Harmonised index of consumer prices (HICP) (prc_hicp)

National Reference Metadata in Euro SDMX Metadata Structure (ESMS)

Compiling agency: Statistics Sweden (SCB), the National Statistical Institute of Sweden

Eurostat metadata
Reference metadata
1. Contact
2. Metadata update
3. Statistical presentation
4. Unit of measure
5. Reference Period
6. Institutional Mandate
7. Confidentiality
8. Release policy
9. Frequency of dissemination
10. Accessibility and clarity
11. Quality management
12. Relevance
13. Accuracy
14. Timeliness and punctuality
15. Coherence and comparability
16. Cost and Burden
17. Data revision
18. Statistical processing
19. Comment
Related Metadata
Annexes (including footnotes)

For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT


1. Contact Top
1.1. Contact organisation

Statistics Sweden (SCB), the National Statistical Institute of Sweden

1.2. Contact organisation unit

Section for consumer prices

Unit for business, foreign trade and prices

Department for Economic statistics and analysis

1.5. Contact mail address

Postal address:

Statistics Sweden
Solna strandväg 86,
171 54 Solna

2. Metadata update Top
2.1. Metadata last certified 26/04/2022
2.2. Metadata last posted 31/03/2022
2.3. Metadata last update 31/03/2022

3. Statistical presentation Top
3.1. Data description

The harmonised index of consumer prices (HICP) is a consumer price index (CPI) that is calculated according to a harmonised approach. It measures the change over time of the prices of consumer goods and services acquired by households (inflation).

Due to the common methodology, the HICPs of the countries and European aggregates can be directly compared.

3.2. Classification system

European classification of individual consumption according to purpose (ECOICOP)

3.3. Coverage - sector

The HICP covers the final monetary consumption expenditure of the household sector.

3.4. Statistical concepts and definitions

The main statistical variables are price indices.

3.5. Statistical unit

The basic unit of statistical observation are prices for consumer products.

3.6. Statistical population

3.6.1. Statistical target population

The target statistical universe is the 'household final monetary consumption expenditure' (HFMCE) on the economic territory of the country by both resident and non-resident households. The household sector to which the definition refers, includes all individuals or groups of individuals irrespective of, in particular, the type of area in which they live, their position in the income distribution and their nationality or residence status. These definitions follow the national accounts concepts in the European System of Accounts.

3.6.2. Coverage error population

No deviations.

3.7. Reference area

3.7.1. Geographical coverage

The HICP refers to the economic territory of a country as referred to in paragraph 2.05 of Annex A to ESA 2010, with the exception that the extraterritorial enclaves situated within the boundaries of a Member State or a country are included and the territorial enclaves situated in the rest of the world are excluded.

3.7.2. Coverage error regions

The target is to have a good representation of the overall (national) exenditure and our samples are designed accordingly. Certain geographical areas are excluded from local price collection since they are deemed not to have a significant effect on the overall estimate i.e. their exclusion is not thought to induce bias. 

3.8. Coverage - Time

3.8.1. Start of time series

The HICP series started in January 1997.

3.8.2. Start of time series - national specifics

Swedish HICP data is publically available since January 1996.

However, calculation started already in January 1995.

HICP-CT is available since December 2002.

See the HICP database

3.9. Base period


4. Unit of measure Top

The following units are used:

  • Index point
  • Percentage change on the same period of the previous year (rates);
  • Percentage change on the previous period (rates);
  • Percentage share of the total (weights).

5. Reference Period Top

HICP is a monthly statistics.

6. Institutional Mandate Top
6.1. Institutional Mandate - legal acts and other agreements

Harmonised Indices of Consumer Prices (HICPs) are harmonised inflation figures required under the Treaty on the Functioning of the European Union. Regulation (EU) 2016/792 of the European Parliament and the Council of 11 May 2016 (OJ L 135) sets the legal basis for establishing a harmonised methodology for the compilation of the HICP and the HICP-CT.

This regulation is implemented by Commission Regulation (EU) 2020/1148 of 31 July 2020.

Further methodological documentation, namely recommendations and guidelines, is available in the HICP dedicated section, under 'Methodology'.

6.2. Institutional Mandate - data sharing


7. Confidentiality Top
7.1. Confidentiality - policy

Regulation (EC) No 223/2009 of the European Parliament and of the Council, of 11 March 2009, on the transmission of data subject to statistical confidentiality to the Statistical Office of the European Communities

The relevant national legislation is the public access to information and secrecy act (2009:400) - this prevents disclosure of data that identify a person or economic entity either directly or indirectly. An exception is if there exist a valid purpose (such as for research) and that the disclosure do not cause damage to an individual or company.

Prices, weights and item descriptions are considered confidential if they reveal a company, person, brand, product name, price or turnover etc. Therefore microdata within most elementary aggregates are confidential. Price data from the government sector (national or local level) is not confidential, such as the fee for water from a municipality.

7.2. Confidentiality - data treatment

The published figures should not reveal any individual data. If the business situation is such that a published index reveals the data source, the index is not published.

8. Release policy Top

In line with the Community legal framework and the European Statistics Code of Practice Eurostat disseminates European statistics on Eurostat's website (see point 10 - 'Accessibility and clarity') respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users.

8.1. Release calendar

The release calendar is publically available and published in September for the full following year.

8.2. Release calendar access

Eurostat's website HICP calendar.

The release of the Swedish HICP on Statistics Sweden’s web page follows the release calendar of the Swedish CPI, a few days before the Eurostat HICP release.

8.3. Release policy - user access

In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see item 10 - 'Accessibility and clarity') respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users.
In line with this protocol and on a strictly regulated basis, data on Harmonised Consumer Prices (HICPs) are sent for information to the European Central Bank (ECB) and to the European Commission Directorate General for Economic and Financial Affairs (DG ECFIN) under embargo the evening before the official release of data.

9. Frequency of dissemination Top


10. Accessibility and clarity Top

HICP and HICP-CT are published on Statistics Sweden's webpage:

  • Time series for HICP total, Index 2015=100, with two decimals
  • Time series for HICP-CT total, Index 2015=100, with two decimals
  • Time series for HICP total, monthly rate of change (m/m-1), one decimal
  • Time series for HICP total, annual rate of change (m/m-12), one decimal
  • Time series for HICP-CT total,  monthly rate of change (m/m-1), one decimal
  • Time series for HICP-CT total, annual rate of change (m/m-12), one decimal

We publish the most recent figures in our press release for CPI and the longer time series in the statistical database (SSD).

No data for sub-indices of HICP/HICP-CT are published at Statistics Sweden's web page.

Statistics Sweden do not produce or publish flash estimates of the HICP.

