Structural business statistics (sbs)

National Reference Metadata in Euro SDMX Metadata Structure (ESMS)

Compiling agency: Annex I-IV: Central Statistical Bureau of Latvia

Time Dimension: 2015-A0

Data Provider: LV1

Data Flow: SBS_ESMS_A


Eurostat metadata
Reference metadata
1. Contact
2. Metadata update
3. Statistical presentation
4. Unit of measure
5. Reference Period
6. Institutional Mandate
7. Confidentiality
8. Release policy
9. Frequency of dissemination
10. Accessibility and clarity
11. Quality management
12. Relevance
13. Accuracy
14. Timeliness and punctuality
15. Coherence and comparability
16. Cost and Burden
17. Data revision
18. Statistical processing
19. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
1.1. Contact organisation

Annex I-IV: Central Statistical Bureau of Latvia

1.2. Contact organisation unit

Annex I: Enterprise Structural Innovation Statistics Section
Annex II: Enterprise Structural Innovation Statistics Section
Annex III: Enterprise Structural Innovation Statistics Section
Annex IV: Enterprise Structural Innovation Statistics Section

1.5. Contact mail address

Annex I-IV:

1, Lāčplēša Str., Riga, LV-1301, Latvia


2. Metadata update Top
2.1. Metadata last certified 13/03/2019
2.2. Metadata last posted 13/03/2019
2.3. Metadata last update 13/03/2019


3. Statistical presentation Top
3.1. Data description

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 product breakdown is based on the Classification of Products by Activity (CPA) as stated in the Regulation establishing CPA 2008 and its amending  Commission Regulation (EU) No 1209/2014  (from reference year 2015 onwards)

3.3. Coverage - sector

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.

3.4. Statistical concepts and definitions

The statistical characteristics are defined in Annex I of Commission Regulation (EC) No 250/2009

3.5. Statistical unit

According to the action plan sent to the Eurostat, first delivery of the SBS final data series, which are fully compliant with the definition of a statistical unit defined in Regulation No 696/93, is planned for the reference year 2018. 

3.6. Statistical population

Coverage of target population includes branches of foreign enterprises which are registered in State Register of Enterprises, excluding Latvian enterprise branches which are doing business abroad in accordance with Tax Office information. 

3.7. Reference area

Latvia

3.8. Coverage - Time

1997-2015

3.9. Base period

Not applicable.


4. Unit of measure Top
  • 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.

 


5. Reference Period Top

SBS data refer to the calendar year, only in some cases the data refer to the fiscal year, however the differences are not essential.


6. Institutional Mandate Top
6.1. Institutional Mandate - legal acts and other agreements

Year 1995 was the first year for the implementation of the Council Regulation No 58/97 (SBS Regulation).

The Council Regulation No 58/97 has been amended three times: by Council Regulation No 410/98, Commission Regulation No 1614/2002 and European Parliament and Council Regulation No 2056/2002. As a new amendment of the basic Regulation it was decided to recast the Regulation No 58/97 in order to obtain a new "clean" legal text. The European Parliament and Council Regulation No 295/2008 was adopted on 14/02/2008 and the provisions of this Regulation are applicable from the reference year 2008. Regulation No 295/2008 has been amended by Commission Regulation (EU) No 446/2014.

6.2. Institutional Mandate - data sharing

Not applicable.


7. Confidentiality Top
7.1. Confidentiality - policy

The confidentiality of information provided by respondents is protected by the Statistical Law, Regulation (EC) No. 223/2009 on European statistics, the Law on the Protection of Personal Data and the Law on State Administration Structure.

7.2. Confidentiality - data treatment

The primary confidentiality rules:

  1. indicator of the aggregates is obtained from one, two or three statistical units;
  2. proportion of a one statistical unit in the respective indicator accounts for 80% and more;
  3. total proportion of two statistical units accounts for 90% or more.

The secondary confidentiality criterion is regarded as fulfilled, if the values of confidentiality indicators are not possible to identify through arithmetical operations. In order to ensure it, sometimes additional indicator has to be marked as confidential. Usually a minimum value other than zero is chosen as the additional indicator.

The secondary confidentiality is calculated by using T-Argus.

