7904_om_not-searchable
/en/energi-og-industri/statistikker/lsi/kvartal
7904_om
statistikk
2011-02-18T10:00:00.000Z
Energy and manufacturing;National accounts and business cycles
en
true

Index of stocks in manufacturing (discontinued), Q4 2010

The statistics has been discontinued.

Content

About the statistics

Definitions

Name and topic

Name: Index of stocks in manufacturing (discontinued)
Topic: Energy and manufacturing

Responsible division

Division for Manufacturing and R&D statistics

Definitions of the main concepts and variables

Local unit (establishment) : An enterprise or part of an enterprise that is located in one particular place and can be identified geographically

Enterprise : The smallest combination of legal units that is an organisational unit producing goods or services and that benefits from a certain degree of autonomy in decision making.

IDUN : Statistics Norway's web service for electronic reporting of data.

NACE : Standard for industrial classification used by EUROSTAT. It is based on the UN's international standard for industrial classification, ISIC Rev. 3.

Standard Industrial Classification (SIC) The standard is primarily a statistical standard. It forms the basis for classifying units according to main activity in the Central Register of Establishments and Enterprises (CRE). The use of common standards is essential in enabling the comparison and analysis of statistical data at national/international level and over time. The standard is identical to NACE. However, a fifth figure (subclass) is added to the standard to create a national Norwegian level.

Imputation : An estimated value for a missing observation.

Processing level : The most detailed level of the statistics.

Seasonal adjusted figures : Time series for which calendar and seasonal effects have been removed. X12-ARIMA is used to calculate these figures.

Unadjusted figures : Raw data figures with primary information from the respondent.

Elementary index : A formula where the estimated value of a variable is divided by the average annual value for the same variable for a previous (base) year - e.g. 2005.

Stock : Stock includes finished goods and work in progress.

Stock of goods : The total stock of finished goods and work in progress at the end of the period. Raw material and merchandise produced by other establishments are not included. The value reported is the expected sales price excluding taxes.

Finished goods : Sales value of the stock of finished goods.

Work in progress : The value of all goods in progress and semi-manufactured articles in stock at the end of the period.

Standard classifications

The survey is classified according to the Standard Industrial Classification 2007 (SIC2007). This is a Norwegian adaptation of NACE Rev. 2. SIC2007 forms the basis for coding units according to principal activity in the Central Register of Establishments and Enterprises. The use of common standards is essential in enabling comparison and analysis of statistical data at national/international level and over time.

Administrative information

Regional level

National level only

Frequency and timeliness

Quarterly

International reporting

Not relevant

Microdata

Non-revised and revised micro data are stored in accordance with Statistics Norway's guidelines for storing of computer files.

Background

Background and purpose

The survey monitors the changes in stock on a quarterly basis. Information about changes in stock gives important background information for the interpretation of the development in the short-term statistics - especially the development of the supply and demand side.

The statistics were first published in 1996 with around 750 units. A revision was conducted in 2003. The revision in 2003 is outlined in more detail in chapter 5.4.

As from Q1 2009, all results will refer to SIC2007 (chapter 4.2). The historical series have been recalculated according to this version of SIC, and results dating back to 2000 are available in the Statbank database. Historical series based on SIC2002 are also available (chapter 6.1). The survey is wholly financed by government appropriations.

Users and applications

The survey is used in Statistics Norway in the preparation of the quarterly national accounts.

Other users include financial and analytical institutions and, to some extent, public institutions (the Ministry of Finance and Norges Bank, among others).

Coherence with other statistics

Information regarding changes in stock is important in the interpretation of the development of the short-term statistics - especially the development in the supply and demand side. The survey should be looked at in correlation with other short-term statistics such as:

The statistics constitute one of several indicators of the economic development. The Statistics on stocks and the Statistics on new orders have joint data collection.

Legal authority

The Statistics Act of 16 June 1989, §§2-1, 2-2 and 2-3.

EEA reference

Not relevant

Production

Population

The population covers all establishments except sole proprietors in the industries textiles and wearing apparel (13-14), paper and paper products (17), chemical and pharmaceutical products (20-21), basic metals (24), fabricated metal products (25), computer and electrical equipment (26-27), machinery and equipment (28), ships boats and oil platforms (301), transport equipment n.e.c (29,30(-301), repair, installation of machinery (33), see Standard Industrial Classification 2007 (SIC2007) . The population is defined by the Central Register of Establishments and Enterprises, and establishment is the observation unit in the survey. (See chapter 4.1 for a complete definition of establishment and enterprise.)

Data sources and sampling

The survey uses investment data collected by questionnaires from the units included in the sample, in addition to information from the Central Register of Establishments and Enterprises.

The sample includes about 940 establishments. The sample includes all establishments with 100 employees or more, or establishments with a turnover of at least 10 per cent of the publishing level. The remaining units are drawn based on stratification and optimal allocation proportional to the size of the unit measured by the number of employees. The sample does not include establishments with ten employees or fewer.

Collection of data, editing and estimations

The survey is based on data collected by questionnaire. The questionnaires are either returned by mail or electronically via IDUN. 85 per cent of the establishments use IDUN (Q1 2010). The questionnaire is sent as close as possible to the first day of the month following the quarter. The deadline for returning the questionnaire is normally the 20th in the same month. Establishments registered with an e-mail address in IDUN are notified by e-mail when the questionnaire is available on the Internet.

