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.
Statistical unit used for the compilation the SBS series is enterprise. The enterprise almost always correspond to legal unit.
3.6. Statistical population
Target population: All the active enterprises (consisting of legal and natural persons) classified in Sections B to J, L to N and Division S95 of NACE Rev 2 (see also concept 3.3. Coverage - sectors) and institutional sectors of non-financial corporations and households (employers and own-account workers). Exceptions are housing associations and non-profit institutions classified into the institutional sector of non-financial corporations.
Frame for the identification of the population: National Statistical Business Register.
3.7. Reference area
Czech Republic
Inclusion/exclusion of the activities of Foreign Affiliates
If a foreign enterprise establishes a branch in the Czech Republic, it has the obligation to register the branch at the Register of Companies, thereby it enters the Statistical Business Register and subsequently the population of the Annual Structural Business Survey carried out by the Czech Statistical Office (CzSO). And the other way round, if an enterprise registered in the Czech Republic establishes a branch abroad, it is requested to report only unconsolidated information (i.e. data not including the activities of foreign affiliates). However, if an enterprise registered in the Czech Republic carries on business abroad, but does not establish an affiliate abroad, the results of these activities are included in the data reported to the CzSO.
3.8. Coverage - Time
2005-2020
3.9. Base period
Not applicable.
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
Data refers to calendar year. If an enterprise reports the data for a fiscal year, it is recalculated to the respective calendar year.
6.1. Institutional Mandate - legal acts and other agreements
All the data provided to Eurostat, unless marked for use as a contribution to European totals only, is provided to users through the regular publications or on request.
Microdata is provided only to the selected public authorities and wholly in conformity with the State Statistical Service Act.
Annual
10.1. Dissemination format - News release
News releases are not used for the publication of the SBS data. The Czech Statistical Office prefers other publication formats.
10.2. Dissemination format - Publications
Paper publications and Statistical Yearbook of the Czech Republic available in paper and electronic versions (English versions, see Annex).
Time series on the website of the Czech Statistical Office. Different indicators are published - SBS indicators as well as indicators constructed according to a national methodology - from the Annual Structural Business Survey.
Access to microdata is granted only to the selected public authorities and wholly in conformity with the State Statistical Service Act.
10.5. Dissemination format - other
All the data from the Annual Structural Business Survey provided to Eurostat or not and not regularly published can be provided, under certain conditions, to all users on request.
10.6. Documentation on methodology
Available as part of the paper/electronic publications and time series.This documentation is available in Czech and also in English.
10.7. Quality management - documentation
The whole process of data production from questionnaires design, metadata definition, through data collection, imputation methods, primary and secondary data validation, confidentiality treatment and dissemination is in detail described in the internal documentation (Technical Projects and Statistical Metadata System) to which all interested parties have an access. If the Annual Structural Business Survey is the subject of the methodology audit, the documentation is available to auditors. This comprehensive documentation is available in the Czech language.
11.1. Quality assurance
The key components of the quality assurance are the following: 1. Strategic documents of the CZSO: Mission, Vision and Priorities of the CZSO, Quality commitment, Priority tasks defined for each year. 2. Irregular methodological audits represent an important tool in quality control. Audits are usually carried out by expert group comprising of internal and external experts (NA experts, representatives of universities, ministries etc.). 3. Important aspects and procedures of quality management within statistical production process are documented by the internal Statistical Metadata System, including so-called Technical projects, event. by instructions for surveyors that are available for every statistics produced.
Thanks to available administrative data, high response rate, chosen imputation method and several stage validation system the overall quality can be assessed as favourable. Data is produced in time and all data requests of the most important users are satisfied. The detail aspects of the quality are described under the relevant concepts (12 to 15).
Two aspects of the quality can be assessed as problematic: quite high miss-classification of the units in the Statistical Business Register and the stability of the administrative data (subject to frequent changes).
12.1. Relevance - User Needs
Internal Users:
User: National Accounts Department. Needs: SBS data plus additional, mainly accounting, data far beyond the scope of SBS.
User: Prices Statistics Department Needs: Breakdown of Turnover by product type (CPA)
User: STS Needs: Weights of base year
User: IFATS Needs: Data referring to the foreign controlled enterprises.
