Turnover index for service activities

Turnover comprises total revenue from market sales of goods or services supplied to third parties. Turnover includes all duties and taxes on goods or services except for the VAT invoiced by the unit vis-à-vis its customers and other similar deductible taxes directly linked to turnover. Turnover also includes all other charges (for example transport and packaging) passed on to the customer, even if these charges are listed separately in the invoice. Reduction in process, rebates, discounts as well as the value of returned packing must be deducted. Price reductions, rebates and bonuses conceded later to clients, for example at the end of the year, are not considered. Income classified as other operating income, financial income and extraordinary income in company accounts is excluded from turnover. Subsidies received from public authorities, or the institutions of the European Union are also excluded.

A kind-of-activity unit (KAU) is a part of an enterprise. A KAU groups together all the offices, production facilities etc. of an enterprise, which contribute to the performance of a specific economic activity defined at class level (four digits) of the Standard Industrial Classification 2007 (SIC2007). A KAU is delimited only based on activity, not location. A KAU can, for example, consists of a combination of all businesses within an enterprise that runs hotel business. Within the same enterprise, there may be another KAU that runs restaurant business.

Standard Industrial Classification 2007 (SIC2007).

Name: Turnover index for service activities
Topic: Wholesale and retail trade and service activities

Division for Business Cycle Statistics

No geographical breakdown available. National level only

Monthly. The statistics are normally published around the 37th day of the following month.

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

Collected and revised data are stored securely by Statistics Norway in compliance with applicable legislation on data processing.

Statistics Norway can grant access to the source data (de-identified or anonymised microdata) on which the statistics are based, for researchers and public authorities for the purposes of preparing statistical results and analyses. Access can be granted upon application and subject to conditions. Refer to the details about this at Access to data from Statistics Norway.

The aim of the statistics is to describe the value development within market-oriented service industries. The statistics must meet the requirements of European Parliament and Council Regulation (EU) 2019/2152 of 27 November 2019 on European business statistics.

The turnover index for services covers main business areas H, I, J, M (except M701, M72 and M75) and N as defined in the Standard Industrial Classification 2007 (SIC2007). The statistics will later be expanded to also cover the main business area L.

The turnover index for services was first published with a monthly frequency in March 2023 with figures from and including the statistical period January 2015. The monthly statistics replaced the quarterly turnover index for services which covered many of the same industries. The transition from quarterly to monthly statistics was a consequence of the new European Parliament and Council Regulation 2019/2152 of 27 November 2019.

Since the data from the VAT register which was previously used in the production of the quarterly statistics neither satisfies the new requirements or frequency, a need arose to use other data sources which entailed major changes in the production systems. The two statistics are therefore not directly comparable, even though today's monthly turnover index essentially covers the same industries as the quarterly turnover index that was published up to and including the 4th quarter of 2022.

For most service industries, the turnover index is produced using data from a sample survey consisting of approximately 2,200 enterprises that report monthly turnover to Statistics Norway, but for some industries other data sources are used.

The reference year for the index is 2015, i.e., 2015=100.

Primary users of the statistics are public sector agencies (Central Bank of Norway, Departments, etc.), private sector agencies and organisations within the industries. The statistic is used to analyse and monitor the development of the economy. At Statistics Norway the statistics is used by the Division for National accounts and other research and analysis activity.

No external users have access to statistics before they are released at 8 a.m. on ssb.no after at least three months’ advance notice in the release calendar. This is one of the most important principles in Statistics Norway for ensuring the equal treatment of users.

Statistics Norway will also produce a monthly production index and a quarterly Producer Price Index for services covering the same service industries.

The service industries are also covered by the annual Business statistics.

European Parliament and Council Regulation (EU) 2019/2152 of 27 November 2019 on European business statistics (EBS) ensures conceptual consistency across different business statistics.

The statistics are developed, produced and disseminated pursuant to Act no. 32 of 21 June 2019 relating to official statistics and Statistics Norway (the Statistics Act).