The above mentioned confidentiality rules are not applied to the variables 11110, 11210, 11310, 16110, 16130, 16140, 16150 – they are not confidential at any case according to the Statistical Law of Latvia.

The turnover (12110) is used as a shadow variable. 


8. Release policy Top
8.1. Release calendar

Release calendar is accessible for everyone and is published on Central Statistical Bureau of Latvia homepage under Theme - Enterprises\Structural Business statistics:  

https://www.csb.gov.lv/en/statistics/calendar

8.2. Release calendar access

Release calendar is accessible for everyone and is published on Central Statistical Bureau of Latvia homepage under Theme - Enterprises\Structural Business statistics:  

https://www.csb.gov.lv/en/statistics/calendar

8.3. Release policy - user access

The data which is not available in the published publications and in the published on-line databases are provided to everyone with a specific request.


9. Frequency of dissemination Top

Annual


10. Accessibility and clarity Top
10.1. Dissemination format - News release

Press releases are not disseminated, and they are not planned for the next reference year.

10.2. Dissemination format - Publications

Electronic versions of CSB publications are available in the CSB homepage free of charge.

SBS data is disseminated annually in Paper/pdf Publications: 

  1. Statistical yearbook is disseminated annually in the 1st Quarter and it is available in both languages - in Latvian and in English. Link to the Statistical yearbook: https://www.csb.gov.lv/en/statistics/statistics-by-theme/economy/gdp/search-in-theme/163-statistical-yearbook-latvia-2017
  2. The brochure: LATVIA. STATISTICS IN BRIEF 2017 is disseminated annually in the 2nd Quarter and it is available in both languages - in Latvian and in English. Link to the brochure: https://www.csb.gov.lv/en/statistics/statistics-by-theme/economy/gdp/search-in-theme/119-latvia-statistics-brief-2017

SBS data tables are available in CSB database:  

  1. SBG010. Entrepreneurship indicators of enterprises - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG010.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
  2. SBG020. Key entrepreneurship indicators of enterprises by number of employees - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG020.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
  3. SBG030. Key entrepreneurship indicators of kind of activity units in industry and construction - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG030.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
  4. SBG040. Entrepreneurship indicators in manufacturing by technological intensity (NACE Rev. 2) - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG040.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
  5. SBG050. Key entrepreneurship indicators of enterprises by statistical region - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG050.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
  6. SBG060. Key entrepreneurship indicators of enterprises in cities under state jurisdiction by main kind of activity and by place of office or main kind of activity - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG060.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
  7. SBG080. Structural business statistics in monetary intermediation, insurance and pension funds - http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/SBG080.px/?rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc
10.3. Dissemination format - online database

SBS data tables are available in national on-line database: http://data1.csb.gov.lv/pxweb/en/uzn/uzn__uzndarb/?tablelist=true&rxid=a47c5ebe-bbdc-40aa-b578-70401736b7dc

Nationally preliminary data is disseminated in T+11, final data in T+19.

 

10.4. Dissemination format - microdata access

In order to encourage deeper analysis of statistical information and its use in research, the CSB provides access to anonymized individual statistical information for scientific purposes, in the meantime strictly observing the principles of confidentiality.

10.5. Dissemination format - other

Not applicable.

10.6. Documentation on methodology

Documentation on methodology is available on the Website (electronic version). 

Key structural business indicators:

https://www.csb.gov.lv/en/statistics/statistics-by-theme/enterprises/structural-business-statistics/tables/metadata-key-structural-business

Business indicators in monetary intermediation, insurance and pension funding:

https://www.csb.gov.lv/en/statistics/statistics-by-theme/enterprises/structural-business-statistics/tables/metadata-business-indicators-monetary

10.7. Quality management - documentation

Quality documentation is available on CSB homepage: https://www.csb.gov.lv/en/documents/official-statistics-system/quality-framework/documents


11. Quality management Top
11.1. Quality assurance

Quality documentation is available on CSB homepage: https://www.csb.gov.lv/en/documents/official-statistics-system/quality-framework/documents

To ensure that NSS of Latvia has common principles and requirements, CSB has developed the Quality Policy of the National Statistical System of Latvia and imposed total quality requirements for statistical institutions which are based on European Statistics Code of Practice. 