External Users:
Users: Czech National Bank, Ministry of Industry and Trade, Ministry of Regional Development Needs: Microdata from the Annual Structural Business Survey
12.2. Relevance - User Satisfaction
Regular consultations with some of our main users are organised.
12.3. Completeness
All the SBS series provided to Eurostat, with the exception of the series on environmental protection (75% completness), are complete.
13.1. Accuracy - overall
With regard to available administrative data, high response rate, chosen imputation method and several stage validation system the overall quality can be assessed as favourable. The possible source of potential errors is the lack of information related to the smallest units in the population, what might result, among others, in the high misclassification of statsitical units in the Statistical Business Register. The potential bias is, however, small with limited effect on the overall accuracy of estimates.
The preliminary results may be biased since at the time of its transmission to Eurostat not all questionnaires nor administrative data are available. However, we consider the potential bias to be relatively low.
13.2. Sampling error
Sampling error is regularly monitored. The coefficients of variation are computed and for indicators determined by the Commission Regulation (EU) No 275/2010 provided to Eurostat.
Methodology on Variance estimation is included in the separate file (see Annex).
The potential non-sampling error that is considered to be small may result from:
Relatively high degree of misclassification particularly of the smallest units (natural persons and legal persons without employees, which are not VAT payers).
Non-response. However, it is not considered to be serious problem.
Relatively frequent modifications of the content or/and the extent of the administrative sources.
The lack of information related to the smallest units (especially natural persons - not VAT payers - without employees)
The methods used for taking into account the unit non-response:
The imputation procedures dealing with unit and item non-response are based on:
the available administrative data (selected indicators from balance sheet and profit and loss statement; approximation of total revenues from the VAT data; number of social insurance policyholders);
the data available from the other surveys (especially annual Labour Cost Survey; quarterly business structural survey; PRODCOM);
the annual structural business data from the previous reference periods.
The quality of administrative data does not allow to be directly used in the imputation procedures. Therefore, prior to their use in the statistical processing they must be verified by the procedures detecting and correcting the errors, e.g. errors in order of the data (e.g. balance sheet data is reported in thousands, while profit and loss statement data in million) or consistency errors (assets do not equal the liabilities, revenues minus expenses do not equal the profit/loss, etc.). Moreover, the process for the verification of administrative data for a subsequent use, includes also the procedures for the imputation of missing or erroneous administrative data.
After the verification, the administrative data and other relevant data enter the process of the imputation. The very basic principle of the imputation process as such is based on the average share of an estimated variable and a variable available from the administrative or other external sources (hereafter referred as the explanatory variable) calculated from the surveyed units that provided CzSO with correctly completed questionnaire belonging to the same stratum as a non-respondent unit or a unit not included in the sample. For the estimation of the estimated variable the average share is applied to the explanatory variable available for the non-respondent unit or the unit not included in the sample.
In the initial stages of the imputation process, the explanatory variables are indicators available from the administrative or other external sources. In the next stages, a previously imputed variable can become the explanatory variable for the estimation of other variables.
The measures taken for minimising the unit non-response:
1) Average unit non-response is taken into account when designing the sample.
2) The questionnaire consists of several individual parts. Each part of the questionnaire is focused on one or more areas (e.g. cost/expenses, revenues and assets; investment; financial assets and liabilities; breakdown of turnover by products; selected indicators broken down by residence of clients; selected indicators broken down by regions; etc.). The individual parts can be combined so that we can tailor different versions of the questionnaire for different groups of respondents according to their size/importance. As a consequence the biggest enterprises complete the version of the questionnaire consisting of all (or most) parts, while the smaller ones those versions consisting of only selected parts. There are altogether 401 different versions of the questionnaire. Quite a high level of personification enables us to lower the burden on especially small respondents and it contributes to the minimising of the unit non-response as every sampled unit completes only the minimum information necessary for the statistical production.
3) Every respondent can choose one of the three ways of questionnaire completion: 1) web based questionnaire 2) interactive PDF form of the questionnaire or 3) the paper form of the questionnaire.
4) If a unit is selected for the sample of one survey, it cannot be selected for any sample of the other surveys unless it is classified to the stratum where all the units are sampled. This method known as negative coordination of the samples contributes to minimising the burden on especially small units. Moreover, it indirectly helps to increase the response rates of the individual surveys since it reduces the risk of non-response due to overburden of respondents.
