Prices and price indices
pif, Price index of first-hand domestic sales, price trends, inflation, wholesale price index, domestic market, import market, product groups (for example food, chemical products, machines)Producer and wholesale price indices, Prices and price indices

Price index of first-hand domestic sales


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Key figures

4.5 %

twelve-month change in PIF in September 2017

Price index of first-hand domestic sales. 2000=100
Product groupsChanges in per centIndexWeights1
August 2017 - September 2017September 2016 - September 2017September 2017
1The weights are updated annually, and are valid for the entire year.
Total index0.94.5153.81 000.0
Beverages and tobacco0.73.5175.816.8
Crude materials, inedible, except fuels3.418.9153.048.4
Mineral fuels, lubricants and related materials8.918.9205.4149.5
Chemicals and related products, n.e.s.-0.61.1157.983.8
Manufactured goods classified by material0.54.2146.6135.0
Machinery and transport equipment0.10.7108.3234.7
Miscellaneous manufactured articles-0.40.6125.2103.7

About the statistics

The price index of first-hand domestic sales (PIF) measures the price development of first-hand sales of goods to the Norwegian market. That means goods from Norwegian production sold in Norway and imported goods.


Definitions of the main concepts and variables

A chained Young formula is used in the production of the index. This is a version of a Laspeyres formula. A Laspeyres formula is characterised by constant weights for a given time period. A chained Laspeyres index is an index made up by several Laspeyres indices with various weights.

The Norwegian market is a joint description of all goods produced and purchased in Norway. Price regarding sales to the Norwegian market is defined ab fabric (ex works) and should refer to the sales price out of the factory on the 15th of the month.

The import market refers to goods produced outside Norway, purchased by Norwegian firms. Prices are measured CIF (cost, insurance, freight) at the national border.

Imputed value is the estimated value where observations are missing.

The elementary level is the lowest level in which indices are computed, the level below the lowest level in which weights are used. In the PIF the elementary level is the HS product.

The weight share is the share of the HS product in the total index.


Standard classifications

HS - (Harmonized System) .

SITC rev 3

Main industrial groupings

Administrative information

Name and topic

Name: Price index of first-hand domestic sales
Topic: Prices and price indices

Next release

Responsible division

Division for Price Statistics

Regional level

National level only.

Frequency and timeliness

Monthly. Released on the 10th of each month.

International reporting



Data concerning establishments, products and prices are stored in Oracle databases, and are subject to confidentiality.


Background and purpose

The purpose of the PIF is to measure the price development of first hand sales of products to the Norwegian market, from Norwegian production and imports. For details on changes made in methodology and dissemination, see the links Revision 2001in the right hand pane

The PIF is an important part of a system for short-term indicators made in order to monitor the Norwegian economy. Together with the Producer Price Index (PPI), the PIF measures the price development in production and imports of goods. Together these two statistics cover the price development in three markets: Domestic production, exports and imports. The prices used to calculate these two statistics are collected in the same survey.

The index is commissioned and financed by The Norwegian Ministry of Finance.

Users and applications

The index is primarily used for regulating the price of different contracts over time. The statistics are used in the production of the national accounts in Statistics Norway.

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 inthe Statistics Release Calendar.

Coherence with other statistics

Together with the Producer Price Index (PPI), the PIF measures the price development in production and imports of goods. Together these two statistics cover the price development in three markets: Domestic production, exports and imports.

The building cost index and the quarterly wholesale price index use some data from the PIF.

Legal authority

Statistics Act §§2-1, 2-2, 2-3 (compulsory fines) Statistics Act §§2-1, 3-2 (administrative data-processing systems) Statistics Act §§ 2-1, 2-2, 3-2 (forms/registers)

EEA reference

Council Regulation (EC) no. 1165/98 of 19. May 1998 concerning short-term statistics. Commission Regulation 586/2001 Commission and Council Regulation 1158/2005 Commission and Council Regulation 1983/2005 Commission Regulation 1503/2006 Commission Regulation 656/2007 Commission Regulation 472/2008



The population is all commodities in first hand sales of products to the Norwegian market, from Norwegian production and imports. The PIF is aggregated according to SITC and this includes food and live animals (SITC0), beverages and tobacco (SITC1), crude materials, inedible except fuels (SITC2), mineral fuels, lubricants and related materials (SITC3), animal and vegetable oils, fats and waxes (SITC4), chemicals and related products (SITC5), manufactured goods classified by material (SITC6), machinery and transport equipment (SITC7) as well as miscellanous manufactured articles (SITC8).

