Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).
SBS covers all activities of the non-financial business economy with the exception of agricultural activities and personal services. Limited information is available on banking, insurance and pension funds.
Main characteristics (variables) of the SBS data category:
Business demographic variables (e.g. Number of enterprises)
"Output related" variables (e.g. Turnover, Value added)
"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments)
3.2. Classification system
Statistical Classification of Economic Activities in the European Community (NACE): NACE Rev.1 was used until 2001, NACE Rev. 1.1 since 2002, and NACE Rev 2 is used from 2008 onwards. Key data were double reported in NACE Rev.1.1 and NACE Rev.2 for 2008. From 2009 onwards, only NACE Rev.2 data are available.
The regional breakdown of the EU Member States is based on the Nomenclature of Territorial Units for Statistics (NUTS). Detailed information about the consecutive NUTS Regulations can be found at Eurostat's website
The SBS coverage was limited to Sections C to K of NACE Rev.1.1 until 2007. Starting from the reference year 2008 data is available for Sections B to N and Division S95 of NACE Rev.2. With 2013 as the first reference year information is published on NACE codes K6411, K6419 and K65 and its breakdown.
The provisions of Regulation 696/93 are partly implemented in the area of SBS according to Regulation 295/2008 Statistical unit is enterprise. Internal transaction in the account data was consolidated and eliminated. Most of the legal units are identical with an enterprise.
The Series 1G and 1D are still based on a legal unit.
3.6. Statistical population
The statistical population is taken from the business register. All Nace sections are covered in national statistics. Data published by Eurostat includes Nace sections B to N excluding K642, K643, K649 and K66.
Market producers that have operated for at least 6 months during the statistical year and meet at least one of the size limits are counted as statistical units. Enterprise meets the size limits if it has turnover of over EUR 11 968, personnel of over 0.5 personyears, investments of over EUR 50 000 or over EUR 170 000 on their balance sheet.
Branches of foreign enterprises are included in the data if the branches are registered in Finland. Foreign activities of an enterprise in Finland are included in the data only if these activities are included in the balance sheet of the enterprise registered in Finland.
3.7. Reference area
Finland (including NUTS)
3.8. Coverage - Time
Based on legal units: 1996 – 2020 Changes to the industrial classification caused discontinuity the series in years 2002 and 2008
Based on enterprise units: 2017-2020
3.9. Base period
A Year
Number of enterprises and number of local units are expressed in units.
Monetary data are expressed in millions of €.
Employment variables are expressed in units.
Per head values are expressed in thousands of € per head.
Ratios are expressed in percentages.
2020
The data refers to fiscal year. The different financial years are converted to correspond to the statistical year.
6.1. Institutional Mandate - legal acts and other agreements
Confidentiality policy is based on the basic statistical law. The statistical authority shall not disclose information that allows the statistical unit to be identified directly.
7.2. Confidentiality - data treatment
Confidentiality rules Primary: If a cell contains less than 3 observations Secondary: Flagging cells so that the least amount of data is lost.
Specific tool (InputHall) maintained by Eurostat for validation of SBS-series.
The quality management framework of the field of statistics is the European Statistics Code of Practice (CoP). The quality criteria of Official Statistics of Finland are compatible with the European Statistics Code of Practice. Further information: Quality management | Statistics Finland (stat.fi)
The quality of the structural business and financial statement statistics is examined as the data accumulate. At aggregate level, the data are compared with the previous year and the most significant changes are examined. Coherence analyses to short term statistics are also carried out.
11.2. Quality management - assessment
We have comprehensive administrative data at our disposal, thus the quality is good.
The top management of SF has made several self-assessments in line with the EFQM model. There have also been external audits by e.g. the EU and IMF experts. Processes are in place to monitor the quality of the statistical process and the processes of individual statistics. Quality considerations are an integral part of the planning and evaluation of the statistical programme. The process owner of statistical production and it’s supporting group monitor the quality and steer the standardisation of work processes. Statistics Finland has an internal quality audit system. The main objectives are to evaluate the ways of working, methods and techniques. An audit is carried out by an audit team of experts who are external in the sense that they do not have any direct connection with the production process in question. About 8 audits are carried out yearly.