Statistics Sweden do not have any methodology document in English especially for the HICP. In the English version quality declaration for CPI, there is some information also about HICP:



10.1. Dissemination format - News release

Statistics Sweden does not have a dedicated news release for the HICP. We do however include the HICP all-items inflation rate in the news release for the Swedish CPI/CPIF (for comparison in one graph):

Konsumentprisindex (KPI) (scb.se)

Eurostats' News release.

10.2. Dissemination format - Publications

In the national publication, the HICP is available at overall level. Breakdowns by ECOICOP sub-aggregates are given for the national CPI but not for the HICP in the national publication. The CPI and the HICP are available on the website of Statistics Sweden (the NSI): www.scb.se.

10.3. Dissemination format - online database

HICP database.

10.4. Dissemination format - microdata access

None if not specifically asked for.

10.5. Dissemination format - other

See also Eurostat's HICP section website.

10.6. Documentation on methodology

The HICP Methodological Manual provides the reference methodology for the production of HICP. (https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-17-015)

10.6.1. Documentation on methodology - national specifics

The national website presents a description statistics in HTML and PDF formats. Specific documents on concepts and methods for the indices can also be found at the website. The national CPI was reviewed by a Government Commission and its report is available online with the reference "SOU 1999:124" (in Swedish, with summary and annexes in English). The report is available at http://www.regeringen.se/sb/d/108/a/1227

10.7. Quality management - documentation

Not available.

11. Quality management Top

Statistics Sweden's quality policy:



European statistics code of practice:


11.1. Quality assurance

11.1.1. Quality management - Compliance Monitoring

Compliance Monitoring

11.1.2. Quality assurance - national specifics

The Data collection department is responsible for data collection and initial data cleaning. The Section for consumer prices carries out further validation (outlier checks etc) of data.

The Section for consumer prices employs a quality assurance system:

- A continual improvement process (reporting and acting on all incidents and errors in the production)

- Written work instructions for all stages in the production process (the aim is to keep such instructions up-to-date)

- A rigorous test model for all changes in the main production system (VB/C#.net)

- Checklists for all changes in secondary calculation systems (SAS/Excel) to ensure that all changes meet the quality standards. On the section there is a team dedicated to the quality process that is responsible to follow-up and remind about the use of checklists.

The main production meetings during a month are:

- Macro validation meeting before finalisation of CPI/HICP. Suspicious index developments are explained.

- Follow-up meeting after the publication, to communicate any deviations, errors and incidents. This output from this meeting are action points (if needed) and a protocol.

As for "suspicious", we make use of a special method for detecting index developments which are extreme as compared to an historical average and which would also (if erroneous) have a large impact on the end result. (The idea behind this method is similar to the one in SELEKT, the generic editing tool described for example in the 2016 JOS paper “SELEKT – A Generic Tool for Selective Editing” by A Norberg.).

A measure of “expected impact” is computed as the product of two other measures; “potential impact” and “suspicion”. Potential impact is in turn computed as the weight times the absolute value of (observed monthly rate of change – expected monthly rate of change), where the expected rate of change is a function of historical values for the same elementary product group and months. The suspicion measure, in turn, takes on values between 0 and 1 and is computed based on a comparison between the current rate of change and the lower and upper quartiles of the historical rates; if the rate of change is extreme, in the sense of being far away from the upper or lower quartile (taking the normal variation of the product group into account), suspicion will be close to one.

If a suspicious index cannot be confirmed (for example if we are unable to get confirmation from the respondent), we are making a decision whether to include the suspicious change based on the effect on total CPI and how plausible the change is.


11.2. Quality management - assessment

11.2.1. Compliance monitoring - last report and main results

The last available compliance or follow-up report can be found in the dedicated HICP section ‘Compliance Monitoring’ of Eurostat’s website, under ‘Methodology’.

11.2.2. Quality assessment - national specifics

The quality of the HICP can be assessed to be very high. Its concepts and methodology have been developed according to international standards and using consumer price statistics experience from all EU Member States. HICPs are considered to be sufficiently accurate for all practical purposes they are put into. In particular it is the best measure of inflation for the euro area and European Union as whole as well as for the comparisons of inflation across countries for which it is compiled. The indices are disseminated around mid-month following a predetermined timetable.
Further work is ongoing to improve the quality and in particular comparability of the index. Key priorities are the treatment of owner-occupied housing (currently excluded) and greater harmonisation of methods for quality adjustment and sampling. Eurostat and the national statistical institutes are also working on additional indices, for example an HICP index at constant tax rates.

Statistics Sweden uses a system for managing process and product quality called ASPIRE (A  System for Product Improvement, Review, and Evaluation). The outcome of an ASPIRE evaluation are quality indicators. Several uncertainty sources are considered such as:

- Overall accuracy
- Sampling
- Frame coverage
- Measurement
- Non-response
- Data processing
- Model assumptions

Each uncertainty source is also assigned a risk rating depending upon its potential impact on the quality for the specific product. ASPIRE is an evaluation conducted by external experts.

More about ASPIRE can be found in the paper 'A System for Managing Process and Product Quality.


Since HICP to a very large extent is based on CPI data, any improvements or deteriorations in the outcome for CPI will affect also HICP.

Latest APSIRE result is from 2021, which was the 9th time the assessment was made. The overall score was the same as in the previous round (58.0). Any improvements following an evaluation according to ASPIRE will affect the HICP. The evaluation is made every second year.

Key recommendations for the coming two years

1. Replacement source for the Household Budget Survey. Work has continued on using turnover data
(such as scanner data) and the Structural Business Survey as alternative sources, on a small scale.
Weights for some areas of the CPI (e.g. clothing and furnishing) remain out of date.
Implementation of this work will not be until 2023, and this work should continue to be given
priority. There will be difficulties in finding appropriate sources in all cases, such as sales from
overseas, and problems in modelling household expenditure estimates from these sources.
Resources will need to be deployed in these areas, and the results of the new approaches evaluated,
including an analysis of the potential impact on the weights, to ensure the resulting revisions to the
CPI weighting methodology are justified.

2. Data processing of scanner data. The current system is described as ’good enough’ but it is not
robust nor efficient. Given its evolution from development systems and the complexity of the
various operations performed it should be considered an organisational risk. The NDK project
should provide a strategic solution, but will need to be prioritised, resourced, timetabled and
developed in close collaboration with the CPI team. The team should also investigate the use of
machine learning techniques for investigative work with scanner data.

3. Monitoring the impact of COVID. COVID has led to changing patterns of consumers’ expenditure and
in changes to prices. It will be necessary to continue to monitor its effects, as the Swedish economy
continues to recover and discuss them with users.

4. Managing risk. The introduction of replacement weights for the HBS, the replacement of survey
data by scanner and web scraped data, and the introduction of a new data processing system will all
come on top of all the regular changes that need to be made to maintain the CPI as fit for purpose.
This will need careful risk management to ensure the impact on the CPI is known and well planned.