Quality policy of the National Statistical System of Latvia is available on CSB homepage: https://www.csb.gov.lv/sites/default/files/dokumenti/Kvalitate/Quality_Policy_of_NSS_of_Latvia.pdf

For implementation of Quality Policy of the NSS of Latvia, the CSB has developed a Memorandum of Understanding, by which the statistical institutions confirm their commitment to implementing and promoting quality standards in their statistical institutions.

Guidelines for Implementation of European Statistics Code of Practice were developed to facilitate common perception and understanding of the requirements imposed by the Code of Practice. Based on the situation in Latvia, the Guidelines explain principles of the Code, as well as indicators and statistical terms used, moreover, requirements are supplemented with binding national legislation, explanatory information, and examples of good practice.

The Quality Policy of CSB consists of the CSB's vision, mission, core values and commitment to meet the requirements, follow good practice and ensure continuous improvement. The quality policy is designed and implemented in accordance with the CSB strategy (3-year period) and the action plan. The document is available on CSB homepage: https://www.csb.gov.lv/sites/default/files/dokumenti/CSB_Kvalitates_politika_2018_EN.pdf

11.2. Quality management - assessment

Overall assessment of data quality is good.


12. Relevance Top
12.1. Relevance - User Needs

Internal users:

  • National accounts – all SBS variables + KAU + larger scope (NACE rev.2 sections A, O – S)
  • Labour statistics section – SBS employment variables + KAU + variable “Number of employees at the main job” + larger scope (NACE rev.2 sections A, O – S)
  • FATS - SBS variables according to the FATS Regulation
  • ICT - Turnover on ICT sector
  • Industry statistics – Value added in industrial activities
  • Price Statistics - SBS variables: turnover, number of persons employed + KAU + larger scope (NACE rev.2 sections A, O – S)

 External users:

  • Ministry of Economics – different SBS variables and breakdowns (NACE, size classes, regions)
  • Ministry of Culture – Turnover on creative industries
  • Ministry of Agriculture – different SBS variables on fishing, aquaculture and fish processing industries (NACE, size classes)
  • Investment and Development Agency of Latvia - different SBS variables and breakdowns (NACE, size classes, regions)
  • United Nations Industrial Development Organization – data for UNIDO General Industrial Statistics Questionnaire
12.2. Relevance - User Satisfaction

The survey related to the user's satisfaction regarding the availability and the quality of the data used were not organised.

12.3. Completeness

LV don't plan to provide in the next reference years any of the data for which the 1%-rule have been applied.


13. Accuracy Top
13.1. Accuracy - overall

Based on sampling error measurements, provided in separate files, sampling errors are assessed as low in the most cases.

Estimated coverage error and non-response error are assessed as low.

Measurement, data processing and modelling errors are not measured and could not be assessed.

Preliminary data used for calibration does not contain information about companies that are legally authorized to submit data to State Revenue Service with the delay. The same reason is for non-respondence (non-response error).

13.2. Sampling error

Sample survey is combined with administrative information. Software R is used for computation of the coefficients of variation. The precision estimation is done by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953) with Taylor linearization for linear statistics and residual estimation from the regression model to take weight calibration into account.

The sampling error is applicable and small. 



Annexes:
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 on NACE Rev.2 Section level for the Sections H to J and L to N and also on the NACE Rev.2 Division level for division 95 and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 for NACE Rev.2 Sections B to E detailed by NACE Section and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 For NACE Rev.2 Section G detailed by NACE Section and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 for NACE Rev.2 Section F detailed by NACE Section and by size class
Coefficients of variation for variable 12110 at NACE 3-digit level for NACE Rev.2 Sections B to J and L to N and Division 95
Coefficients of variation for the variables 11110, 12110, 12150, 13310, 15110 and 16130 on NACE Rev 2 Section level for the sections B to J and L to N and also on NACE Rev 2 Division level for divisions 45, 46, 47 and 95
13.3. Non-sampling error

Unit non-response

The unit non-response is adjusted using re-weighting and imputation. Reminding e-mails are sent to non-respondents, notification about not filled in questionnaire has been shown in the application of web-questionnaire and reminding phone calls are also made in order to minimise the unit non-response.