European Parliament and Council Regulation (EU) 2019/2152 of 27 November 2019 on European Business Statistics (EBS)

The population includes all market-oriented KAU in H, I, J, M (except M701, M72 and M75) and N as defined in the Standard Industrial Classification 2007 (SIC2007).

The sample consists of a selection of approx. 2,200 enterprises where the information is obtained using electronic reporting in Altinn. In addition, the VAT register and information from Statistics Norway's own Business and Enterprise Register are used.

The VAT data as well as annual turnover from business statistics and the number of employees are used in the sampling process.

Data is collected using electronic reporting in Altinn. The form is sent out on the first working day after the end of the month and has a response deadline of about 16 days later.

Mathematical and logical controls have been added to the data registration routine. Reported figures are compared with previously reported figures, as well as historical figures for annual turnover and data from the VAT register.

See separate tab "About seasonal adjustment"

Employees of Statistics Norway have a duty of confidentiality.

Statistics Norway does not publish figures if there is a risk of the respondent’s contribution being identified. This means that, as a general rule, figures are not published if fewer than three units form the basis of a cell in a table or if the contribution of one or two respondents constitutes a very large part of the cell total.

Statistics Norway can make exceptions to the general rule if deemed necessary to meet the requirements of the EEA agreement, if the respondent is a public authority, if the respondent has consented to this, or when the information disclosed is openly accessible to the public.

More information can be found on Statistics Norway’s website under Methods in official statistics, in the ‘Confidentiality’ section.

As of 18 May 2021, the statistics Turnover index for business services and Turnover index for transport, tourism and ICT were published together as the quarterly statistics Turnover index for services.

In 2023, the frequency of the turnover index for services was changed from quarterly to monthly. The monthly time series was published with figures from and including the statistical month of January 2015. The change entailed the need for other basic data and significant restructuring of the production process, which means that the monthly index is not directly comparable to the previous quarterly index, even though they cover the same industries.

Historical value development for January 2015 up to and including December 2020 has been calculated by disaggregating the base figures for the quarterly index using a calendar with the number of working days as an explanatory variable.

As of January 2021, monthly value development for most service industries is based on direct data collection. For some individual industries, other data sources are used.

According to requirements from Eurostat, the statistics have reference year 2015=100.

Measurement errors because of incorrect information and processing errors due to incorrect coding have been tried to be avoided by building up a control system.

Enterprises that do not respond are warned and eventually imposed a compulsory fine. Enterprises that do not respond are treated in the same way as enterprises that are not included in the selection. This means that they are allocated the same percentage change in turnover as the average change for the industry to which the unit belongs. The response rate is around 98 per cent at the time of publication.

The results are based on information from a selection of enterprises, and there is therefore a certain degree of uncertainty attached to them. The sample used to calculate the index is updated once a year. Errors in the sample can also occur as a result of errors in the information by which the sample is stratified.

A revision is a planned change to figures that have already been published, for example when releasing final figures as a follow-up to published preliminary figures. See also Statistics Norway’s principles for revisions.

Revisions in previously published seasonally adjusted figures can take place when new observations (or revised previous observations) are included in the basis of calculation. The scope of the revision is usually greatest in the most relevant part (last 1–2 years) of seasonally adjusted time series. A corresponding revision in trends is also typical, particularly at the end of the time series. The extent of the revision of trends and seasonally adjusted figures is partly determined by the revision policy, see Section 4 of the European Statistical System (ESS) Guidelines on Seasonal Adjustment on the Eurostat website. For more information on the revision of seasonally adjusted figures, see the ‘About seasonal adjustment’ section in the relevant statistics.

For monthly and quarterly figures, there are often significant seasonal variations which make it difficult to directly interpret the development from period to period. To facilitate the interpretation of such time series, many number series are seasonally adjusted using X-12-ARIMA or other seasonal adjustment tools.

For more general information about seasonal adjustment and the terms associated with it, see General information about seasonal adjustment (pdf).