5) Every non-respondent unit is contacted successively by phone or e-mail and then repeatedly by registered letter sent in most cases to so called data box. The data box is an electronic storage site, intended for delivery of official documents and for communication with public authority bodies.
Bias:
If characteristics of the non-respondent units significantly differed from those of respondent units, the estimated part of the population could be biased. In addition, a certain bias can be caused by the high misclassification of the smallest units (especially natural persons) in the Statistical Business Register.
The potential bias is, however, small with limited effect on the overall accuracy of estimates.
14.1. Timeliness
Deadline for data-collection:15/04/2021
Deadline for post-collection phase: 31/01/2022
Deadline for the Annual Structural Business Survey termination: February 2022
Dissemination of SBS data: 30/06/2022
14.2. Punctuality
No delay
15.1. Comparability - geographical
As regards NUTS classification, data is fully comparable.
15.2. Comparability - over time
2005-2020
15.3. Coherence - cross domain
Number of enterprises in the business register - Business register contains units that are from the administrative point of view active, however, as far as statistics is concerned they are not active (e.g. units that were established, but have not started their activity yet, units that finished their activity but have not been erased from the register yet etc.). In this respect, it is important to underline that business statistics consider a unit to be active only if there is an administrative “trace“, i.e. the unit is VAT payer or statistics have information on the unit from the income tax return or on its employees from the Czech Social Security Administration.
Value added of national accounts - There are numerous methodological and conceptual differences between VA of National Accounts and VA of Business Statistics. The inclusion of the non-observed economy in NA can be given as an example. Generally, however, the cross-domain coherence between SBS and NA data is considered favourable.
Evolution of turnover and persons employed from short term statistics - Minor inconsistencies in the evolution of turnover - resulting from the fact that when STS data are compiled, the accounting data are considered provisional - can be observed.
15.4. Coherence - internal
Fully consistent.
Cost and burden is regularly monitored for the whole Annual Structural Business Survey (ASBS) covering requirements far beyond the SBS requirements.
On the basis of the ASBS burden model, it was estimated the burden resulting from the SBS regulation (Annexes I to IV and VIII). Number of respondents (irrespective of response) for whole ASBS: 33 781, of which 29 568 for NACE B to J, L to N and S95. Average time to complete the questionnaire: 12 hours, of which 2,5 hours for items entering the computation of SBS indicators. The burden resulting from the SBS regulation: 73,9 thousand person-hours.
At present, unfortunately we are not able to make a reliable estimation of the costs resulting from the SBS Regulation using ASBS costs model.
17.1. Data revision - policy
When compiling the final data for the year Y - usually in February Y+2, if needed, data for Y-1 can be revised.
17.2. Data revision - practice
Preliminary data versus final data
The methodology is the same as the methodology used for the final data compilation. The only difference is that at the time when preliminary data are compiled, neither all surveyed data nor all administrative data are available.
Relative Mean Absolute Revisions (RMAR) of preliminary data versus published final data were 0,0078 in case of variable 12110 and 0,0062 in case of variable 16110.
18.1. Source data
TYPE OF SOURCE: Sample survey combined with administrative sources
Following sources were used for the compilation of the SBS data:
1.Annual Structural Business Survey named P5-01 - surveyed are legal persons, market producers, classified in the institutional sector S.11 with a few exceptions, e.g. excluded are enterprises with the principal activity NACE 01, non-profit institutions classified in the sector S.11 or housing associations, on the contrary included are certain types of enterprises classified in the sector S.12.
2. Labour costs survey named UNP 4-01- some data referring to number of persons employed and personnel costs is estimated exploiting information from the Annual Labour costs survey.
3. Quarterly business statistics survey named P 6-04: auxiliary sources used in the imputation.
4. PRODCOM survey
5. Administrative data:
Selected information from the income tax returns
VAT data
Number of social insurance policyholders and the basis of social security insurance assessment
SURVEY (corresponds to Annual Structural Business Survey):
Data for the base population corresponds to the state before the update on later available administrative data.
A random stratified sampling is combined with an intentional sampling of statistically significant units.
The units classified in the institutional sector 14 are not sampled. The required data are estimated solely on the basis of the available administrative data.