Weighted unit non-response rate

The number of employees is used for calculation of the weighted unit non-response rate. For the data collected by survey imputed items has non-respondent status, otherwise for data set has not collected by survey and data has been extracted from administrative sources, imputed items have respondent status. Recorded unit non-response rate is evaluated as low.

Bias

Small bias (with a limited effect in the overall accuracy of the estimate) describes the bias of the estimate.

Evaluation of the impact of imputation

The impact of imputation on CVs is not important. The CVs just using the reweighting procedures are not compared with the CVS after imputation procedures.

Frame

The impact of imperfection of the relevant business register on the quality of the key statistics is assessed as medium.

Out-of-scope units

Out-of scope units are detected during the data collection and using administrative sources. Estimated number of those units is 2%. 



Annexes:
Weighted unit non-response rates for NACE Rev.2 Section B to J and L to N


14. Timeliness and punctuality Top
14.1. Timeliness

Data collection:08/2016

Post-collection phase: 10/2016 (preliminary data), 05/2017 (final data)

Dissemination nationally: 11/2016 (preliminary data), 07/2017 (final data) 

14.2. Punctuality

Not applicable.


15. Coherence and comparability Top
15.1. Comparability - geographical

Data are comparable.

15.2. Comparability - over time

2005 - 2015

Due to the implementation of new classification NACE rev.2 the data series are not comparable later than 2005. 

15.3. Coherence - cross domain

Inconsistencies between STS Production in Construction and SBS Production value

Differences are explained as:

  • variable Construction production in STS is calculated taking into account only construction works done on own account, however in SBS construction activities also includes the work of subcontractors,
  • STS sometimes do not cover enterprises with small number of persons employed but with large turnover (very often in activity “Development of building projects”), such enterprises frequently are detected and imputed only, when compiling SBS results.

Inconsistencies between STS Industrial Production output and SBS Production value

Differences are explained as:

  • Different coverage; STS covers enterprises with more than 20 employees only, SBS covers entire population,
  • Data corrections, precisions, when annual results are reported,
  • Differences in reporting periods; STS covers calendar year, SBS covers financial year,
  • Differences in definitions; capitalised production is included in SBS variable, however due to difficult reporting such positions are not included in STS variable.

Inconsistencies between STS Turnover in distributive trade and SBS Turnover

Differences are explained as:

  • Differences in definitions; turnover in SBS excludes subsidies, but turnover in STS should not exclude such subsidies if they are included in turnover,
  • Different coverage; SBS includes data on self-employed physical persons, but STS do not cover them,
  • Different stratification criteria for sampling; the sample for SBS is stratified according NACE code, number of employees and turnover, but in STS stratification has been made only according NACE codes and turnover (not taking into account number of employees),
  • Data corrections, precisions, when annual results are reported,
  • Different methods for data imputation; SBS mainly impute information from annual enterprise reports, but STS have been based on imputation of previous period data with corrections for tendencies and on use of VAT data.

Inconsistencies between LCS wages and salaries and number of persons employed and SBS wages and salaries and number of persons employed

Differences are explained as:

  • Different coverage; SBS includes data on self-employed physical persons, but LCS do not cover them,
  • SBS data do not include remuneration paid in kind, but include employer payments in the event of illness, however LCS include remuneration paid in kind, but excludes employer payments in the event of illness.

Inconsistencies between value added in Nacional Accounts (NA) and SBS value added

Differences are explained as:

  • NA adjustments (for instance, shadow economy),
  • SBS value added does not include taxes, however NA value added includes taxes and duties linked to production,
  • NA value added includes imputed rent for NACE 68.
15.4. Coherence - internal

The outputs of the statistical activity are coherent.


16. Cost and Burden Top

Burden on respondents for filling in the questionnaire is 12 286 person-hours. Respondents' burden has been reduced for the reference period 2017, when stratification criteria - number of persons employed were increased, as well as the necessity of each indicator included in the survey was evaluated.


