363070
/en/varehandel-og-tjenesteyting/statistikker/doi/maaned
363070
statistikk
2019-09-30T08:00:00.000Z
Wholesale and retail trade and service activities;Income and consumption
en
doi, Index of retail sales, retail sales, volume index, value index, retail trade, commodity trade, turnover (for example groceries, clothing, building materials)Wholesale and retail trade , Consumption, Income and consumption, Wholesale and retail trade and service activities
true

Index of retail sales

Updated

Next update

Key figures

0.0 %

change in retail sales (seasonally-adjusted volume index) from July 2019 to August 2019

Index of retail sales. Per cent change
Volume index. Seasonally adjusted1Volume index. Calendar adjusted2
Monthly change3-months change12-months change
August 2019 / July 2019June 2019 - August 2019 / March 2019 - May 2019August 2019 / August 2018
1In addition to be adjusted for calendar effects this is also adjusted for the seasonal pattern during the year.
2Adjusted for different number of business days and different turnover intensity between weekdays and also adjusted for public holidays in Norway.
3For more details about the industries, see G, 47 here: https://www.ssb.no/en/klass/klassifikasjoner/6
47 Retail trade30.0-0.20.4
47.1 Ret. sale in non-specialised stores0.1-0.91.0
47.2 Ret. sale of food, beverages etc.0.00.82.7
47.3 Ret. sale of automotive fuel2.9-0.40.7
47.4 Ret. sale of ICT-equipment-0.5-0.9-6.5
47.5 Ret. sale of other household equip.0.1-0.8-0.3
47.6 Ret. sale of cultural/recreation goods-3.51.4-3.3
47.7 Ret. sale of other goods-0.20.2-0.2
47.9 Ret. trade not in stores/stalls etc.-1.34.94.3

See selected tables from this statistics

Table 1 
Retail sales. Index of volume. Seasonally adjusted 2015=100

Retail sales. Index of volume. Seasonally adjusted 2015=100
August 2019Monthly change in per cent
June 2019 / May 2019July 2019 / June 2019August 2019 / July 2019
1For more details about the industries, see G, 47 here: https://www.ssb.no/en/klass/klassifikasjoner/6
47 Retail trade1103.3-0.40.80.0
47.1 Ret. sale in non-specialised stores103.8-1.52.00.1
47.2 Ret. sale of food, beverages etc.102.5-0.10.50.0
47.3 Ret. sale of automotive fuel99.2-0.5-1.52.9
47.4 Ret. sale of ICT-equipment86.24.1-4.7-0.5
47.5 Ret. sale of other household equip.100.4-1.00.60.1
47.6 Ret. sale of cultural/recreation goods99.2-0.5-0.1-3.5
47.7 Ret. sale of other goods101.11.00.2-0.2
47.9 Ret. trade not in stores/stalls etc.145.23.61.5-1.3
 
47_ub Ret. sale, except motor vehicles and gas stations103.8-0.41.1-0.3

Table 2 
Retail sales. 3 months index of volume, seasonally adjusted. 2015=100

Retail sales. 3 months index of volume, seasonally adjusted. 2015=100
3 months average3 months change in per cent
June 2019 - August 2019December 2018 - February 2019 / September 2018 - November 2018March 2019 - May 2019 / December 2018 - February 2019June 2019 - August 2019 / March 2019 - May 2019
1For more details about the industries, see G, 47 here: https://www.ssb.no/en/klass/klassifikasjoner/6
47 Retail trade1103.0-0.81.3-0.2
47.1 Ret. sale in non-specialised stores103.10.30.9-0.9
47.2 Ret. sale of food, beverages etc.102.30.61.30.8
47.3 Ret. sale of automotive fuel97.80.9-0.8-0.4
47.4 Ret. sale of ICT-equipment87.9-4.71.1-0.9
47.5 Ret. sale of other household equip.100.1-1.92.2-0.8
47.6 Ret. sale of cultural/recreation goods101.6-1.82.51.4
47.7 Ret. sale of other goods101.2-0.10.60.2
47.9 Ret. trade not in stores/stalls etc.145.7-9.45.94.9
 