ADMINISTRATIVE SOURCES:
Micro data for the entire taxpayers population from the corporate income tax return is available for the following indicators:
Balance sheet:
1) Indicators available for all legal persons and those natural persons keeping accounts:
- Total assets (brutto, netto)
- Intangible assets
- Tangible assets
- Long-term financial assets
- Current assets
- Inventory
- Long term receivables
- Short-term receivables
- Short-term financial assets
- Total equity&liabilities
- Equity
- Registered capital
- Profit/loss of current period
- Liabilities
- Provisions
- Long-term payables
- Short-term payables
- Bank loans and financial assistance
- Accruals
2) Indicators available only for some legal persons
- Long-term bank loans
- Social security and health insurance payable
3) Indicators available for the natural persons who do not keep accounts only tax records:
- Fixed assets
- Money in cash
- Money on the bank account
- Stocks (inventory)
- Receivables including provided credits and loans
- Other property items
- Payables including received credits and loans
- Reserves
Profit and loss statement
1) Indicators available for all legal persons and those natural persons keeping accounts:
- Revenues from merchandise
- Cost of goods sold
- Production
- Production consumption
- Operating profit/loss
- Interest revenues
- Interest expenses
- Profit/loss from financial operations
- Income tax on ordinary income
- Profit/loss of current period (before/after tax deduction)
- Transfer of ratio in profit/loss to partners
Other data
Indicators available only for some legal persons:
- Sales of products, goods and services
Indicators available for the natural persons who do not keep accounts only tax records:
- Income
- Expenses pursuant to income
- Wages
The indicators available for the rest of natural persons:
- Income
VAT data
All the information from the tax return form and the part of the information from the annexes is available.
Czech social security administration data
- Number of social insurance policy holders
- Basis of social security insurance assessment
FRAME
The sample of the Annual Structural Business Survey is drawn every year from the Statistical Business Register (SBR).
SBR is updated continually throughout the year. The principal and secondary activities are identified using the top-down method and variables value added, number of employees or turnover.
18.2. Frequency of data collection
Annual data collection
18.3. Data collection
Annual Structural Business Survey named P5-01: surveyed are legal persons, market producers, classified in the institutional sector S.11 with a few exceptions, e.g. excluded are enterprises with the principal activity NACE 01, non-profit institutions classified in the sector S.11 or housing associations, on the contrary included are certain types of enterprises classified in the sector S.12.
Every respondent can choose one of the three ways of questionnaire completion: 1) web-based questionnaire 2) interactive PDF form of the questionnaire or 3) paper form of the questionnaire.
There are several types of checks built-in the web-based and interactive PDF form of the questionnaire classified into the two types: interactive and batch checks and four categories: errors (Occurrence of the phenomenon is not possible), anomalies (Occurrence of the phenomenon is possible, but unlikely. The phenomenon must be commented. If found out by a statistician, it must be always verified by contacting the respective unit), non-response (The respective cell of the form is not filled in, even though according to the available information it should be) and information (Occurrence of the phenomenon is possible, but unlikely. Respondent should comment the phenomenon. If found out by the statistician, it does not have to be always verified. Decision whether it should be verified or not is up to consideration of the statistician responsible for data verification).
The web based questionnaire contains all defined checks, interactive PDF form only selected ones. Notwithstanding the way of the questionnaire completion, every questionnaire is verified by the internal verification application – the off-line version of the on-line web application for the questionnaires completion.
After the initial verification the data is sent to a central database for further processing. In the central database the surveyed data together with administrative and other external data enters the imputation procedures in order to be grossed up to the whole population. Subsequently, it is analysed and checked by the branch responsible experts.
The cycle - collection of the questionnaires, their verification, grossing up to the whole population and analysis and verification of the data concerning the whole population - is repeated several times. Particularly when there are new input data (from the survey or administrative sources).
The partner organizations provide CzSO with the administrative data in the form of extracts from the internal databases.
18.4. Data validation
See 18.3 Data collection.
18.5. Data compilation
The method of imputation/grossing-up: Estimates were based on the theory of superpopulation model of regression estimates. Simply speaking for regression estimates, the subject of the random variable is not variable Yi, but it is the remainder (random error) of the regression model.