Other areas for consideration

1. Learning more from best practice outside the EU. Sweden is actively involved in EU Task Forces and
Working Groups and in receipt of grant funding for projects. Given that all this is within the
constraints of the Harmonised Index of Consumer Prices, the team could consider developing
further the already good links they have outside Europe with the Ottawa City Group on Price
Statistics to ensure they benefit from a wider range of peer discussion.

2. Researching the impact of growing sales from overseas. This is a particular weakness of the reliance
on domestic sales weights for the CPI, given the growth in this area, and the potential for price
development for overseas purchases to differ from domestic price development.

 Apart from ASPIRE, parts of the statistical production process are also reviewed internally. These reviews are regular, but only a sample of survey/process steps are evaluated every year.

12. Relevance Top
12.1. Relevance - User Needs

In addition to being a general measure of inflation, the HICP is also used in the areas of:

  • wages, social benefit and contract indexation;
  • economic forecasting and analysis;
  • measuring specific price trends;
  • accounting purposes and deflating other series;
  • inflation targeting by central banks;
  • cross-country economic comparisons.


The euro area (evolving composition) index is used by the European Central Bank (ECB) as the main indicator for monetary policy management. The ECB and the European Commission's Directorate-General for Economic and Financial Affairs (DG ECFIN) use the HICP for assessing price stability and price convergence required for entry into European Monetary Union.

Other users include: National Central Banks, financial institutions, economic analysts, the media and the public at large.

12.1.1. User Needs - national specifics

The main users of the CPI and measures related to the CPI:

• The Ministry of Health and Social Affairs: for establishing the price base amount which is linked to certain pensions, other social benefits and student loans.

• The Riksbank: the CPIF is used as a target variable for the monetary policy decisions. The CPIF is calculated the same methodology and data as CPI, but with fixed mortgage interest rate in ECOICOP 04.

• The Ministry of Finance: as a basis for decisions on economic policy and stabilisation policy.

• Swedish National Institute of Economic Research: for economic analyses.

• The Swedish Tax Agency: for the calculation of conversion ratios for the taxation of capital gains on property and for calculating break points in income tax rates.

• Statistics Sweden: for deflating in the national accounts as well as the service industry statistics (concerning turnover and inventory).

• Other government administration: including the Swedish Board of Agriculture, which monitors and analyses consumption figures and price trends on the consumer level.

• Organisations, enterprises and individuals: for indexation of agreements and conversions of value amounts to fixed monetary values.

• Asset management enterprises and institutions: as a basis for assessing future interest rates and real returns.


National users of HICP are:

• Economic analysts in the private banks are at least to some extent using HICP data. Otherwise no principal national users of HICP are known.

12.2. Relevance - User Satisfaction

Statistics Sweden has a CPI board where important main users are members. These include:

- Swedish central bank (Riksbank)

- Swedish Social Insurance Agency

- Swedish Pensions Agency

- National Institute of Economic Research

- Ministry of Finance

- Consumer Agency

The board has an advisory role for methodologies and principles used for CPI. All decisions on implementing a methodology or principle are taken by Statistics Sweden, not the CPI board.

No general CPI user survey is carried out.


12.3. Completeness

All ECOICOP indices at 5-digit level are produced.

13. Accuracy Top
13.1. Accuracy - overall

The accuracy of HICP is generally considered to be high. The accuracy of source data is monitored by assessing the methodological soundness of price and weight sources and the adherence to the methodological recommendations. There is a variety of data sources both for weights (National Account data, Household Budget Survey data, etc.) and prices (visits to local retailers and service providers and central collection via scanner data, mail, telephone, e-mail and the internet are used). The type of survey and the price collection methods ensure sufficient coverage and timeliness. The outlets, from which prices are collected, are chosen to represent the existing trade and services network and they are based usually on three main criteria:

  • Popularity with consumers,

  • Significant turnover from consumer sales and

  • Availability of goods and services included in the HICP basket.

All the private households in the economic territory of the country are covered, whether resident or not and irrespective of their income.
Furthermore, Eurostat and the Member States are actively following up an Action Plan concerning quality adjustment and sampling issues. Concrete best practices have been agreed for a range of specific goods and services (in particular cars, consumer durables, books and CDs, clothing and computers).


Main sources of random and systematic error in the statistical outputs

The most important sources of inaccuracy in the HICP are the composition of the basket of goods (i.e. the weights), the sampling of retail outlets, products and product offers, and the incorporation of products that are new in the market. It is believed that new products in the market are the greatest source of inaccuracy.

The source of inaccuracy caused by sampling is relatively large, but it can be estimated and can therefore guide the users of the statistics. New products can be a source of bias, a systematic under- or overestimation, most likely an overestimation; consumers choose products that are new in a market because they consider them to be better in relation to the price than existing products. The evaluation of quality differences in case of necessary changes is also very difficult. According to the principles, the valuation should be carried out based on the consumers’ valuations, which is very difficult in practice.


Bias in the Swedish HICP

# Bias source Example Magnitude Sign Counter-measure
1 Sample representativity bias HICP weights and samples updated once a year while actual consumption pattern changes also within the year Small Depending on the sample and position in the economic cycle None.
2 New products and quality changes Smarter cell phones Important Probably mostly upwards? Annual resampling
3 New sales channels Low cost food stores Important Upwards To be monitored
4 Cross-border transactions Internet purchases from abroad Increasing importance Upwards? To be included in the future
5 Other coverage issues Narcotics; Prostitution; Games of chance; Implicit service charge in life-insurance Partly important Ambivalent Defined away from the HICP coverage. 
6 Certain discounts Cars; scanner data Potential risk for bias Ambivalent To be monitored and consider if improvements are possible
7 Selection bias through chaining Selection of products in the resampling Possibly important Downwards Starting resampling procedure before December
8 Quality adjustment methods Supported judgemental adjustment Potential risk for bias Uncertain To be monitored and consider if better methods can be implemented
9 Shortcomings of data sources Old household budget survey Possibly important Ambivalent To be monitored. Initiatives of finding alternatives to the household budget survey.


1. Even though weights and most samples are updated annually in the HICP/CPI, to the extent that consumers' expenditure pattern change also within the survey year, sample representativity bias may occur. The magnitude of this bias is thought to be small.

2. An example of the bias risk connected to new products, is the treatment of high technology products, such as smartphones. The decision of when in the lifecycle to start the measurement of such products affects HICP/CPI. The typical pattern is a higher initial price and a subsequent decline. On one hand, the initial price level can be seen as an expression for quality and therefore should be included in HICP/CPI. On the other, it can be argued that such a high price may be less representative of what the average consumer is paying, since the product initially only attracts a limited number of consumers. Carlsson et al (2020) carried out an assessment for specifically computers and smartphones. The combined estimated bias, i.e. for both smartphones and computers, amounted to around -0.02 on the HICP all items annual rate of change (m/m-12).