47_ub Ret. sale, except motor vehicles and gas stations103.6-0.91.5-0.2

Table 3 
Retail sales. Monthly index of volume, calendar adjusted. 2015=100

Retail sales. Monthly index of volume, calendar adjusted. 2015=100
August 201912-month change in per cent
June 2019 / June 2018July 2019 / July 2018August 2019 / August 2018
1For more details about the industries, see G, 47 here: https://www.ssb.no/en/klass/klassifikasjoner/6
47 Retail trade1107.00.40.90.4
47.1 Ret. sale in non-specialised stores104.6-1.30.01.0
47.2 Ret. sale of food, beverages etc.98.21.22.12.7
47.3 Ret. sale of automotive fuel105.2-2.3-3.90.7
47.4 Ret. sale of ICT-equipment89.8-4.2-4.5-6.5
47.5 Ret. sale of other household equip.112.01.33.7-0.3
47.6 Ret. sale of cultural/recreation goods106.93.32.8-3.3
47.7 Ret. sale of other goods101.21.90.5-0.2
47.9 Ret. trade not in stores/stalls etc.157.06.89.04.3
 
47_ub Ret. sale, except motor vehicles and gas stations107.20.61.40.4

Table 4 
Retail sales value index. Exclude value added tax. August 2019. 2015=100

Retail sales value index. Exclude value added tax. August 2019. 2015=100
 August 2019Weight August 2019Change in per cent
 August 2018-August 2019June 2018-August 2018 - June 2019-August 2019January 2018-August 2018 - January 2019-August 2019
Retail sale, except sales of motor vehicles115.8100.01.41.31.6
47.1 Retail sale in non-specialized stores115.639.03.11.42.0
47.11 Retail sale in non-specialised stores with food, beverages or tobacco predominating115.836.42.91.11.8
47.2 Retail sale of food, beverages and tobacco in specialized stores111.94.64.93.52.9
47.251 Retail sale of wines and spirits in specialised stores107.43.03.92.42.3
47.3 Retail sale of automotive fuel in specialised stores117.510.0-0.6-1.1-0.1
47.4 Retail sale of information and communication equipment in specialised stores95.30.9-5.9-5.1-5.1
Retail sale of other household equipment in specialised stores118.918.4-0.11.42.3
47.51 Retail sale of textiles in specialised stores115.40.9-4.80.83.3
47.521Retail sale of variety of hardware, paints and glass in specialised stores121.85.83.05.04.8
47.54 Retail sale of electrical household appliances in specialised stores128.83.8-1.2-0.50.3
47.59 Retail sale of furniture, lighting equipment and other household articles in specialised stores110.96.0-2.40.01.8
47.591 Retail sale of furniture in specialised stores117.34.2-1.02.24.5
47.6 Retail sale of cultural and recreation goods in specialised stores110.45.1-1.41.0-0.4
47.61 Retail sale of books in specialised stores134.21.1-1.30.6-0.1
47.641Retail sale of sporting equipment in specialised stores112.93.1-2.0-1.4-3.3
47.7Retail sale of other goods in specialised stores106.116.60.41.20.6
47.71Retail sale of clothing in specialised stores97.95.9-6.0-3.6-3.8
47.72 Retail sale of footwear and leather goods in specialised stores93.30.9-4.3-2.1-2.5
47.721 Retail sale of footwear in specialised stores85.60.7-10.2-6.9-5.0
47.73 Dispensing chemist in specialised stores116.44.33.31.92.4
47.75 Retail sale of cosmetic and toilet articles in specialised stores105.71.15.32.50.5
47.761 Retail sale of flowers, plants, seeds and fertilisers92.41.213.519.110.6
47.78 Other retail sale of new goods in specialised stores123.52.16.03.93.7
47.9 Retail trade not in stores, stalls and markets160.45.42.86.45.6
Retail sale via mail order houses or via Internet158.64.90.83.74.1

Table 5 
Retail sales. Yearly index of volume, not adjusted. 2015=100

Retail sales. Yearly index of volume, not adjusted. 2015=100
2018Yearly change in per cent
2016 / 20152017 / 20162018 / 2017
1For more details about the industries, see G, 47 here: https://www.ssb.no/en/klass/klassifikasjoner/6
47 Retail trade1100.9-1.51.70.7
47.1 Ret. sale in non-specialised stores102.1-0.11.60.6
47.2 Ret. sale of food, beverages etc.99.6-0.2-1.91.7
47.3 Ret. sale of automotive fuel99.20.60.5-1.9
47.4 Ret. sale of ICT-equipment93.5-6.71.8-1.6
47.5 Ret. sale of other household equip.98.7-3.32.5-0.4
47.6 Ret. sale of cultural/recreation goods94.9-6.60.61.0
47.7 Ret. sale of other goods99.8-1.50.11.2
47.9 Ret. trade not in stores/stalls etc.134.34.212.714.4
 