12.1. Relevance - User Needs
Main users of the data are Internal: Other departments in our institution (national accounts), Researchers, Enterprises/businesses, Media, Minstries etc. External: Eurostat SBS data published at national level is partly different from the data sent to Eurostat. Enterprise size classifications is different due to number of enterprises. KAU-level statistics are not published. Regional level of breakdown is different, as we publish the data at more detailed regional level instead of NUTS. Special aggregates are not published (Serie 1E). Variable 12120 is not published at enterprise level but rather at local level. Variable concerning investment is published as net investment instead of gross investment.
We provide additional variables based on balance sheet information
12.2. Relevance - User Satisfaction
Co-operation between SF and important users with regard to the relevance of statistics and the users’ needs consists of an extensive feedback system and co-operative working groups with the main users, such as users of national accounts. There are regular meetings of SF directors and experts with the users, even at the senior management level. Users are usually also invited to participate in discussions concerning the establishment of new statistics or revisions of existing ones.In addition, there are specific feedback systems for receiving the users’ opinions at SF. These systems consist of an anonymous feedback channel on the web, media monitoring, surveys among different user groups for the evaluation SF’s performance, user surveys (every second year, latest in 2015), and a system for collecting and disseminating information that is strategically important for SF. Specific statistical products conduct their own user surveys and keep in regular contact with their main interest groups.
12.3. Completeness
We are providing all the relevant data required by SBS regulations.
13.1. Accuracy - overall
The main source of errors is non-response
13.2. Sampling error
Not relevant because total data is available.
13.3. Non-sampling error
The influence of non-sampling error is small. The unit non-response are imputed based on nearest neighbour with distance measure.
The recorded unit non-response rate: Low
The bias of the estimate: Small bias
Coverage error: Almost none
Out of scope units: For the reference year 2020 the business Register annual Quality Control Survey covered 1411 legal units. The response rate was 64,4 percent. Based on the survey results 3 percent were found to be misclassified (5-digit level).
Based on legal units: Length of comparable time series: 2013-2020
Statistical Finland has renewed and harmonized the production of business statistics from the statistical reference year 2013 onwards. The renewal improves the uniformity of business statistics. In connection with the renewal, uniform industries defined by the Business Register were taken into use. Due to changes in industries, the time series (SBS) are not in all respects fully comparable. The definition of an enterprise to be included in the statistics has not been changed in SBS series.
Based on enterprise units: 2017-2020
15.3. Coherence - cross domain
- Number of enterprises, number of persons employed, number of employees in Business register
- Number of enterprises, number of persons employed, number of employees in Business demography
- Production value of Prodcom
- Value added of national account
- Evolution of turnover and persons employed from short term statistics
- Business services (turnover in Annex VIII versus Annex I)
The inconsistencies are evaluated as part of the manual editing process of statistics
Description of coherence:
Coherence between Business register and SBS is good. Between other statistics especially short term statistics there are partly uncoherence.
Explanation of differences:
Differences are caused by differences in statistical target population
15.4. Coherence - internal
The aggregates are always consistent with their main sub-aggregates
We use administrative data for most of SBS variables.
We did a burden measuring survey for enterprises who are part of our financial statement survey in 2018:
900 enterprises answered
the average time spend to answer was 188 minutes (median was 120 minutes)
29 % of the enterprises answering to the survey used over 3 hours to answer
55 % thought that the survey was very burdensome
17.1. Data revision - policy
Different versions of administrative data cause revisions. In general revisions are small/moderate if compared to the known use of data.
17.2. Data revision - practice
The methodology is the same for the preliminary data as the final data. All the revisions are due to revised source data.