Another example of such bias relates to the fact that many products are brought into the HICP using the overlap method in connection with annual resampling. Using this, any price level difference between two baskets are attributed to quality. In many cases this may be reasonable, but it rests on assumptions that are sometimes not valid (e.g. the existence of a free and functioning market as well as rational consumers). Carlsson et al (2020) estimated the following bias effects:

- New cars: 0.00 to -0.01 percentage points on HICP all-items annual rate of change (m/m-12)

- Prescribed pharmaceuticals 0.02 percentage points on HICP all-items annual rate of change (m/m-12)

- Rents in rental apartments -0.01 percentage points on HICP all-items annual rate of change (m/m-12)

3. A challenge when measuring price development is the phenomenon of new sales channels. New channels may provide consumers better value through a trade-off between price and service-level. In connection to this there are risks of not adequately capturing the pure price change. As an example of this there is the entrance of low cost food stores in the 2000s on the Swedish market. The standard methodology is to include new outlets using the annual linking procedure, which implicitly attributes the entire average price level difference as a quality difference. The potential bias in the particular example of low cost food stores is estimated to 0.06 percentage units on the HICP all items annual rate of change (m/m-12). Source: Carlsson et al (2020)

4. The Swedish HICP in general does not include cross-border transaction in line with the recommendation on cross-border purchases , and we thus mainly price products that are sold by firms within Sweden. There are however a few exceptions such as online clothing stores located in other European countries.

5. The coverage of the Swedish HICP is assessed to be good and fulfils the standards set out in the regulations. However there are a few consumption areas that are in principle included in the household monetary consumption expenditure, but are not covered in HICP for practical reasons. These include games of chance, narcotics, prostitution and the implicit service charge in life-insurance premiums.

6. For new cars we measure list prices, not taking into account individual discounts. For second-hand cars, the data provider estimates approximate transaction prices using a model, taking into account for example how long the car has been available for purchase. A second-hand car that has been on the market for a long time is assumed to be sold for a lower price than initially asked (due to bargaining).

In scanner data, we do not always have full control of which discounts are included (some may be conditioned). 

7. In the annual resampling procedure it is assumed that any differences in price level between previous and current year can be attributed to quality differences. This may be a valid assumption for example in a market with perfect competition, while in other cases not so.

In the Swedish CPI/HICP we saw that our price collectors tended to avoid products with discounted prices in the reference period December. Discount products have a higher probability of being sold out the next month, thus implying a higher expected work burden (negative incentive) the coming month when a replacement has to be made.

As a counter measure for some product categories such as electronics and household textiles, price collectors now select new products for the coming year already in September the year before (instead of in December).
For clothing we apply a correction factor to mitigate this problem.

8. The various quality adjustment methods used have their advantages and drawbacks. For example, in supported judgemental quality adjustment, there is always a certain degree of uncertainty in the assessment.
New techniques such as web scraping of product characteristics may in the future facilitate a transmission to better quality adjustments methods such as hedonic quality adjustment.

9. The household budget survey (HBS) has traditionally served as a good source for distributing HICP weights on lower aggregate levels. There has however been an increasing non-response rate for the HBS and due to this the most recently planned survey (2016) was cancelled. Weights for parts of the lower-level aggregates have there not been updated since 2012, such as distribution between different types of clothing, furniture and household utensils. This mainly concerns lower-level weights within COICOP 03, 05, parts of 07, 09, 11.1 and 12.


Carlsson, E. , Hillström, E. Norberg, A., Olsson, K., Ottosson, M., Ståhl, O., Tongur, C. (2020) Grants project report for a study on the impact of overlap methods on the CPI and HICP, Statistics Sweden. The report was written under the Eurostat Grant Agreement number 210549154 “New methodologies for HICP – Activity 1A”.

13.2. Sampling error

The HICPs are statistical estimates that are subject to sampling errors as they are based on a sample of consumer prices and weights relating to household expenditures from surveys, hence these are not the complete universe of all prices and expenditures but rather estimates. Also, the presence of purposive sampling in certain areas may influence variability which is remedied by keeping samples large if possible, e.g. within clothing (COICOP03). However, rich use of transaction data, API and web scraping has been a way of reducing variability and increasing sample sizes.

Sampling error for Swedish HICP total

Statistics Length of 95% Confidence Interval Comments
Monthly Change ±0.14 Somewhat shorter for April, May, June and November
Annual Change (Inflation Rate) ±0.23 Somewhat shorter for December
Monthly Change in Inflation Rate (low) ±0.15 For April, May, June and November
Monthly Change in Inflation Rate (high) ±0.20 Other months

Sampling error for Swedish CPI total is published annually in the Quality Declaration, accessible for 2022 through https://scb.se/contentassets/a1e257bb3a574420b9d3f2ff59851c0a/pr0101_kd_2022-eng.pdf

13.3. Non-sampling error

Not estimated.

14. Timeliness and punctuality Top
14.1. Timeliness

The full set of HICPs is published each month according to a pre-announced schedule, usually between 15 and 18 days after the end of the reference month. Each year, the January news release is published at the end of February to allow for the annual update of the weights of individual product groups and the relative country weights of Members States in the country-group aggregates.

The euro area flash estimate is published on the last working day of the reference month or shortly after that.

14.2. Punctuality

Since the March 1997, launch of the HICP release, the HICP for the country groups aggregates has always been published on the pre-announced release dates.

15. Coherence and comparability Top
15.1. Comparability - geographical

HICPs across Member States aim to be comparable. Any differences at all levels of detail should only reflect differences in price changes or expenditure patterns.

To this end, concepts and methods have been harmonised by means of legislation. HICPs that deviate from these concepts and methods are deemed comparable if they result in an index that is estimated to differ systematically by less than or equal to 0.1 percentage points on average over one year against the previous year (Article 4 of Council and European Parliament Regulation (EU) 2016/792).

15.2. Comparability - over time

HICP data are fully comparable over time. There have been several improvements in methodology since HICP was introduced with the aim of improving reliability and comparability of the HICP. These changes may have introduced breaks in time series. However back calculations under the newer standards were performed when appropriate basic data was available.

15.3. Coherence - cross domain

Differences between the HICP and national CPI
The following expenditure is included in the HICP but excluded from the national CPI:

  • ECOICOP 06.3: Hospital services.

  • ECOICOP 12.6.2, part of: Other financial services n.e.c.: Services that are charged in proportion to transaction value.

The following consumption expenditure is included in the national CPI but excluded from the HICP:

  • ECOICOP 04, part of: User capital cost of owner-occupied housing (including real-estate tax).

  • ECOICOP 04, part of: Monthly charges in housing co-operatives (bostadsrätter).

  • ECOICOP 09.4.1, part of: Games of chance (service charge).

  • ECOICOP 12.6.2 part of; Brokerage fees (real estate).