47_ub Ret. sale, except motor vehicles and gas stations101.2-1.51.80.9

Table 6 
Retail sales. Yearly index of value, not adjusted. 2015=100

Retail sales. Yearly index of value, not adjusted. 2015=100
2018Yearly change in per cent
2016 / 20152017 / 20162018 / 2017
1For more details about the industries, see G, 47 here: https://www.ssb.no/en/klass/klassifikasjoner/6
47 Retail trade1107.72.72.32.5
47.1 Ret. sale in non-specialised stores108.23.21.92.9
47.2 Ret. sale of food, beverages etc.107.23.6-0.54.0
47.3 Ret. sale of automotive fuel110.4-1.86.75.3
47.4 Ret. sale of ICT-equipment95.5-5.80.41.0
47.5 Ret. sale of other household equip.103.81.62.00.2
47.6 Ret. sale of cultural/recreation goods101.03.0-0.5-1.5
47.7 Ret. sale of other goods104.53.90.20.4
47.9 Ret. trade not in stores/stalls etc.140.49.812.513.7
 
47_ub Ret. sale, except motor vehicles and gas stations107.43.21.82.2

Table 7 
Petrol stations. Turnover statistics. NOK million

Petrol stations. Turnover statistics. NOK million
August 201912-months change in per cent
June 2019 / June 2018July 2019 / July 2018August 2019 / August 2018
Total4 476.7-2.0-3.3-1.9
Engine fuel and lubricants3 678.3-3.0-2.5-1.6
Car equipment35.5-4.0-0.9-2.1
Everyday commodities401.74.3-10.5-3.1
Other goods including music and video sales21.8-17.3-15.0-21.4
Food service225.08.72.50.5
Workshop service and car wash56.4-11.9-6.50.6
Renting8.3-12.3-6.3-9.9
Games/commision49.8-1.5-6.8-13.1

About the statistics

The Retail sales index describes developments in values and volumes in the retail trade (excluding sales of motor vehicles). Retailers are companies that sell goods to private households. Previously published seasonally-adjusted figures may be revised when figures are added for a new month in the series.

Definitions

Definitions of the main concepts and variables

Turnover includes dutiable and duty-free sales income from goods and services as well as rents, commission fees and royalties. Financial revenues are not included.

Standard classifications

Standard Industrial Classification (SIC2007) ( http://www.ssb.no/nace ).

Administrative information

Name and topic

Name: Index of retail sales
Topic: Wholesale and retail trade and service activities

Next release

Responsible division

Division for Business Cycle Statistics

Regional level

No geographical breakdown available. National level only.

Frequency and timeliness

Monthly. The statistics are normally published on the 28th or 29th day of the following month.

International reporting

The statistics are reported to Eurostat at the time of publication in Norway.

Microdata

Primary data and compiled statistics are stored electronically in SAS files.

Background

Background and purpose

The objective of the Index of retail sales is to describe the value and volume development in retail sales, excluding sales of motor vehicles. Retail sales consist of enterprises involved in the sale of new and used goods to private households. The sale is executed from either a fixed or moveable sales outlet, a market place or via the Internet or mail order. Examples of retail sales are the sale of food, beverages, clothing, shoes, domestic electrical appliances, furniture, building equipment and so on. Retail sales are the main component in the calculation of household consumption. Statistics have been published since 1936.

From November 1999, the sample of retail stores has been supplemented gradually with turnover figures for chain stores reported directly by head office. As a consequence, figures for newly established and closed down units for the above-mentioned stores are also more up to date.

Users and applications

Users include public and private sector agencies and organisations. Statistics Norway's national accounts statistics rely on timely production of the retail sales index. Other users include Statistics Norway’s research department.

Equal treatment of users

No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 8 am. Prior to this, a minimum of three months' advance notice is given in the Statistics Release Calendar. Principles of communication and dissemination

Coherence with other statistics

The index of household consumption of goods is published at the same time as the index of retail sales. The former is more extensive than the latter, which can lead to different developments in the two indices. Price indices of retail sales are applied when calculating the volume indices of the retail sales index. Changes in turnover are later compared with bimonthly retail sales statistics, which are based on the VAT register.

Legal authority

The Statistics Act, § 2-1, 2-2 and 2-3

EEA reference

EU Council Regulation No 1165/98. Commission Regulation 586/2001.

Production

Population

All establishments in retail trade, except of motor vehicles and motorcycles (SN2007: 47.).

Data sources and sampling

Monthly statistical surveys (simplified questionnaire), Statistics Norway’s annual survey of retail trade establishments (detailed questionnaire), the VAT register and Statistics Norway's Register of Establishments and Enterprises.