17. Data revision Top
17.1. Data revision - policy

CSB Revision policy has been lain down in accordance with the European Union and international recommendations and good practice. 

Revision policy guidelines are available on CSB homepage: https://www.csb.gov.lv/sites/default/files/dokumenti/revision_policy_ENG.pdf

The first chapter of the present document explains the terms applied in the Revision policy, the second chapter characterises shortly the CSB revision policy, whereas the third chapter stipulates the revision cycle of the statistical data prepared by the CSB.

Revision policy according Annex I-IV: Data at T+10 has been considered as provisional but at T+18 as final and has not been changed.

17.2. Data revision - practice

Entire population has been covered, when preliminary data is compiled. Differences with the final data occur due to corrections of imputed data and adjustments of population frame.


18. Statistical processing Top
18.1. Source data

Data source is statistical survey combined with administrative sources. 

The type of sample design - stratified.

Stratification criteria - by activity, employment size class, turnover size class and form of entrepreneurship.

Selection schemes (sampling rates) - Neyman optimal allocation is used to calculate the optimal sample size for each stratum based on information about the amount of net turnover of enterprise and population size. Sample size was optimized so that theoretical CV in each NACE rev.2 group was less or equal to 3%. Sampling rate is 76%.

Threshold values - the strata according to the size groups by employees are as follows: 10-19, 20-49, 50-99, more than 100. All enterprises with 100 or more persons employed and all enterprises with turnover that is higher than threshold defined for the corresponding NACE group are surveyed exhaustively.

Sample size - 10 721. 

Several statistical questionnaires are used to compile SBS data, but different sample scopes are always adjusted to the sample of the main SBS survey “Annual enterprise survey” using imputation methods to obtain the data on those enterprises, which were not surveyed with those additional questionnaires. Such questionnaires as "Annual questionnaire on investments", "Quarterly questionnaire on labour force" and "Questionnaire on costs" are additionally used to compile SBS data.

Administrative data sources are provided by The State Revenue Service of Latvia. For the compiling of SBS data, the following sources are used:

  • annual financial statement of enterprises; balance sheet, profit and loss account
  • declaration on income from economic activity
  • different declarations on taxes paid (natural resources tax, lottery and gambling tax, customs tax and other governmental charges and payments)
  • employers' declaration on salary tax
  • declaration on mandatory state social insurance contribution
  • declaration on Value Added Tax (VAT) 
  • declaration on micro-enterprise tax.

The characteristics which are directly available or with a good proxy in the administrative source - 12110, 13210, 13211, 13213, 13320, 16110.

The extent to which the administrative source is used:

  • data source for imputation in case of non-response
  • data source for 'mass imputation' (imputation of units not selected in the sample).

Access to administrative data is at micro data level.

The frequency to which the used administrative data sources are updated is assessed as satisfactory.

The administrative data subject to several revisions are with (increasing) degree of completeness.

Reporting unit is enterprise.

Regarding the frame used for the SBS, turnover and employees' variables are used for identifying principal and secondary activity.

The method used for identifying activities is top-down.

Updating of the unit's principal activity is carried out in accordance with methodological guidelines and after careful economic indicators analysis (statistical surveys and Tax Office data). 

Business Register is updated on a monthly basis using the Tax Office data on the employees and tax contributions, as well as using State Register of Enterprises about information on established and liquidated companies.

Once a year information is updated about turnover and total balance sheet.

Frequency of data collection is annual.

18.2. Frequency of data collection

Annual data collection

18.3. Data collection

The type of questionnaires used in the survey are electronic format in Electronic Data Collection system on CSB homepage, as well as respondents can print out the paper questionnaires in CSB homepage and submit them by post, fax, send by e-mail or bring them personally. 

Reminder e-mails are sent as well as reminders by phone are made to companies in order to speed up or increase the rate of response.

 

18.4. Data validation

The following validation rules are applied before the data transmission to Eurostat:

  • completeness checks (data integrity rules);             
  • validity checks (internal consistency);                      
  • plausibility checks.

Data editing is done during the data collection period. Data validation rules are described in the data production system MD ISDMS (Metadata Driven Statistical Data Management System) and appear automatically after data entering in the system. Data is corrected manually. Some primary data validation rules are also worked-in the electronic version of the survey (respondent can not transmit the data before those errors are corrected).