18.1. Source data
Type of source
For the compilation of the Structural Business Statistics following source datas are used: Business register, a direct inquiry and Tax Authority administrative data. Administrative data from Tax Administration provides financial statements data for all enterprises (main source). The BR provides information on principal activity and number of personnel. The direct inquiry is a census which covers all enterprises with more than 50 employees. Some enterprises with more than 10 employees are also included in sample survey. The direct inquiry data are mainly collected for the national data needs (more detailed data on certain variables/items for the calculation of national accounts)
- any possible threshold values: not applicable - the effective sample size is 5500
- the used administrative sources: Income tax data from tax authorities and Business register - the characteristics directly available or with good proxy in the administrative source: 11110, 11210, 11310, 12110, 12120, 12130, 12150, 12170, 13110, 13120, 13131, 13210, 13211, 13213, 13310, 13320, 13330, 13411, 15110, 15120, 15130, 15140, 15150, 15210, 15420, 15441, 16110, 16120, 16130, 16140, 16150, 18110, 18120, 18121, 18122, 18150, 18160, 18310, 18320, 20110, 23110, 23120 - the extent to which the administrative source are used?: data source - what kind of administrative data do you access?: micro data - how do you assess the frequency to which the used administrative data sources are updated?: good - whether the administrative data are subject to several revisions with (increasing) degree of completeness?: yes - the relation between the reporting unit for the survey/adminitrative data and the enterpise?: Survey/ administrative data is received from the legal unit
Frame - What is the variable used for identifying principal and secondary activities?: Personnel (first the industry level value added multipliers are calculated) - What is the method used for identifying activities?: bottom-up - Please comment on the frequency of updating the unit's principal activity (stability rules)?: The unit’s principal activity is updated mainly once a year. We have account stability rules so that for certain cases (near 50/50 between principal and secondary) the unit’s principal activity is more stable over time. - Please comment on the frequency of updating the business register in your NSI: in principle the business register is updated continuously, however most of the units and characteristics are updated?: once a year - Please indicate the frequency with which the sample is updated?: new sample is drawn every year
18.2. Frequency of data collection
Annual data collection
18.3. Data collection
Administrative data: Direct access to an administrative data base. Part of the administrative data are sent to Statistics Finland.
Scoring model is introduced to detect and evaluate errors.
18.4. Data validation
1. Validation of format and file structure checks. This validation is made right after extraction the data from administrative data base. The same prosedure is applied to the survey data.
2. Intra-dataset checks. For further information see 18.5.
3. Inter-dataset checks. Not applicable
4. Intra-domain, intra-source checks. This includes revision checks and time series checks by industries.
5. Plausibility or consistency checks between two domains available in the same Institution. Short term incoming data and annual incoming data is compared (eg. turnover) by industries. 6. Plausibility or consistency checks between the data available in the Institution and the data / information available outside the Institution. For the time being there is no comparable outside database for this type of plausibility or consistency checks.
18.5. Data compilation
Imputation methods: Tax data is treated automatically using mass editing and imputation techniques. The errors and outliers are edited in following order: Logical edits, Outlier detection, Small errors (<5%) from turnover are re-scaled. Two types of imputation methods are used in the SBS data. First type is donor imputation which is applied for unit non-response in tax data. The data is imputed using nearest neighbour imputation with distance measure. Unit non-response includes those units that have not sent their accounting data to Tax Authority: Second step is item non-response. Item non-response refer to mass imputation of the variables included in direct inquiry and not received from Tax Authority. Primary method used is regression imputation with outlier detection and weighting if necessary.
Survey data is mainly checked manually. It forms the basis for imputation process.
Tax data and survey data is compiled together thus forming our structural business data.
18.6. Adjustment
The accounting year is not necessarily same as the calendar year. No corrections are made to convert accounting year data to calendar year data.
No further comments
Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).
SBS covers all activities of the non-financial business economy with the exception of agricultural activities and personal services. Limited information is available on banking, insurance and pension funds.
Main characteristics (variables) of the SBS data category:
Business demographic variables (e.g. Number of enterprises)
"Output related" variables (e.g. Turnover, Value added)
"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments)
The provisions of Regulation 696/93 are partly implemented in the area of SBS according to Regulation 295/2008 Statistical unit is enterprise. Internal transaction in the account data was consolidated and eliminated. Most of the legal units are identical with an enterprise.
The Series 1G and 1D are still based on a legal unit.
The statistical population is taken from the business register. All Nace sections are covered in national statistics. Data published by Eurostat includes Nace sections B to N excluding K642, K643, K649 and K66.