A sample of about 16 100 units is selected from the population of retail trade establishments in the VAT register, representing about a quarter of all units. This includes a sub-sample of 14 600 chain stores with direct reporting from head office.

Another sub-sample of 1 500 units is selected from other existing establishments, i.e. independent shops and the remaining chain stores not yet included in the sample above. This population is stratified according to size in terms of number of employees. The sample is adjusted as necessary to ensure a reasonably even geographical coverage and to incorporate available new information from the annual survey of retail trade establishments and the bimonthly updated VAT register. In the latter case, the adjustments focus on diverging trends between turnover as measured by the sample and turnover as measured by a survey of the VAT register. In 1997, the sample was adjusted to take into account the variation of strata variances. The sample is rotated annually based on the second term of the turnover statistics. Establishments are retained in the sample for a maximum of four years unless they are part of a full coverage stratum.

Collection of data, editing and estimations

Questionnaires are submitted by mail, via the Internet or electronically. If the respondents need help in filling in the questionnaire, Statistics Norway can be contacted by telephone. The establishments normally receive the questionnaire before the expiry of the survey month. The deadline is the 12th day of the following month. Failure to respond is subject to fines.

Prior to the statistical compilation, arithmetic and logical checks are carried out. This procedure also includes comparing the results with other data sources, mainly the wholesale and retail trade statistics.

Results broken down by sector and stratum are compared with data for the previous period and the corresponding period in the previous year. If the discrepancy is considerable, the respondent is consulted.

A ratio estimator is applied to each stratum to inflate sample data to population level. The ratio estimator uses turnover figures from the VAT register as auxiliary variables.

The establishments are divided into identical units and newly established units. These concepts are defined in Notater 93/17 Detaljomsetningsindeksen (in Norwegian only).

Newly established units are enterprises that have been registered in the Central Register of Establishments and Enterprises after the last rotation of the sample. The turnover of these enterprises is based on estimates as this information is not available. The estimates are based on information about newly established units in the same period in the previous year. The average turnover per establishment is calculated for newly established units in the same period in the previous year. On this basis the turnover for all recent establishments for the period in question is aggregated. The estimates for newly established units are made on stratum and NACE level.

Volume indices at NACE four-digit sector level are calculated by deflating value indices directly by means of the price index for retail sales. At NACE two and three-digit sector level the volume indices are calculated as a weighted sum of the volume indices at the NACE four-digit sector level using value shares in the reference year as weight.

The index is adjusted for seasonal variations applying the X12ARIMA method with non-fixed seasonal effects and multiplicative model. As a supplement to seasonal variations, the new model takes into account the effect of weekdays, fixed holidays as 1 st and 17 th May, Easter, Pentecost, Ascension Day and 1 st New Year's Day. 24 th to 26 th of December are considered as seasonal variations with a method developed by Statistics Norway.

Seasonal adjustment

Refers to the separate tab "About seasonal adjustment" on statistics website.

Confidentiality

Not relevant

Comparability over time and space

The index has been published since 1936. A new version of Norwegian industry classification (SIC2007) has been implemented from January 2009. The toal index, as well as three-digit sector levels, has been recalculated back to 2000 according to SIC2007. In January 2003, Statistics Norway altered the calculation method for the price index of retail sales. The new method is based on the price development of product groups from the consumer price index, as well as on sector-wise product allocation. The product allocation comes from a survey with that particular focus. The price index of retail sales is calculated using the same methods used to calculate the deflator of the retail sales component in the index of household consumption of goods. Volume figures have been calculated back to August 1999 using the current deflator.

The volume indices at NACE four-digit sector level are calculated by deflating value indices using the aid of the price index for retail sales. From January 2002, volume indices at NACE two and three-digit sector level are calculated as a weighted sum of the volume indices at NACE four-digit sector level with value shares in the reference year as weight. In the past, volume indices at the NACE two and three-digit sector level were calculated using immediate deflation of value indices.

In compliance with Eurostat regulations the reference year was changed to 2015=100 when indices for January 2018 were published. Prior to this the reference year of the retail sales index was 2010=100.

Accuracy and reliability

Sources of error and uncertainty

Measurement errors (the respondent supplies erroneous data) and processing errors (wrong interpretation of figures and letters during optical scanning) may occur.

Three types of errors are common:

  • The respondent does not supply turnover figures for the establishment, but for a part of the establishment or the enterprise that the establishment is part of.
  • The respondent does not report data for the correct time period (calendar month).
  • Figures are in the wrong unit of measurement (usually in NOK instead of NOK 1 000).