The establishment's local office normally fills in the questionnaire, but in some cases the head office reports data for several units. Establishments that fail to return the questionnaire receive a reminder within a week of the deadline. A new deadline of seven days is set. Establishments that still fail to return the questionnaire receive a second reminder and a compulsory fine if they do not return the questionnaire within five days.

The questionnaires are optically read or downloaded from the Internet, and the data are automatically checked for duplicates and errors in totals. The figures are revised on the basis of a revision programme (for example errors regarding reporting in NOK million or large deviations from previous reported figures). Where there are considerable deviations, the establishment is contacted. In case of extreme deviations, further revisions are carried out.

The sample data are inflated to population level using a ratio estimator. The ratio estimator uses turnover figures from the VAT Register as auxiliary variables.

Time series sometimes contain significant seasonal variation that makes it difficult to interpret the results from one period to another. In the survey, seasonally adjusted figures and trend figures are calculated with X12-ARIMA for the manufacturing industry.

Confidentiality

Confidential micro data : According to § 2-4 of the Statistics Act , collected data are subject to secrecy and are to be kept or destroyed in a secure manner. Any use of the data must be in accordance with the rules set out by the Data Inspectorate.

Time series that are not to be published : The publication of data is subject to the provisions of § 2-6 of the Statistics Act . The main rule is that data should not be published if they can be traced back to the respondent, i.e. figures for which less than three respondents make up the foundation for a cell in the table, figures where one respondent represents more than 90 per cent of the total value or figures where two respondents represent at least 95 per cent of the total value.

Unpublished data : Revised data that are not published are subject to secrecy. This implies that they are unavailable to users without distinct approval. Such agreements only apply to internal users.

Comparability over time and space

As from January 2009, SIC2002 is replaced by SIC 2007 (see paragraph 2.1). The historical series based on this new version of SIC have been recalculated back to 2000. Users of the data must ensure they use results based on the same version of SIC when making comparisons over time. When looking at changes from 2000 to 2008. Either the series based on SIC2002 or the series based on SIC2007 must be used. Historical series based on SIC2002 for the period 1989 to 2008 remain available in the Stabank database under Completed time series. However, as from January 2009, only series based on SIC2007 will be continued. To get an overview of possible changes in industrial groupings, see the article on new Standard for Industrial Classification .

Accuracy and reliability

Sources of error and uncertainty

Measurement errors are caused by the questionnaire or the respondents internal system for obtaining the data. Examples are ambiguous questions, misunderstood questions or erroneous data from the respondents. In the Statistics on stocks, errors in reported figures may originate from misunderstandings of the concept of stocks or the definition of the main variables used in the survey. Unambiguous guidelines and definitions are therefore emphasised. The use of incorrect units of measurement may occur since the figures should be reported in NOK million. This type of error will become evident during the revision of the data. Processing errors may occur when Statistics Norway processes the data. Typical examples are misinterpretations of answers (1 may be interpreted as 7 and so on) or that correct answers for some reason are assumed to be false and corrected. Paper questionnaires are optically read with automatic verification and transmission to electronic medium. The current techniques for optical reading are of high quality, and few errors are found in this phase of the production. The introduction of IDUN has also contributed to reduce such errors, as data from electronic questionnaires are loaded directly into the system. Questionnaires that are not verified by the optical reading are treated manually. Thus there is room for human error, but considerable deviations will normally become evident during the revision of the data.

Errors of non-response refer to errors that either occur due to missing questionnaires or blank boxes in the questionnaire.

The response rate after the deadline has expired is around 95 per cent. Critical units, i.e. units that have a considerable impact on the results on a detailed level aggregation (2-digit NACE), are contacted by telephone. Calculations of the effect of missing units have been carried out, but no skewness has been uncovered. Missing questionnaires are mainly imputed automatically, based on previous reported figures (cold-deck method). Large units are imputed manually using rates of change at processing level and the reported figures from the enterprise in the previous quarter (type of hot-deck). An imputed value is not imputed in the following quarter.

Boxes that are left blank (partial non-response) are imputed manually.

Sampling errors refer to uncertainty that occur in sample surveys as opposed to full counts. The sample variance equals the expected deviation between a sample survey and a full count. In the Statistics on stocks the sample represents 15 per cent of the population that covers about 80 per cent of the turnover in the population. In order to ensure a high degree of relevance at the lowest cost possible, great effort is put into including all large units in the population in the sample.

Calculations of the size and significance of this type of error have not been carried out.

Establishments that close down may be a source of skewness if the proportion of closing downs in the sample deviates from the population. The Statistics on stocks are mainly based on a fixed sample (panel). Periodic updates of the sample ensure that the sample is in accordance with the population.

Coverage errors refer to errors in registers that define the population, in this case the Central Register of Establishments and Enterprises. As a result of such errors, units may be incorrectly included in or excluded from the population. Other problems are related to delays in the update of the registers and units that are incorrectly classified. From experience a limited share of the population units are incorrectly classified. This is usually due to misleading or insufficient information at a certain time. Calculations of the size and significance of such errors have not been carried out. However, such errors are not considered to be greater than for other quantitative short-term statistics.

Modelling errors are mainly related to problems with seasonal adjustment of time series. Such problems are caused by deviation from the conditions that form the basis for the model used. Typical problems are related to movable public holidays such as Christmas and Easter. However, such problems are considered greater for surveys published on a monthly basis. X12-ARIMA generates a number of indicators that are used to evaluate the quality of the seasonal adjustment. These indicators have identified a stable seasonal pattern.

Relevant documentation