable on the basis of criteria deﬁned by ISTAT. In the month when the replacement

is done, the elementary price is ﬂagged, the reason of the replacement is ticked and an automatic

procedure estimates the price of the previous month of the new product oﬀer in order to build the

bridge between the two consecutive months. To calculate the price of the previous month, the rate

of change m/m-1 of the geometric mean of the micro indices is used, within the same product of

the basket in the same town, both of the product oﬀers for which no replacements take place and

for the product oﬀers for which direct comparison was carried out. The information available within

the same product, in the same town is used only if at least for the 50% of the sample for that spe-

ciﬁc product aggregate in that speciﬁc town, prices are collected for the same product oﬀers of the

previous month (indeed without replacements) or for product oﬀers for which direct comparison was

carried out. If the latter condition is not respected, the overlap approach is adopted.

• Overlap:

– for the other products, for which prices are collected monthly at territorial level (except clothing and

footwear, products for which prices are collected centrally, fresh and processed food for which direct

comparison is carried out). In the month when the replacement is done, if the data collector has

collected the price of the previous month of the replacing product oﬀer (or the seller is able to provide

this information) this information is used and the elementary price is ﬂagged. If the data collector

has not collected the price of the previous month of the replacing product oﬀer (and the seller is

not able to provide this information), an automatic procedure estimates the price of the previous

month of the new product oﬀer using the current price of the replacing product oﬀer (link-to-show-

no-price-change). However, in order to avoid (when it is possible) the link-to-show-no-price-change,

the software for the data collection allows ‘booking’ a product oﬀer when the sampled product oﬀer

is on the way to exit the market.

• Explicit quality change:

– it is adopted only when the diﬀerence between a product oﬀer in a month and a product oﬀer in the

following one, is due exclusively to packaging and the replacing product oﬀer is actually new. In this

case the data collector has to specify the reason of the replacement and to insert the information

ECB Working Paper Series No 2773 / February 202354about the quantity of the new product oﬀer; the elementary price registered is ﬂagged. An auto-

matic procedure calculates the price of the new product in the previous month taking into account

the diﬀerence between the new and the old quantity collected. If the quantity increases or declines

by 50% as compared to the previous month, the procedure estimates the price of the new product

oﬀer in the last month, calculating the cost of the new quantity on the base of the previous price. If

the new one is diﬀerent from the previous one of an amount that is more than 50%, the procedure

calculates the price of the previous month of the new product oﬀer as in the case of overlap when

the link is built to show no price change). When the replacing product oﬀer was already available in

the previous month, the situation is ascribable to cases when it is adopted the overlap approach.

Latvia:

Monitoring Report 2013:

Decision about the quality adjustment is always based on the speciﬁc conditions reported by the price collectors.

Where there are agreed European standards, mainly methods that can be classiﬁed as B-methods (in accordance

with Article 5 of Regulation no 1334/2007) are applied. Option pricing is applied for new and used cars and

desktop computers. In other cases bridged overlap, quantity adjustment, judgemental (expert) adjustment and,

in most cases, direct comparison (equivalent replacement) is applied.

Reference Metadata:

Price collectors perform the initial steps for quality adjustment, by selecting the most suitable replacement, es-

timating the comparability of both products and reporting on the diﬀerences in characteristics between them.

The instructions and examples are described in the manual for price collectors. Staﬀ of the CPI unit makes the

ﬁnal quality adjustment. For fruit, vegetables, clothing, footwear, audio recordings, books in rapidly-changing

market direct comparison is the primary (most often used) quality adjustment method. For other product cate-

gories, mostly the bridged overlap in case of major changes and direct comparison in case of minor changes is used.

Divisions % Bridged overlap % Direct compariso % Option cost, expert estimation, etc.