Reminders are sent to enterprises that fail to respond in time. Failure to respond is subject to fines. Large enterprises that do not respond are reminded via telephone shortly before publication. These enterprises are treated in the same way as enterprises that are not included in the sample. This means that the change in turnover applied to these enterprises is the average percentage change that is applied to their stratum. Non-respondents normally constitute about 4 per cent of the total sample at the time of publication.

The results are uncertain as they are based on information from a sample of enterprises. The sample is updated once a year. The coverage exceeds 80 per cent of the population in terms of turnover, including the sub-sample of chain stores. Sample variance accounted for by the sub-sample of other stores than chain stores is estimated to about 1 per cent. Sample errors may also occur as a result of errors in the information that the sample is stratified according to.

Revision

Not relevant

About seasonal adjustment

General information on seasonal adjustment

Montly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.

For more information on seasonal adjustment: metadata on methods: seasonal adjustment

Why seasonally adjust these statistics?

Due to our shopping habits the retail sales index will vary from month to month. For instance the month of December shows higher sale than the rest of the months. This combined with the influence of how the Easter holiday varies between March and April and also the influence of movable public holidays make a comparison from one month to the next difficult. To adjust for these circumstances the retail sales index is adjusted for seasonal variations, so the underlying development of the index can be analyzed.

Seasonally adjusted series

The retail sales index is published in a three-digit NACE level, and constitutes 10 seasonally adjusted series.

Pre-treatment

Pre-treatment routines/schemes

Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.

Calendar adjustment

To perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, such as regression or RegARIMA (a regression model with an ARIMA structure for the residuals). The regression variables for the calendar adjustment are adapted to reflect the working days, public holidays and so forth specific to Norway.

Methods for trading/working day adjustment

RegARIMA correction – in this case, the effect of trading days is estimated in a RegArima framework. The effect of trading days can be estimated by using a correction for the length of the month or leap year, regressing the series on the number of working days, etc. In this case, the residuals will have an ARIMA structure.

Comments:

For 1 January , 1 May and 17 May the correction of working days has been modified so that these days are regarded as a Sunday.

Correction for moving holidays

Correction based on an estimation of the duration of the moving holidays effects, specifically adjusted to Norwegian circumstances.

National and EU/euro area calendars

A calendar based on Norwegian holidays is used.

Treatment of outliers

Outliers are detected automatically by the seasonal adjustment tool. The outliers are removed before seasonal adjustment is carried out, and then reintroduced into the seasonally adjusted data.

Model selection

Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.

Automatic model selection by established routines in the seasonal adjustment tool.

Decomposition scheme

The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.

Manual decomposition scheme selection after graphical inspection of the series.

Seasonal adjustment

Choice of seasonal adjustment approach

X-12-ARIMA

Consistency between raw and seasonally adjusted data

Do not apply any constraint.

Consistency between aggregate/definition of seasonally adjusted data

In some series, consistency between seasonally adjusted totals and the original series is imposed. For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.

Do not apply any constraint.

Direct versus indirect approach

Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.

Indirect approach where the seasonal adjustment of components occurs using the same approach and software, and then totals are derived by aggregation of the seasonally adjusted components.

Horizon for estimating the model and the correction factors

When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.

The whole time series is used to estimate the model and the correction factors

Audit procedures

General revision policy

Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.

Both raw and seasonally adjusted data are revised between two consecutive official releases of the release calendar.

Comments:

Raw data is not revised.

Concurrent versus current adjustment

Partial concurrent adjustment: the model is identified and estimated yearly, while filters, outliers and regression parameters are re-identified and estimated continuously as new or revised data become available.

Comments:

Factors concerning the Easter holyday are estimated yearly.

Horizon for published revisions

The entire time series is revised in the event of a re-estimation of the seasonal factors.

Quality of seasonal adjustment

Evaluation of seasonally adjustment data

Evaluation of quality based only on graphical inspection and descriptive statistics.

Comments:

A model where the various quality indicators will be evaluated continuous/periodically in the future.

Quality measures for seasonal adjustment

For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.

Special cases

Seasonal adjustment of short time series

All series are sufficiently long to perform an optimal seasonal adjustment.

Treatment of problematic series

None of the published series are viewed as problematic.

Posting procedures

Data availability

Raw data, pre-treated data and seasonally adjusted series are available.

All metadata information associated with an individual time series is available.

Press releases

In addition to raw data, at least one of the following series is released: Seasonally plus working day adjusted.

Relevant documentation

Contact