Because the PIF is closely connected to the PPI, commodities in the following industries are not covered: mining of uranium and thorium ores (07.21), processing of nuclear fuel (24.46), manufacture of weapons and ammunition (25.4), building of ships and boats (30.1), manufacturing of aeroplanes and spacecraft (30.3) and manufacture of military fighting machinery (30.4).

The establishments are sampled from Statistics Norway's Register of Establishments and Enterprises, based on data from the Norwegian customs and excise as well as the PRODCOM survey.

Data sources and sampling

The main data source is the price survey which together cover the PIF and the PPI. Prices are mainly surveyed using electronic questionnaires to the esteblishments specified above. In addition, data are collected from various sales organisations, the Division for Foreign Trade, the quarterly electric energy prices, and in some cases from international sources (spot prices).

The sample consists of about 273 commodity groups (three digits SITC groups, see 4.3). These groups are split into various HS products (see 4.2). The products are selected based on foreign trade data and the PRODCOM survey, where the HS products are selected to cover the SITC products. The number of HS products is usually about 3500 total to cover the domestic, export and import market. A HS product is included in the sample because of the weight in at least one of the three markets. This means that the HS is not necessarily included in the index calculations in all three markets. SITC commodities are selected in cooperation with the Division for National Accounts and cover important commodities in the national accounts. Furthermore, the HS products are selected using data from foreign trade and Norwegian manufacturing statistics, and cover the chosen SITC commodities. The range of CPA commodities and HS products is adjusted according to changes in the national accounts and foreign trade - even so, the annual changes are modest. The changes are carried out to introduce new products into the sample.

A selection of establishments is used in the monthly price collection. In cooperation with these companies a range of goods are chosen to cover the selected HS products (and CPA commodities). The sample consists of about 1300 establishments and roughly 5000 products. Companies with 100 employees or more are included in the sample on a permanent basis (cut-off principle). The sample is updated continuously with regard to liquidation, foreclosures etc.

The table below show the number of price observations by commodity group, including the domestic markets and the import markets. The numbers vary some from month to month depending on the response rates.

Commodity group 1

Price observations

as per January 2012

0 548
1 90
2 252
3 76
4 4
5 462
6 1077
7 1149
8 861
9 65
Sum 4584

1 Standard International Trade Classification.

Collection of data, editing and estimations

The PIF and the PPI are collected in the same survey. The survey is based on the same electronic questionnaires as the producer price index (PPI) . Respondents who register an e-mail address in Altinn are notified by e-mail when a new questionnaire is released. The electronic questionnaires via the Internet (Altinn) are avalible online around the 10th and have deadline around the 17th. Figures from foreign trade are used if they are found to be an adequate indicator of the price development of a good. Prices of electric energy prices are collected from NordPool . In addition some prices are collected from international sources such as Bureau of Labour Statistics , London Spot Markets og London Metal Exchange .

All forms go through a manual check where administrative information is checked. A computerised control then checks for punching errors, duplicates and observations with large price changes from the previous month. The checks are done for each market separately. Respondents are contacted if one or several of their commodities show large changes in price and no explanation is given in the questionnaire.

Non-response: Non-response is imputed mechanically based on the price development at a higher aggregation level. The firm is contacted in case of non-response, and if prices are not available price data are imputed. SITC products which are not covered by actual price obeservations are imputed using a similar algorithm. Which aggregate level is used depends on the number of actual price observations.

On the micro level the a Jevons formula is used, which is the geometric average of products within the same HS product (see 4.3). Higher level indices are calculated using a chained Young formula. For the PIF, total indices on the HS level (domestic market and imports) are weighed together and aggregated according to the SITC nomenclature . Higher level indices are are first calculated as weighted averages to the SITC five digit products, then according to the hierarchical structure of SITC.The weights are updated annually, and are calculated from production and export values from the national account's latest final figures. On detailed levels the weights are based on the national account's latest final figures. To keep the weight as up-to-date as possible aggregated levels are adjusted by using the quarterly results from the national accounts. This way weights always relate to the year previous to each index period (calendar year). The reference year is 2000 (2000=100).

Seasonal adjustment

Not seasonally adjusted.