01

02

03

04

05

06

07

08

09

10

11

12

1.3

0.9

1.0

1.3

2.2

0.6

1.2

1.2

2.0

0.1

1.0

1.2

2.9

0.5

7.1

1.5

1.8

0.6

2.1

6.4

2.7

5.0

1.3

2.1

0.3

0.3

0.4

0.3

0.6

0.1

0.1

1.4

0.3

0.0

0.1

0.4

ECB Working Paper Series No 2773 / February 20235550% option cost or indirect option cost regarding fuel consumption approach for new cars in case of minor changes

is used. For second-hand cars, supported expert judgement based on age and mileage is used, combined with

direct comparison or bridged overlap. In special cases judgmental (expert) adjustment and quantity adjustment

are used as well. There are no automatic quality adjustment procedures for any product group. Instead, the

choice of method depends on the speciﬁc replacement situation and selected replacement product-oﬀer.

Lithuania:

Monitoring Report 2013:

Quality adjustment is usually done for prices of food and beverages, clothing and footwear, furnishings, household

equipment, new cars, equipment for the reception, recording and reproduction of sound and picture, personal

computers, and books. Where there are agreed European standards, methods that can be classiﬁed as B-methods

or A-methods (in accordance with Article 5 of Regulation no 1334/2007) are applied. Option pricing is applied for

new cars. Prices of used cars are compared directly. For minor quality diﬀerences for clothing, direct comparison

is used, while for substantial quality diﬀerences, expert judgement or bridged overlaps are applied. Hedonic re-

gressions are applied for top-ten ﬁction books. In other cases bridged overlap, quantity adjustment, judgemental

(expert) adjustment, option pricing and, in most cases, direct comparison (equivalent replacement) is applied.

Reference Metadata:

Explicit (”expert judgment”, ”option pricing”, ”hedonic”, ”quantity adjustment”) and implicit (”bridged over-

lap”) quality adjustment methods are used for particular goods. In some cases, the overlap method is used.

Using ”expert judgement” method, the impact of the change in quality on the price is estimated by the price

collector assisted by the employee of the outlet. The price collector has to inform the Price Statistics Division

about the change in quality and the impact thereof on the change in the price. The information obtained is

analysed and, taking into account the impact of the change in quality, the staﬀ of the Price Statistics Division

recalculates the price of the replaced product of the previous month and the price of the replacement product in

the current month is compared with adjusted price of replaced product in the previous month.

Using ”option pricing” method, expenditure is assessed for the new characteristic of the product that was not

present in the replaced product. Then, the price of the replaced product in the previous month is recalculated

based on the assumption that the new characteristic was presented in the replaced product, and then, the price

of the replacement product in the current month is compared with the recalculated price of the replaced product

of the previous month.

”Hedonic” method based on a regression equation, which expresses the price of a product as a function of its

qualitative characteristics which determine the change in the product price.

According to ”quantity adjustment” method, the price of a replaced product is recalculated proportionally, taking

into account the quantity ratio of the replaced to the replacement products.

A ”bridged overlap” method is used in case when the price collector cannot record the price of the replaced

product in the current month and selects a replacement product whose price in the previous month cannot be

determined. The price of the replaced product in the previous month is computed on the basis of a short-term

price ratio of an identical or similar product calculated on the basis of prices recorded in other outlets of the same

ECB Working Paper Series No 2773 / February 202356territorial unit or other territorial units.

According to the ”overlap” method, the prices for replacement product and replaced product are collected at the

same time, when both products are available in the outlet.

Hedonic quality adjustment methods is used for the books.

Divisions Bridged overlap, % Direct comparison, %

0.0

0.1

3.5

0.2

1.0

0.0

1.0

1.6

0.4

0.2

0.0

0.4

01

02

03

04

05

06

07

08

09

10

11

12

0.8

0.4

1.3

0.4

1.2

0.1

0.5

2.1

1.5

0.4

0.3

1.3

Luxembourg:

Monitoring Report 2012:

No information provided.

Reference Metadata:

In most cases, the following methods are used in case of replacements:

• Bridged Overlap

• Overlap pricing (in case the price of t-1 of the replacement variety is known)

• Direct comparison

For some speciﬁc product groups (e.g. cars), explicit quality adjustment (QA) is performed.