Modification of RATIO estimators (or Superpopulation model) was applied in the CZSO and used for estimation of expected values for all unique units with no response or not in the sample in corresponding stratum in the frame. Additivity of estimated values was preserved at all level breakdowns by stratification variables. A regression estimator using model with one or two auxiliary variables (without an intercept) was applied for mass imputation. Weights were calibrated subject to totals of two auxiliary variables for all active units in the corresponding stratum. Given the difference of the indicators measured, seven different weights were calculated. The variable number of units at the end of the year was combined with one of the auxiliary variables:
Number of social insurance policyholders
Total revenues from tax income statement
Total assets
Tangible fixed assets
Intangible assets
Basis of social security insurance assessment
The auxiliary variable to calculate regression coefficients are given below:
Number of social insurance policyholders
Revenues from tax income statement
Statistical Business register (SBR) data as of the date of sampling (generation of samples) were the source of values for auxiliary variables.
Initial weights were calculated from SBR data both as inverse values of probabilities with which a unit would have been included in the sample in a given stratum. The adjustment of the weights to final values for the calculation of estimates takes into account non-activity of units, non-responses and shifts between CZ-NACE divisions (resulting from differences between the BR and actual information found by the statistical survey). Then weighted least squares method was applied in each stratum. The regression coefficients are then used to impute all surveyed variables for non-respondent units or units not in the sample. Different regression models are applied to different group of variables (e.g. turnover based variables, employees based variables).
For the strata with inadequate number of correctly filled in questionnaires, data from neighbour strata were employed to obtain estimates via regression model (so called “borrowing strength”).
As regards estimates for detailed breakdown, they were computed as an aggregate of unique unit estimated values.
Further relevant information can be found in the chapter 13.3.
18.6. Adjustment
We applied a correction in case the reference period differs from the calendar year.
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)
Statistical unit used for the compilation the SBS series is enterprise. The enterprise almost always correspond to legal unit.
Target population: All the active enterprises (consisting of legal and natural persons) classified in Sections B to J, L to N and Division S95 of NACE Rev 2 (see also concept 3.3. Coverage - sectors) and institutional sectors of non-financial corporations and households (employers and own-account workers). Exceptions are housing associations and non-profit institutions classified into the institutional sector of non-financial corporations.
Frame for the identification of the population: National Statistical Business Register.
Czech Republic
Inclusion/exclusion of the activities of Foreign Affiliates
If a foreign enterprise establishes a branch in the Czech Republic, it has the obligation to register the branch at the Register of Companies, thereby it enters the Statistical Business Register and subsequently the population of the Annual Structural Business Survey carried out by the Czech Statistical Office (CzSO). And the other way round, if an enterprise registered in the Czech Republic establishes a branch abroad, it is requested to report only unconsolidated information (i.e. data not including the activities of foreign affiliates). However, if an enterprise registered in the Czech Republic carries on business abroad, but does not establish an affiliate abroad, the results of these activities are included in the data reported to the CzSO.
2020
Data refers to calendar year. If an enterprise reports the data for a fiscal year, it is recalculated to the respective calendar year.
With regard to available administrative data, high response rate, chosen imputation method and several stage validation system the overall quality can be assessed as favourable. The possible source of potential errors is the lack of information related to the smallest units in the population, what might result, among others, in the high misclassification of statsitical units in the Statistical Business Register. The potential bias is, however, small with limited effect on the overall accuracy of estimates.
The preliminary results may be biased since at the time of its transmission to Eurostat not all questionnaires nor administrative data are available. However, we consider the potential bias to be relatively low.
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.
The method of imputation/grossing-up: Estimates were based on the theory of superpopulation model of regression estimates. Simply speaking for regression estimates, the subject of the random variable is not variable Yi, but it is the remainder (random error) of the regression model.
Modification of RATIO estimators (or Superpopulation model) was applied in the CZSO and used for estimation of expected values for all unique units with no response or not in the sample in corresponding stratum in the frame. Additivity of estimated values was preserved at all level breakdowns by stratification variables. A regression estimator using model with one or two auxiliary variables (without an intercept) was applied for mass imputation. Weights were calibrated subject to totals of two auxiliary variables for all active units in the corresponding stratum. Given the difference of the indicators measured, seven different weights were calculated. The variable number of units at the end of the year was combined with one of the auxiliary variables:
Number of social insurance policyholders
Total revenues from tax income statement
Total assets
Tangible fixed assets
Intangible assets
Basis of social security insurance assessment
The auxiliary variable to calculate regression coefficients are given below:
Number of social insurance policyholders
Revenues from tax income statement
Statistical Business register (SBR) data as of the date of sampling (generation of samples) were the source of values for auxiliary variables.