The national CPI is designed for several kinds of use, for example compensation. For many years now, the national CPI has been defined as a conditional Cost-of-Living Index (coli). This has implications for the upper level aggregation, which deviates from HICP rules. As from 2005, the national CPI uses a superlative index formula (Walsh) for annual link chaining between full years. As far as possible, the HICP and the national CPI share the same source data, data preparation and low-level aggregation. The national CPI is published with a breakdown according to ECOICOP. There is a CPI Board of Experts that has an advisory role for CPI principles and methodology.

15.4. Coherence - internal

The HICPs are internally coherent. Higher level aggregations are derived from detailed indices according to well-defined procedures.

16. Cost and Burden Top

Not available

17. Data revision Top
17.1. Data revision - policy

The HICP series, including back data, is revisable under the terms set in Articles 17-20 of Commission Implementing Regulation (EU) 2020/1148.

17.1.1. Data revision - policy - national specifics


After the official release of the CPI index figure for a specific month it cannot be revised. It will therefore apply in all contexts where references are made to the consumer price index, such as laws, ordinances or agreements. On a few occasions, mistakes in the creation or processing of data have resulted in “incorrect” index numbers. As of 8 May 2000, the Statistical Database refers in part to the unrevised adopted total index numbers as of 1980 and in part to revised shadow index numbers for the total CPI and revised index numbers for product groups that are consistent with the shadow index numbers. The shadow index is used in case we detect major errors in the calculation or get revised information from our data sources. One main use of the CPI is indexation (in e.g. legal contracts) and for such purposes the unrevised series is often preferred.


Published inflation rate figures can be revised, but it is rare. CPI inflation is not calculated based on the adopted series but based on the shadow index numbers.

Shadow index and revised inflation rates should be calculated if the effect of the error is larger than 0.1 percentage points on the monthly rate of change or annual inflation rate.


According to the EU regulations, all HICP numbers can both be revised and disseminated in preliminarily form under certain circumstances. However, Eurostat and Statistics Sweden have different policies on when inflation figures should be revised. This means that Eurostat may revise Swedish HICP numbers while Statistics Sweden does not revise them. When such situations have occurred, however, Statistics Sweden have for practical reasons also chosen to revise HICP, to ensure that the statistics are consistent.

17.2. Data revision - practice

A technical revision of the HICP series was carried out in January 2006, in preparation for the general change of the HICP index reference year to 2005=100.


Revisions of HICP the past 10 years        
Date that the revision was carried out Time series period revised Description Effect on aggregate level Total level Main reason
February 2013 December 2012 The Swedish calculation system was using rounded index figures, while Eurostat required "full precision".
Other aggregates: between +/-0.00 to 0.1 on the monthly rate
HICP Total: -0.03 p.e. units on monthly rate Sweden has revised figures for December 2012 due to a re-computation using a different rounding precision.

18. Statistical processing Top
18.1. Source data

18.1.1. Weights

i) The starting point for our HICP item weights calculation is t-2 household consumption data from national accounts. Furthermore, for the breakdown at more detailed levels we use other sources (see table "Weight sources used to derive HICP weights during normal circumstances" below).

ii) The expenditures in (i) are then multiplied by an adjustment factor, to make them representative of year t-1. The adjustment factor (F) for aggregate K is calculated based on preliminary quarterly national accounts data as

FK=EK, Q1-Q3 t-1 / EK, Q1-Q3 t-2

where EK, Q1-Q3 t-1 being the national accounts expenditure for aggregate K and quarter 1-3 in t-1.

The assumption is here that the development from Q4 2020 to Q4 2021 is equal to the development for Q1-Q3. The quarterly national accounts data is available at 4-digit COICOP level. We thus calculated adjustment factors at this level and applied them to our elementary aggregates.

We applied such adjustment factors to all product areas in our HICP basket, not only those most heavily hit by the pandemic.

There are often more than one elementary aggregate within a 4-digit COICOP and in almost all such cases we applied the adjustment factor proportionally to the included elementary aggregates.

Exceptions were flights, accommodation and package holidays. For flights and accommodation we used additional data sources for the more detailed breakdown within each 4-digit COICOP.

For flights (COICOP 07.3.3), we used airport statistics for full years 2021 and 2020 to refine the estimates for domestic and international flights respectively.

For accommodation (COICOP 11.2.0), we used statistics from the Swedish Agency for Economic and Regional Growth, that enabled us to calculate specific adjustment factors between 2020 and 2021 for the elementary aggregates within 11.2.0.

For package holidays we used transaction data to adjust the implicit assumption for the fourth quarter of 2021.

Except for these areas, we used no other sources than preliminary national accounts data for the adjustment factor.

We carried out consistency checks and compared the sums of the adjusted elementary aggregate expenditures per COICOP with the corresponding higher aggregates in national accounts. We found no significant inconsistencies.

The table below describes the main weight sources used to derive HICP weights during normal circumstances (also before 2020).         
Table. Weight sources used to derive HICP weights during normal circumstances.        
Source Producer Used at this level Product categories/divisions Reference period
National accounts Statistics Sweden ECOICOP and some  elementary aggregates ECOICOP 1-12 t-2
Household budget survey Statistics Sweden Detailed breakdown within ECOICOP-groups Part of the detailed breakdown within ECOICOPs 03, 05, parts of 09 and 12 2012*
Business turnover statistics Statistics Sweden Detailed breakdown within ECOICOP-groups ECOICOP 01 t-2
Energy statistics Statistics Sweden Detailed breakdown within ECOICOP-groups ECOICOP 04 t-2
Dwelling statistics Statistics Sweden Detailed breakdown within ECOICOP-groups ECOICOP 04 t-2
Market reports Retail industry organizations, market research institutes Detailed breakdown within ECOICOP-groups Consumer electronics in ECOICOPs 08 and 09 t-2
Statistics from Government agencies E.g. Swedish Post and Telecom Authority, Swedish Tax agency, Swedish Transport Agency Detailed breakdown within ECOICOP-groups ECOICOP 05, 07, 08 t-2
*Statistics Sweden currently develops an alternative design for estimating the part of household consumption that was previously derived from the household budget survey. The new approach will primarily rely on new data sources such as scanner and payment card data and is expected to be implemented around 2023-2024.        

Price updating from full year t-1 to December t-1 is carried out at elementary aggregate level, with the corresponding price index series for each particular aggregate.
Exceptions are insurance services where we price update using HICP All items (in line with pp 255-256 in HICP-manual). Compilation at elementary aggregate level

The availability and use of detailed weights differ between product categories. For these aggregates, weights on the level of the product or outlet are used (i.e. within the elementary aggregate): 

02.1 Alcoholic beverages,

03 Clothing and footwear

04.5 Electricity and fuel

06.1.1 Pharmaceutical products

06.2.2 Dental services

07.2.2 Fuels and lubricants for personal transport equipment

07.3 Transport services

08 Communications

09.1 Audio-visual, photographic and information processing equipment

09.4 Recreational and cultural services

09.5 Newspapers, books and stationery

09.6 Package holidays

10 Education

12.5 Insurances

12.6 Other financial services

12.7 Other services.

For other product categories than those mentioned above, we tend to use PPS sample, and therefore no explicit weights are used within the elementary aggregate (the number of observation per product/outlet tend to reflect the size component in the PPS, e.g. outlet or product revenue).