ECB Working Paper Series No 2773 / February 202357Division Bridged Overlap Direct Comparison Other QA Total Replacement No Replacement

0.5%

0.4%

1.7%

0.1%

1.2%

0.1%

0.4%

2.9%

1.1%

0.5%

0.4%

0.6%

1

2

3

4

5

6

7

8

9

10

11

12

0.8%

0.9%

1.0%

0.5%

2.0%

0.2%

0.7%

2.6%

1.4%

0.3%

1.1%

0.7%

Malta:

Monitoring Report 2019:

0.1%

0.0%

0.1%

0.1%

0.3%

0.2%

0.9%

0.2%

0.6%

0.0%

0.1%

0.1%

1.4%

1.3%

2.8%

0.7%

3.5%

0.5%

2.0%

5.7%

3.0%

0.8%

1.5%

1.4%

98.6%

98.7%

97.2%

99.3%

96.5%

99.5%

98.0%

94.3%

97.0%

99.2%

98.5%

98.6%

Price collectors work with relatively broad item descriptions and choose a well-sold product that falls under the

item description. If the product is not available anymore or not well-sold the price collector choses a replacement

and notes all relevant information. The commonly used methods for quality adjustment are direct comparison, if

the diﬀerences are considered negligible or else bridged overlap. Hedonic quality adjustments are made for new

and second-hand cars, laptops, mobile phones and cameras.

Reference Metadata:

When a replacement model diﬀers signiﬁcantly from the replaced model, an implicit method or explicit method

of quality adjustment is used. We use three diﬀerent quality adjustment methods, namely the Direct Comparison

method (comparing prices of the old and the new product over two periods without adjustment), the Bridged

Overlap method (derives the price change between the old and new item from the observed price change of other

items in the sub-index) and the Hedonic method (regression analysis is used to assess the extent to which the

observed variability in the price of a product can be explained by variability in the product’s attributes).

All changes are assessed on a case-by-case basis. The Direct Comparison Method is used only when the change

between the replacement and replaced models is not radical.

For Clothing and Footwear, the direct comparison method (B method) is used for all replacements which are

deemed essentially equivalent. In the rare cases where a replacement model diﬀers signiﬁcantly from the replaced

model, the bridged overlap method used. For Books, recorded media and computer games, the items falling within

this group of products are characterised by ”rapidly-changing markets”. The HICP does not cover any books

or other recorded media which have a relatively long commercial life-span, and low rates of introduction of new

products, as these are deemed unrepresentative. The direct comparison method of quality adjustment, which is

considered a B method, is used in all cases. The Hedonic method (B method) of quality adjustment is used in

compilation of both new and used cars indices. For the latter we use the hedonic re-pricing method described in

ECB Working Paper Series No 2773 / February 202358great detail in the handbook drafted by CENEX HICP Quality Adjustment (16th draft). In the case of new cars

the coeﬃcients of the regression are used for quality adjustment.

Products which are characterised by continuous technological advancements are regularly quality adjusted.

Where quality changes occur, in most cases we carry out direct comparison.

Netherlands:

Monitoring Report 2012:

No information provided.

Reference Metadata:

Hereby an overview of all the quality adjustment methods we are using:

• Direct comparison (e.g. clothing)

• Quantity adjustment (e.g. for tobacco)

• Option Prices (e.g. for cars)

• Expert Judgement (e.g. for electronics and boats)

• Overlap (not commonly used)

• 50% method (not commonly used)

• Targeted Mean imputation (not commonly used)

We can’t indicate per ECOICOP division as a percentage of all prices, how often a method was used. It is di-

verse and it doesn’t depend on the COICOP but on the article. Hedonic quality adjustment methods are not used.