Due to seasonal variations, turnover will vary from month to month. In the holiday months of June, July and August, for example, there will often be high turnover in the hotel and tourism industries compared to the other months of the year.

The index series are also corrected for the number of working days, variable public holidays and leap years. The Easter holidays and other moving holidays are examples of effects that make it difficult to compare from one month to the next. To be able to analyse the underlying development in the Turnover index for services, it is therefore adjusted for these conditions.

Seasonally adjusted series

The seasonally adjusted series for turnover for services are published at the 2-digit industry level, as well as by main industry area. The seasonally adjusted series are published for 24 2-digit industries as well as 5 main business areas. 1 total aggregate of all service industries H, I, J, M (except L68, M701, M72 and M75) and N is also published.

The statistics will be expanded to cover the main industry area L, which consists of one 2-digit industry, L68.

Pre-treatment routines/schemes

A detailed pre-treatment is done. This means that the tools are based on special adjusted models and not based on standard options in the seasonal adjustment tools.

Calendar adjustment

Calendar adjustments are performed 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).

Methods for trading/working day adjustment

The series are corrected using RegARIMA modeling: The effect of working days is estimated by correcting for then length of each month when one also takes into account the occurrence of leap years. The regressor used is given by the number of working days. Within RegARIMA modeling, the effect of the working days is estimated, and an ARIMA structure is obtained for the residuals.

Correction for moving holidays

Correction for moving holidays is done by counting these days as Sundays.

National and EU/euro area calendars

In the seasonal adjustment of the production index for construction, a calendar based on Norwegian public holidays is used.

Treatment of outliers

Extreme values, also called outliers, are abnormal values ​​in the series.

Extreme values ​​are automatically identified in the seasonal adjustment tool and are removed before seasonal adjustment is carried out. The extreme values ​​are subsequently included in the seasonally adjusted figures. The seasonal adjustment during the corona crisis has been done in such a way that a level shift has been introduced in March and April 2020 and these months are not included in the basis for the calculations of the seasonal pattern. Technically, in the seasonal adjustment routine, this is done by specifying March and April 2020 as extreme values.

Model selection

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

Model selection is primarily automatic, but in some cases models are selected manually. A log transformation of unadjusted (raw) data is performed for pre-treatment of the series in the production index.

After the automatic model selection, the model parameters are held constant throughout the year.

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.

For decomposition of the index a multiplicative decomposition is in use.

Choice of seasonal adjustment approach

X-13-ARIMA

Consistency between raw and seasonally adjusted data

In some series, consistency between raw and seasonally adjusted series is imposed.

For the turnover index for services, no constraints are applied.

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.

For the turnover index for services, there is no consistency imposed.

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.

In seasonal adjustment of the turnover index for services, a direct method is used, where raw data is aggregated, and the components and aggregates are seasonally adjusted directly using the same approach and software. Inconsistencies across the aggregation structure are not removed.

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.

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.

Seasonally adjusted data are revised between two consecutive official releases of the release calendar.

Concurrent versus current adjustment

The model, filters, outliers and regression parameters are re-identified and re-estimated continuously as new or revised data become available.

Horizon for published revisions

The revision period for the seasonally adjusted results is limited to approximately 3 years prior to the revision period of the unadjusted data, while older data are frozen.

When changing the method, the entire time series can be recalculated and updated.

Evaluation of seasonally adjustment data

Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.

Quality measures for seasonal adjustment

To analyse the time series, a limited range of diagnostics and graphics are used by the seasonal adjustment tool.

A monthly graphical and detailed empirical analysis is available.

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.

Data availability

Unadjusted figures (orignial series or raw data), calendar adjusted, seasonally adjusted and smoothed seasonally adjusted figures are available.

Press releases

In addition to unadjusted figures (raw data), the following series are released: calendar adjusted, seasonally adjusted and smoothed seasonally adjusted figures.

ESS-Guidelines on seasonal adjustment

EUROSTAT: Handbook on Seasonal Adjustment 2018 edition