See 18.1.1 Weights for a table with sources of weights at the elementary aggregates level.

No weighting for regions used.

Source for outlet weights: Statistics Sweden's business register (turnover) Compilation of sub-index weights.

The expenditure shares at sub-index level should be derived from data sources for year t-2 and these shares should be updated to make them representative for t-1. How have you applied these principles in the most recent weight update?

See the field 18.1.1 Weights.

Sources for adjustment from t-2 to t-1

For the COVID related adjustment factor from t-2 to t-1 (described in question 1) at the 4-digit COICOP level, we use national accounts quarterly data Q1-Q3 for 2020 and 2021. For the detail breakdown within flights (COICOP 07.3.3), we used airport statistics for full years 2021 and 2020 to refine the estimate for domestic and international flights respectively. For accommodation (COICOP 11.2.0), we used preliminary domestic accommodation statistics from the Swedish Agency for Economic and Regional Growth. We used data up until October 2021. For package holidays we used transaction data to adjust the fourth quarter estimate.

Which main data sources did you use to derive the expenditure shares at sub-index level? Specify the reference period of the data sources.

See section 18.1.1 Weights for a table with the main sources and each respective reference period.

Describe how the 4th quarter of t-1 is integrated in the calculations of the sub-index weights.

Since we apply the adjustment factor previously described, the 4th quarter is assumed to have the same development as quarter 1-3 between 2020 and 2021.

Provide details for specific product categories that have the biggest changes when compared with the previous weight derivation exercise. Specify the reference period of the data sources.

The broad product categories that changed the most in terms of their relative importance in the basket (2021-->2022) are the following:

  • 04.4 Water supply and miscellaneous services relating to the dwelling: +24%
  • 04.5 Electricity, gas and other fuels: + 17 %
  • 06.1.1 Pharmaceutical products: -16 %
  • 07.1 Purchase of vehicles: + 24 %
  • 07.2.2 Fuels and lubricants for personal transport equipment: +22%
  • 07.3 Transport services: -16 %
  • 08 Communication: -15%
  • 09.4.1 Recreation and sporting services: + 22%
  • 10 Education: -63 %
  • 12.5 Insurance: +54%

The change is calculated as [Expenditure share 2022] / [Expenditure share 2021] -1. Reference period higher levels

The reference period for the national accounts data used to calculate sub-indices weights is t-2.

Reference years to get a more detailed distribution of expenditure shares:

- Statistics Sweden's business turnover statistics (t-2)

- Statistics Sweden's housing expenditure survey (t-2 or t-3, since the survey is carried out every two years)

- Industry organization reports (mostly t-2)

- Household budget survey (2012)

- Reports from other Swedish agencies (t-2) Weights – plausibility checking

Plausibility checking for annual updating of weights at higher level

Two parallel calculations of weights are carried out and we make sure they give the same results in the end. Furthermore, we check the following:
- Suspicious change in weight share from previous survey year to current survey year in percentage units
- Consistency in weight aggregation, e.g. that weights at each level aggregate to the superordinate aggregate and that weight sum is 1000.

Plausibility checking for periodic review of weights at lower levels
We carry out targeted checks for product areas that are considered to be suspicious and try to find alternative sources to compare with. New sources may be assessed to give better estimates than the existing weight source and can replace the existing source. Price updating

Do you apply price-updating between t-2 and t-1 to make the expenditure shares representative for t-1?

At the 4-digit COICOP level, we use national accounts data in current prices when calculating the adjustment factor between full year t-2 and full year t-1. At that stage, no further price updating is carried out.

For the detailed breakdown within flights and accommodation, we use volume data as a basis for the adjustment factors and therefore we also carried out price updating from t-2 to full year t-1. We did this using the indices for each specific elementary aggregate.

Describe your price-updating procedures to December t-1.

Price updating from full year t-1 to December t-1 is carried out at elementary aggregate level, with the corresponding price index series for each particular aggregate.
Exceptions are insurance services where we price update using HICP All items (in line with pp 255-256 in HICP-manual).

At which level is the price-updating applied?

Elementary aggregate level (the level below five digit ECOICOP). Compilation of total household final monetary consumption expenditure

Which data sources do you use and which adjustments do you make to derive the total country HFMCE for t-1. Describe how the 4th quarter of t-1 is included in the calculations. How did you ensure consistency between the total and the sum of the sub-indexes?

We use the same method as for the ITEM weights, namely by multiplying the expenditure of HFMCE with the adjustment factor FK=EK, Q1-Q3 t-1 / EK, Q1-Q3 t-2 . Here K is the total domestic household consumption on goods and services that are within the scope of HICP. The consistency compared to the adjusted aggregate expenditures used for item weights, seems satisfactory.

18.1.2. Prices

Please see the Excel file. Data Source - overview  

The following sources are used for price data in the Swedish HICP:

  • Survey data collected from physical stores, online stores and by using questionnaires. 800 outlets are sampled for the local price collection, from a Business Register maintained by Statistics Sweden.  Approximately another 800 outlets in the form of websites, shops, banks etc are used for the central price collection. 700 landlords are included in the sample for rents.
  • Scanner data for daily necessities, alcoholic beverages, pharmaceuticals, dentists, package holidays, electronics ,white goods, train tickets and motor fuel.
  • Tariffs are collected from municipalities and governmental agencies (e.g. for social protection, water supply, chimney sweeping, refuse collection and TV-licence).
  • New cars: list price data from a market analysis company
  • Second-hand cars: estimated approximate transaction prices per model from a market analysis company
  • Additional sources in specific cases, e.g. Statistics Sweden's income statistics used in order to estimate the index for social protection Scanner data - general information

Statistics Sweden processes scanner data for these retailer types:

- Daily necessities: supermarkets, smaller markets, hypermarkets (~80% market coverage)

- Alcoholic beverages: shops owned by the government monopoly (100 % market coverage of alcoholic beverages sold legally in Sweden and to be classified within 02.1 Alcoholic beverages (i.e. not including alcohol in restaurants etc))

- Pharmaceutical products sold in pharmacies (100% market coverage). For non-prescribed pharmaceutical sold in supermarkets, please see the figure for daily necessities.