Portugal:

Monitoring Report 2019:

When a product-oﬀer cannot be observed, a price is estimated at most for two months, after which a replacement

product-oﬀer is selected. A temporarily missing price is imputed using the average price change of the prices

collected in other outlets in the same town for the same product. INE uses mainly bridged overlap as a quality

adjustment method for non-comparable replacements. In some circumstances, the prices of the old and the new

product-oﬀer are available in the same month, which makes it possible to apply overlap pricing. If the replacement

product-oﬀer is considered to be of the same quality, the prices are directly compared. Changes in package-sizes

are taken into account for those products where the collected prices are transformed into a standard unit. For

new cars, option pricing is applied on some occasions. Except for new cars, explicit quality adjustment methods,

which estimate the quality diﬀerence based on the product characteristics, are generally not used. The current

production system does not record the type of quality adjustment method that is applied in a given replacement

situation. Direct comparison is mostly used for clothing and footwear. It is also applied in the context of mobile

phones which had led to an upward impact on the indices. To mitigate this eﬀect, INE now restricts the selection

of replacement mobile phones to more similar models. INE also applies direct comparison for cars when it is

ECB Working Paper Series No 2773 / February 202359diﬃcult to quantify small quality diﬀerences. A downward bias may appear in the indices if replacements and

quality adjustments are carried out for products which enter the sample at high prices and exit the sample at low

prices.

Reference Metadata:

The following quality adjustment methods are used:

• Simple overlap, when the prices of the old and replacement items are available in the same period

• Bridged overlap, when the price change of the product within the same municipality used as a bridge for

the price changes of the replacement product

• Direct comparison, when the replacement product is deemed comparable

• Option pricing, when the price of the options of the item are available (used only for new cars)

• Judgmental quality adjustment and supported judgmental quality adjustment, when additional information

is available and assessed by expert judgment.

The most commonly used are bridged and simple overlap. Direct comparison is used mostly when the price

collector indicates that products are comparable (usually for clothing and footwear).

Slovakia:

Monitoring Report 2018:

Missing prices are carried forward for at most two consecutive months before they are replaced within the product

range according to the description and, if possible, the same brand. Exception to this rule is seaside package

holidays that are only collected during the summer season and the last price is carried forward until the beginning

of the next season. (See also point 8.) Quality adjustments are made at the head oﬃce of SO SR. In most cases

bridged overlap or direct comparison is used. The calculation software allows for the application of explicit quality

adjustment methods but such methods are rarely used. Option pricing is only applied for new cars, where it is

applied on a rather ad hoc basis. For minor changes in models, direct price comparison is carried out in most

cases. For used cars, the quality adjustment is carried out by the external data provider, who takes into account

technical speciﬁcations of the car and the mileage. The bridged overlap approach is not the most appropriate

approach to quality adjustment of products if it is applied by default. The bridged overlap approach is applied to

nearly 0.5% of the observed prices. The SO SR has run a project aiming to apply explicit adjustment methods.

However, it is deemed that relevant information to run the calculations cannot be obtained.

Reference Metadata:

The following introduction part regarding the quality adjustment process is given in the ”Methodological instruc-

tions for price collection, verifying and reporting of data on consumer prices”: If the price collector replaces a

previously priced product oﬀer by a new product oﬀer and if in the price of replacement also the change of quality

of the new product oﬀer in comparison with the previously priced product oﬀer is reﬂected, then the change of

quality shall be adjusted from the price development (in the compilation of price index for this product). The

process of quality adjustment (QA) is aimed at the fact that consumer price index shall reﬂect the pure price

change not the price change inﬂuenced by the change in quality of the priced products. However the quality

ECB Working Paper Series No 2773 / February 202360adjustment is in practice a complex process. A prerequisite for proper quality adjustment and for assessment of

quality change are properly ﬁlled ”Table comments” by the price collectors in the prescribed structure (maximum

20 characters for ”brand” of the product oﬀer and maximum 80 characters for other important information re-

lated to the product oﬀer). Though the software introduced within the grant projects on the implementation of

Commission Regulation (EC) No 1334/2007 on sampling and QA enables the use of explicit QA methods (e.g. the

option price method), in practise, in most of the cases the implicit QA methods (bridged overlap) and/or direct

price comparison are used. The reason for a rare use of explicit QA is the fact that in practise it is diﬃcult to

obtain enough ”explicit” information related to the characteristic of the products needed for evaluation of quality

change.

The QA process is done at the central level of the compilation of consumer price indices. As we mentioned above,

the properly ﬁlled ”table comments” are very important for the assessment of quality change and for the QA.