The data collected from the firms will be used in accordance to the rules specified by the Norwegian Data Inspectorate. Collected data are subject to confidentiality specified in the Statistics act, §2-4.

Comparability over time and space

The PIF has been compiled since 1926, and was called the wholesale price index prior to 1989. From 1953 to 1977 only the total and the main groups were published (1-digit SITC) as well as the indices grouped according to end use - consumption, investment and input in building and construction. Prior to 1953 only the total index was published. From 1977 to 2001 the PIF was published down to 2-digit SITC, and the indices grouped according to end use. These latter series have been discontinued since December 2000. In 2000 a number of improvements in methods were implemented, see Revision in year 2000 .

Accuracy and reliability

Sources of error and uncertainty

A correct answer to the questionnaire requires that the respondent has understood the definitions of the main variables. To reduce the effects of possible errors each product is specified in text as well as recommended unit/quantity. Furthermore, the respondent is encouraged to put the establishment's own product description and/or product code on the questionnaire. The price reported in the previous month is also pre-printed. In addition, a guideline containing important definitions is provided. This system gives a good overview for the respondent, but there is still a possibility for errors.

Sometimes the respondents report the same price over longer periods of time. In many cases this is correct because of costs due to changing prices (menu costs). However, in some cases this may be a source of error due to lack of motivation by the respondents. In extreme cases of constant prices the respondents are contacted.

In cases where the respondent's selected product is out of production or out of stock, and the respondent is reporting a different product, this should be specified on the form. If this is not done, product changes may be registered as price changes. Normally, these kinds of errors are identified.

At the time of deadline, the response rate varies between 85 and 90 per cent. After reminders have been sent out and before publishing the response rate normally increases to about 97 per cent. In cases of total non-response, meaning the whole form is missing, the establishments are contacted. Missing prices are imputed. Important respondents are given priority when re-contacting for missing forms.

In order to secure a high level of accuracy at a low cost, emphasis has been put on covering large units that dominate the population.

Variance: Is not calculated.

Skewness: The products in the population are updated continuously. The sample of products for each establishment is selected in a joint effort between the establishment and Statistics Norway. The respondent is asked to continuously update the sample. This means replacing all discontinued products, products that have become less important for the establishment's turnover, and products where the price development no longer represents the overall price development for the establishment. If the respondent does not update the product sample, this may represent a source of skewness. No calculations of the sample skewness have been made, but based on experience these kinds of errors may go in both directions. Extreme cases are normally identified through checks. Also, by contacting the establishments the effects of this type of error/skewness are reduced.

Furthermore, commodity-group levels are checked in connection with the annual weight changes. Here all commodity groups (with weight) are checked against the sample. In commodity groups with poor coverage, efforts are taken to improve the coverage.

The sample of establishments is changed continuously as old respondents are replaced by new ones. The relatively large proportion of large units (establishments) represents a potential source of skewness. A large number of small units in an industry may cause skewness if their price development is different from the larger units.

Experience show that a share of the units (normally small) in the population is misplaced as regards to commodity code (HS). This is due to changes in the classification, and/or insufficient or misleading information at a certain time. Information about the commodities in the sample and in the population is improving over time. No attempts have been made to estimate the extent and significance of these types of errors, but it is assumed to be small.

The quality of the products will also change over time. This might lead to an overestimation or underestimation of the price development. In the cases where we know there has been a change in quality, this is adjusted by ordinary methods of quality adjustment.

For computers a so-called hedonic method is used in order to deal with the frequent quality changes within this commodity group. This method is based on the assumption that the price of a commodity is decided by its quality characteristics. It is therefore essential to establish a descriptive relationship between price and quality characteristics, i.e. price as a function of these characteristics.

As mentioned before, sometimes prices are constant over a longer period of time. This might be because the respondents report list prices instead of transaction prices. In cases where for instance demand or the competitive conditions change - thereby changing the level of discount - the developments in the index may over- or underestimate the price development.

Effects of changes in relative prices may represent another non-sampling error. The relative price between different products changes over time, often because of changes in demand. This may affect the producer's/importer's prices and price development. To identify such substitution effects the weights, and the sample of products, should be revised frequently. The size of this skewness is not estimated, but as both the sample of products and the weights are subject to an annual revision, this skewness is assumed to be very small.

The product sample is mentioned in 5.3.


The PIF is not subject to revisions.