- Dental care: dentists (100 % coverage)

- Train tickets (~70% coverage)

- Package holidays (~75-80% coverage)

- Home electronics (75%), Cell phones (80%)

-Fuel products: fuel station (~90% coverage) Bulk web scraping - general information

Statistics Sweden use an application programming interface (API) to automatically collect the prices for domestic and foreign flights from a Swedish price comparison site. This procedure has allowed us to change the price collection 100% from manual to automated by increasing the sample size for both domestic and foreign flights. These flight prices are also used as a proxy for airline ticket purchases from other sales channels (e.g. travel agencies in physical stores).

From 2021 Statistics Sweden use web scraping to collect prices and other relevant information in order to calculate price development, especially in ECOICOP 05 where approximately 50 percent of the sub classes are web scraped. There are also some web scraping in ECOICOP 03, 04, 06, 07, 09 and 12, but to a much lesser extent. The types of retailers for which Statistics Sweden web-scrape prices for are some furniture stores, home decore stores, hardware stores, home electronic stores, car accessories stores, pet stores and eye glasses stores. The specific percentage of the weight covered by web-scraped data within each sub-class will be specified on the next sheet. Statistics Sweden is web scraping websites which apply identical pricing in their physical shops, and therefore web scraping is used as a proxy for physical shops. We web scrape web-sites that target the domestic market.


Subclass The percentage of the weight covered by web-scraped data 27% 63% 76% 64% 76% 74% 69% 67% 40% 23% 22% 27% 23% 20% 44% 42% 58% 54% 80% 32% 16% 9% 28% 47% 9% 11% 23% 26% 10% 6% 62% 13% 16%

18.1.3. Sampling Sampling design: regions - general information

How the sample is geographically stratified.

"Local" price collection (i.e. on-site sampling of specific outlets in Sweden): No geographical stratification is used.

"Central" price collection: In the survey of monthly condominium fees, the three larger metropolitan areas “Stockholm metropolitan area”, “Gothenburg metropolitan area”, "Malmö metropolitan area" are clustered in stratification, in contrast to the two other clusters “Other large municipalities” and “Other small municipalities” which have no geographical adjacency.

Regarding the "local" price collection: The frame covers approximately 92.5% of the value of total commerce in Sweden, according to the central Business Register. Areas are subject to cut-off only if their aggregate total commerce value according to the Business Register is low.

When it comes to sub-surveys using “central” price collection, certain geographical areas can also be excluded as part of the (often purposive) sampling design, if they are deemed not to have a significant effect on the overall price development.

Indicate which regions are included.

Regarding the "local" price collection: The frame covers approximately 92.5% of the value of total commerce in Sweden, according to the central Business Register. Areas are subject to cut-off only if their aggregate total commerce value according to the Business Register is low.

When it comes to sub-surveys using “central” price collection, certain geographical areas can also be excluded as part of the (often purposive) sampling design, if they are deemed not to have a significant effect on the overall price development. Sampling design: outlets - general information

"Local price collection": The outlet sampling is done with a sequential PPS design (i.e. with selection probability proportional to size). The size measure used for the PPS sampling is defined as a combination of the number of employees (plus one) and total turnover, broken down to entity level. The sample is drawn from the Business Register in connection to the central system for coordinating frames and samples (SAMU). The objects that are included in the HICP's local price collection are divided into some 40 strata by industry according to Swedish Standard Industrial Classification (SNI 2007). Information from previous Household Budget Survey, transaction data and market analysis are accounted for to increase frame relevance. Samples are updated annualy with respect to relevance and to account for market changes. Rotation is employed when necessary, with a target of annual 20% sample updates. Retail chains surveyed through alternative data sources are obtained are not subject to rotation. Outlet weights are computed annually due to this process.

Outlet samples for central price collection sub-surveys are updated either annually or with three to five year intervals (depending on the weight of the product group and dynamics of the product area). The most common sampling method is cut-off sampling based on a measure of total turnover in a previous year according to the business register. Sampling design: products - newly significant goods and services

How we identify and introduce new goods and services in the index

Newly significant goods and services are identified by staff at the Section for consumer prices (centrally) in the annual review of product specifications and product groups. The process is supported by information from price collectors, from the industries concerned, and from Eurostat. Household consumption and market shares are considered relevant for introducing new products.

New products within an already existing product group, such as specific models of smartphones, computers, printers and tablets are introduced as a new representative item in the course of the year if it replaces an old outgoing model. Broader product categories that constitutes entire product groups/elementary aggregates are brought into CPI once per year.

Goods or services introduced the last year

Broad product category introduced last year:

 Personal grooming treatments (survey year 2021)

18.2. Frequency of data collection

Price data is collected every month.

18.3. Data collection

18.3.1. Price collection surveys

Survey data is collected by the following means:

- local price collectors doing visits in physical outlets using tablets

- an electronic web questionnaire (actual rentals)

- web scraping

- staff at the central office sending questionnaires via email

- internet price collection for flight tickets in a more or less fully automated way

- scanner data delivered from companies and governmental agencies

18.3.2. Timing of price collection

  • For the central price collection, prices are normally collected in the calendar week that contains the 15th day of the month.

  • For the prices collected by price collectors directly from shops the prices are collected during three weeks. The week before the week that contains the 15th day of the month, the week that contains the 15th day of the month and the week after the week that contains the 15th day of the month. 

  • Electricity prices are collected once a month, but the spot price collected from a web site that has compiled an average for the entire month. Hence we catch into any volatility in the variable portion of electricity prices.

  • Price collection for actual rents is carried out every month until the new rent for the year is negotiated between the landlord and the Swedish Union of Tenants. Rents are paid per month and refer to the whole month, therefore there is a need for temporal sampling within the month. In December, the questionnaire is sent out to the full sample again in order to be able to take into account changes that occurred since last collection (usually only a small change with minor effect).

  • Prices for products with dynamic pricing such as transport services are collected more frequently according to booking schedules (e.g. collection several times per month).
18.4. Data validation

Data validation is done by National Statistical Institutes; additional quality checks are carried out also by Eurostat.


18.4.1. Data validation - price data

A first type of validation check is carried out in connection with the price collection itself. For locally collected products, the price collector receives a warning in the data entry software if the value is illogical or missing. For centrally collected prices that are manually registered, central staff carry out similar checks. The carefulness of such checks depends on the survey’s importance (weight and variance).

In the next step, another validation at the micro level is carried out by the central staff at the Section for consumer prices. This step starts when prices for a certain month and product group have been entered into the production system and are shown in the interface. Automatic checks are carried out in the system:

  • A check if the price is outside of a pre-defined price level interval

  • A check for large price relatives

  • A check if the price signal (signals: regular price, discounted price, replacement, sold out) is logically consistent with the entered price.

  • A check for missing prices

  • A check for missing quality adjustment value (if product replacement occurred but no adjustment is made)

  • A check for suspicious quality adjustments (e.g. if the price of the new good is lower but the reported quality is reported as considerably higher)

A warning is shown if the system find suspicious values and the central staff can correct the price, write a comment or confirm if the price is ok.