Data checking and editing of imputed data is also done within the data production system MD ISDMS – errors are detected automatically, corrected and re-imputed.

Eurostat provided EDIT tool is used for SBS aggregated data checking, where single series, inter series and year to year validation is done.

 

18.5. Data compilation

Imputation procedure rely on available administrative data as well as information available from the short-term statistics surveys.

The characteristics for which a model-based estimate is used: 13110, 13120 (breakdown of expenditures in material costs, services costs and costs for the purchase of goods and services for resale), and all variables starting with 15xxx.

SBS characteristics which are not included in the data sources: 

  • Variables 11210, 12130, 13131, 13411, 20110, 23110, all variables starting with 18 and breakdown by KAU are compiled with statistical surveys (in some cases estimations are done if the sampling frame of particular survey, from which characteristic is taken, differs from the sampling frame of the annual enterprise survey or in the case of KAU statistics it is assumed that all enterprises under the thresholds, which are estimated from administrative sources, can consist of one KAU only).
  • Investment variables - estimation is done taking into account the values of fixed assets reported in balance sheets at the beginning and at the end of the year.

Unit non-response:

Enterprises, which are included in “large enterprise group” and are surveyed exhaustively and, which did not respond with statistical survey as well as non-sampled enterprises, which are covered by mass imputation, are imputed using the data from administrative sources. Imputation of other statistical survey data, data of previous period (with correction) or donor data imputation is done if no administrative data have been found.

Non-response for enterprises, which are included in the sample survey part (as mass imputation part) are corrected in the weighting procedure.

Item non-response:

Item non-response has been detected and corrected during the data collection period. 

Inference (grossing-up):

The design weights are applied for grossing-up the figures covered by the SBS Regulation in order to cover the entire population of enterprisesThe design weights are calculated according to sample design and are calculated as ratio of the number of enterprises in the population to the number of enterprises in the sample within each stratum. The design weights are adjusted taking into account non-response in each stratum.

Remark: 1) over-coverage errors are assumed as respondents because they represent the total over-coverage in the frame. 2) In the exhaustive enumeration part of survey unit non-response is adjusted using imputation procedure. 

18.6. Adjustment

Generally, SBS data refer to the calendar year, only in some cases the data refer to the fiscal year, however the differences are not essential. If the reference period differs from the calendar year, there is no correction to bring it in accordance with the reference period.


19. Comment Top

Not applicable.


Related metadata Top


Annexes Top
Weighted unit non-response rates for NACE Rev.2 Section B to J and L to N
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 on NACE Rev.2 Section level for the Sections H to J and L to N and also on the NACE Rev.2 Division level for division 95 and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 for NACE Rev.2 Sections B to E detailed by NACE Section and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 For NACE Rev.2 Section G detailed by NACE Section and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 for NACE Rev.2 Section F detailed by NACE Section and by size class
Coefficients of variation for variable 12110 at NACE 3-digit level for NACE Rev.2 Sections B to J and L to N and Division 95
Coefficients of variation for the variables 11110, 12110, 12150, 13310, 15110 and 16130 on NACE Rev 2 Section level for the sections B to J and L to N and also on NACE Rev 2 Division level for divisions 45, 46, 47 and 95
Coefficients of variation for variable 12110 at NACE 3-digit level for NACE Rev.2 Sections B to J and L to N and Division 95
Coefficients of variation for the variables 11110, 12110, 12150, 13310, 15110 and 16130 on NACE Rev 2 Section level for the sections B to J and L to N and also on NACE Rev 2 Division level for divisions 45, 46, 47 and 95
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 on NACE Rev.2 Section level for the Sections H to J and L to N and also on the NACE Rev.2 Division level for division 95 and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 for NACE Rev.2 Sections B to E detailed by NACE Section and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 For NACE Rev.2 Section G detailed by NACE Section and by size class
Coefficients of variation for the variables 11110, 12110, 12150 and 16110 for NACE Rev.2 Section F detailed by NACE Section and by size class
Weighted unit non-response rates for NACE Rev.2 Section B to J and L to N