From this point of view, ”The report of Table comments” produced by the software is important. It contains

information related to the collected prices in the prescribed structure (maximum 20 characters for ”Brand” and

maximum 80 characters for ”Comments” with the other important information related to the collected price).

The table comments refer ﬁrst of all to the prices collected for the new (replacement) product oﬀers and/or to the

product oﬀers collected in the new outlets and also to the prices with the month-on-month change greater than

20%. This report of table comments also serves for the assessment whether the collected price for the replacement

product oﬀer had been collected in compliance with the descriptions of the representative items. If not, the price

may be excluded from further processing. The QA is done on the basis of evaluation of the information given

in the table comments. In most of the cases the implicit QA (bridged overlap) and/or direct price comparison

is used. In the cases of bridged overlap, the software calculates the month-on-month price development of the

products “without” quality change and according to this month-on-month movement, the software adjusts the

price of December of the previous year for those representative items in which QA was done. Every QA process

is documented in the special kind of report related to the process of QA: every month the software developed in

Oracle forms produces output report not only concerning source prices data but also the Production (Output)

data - aggregated data for the Slovak Republic, average prices and price indices for the individual representative

items, as well as for all ECOICOP categories (including the divisions, groups, classes and subclasses) compiled in

4 phases of the compilation of consumer price indices:

• Production (Output) data compiled from the ”gross-unveriﬁed” data

• Production (Output) data compiled after the correction of ”typo” mistakes in prices

• Production (Output) data compiled after the process of quality adjustment taking into account the change

of quality of priced products

• Final production (Output) data compiled after the imputation of seasonal goods prices outside the season

The reports on QA are available on monthly basis.

Quality adjustment (QA) issues for Clothing and footwear, Books, CDs and computer games and for Cars:

• Clothing and footwear

ECB Working Paper Series No 2773 / February 202361In compliance with the standards, the direct price comparison (B method) is used in Quality adjustment for

clothing and footwear. The bridged overlap method is used very rarely for QA of clothing and footwear.

• Books, CDs and computer games

In compliance with the standards, the direct price comparison is used for Long-selling markets approach as well

as for rapidly-changing markets approach.

• Cars

Regarding QA for new cars: direct price comparison is used most often at present. The option pricing method

is used rarely. However the option pricing method was used in the cases where additional equipment of car was

provided for 1Eur “additional” charge or the additional equipment of car was oﬀered as a special bonus free of

charge. SOSR is provided with the prices of second hand cars from external company – specialists at the pricing

of cars. They make some QA taking into account the technical stage of the car and the milage.

Slovenia:

Monitoring Report 2016:

All quality adjustments are performed by the central oﬃce staﬀ. For the quality adjustment a case by case

approach is adopted, i.e. there are no automatic procedures. The quality adjustment method chosen depends

on the speciﬁc replacement situation. Bridged overlap is the most commonly used method e.g.

for technical

products, computers and household appliances. Both option pricing and supported expert judgment are used for

cars. In rare cases, if there is a signiﬁcant quality change in assortment of clothing, only 50% of the price change

is shown in the index.

Reference Metadata:

Explicit methods: Direct price comparison is used for clothing and footwear, books (the list of most read books

is changed every month, since we monitor the major publishing house in the country, which publishes monthly

the list of most sold, i.e. popular, books - Top 10 for adults and children/youth). DVDs and computer games

are replaced on a monthly basis with more popular ones - direct comparison is used. As regards new cars,

mixed/combined approaches are used; in the case of signiﬁcant change, quality adjustment is made (mostly option

pricing complemented with supported judgemental methods). For quantity adjustment, package size adjustment

is used (e.g. medicaments). Option pricing is the most commonly used explicit method (a half of the price

diﬀerence between the two models is attributed to the diﬀerence in quality). Sometimes, when other methods

can’t be applied, we change the base price of a replaced model with the replacement’s base price (only if the

December price of the replacement is known).

Implicit methods: The choice of a method also depends on the data available: overlap method for audio-video,

photo equipmet (if data on prices in two consecutive periods are available). Bridge overlap method is applied

in most cases when quality changes are detected. It’s applied for PCs, technical products, household appliances,

audio-video goods and in a case of signiﬁcant change in quality when no other useful information is available.