Initial weights were calculated from SBR data both as inverse values of probabilities with which a unit would have been included in the sample in a given stratum. The adjustment of the weights to final values for the calculation of estimates takes into account non-activity of units, non-responses and shifts between CZ-NACE divisions (resulting from differences between the BR and actual information found by the statistical survey). Then weighted least squares method was applied in each stratum. The regression coefficients are then used to impute all surveyed variables for non-respondent units or units not in the sample. Different regression models are applied to different group of variables (e.g. turnover based variables, employees based variables).
For the strata with inadequate number of correctly filled in questionnaires, data from neighbour strata were employed to obtain estimates via regression model (so called “borrowing strength”).
As regards estimates for detailed breakdown, they were computed as an aggregate of unique unit estimated values.
Further relevant information can be found in the chapter 13.3.
TYPE OF SOURCE: Sample survey combined with administrative sources
Following sources were used for the compilation of the SBS data:
1.Annual Structural Business Survey named P5-01 - surveyed are legal persons, market producers, classified in the institutional sector S.11 with a few exceptions, e.g. excluded are enterprises with the principal activity NACE 01, non-profit institutions classified in the sector S.11 or housing associations, on the contrary included are certain types of enterprises classified in the sector S.12.
2. Labour costs survey named UNP 4-01- some data referring to number of persons employed and personnel costs is estimated exploiting information from the Annual Labour costs survey.
3. Quarterly business statistics survey named P 6-04: auxiliary sources used in the imputation.
4. PRODCOM survey
5. Administrative data:
Selected information from the income tax returns
VAT data
Number of social insurance policyholders and the basis of social security insurance assessment
SURVEY (corresponds to Annual Structural Business Survey):
Data for the base population corresponds to the state before the update on later available administrative data.
A random stratified sampling is combined with an intentional sampling of statistically significant units.
The units classified in the institutional sector 14 are not sampled. The required data are estimated solely on the basis of the available administrative data.
ADMINISTRATIVE SOURCES:
Micro data for the entire taxpayers population from the corporate income tax return is available for the following indicators:
Balance sheet:
1) Indicators available for all legal persons and those natural persons keeping accounts:
- Total assets (brutto, netto)
- Intangible assets
- Tangible assets
- Long-term financial assets
- Current assets
- Inventory
- Long term receivables
- Short-term receivables
- Short-term financial assets
- Total equity&liabilities
- Equity
- Registered capital
- Profit/loss of current period
- Liabilities
- Provisions
- Long-term payables
- Short-term payables
- Bank loans and financial assistance
- Accruals
2) Indicators available only for some legal persons
- Long-term bank loans
- Social security and health insurance payable
3) Indicators available for the natural persons who do not keep accounts only tax records:
- Fixed assets
- Money in cash
- Money on the bank account
- Stocks (inventory)
- Receivables including provided credits and loans
- Other property items
- Payables including received credits and loans
- Reserves
Profit and loss statement
1) Indicators available for all legal persons and those natural persons keeping accounts:
- Revenues from merchandise
- Cost of goods sold
- Production
- Production consumption
- Operating profit/loss
- Interest revenues
- Interest expenses
- Profit/loss from financial operations
- Income tax on ordinary income
- Profit/loss of current period (before/after tax deduction)
- Transfer of ratio in profit/loss to partners
Other data
Indicators available only for some legal persons:
- Sales of products, goods and services
Indicators available for the natural persons who do not keep accounts only tax records:
- Income
- Expenses pursuant to income
- Wages
The indicators available for the rest of natural persons:
- Income
VAT data
All the information from the tax return form and the part of the information from the annexes is available.
Czech social security administration data
- Number of social insurance policy holders
- Basis of social security insurance assessment
FRAME
The sample of the Annual Structural Business Survey is drawn every year from the Statistical Business Register (SBR).
SBR is updated continually throughout the year. The principal and secondary activities are identified using the top-down method and variables value added, number of employees or turnover.
Annual
Deadline for data-collection:15/04/2021
Deadline for post-collection phase: 31/01/2022
Deadline for the Annual Structural Business Survey termination: February 2022
Dissemination of SBS data: 30/06/2022
As regards NUTS classification, data is fully comparable.