Market producers that have operated for at least 6 months during the statistical year and meet at least one of the size limits are counted as statistical units. Enterprise meets the size limits if it has turnover of over EUR 11 968, personnel of over 0.5 personyears, investments of over EUR 50 000 or over EUR 170 000 on their balance sheet.
Branches of foreign enterprises are included in the data if the branches are registered in Finland. Foreign activities of an enterprise in Finland are included in the data only if these activities are included in the balance sheet of the enterprise registered in Finland.
Finland (including NUTS)
2020
The data refers to fiscal year. The different financial years are converted to correspond to the statistical year.
The main source of errors is non-response
Number of enterprises and number of local units are expressed in units.
Monetary data are expressed in millions of €.
Employment variables are expressed in units.
Per head values are expressed in thousands of € per head.
Ratios are expressed in percentages.
Imputation methods: Tax data is treated automatically using mass editing and imputation techniques. The errors and outliers are edited in following order: Logical edits, Outlier detection, Small errors (<5%) from turnover are re-scaled. Two types of imputation methods are used in the SBS data. First type is donor imputation which is applied for unit non-response in tax data. The data is imputed using nearest neighbour imputation with distance measure. Unit non-response includes those units that have not sent their accounting data to Tax Authority: Second step is item non-response. Item non-response refer to mass imputation of the variables included in direct inquiry and not received from Tax Authority. Primary method used is regression imputation with outlier detection and weighting if necessary.
Survey data is mainly checked manually. It forms the basis for imputation process.
Tax data and survey data is compiled together thus forming our structural business data.
Type of source
For the compilation of the Structural Business Statistics following source datas are used: Business register, a direct inquiry and Tax Authority administrative data. Administrative data from Tax Administration provides financial statements data for all enterprises (main source). The BR provides information on principal activity and number of personnel. The direct inquiry is a census which covers all enterprises with more than 50 employees. Some enterprises with more than 10 employees are also included in sample survey. The direct inquiry data are mainly collected for the national data needs (more detailed data on certain variables/items for the calculation of national accounts)
- any possible threshold values: not applicable - the effective sample size is 5500
- the used administrative sources: Income tax data from tax authorities and Business register - the characteristics directly available or with good proxy in the administrative source: 11110, 11210, 11310, 12110, 12120, 12130, 12150, 12170, 13110, 13120, 13131, 13210, 13211, 13213, 13310, 13320, 13330, 13411, 15110, 15120, 15130, 15140, 15150, 15210, 15420, 15441, 16110, 16120, 16130, 16140, 16150, 18110, 18120, 18121, 18122, 18150, 18160, 18310, 18320, 20110, 23110, 23120 - the extent to which the administrative source are used?: data source - what kind of administrative data do you access?: micro data - how do you assess the frequency to which the used administrative data sources are updated?: good - whether the administrative data are subject to several revisions with (increasing) degree of completeness?: yes - the relation between the reporting unit for the survey/adminitrative data and the enterpise?: Survey/ administrative data is received from the legal unit
Frame - What is the variable used for identifying principal and secondary activities?: Personnel (first the industry level value added multipliers are calculated) - What is the method used for identifying activities?: bottom-up - Please comment on the frequency of updating the unit's principal activity (stability rules)?: The unit’s principal activity is updated mainly once a year. We have account stability rules so that for certain cases (near 50/50 between principal and secondary) the unit’s principal activity is more stable over time. - Please comment on the frequency of updating the business register in your NSI: in principle the business register is updated continuously, however most of the units and characteristics are updated?: once a year - Please indicate the frequency with which the sample is updated?: new sample is drawn every year
Based on legal units: Length of comparable time series: 2013-2020
Statistical Finland has renewed and harmonized the production of business statistics from the statistical reference year 2013 onwards. The renewal improves the uniformity of business statistics. In connection with the renewal, uniform industries defined by the Business Register were taken into use. Due to changes in industries, the time series (SBS) are not in all respects fully comparable. The definition of an enterprise to be included in the statistics has not been changed in SBS series.