In 2019, the percentage of quality adjustments (ECOICOP 03-12) was 0.4%.

ECB Working Paper Series No 2773 / February 202362ECOICOP

QA method (%)

Division

Option pricing and

Bridged overlap

Package size adjust-

Other Base

price

mixed approaches

ment

change

01

02

03

04

05

06

07

08

09

10

11

12

0.09

0.06

0.18

0.40

0.10

0.02

Spain:

Monitoring Report 2020:

0.01

0.30

0.06

2.51

0.30

0.04

0.004

0.11

0.43

0.21

0.23

0.25

0.40

0.40

0,10

0.16

The chosen quality adjustment method depends on the speciﬁc replacement situation. INE assess the comparabil-

ity of the replacement with the obsolete product and decides the most appropriate quality adjustment procedure.

The regional oﬃces make the quality adjustments for regionally collected prices and the central oﬃce checks these.

The most commonly used method is direct comparison. When the direct comparison is not possible due to impor-

tant changes in the item characteristics, the bridge overlap is applied. Besides, for some speciﬁc products, other

methods are also used. For instance, INE uses expert judgment, combined with option pricing, for the adjustment

of quality changes in the price indices covering the purchase of motorcars and motorcycles. The overlap pricing

method is used for the quality adjustment of books and music.

Reference Metadata:

The diﬀerent quality adjustment methods that are commonly applied in the Spanish HICP can be summarized

as follows:

• Implicit methods: overlap, bridged overlap, class mean imputation, direct comparison, link-to-show-no

price change.

• Explicit methods: expert judgement, option costs and quantity adjustment.

All the items in the HICP basket are grouped by ”types”, according to similar features, and there is a list of

advised and unadmitted quality adjustment methods for each, depending on product features. Quality adjustment

procedures are applied every time there is a replacement, in order to assess whether there is any quality diﬀerence

and decide the most appropriate treatment accordingly.

The percentage of prices for which each method is applied is not available because adjustments for quality changes

ECB Working Paper Series No 2773 / February 202363are not counted. Nevertheless, the quality adjustment methods that are usually applied by groups of products

are the following:

• Fast-moving consumer goods (food, medicines and personal care products)

Price imputation, direct comparison and quantity adjustments. Also class mean imputation and expert judgement

for fresh products (meat, ﬁsh, fruit, vegetables and eggs)

• Centrally collected products

Expert judgement, overlap, option pricing.

• Clothing and footwear

Direct comparison, bridged overlap, class mean imputation, expert judgement.

• Furniture, household appliances and equipment

Expert judgement.

• Restaurants

Overlap and price imputation.

ECB Working Paper Series No 2773 / February 202364Acknowledgements 

We thank Fabio Rumler for a thorough discussion of this paper. We are also grateful to Henning Weber, Malte Knüppel, Annette Fröhling 
and Stefan Schaefer for valuable comments and suggestions. Concerning the German CPI micro data, we are especially grateful to 
Malte Kaukal and Markus Stahl (Statistical Oﬃce of Hesse, Germany) for explaining to us the underlying data in great detail. We also 
thank Christoph Fischer (Deutsche Bundesbank) for his valuable comments and for sharing the underlying cross-country micro price 
data set on washing machines with us. Finally, we are very grateful to Günter Beck (University of Siegen, Miggroprices) for providing 
updated estimates of transaction-based price indices on washing machines for the more recent sample. 
The views expressed in this paper are those of the authors only and do not necessarily reﬂect those of the Deutsche Bundesbank or the 
Eurosystem. 

Jan-Oliver Menz 
Deutsche Bundesbank, Frankfurt am Main, Germany; email: jan-oliver.menz@bundesbank.de 

Elisabeth Wieland 
Deutsche Bundesbank, Frankfurt am Main, Germany; email: elisabeth.wieland@bundesbank.de 

Jens Mehrhoﬀ 
Deutsche Bundesbank, Frankfurt am Main, Germany; email: jens.mehrhoﬀ@bundesbank.de 

© European Central Bank, 2023 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

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www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

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