The production system stores the history of data editing actions.

A generic validation tool, SELEKT, for selective validation has been developed at Statistics Sweden. The system aims to reduce the number of warnings but at the same time discover important errors. For more information about this system, see the article by Anders Norberg (2016), "SELEKT - A Generic Tool for Selective Editing", Journal of Official Statistics, Vol. 32, No. 1, 2016, pp. 209-229.

18.5. Data compilation

18.5.1. Index formulae

In general:

  • The Swedish HICP (and -CT)  uses a Laspeyres-type index above the elementary aggregate level.
  • At the elementary aggregate level we use Jevon's index (ratio of geometric mean prices or geometric mean of price relatives) in almost all product areas, since it it has proven to have appealing features, it is international consensus to use this index. In a few cases where substitution is considered to be negligible or absent and where price levels are similar, we use Dutot-type index (ratio of arithmetic mean prices). Both Jevons and Dutot indices are also allowed according to HICP implementing act 2020/1148. The exceptional cases are municipality services, where the consumer can substitute only by moving to another municipality, which is unlikely since the service charge is only a small share out of all expenditures, and a component in the electricity charges, which is also connected to where the consumer resides.

The number of decimals that we apply in HICP/HICP-CT for

  • price observations: 2
  • weights: 8
  • the aggregation/chaining: 8
  • the transmission of index figures: 7
  • the transmission of rates of change: N/A (we do not transmit rates of change to Eurostat)
  • the publication  of index figures: (by Statistics Sweden): 2
  • the publication of rates of change (by Statistics Sweden): 1

18.5.2. Aggregation method

Aggregation steps from bottom to up:

1) Elementary aggregate indices (December previous year=100) are aggregated using Jevons index in most cases with prices from reference period and comparison period (quality adjusted prices where necessary). Exceptions are two EAs where Dutot index is used (municipality services and a sub-aggregate for electricity).

2) Indices (December previous year=100) for each ECOICOP-level are then aggregated using the elementary aggregates directly. This means that the index for HICP total is based directly on elementary aggregates and not on the indices for intermediate ECOICOP groups. These indices (December previous year=100) for all ECOICOP are then used in the chaining.

18.5.3. Chaining and linking method

The procedure for chaining for each respective aggregation level (above elementary aggregate level) within the HICP is to multiply the index for the current month (December y-1=100) with the longer index series (2015=100) for each particular aggregate. No splicing is made (other than chaining the index).

Chaining method

18.5.4. Quality adjustment

The approach used for products in ECOICOP divisions 05, 09 and 12:

Supported judgmental quality adjustment. Performed by local price collectors, except for most consumer electronics, where it is performed by staff at the central office. The price collector/central staff indicates the judged value in SEK of the quality difference between the replaced and the replacing model. The price collector often asks the sales person for information, but the price collector is instructed to make an independent judgment from the consumer perspective. For electronic goods, staff in central office make a judgement supported by information from the internet and in a number of cases also supported by hedonic models (coffee machines, TVs, digital cameras, mobile phones, computers and computer accessories). All quality adjustments are validated and approved centrally.

Package size adjustment: For changes in quantity (e.g. from 200 g to 400 g), the price is adjusted in proportion to the product quantity change. Commonly used for e.g. food packages as well as pharmaceuticals.

Approach used for clothing (garments) and footwear (03.1.2; 03.2): Hedonic regression, adjusting for major product features, is used. Furthermore, an adjustment factor is used to correct for a downward bias occurring when local price collectors tend to choose a lower share of discounted prices in the new sample for reference period December, compared to the share in survey month December. The propensity to choose a lower share of discounted products in the reference period is because a discounted price is a signal to the price collector that the product is likely to only be sold for a short period, thus meaning the product would soon have to be replaced and imply future workload.

Approach used for rental housing (04.1): Staff of the consumer price section make supported judgmental quality adjustments. Detailed information about all quality changes are collected in a phone interview with the property owner, facilitating the adjustment. In addition, option cost lists are utilized that are available on many landlords webpages, e.g. it costs X SEK/month extra to have a dish machine. If this amount is found in the list of more than one landlord, the lowest value is used.


Approach used for cars (07.1.1):

New cars:

- For change in equipment option pricing is used (including a 50% reduction of the quality change value)

- For changes in horsepower and fuel consumption, supported judgemental adjustment is used.

- For a car of new model year, either an expert judgement is carried out (by our data provider) or option pricing.

- For a new model generation, either we do no QA (the car exits the sample) or we do a bridged overlap.

Option pricing is used, from year 2007 in the usual form of adjusting for added or deleted features by 50 percent of their market prices as separate options. Changes in engine power and changes in fuel economy are included as features to adjust for.

Used cars: A simple hedonic regression model, adjusting for mileage, is used in combination with a successive re-weighting of model year to adjust for age.



Approach used for computer/video games, music recordings, video recordings, cinemas and books (parts of ECOICOP 09.1.4, 09.4.2, 09.5.1): A bestseller list approach is used.

Direct comparison is used for certain product groups (e.g. curtains, sleeping sheet, bags and saucepan) where the product life cycle was assessed to be long enough and the product description could be narrowly defined. An analysis was carried out to determine which product groups are suitable for this.

The method "link to show no price change" is generally not applied in the Swedish CPI.


  Indicative frequency of replacements (%) for survey year 2021 of which treated with…  
ECOICOP ... direct comparison ...package size adjustment ...single variable adjustment …option pricing …supported judgmental adjustment …hedonic repricing ...monthly chaining and replenishment …bestseller list method Total
01 0 < 1 0 0,0 0 0 0 0 < 1
02 0 0 0 0,0 0 0 0 0 0
03 0 0 0 0,0 0 10-15 0 0 10-15
04 0 0 0 0,0 0 0 0 0 0
05 <1 0 0 0,0 1-3 0 0 0 2-4
06 0 0 0 0,0 < 1 0 0 0 < 1
07 0 0 0 < 1 < 2 0 0 0 3
08 0 0 0 0,0 0 0 0 0 0
09 < 1 0 0  < 1 1-3 <1 <1 5-7 8-12
10 0 0 12 0,0 0 0 0 0 12
11 5-10 0 0 0,0 0 0 0 0 5-10
12 <1 0 0 0,0 < 1 0 0 0 1-4

18.5.5. Seasonal items - general information

We treat the following items as seasonal items, with a method in line with Regulation (EC) 330/2009, repealed and replaced by Commission Implementing Regulation (EU) 1148/2020:

  • 03 Clothing and footwear (strict annual weights index with all-seasonal estimation)
  • Package holidays (strict annual weights index with counter-seasonal estimation)



18.6. Adjustment

Not applicable.

19. Comment Top

No information.

Related metadata Top

Annexes Top