1.1. Contact organisation
Statistics Finland
1.2. Contact organisation unit
Economic Statistics
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
FI-00022 Statistics Finland
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
21 October 2024
2.2. Metadata last posted
21 October 2024
2.3. Metadata last update
31 October 2024
3.1. Data description
The Community Innovation Survey (CIS) is a survey about innovation activities in enterprises. The survey is designed to collect the information on types of innovation, processes of development of innovation like cooperation patterns, financing and expenditure, objectives of innovation activities or barriers for initiating or implementing innovation.
The CIS provides statistics by type of innovators, economic activity and size class of enterprises. The survey is currently carried out every two years across the EU Member States, EFTA countries and EU candidate countries.
In order to ensure comparability across countries, Eurostat together with the countries develops a Harmonised Data Collection (HDC) questionnaire and drafts the methodological recommendations for implementation of each survey round.
The CIS 2022 implements the concepts and methodology of the Oslo Manual 4th Edition revised in 2018. The changes that the CIS has undergone due to the revision of the manual and their impact on the indicators collected are described in the Statistics Explained article: Community Innovation Survey – new features.
The legal framework for CIS 2022 is the Commission Implementing Regulation (EU) 2022/1092, which sets out the quality conditions and identifies the obligatory cross-coverage of economic sectors, size class of enterprises and innovation indicators. The target population is enterprises with at least 10 employed persons (sum of employees and self-employed persons) classified in the core NACE economic sectors (see 3.3). Further activities may be covered on a voluntary basis in national datasets. Most statistics are based on the 3-year reference period (t, t-1, t-2), but some use only one calendar year (t or t-2).
3.2. Classification system
Indicators related to the enterprises are classified by country, economic activity (NACE Rev. 2), size class of enterprises and type of innovation.
The main typology of classification of enterprises in reference to innovation is the distinction between innovation-active enterprises (INN) and not innovation-active enterprises (NINN).
The enterprise is considered as innovative (INN) if during the reference period it successfully introduced a a) product or a) business process innovation, c) completed but not yet implemented the innovation, d) had ongoing innovation activities, e) abandoned innovation activities or was f) engaged in in-house R&D or R&D contracted out. Non-innovative (NINN) enterprises had no innovation activity mentioned above whatsoever during the reference period.
3.3. Coverage - sector
3.3.1. Main economic sectors covered - NACE Rev.2
In accordance with the Commission Implementing Regulation (EU) 2022/1092 on innovation statistics, the following sectors of the economic activity are included in the core target population: NACE Sections B, C, D, E, H, J, K, and Divisions 46, 71, 72 and 73.
3.3.1.1. Main economic sectors covered - NACE Rev.2 - national particularities
According to the Commission Implementing Regulation (EU) 2022/1092, no deviations
3.3.2. Sector coverage - size class
In accordance with Commission Implementing Regulation (EU) 2022/1092 on innovation statistics, only the enterprises with 10 or more employed persons (sum of employees and self-employed persons) are included in the core target population.
3.3.2.1. Sector coverage - size class - national particularities
According to the Commission Implementing Regulation (EU) 2022/1092, no deviations by definition. Small number of observations in the data with less than 10 employed persons (this is due to decrease in number of employed persons in some of the enterprises after the point of time of sampling).
3.4. Statistical concepts and definitions
The description of concepts, definitions and main statistical variables will be available in CIS 2022 European metadata file (ESMS) Results of the community innovation survey 2022 (CIS2022) (inn_cis13) in Eurostat database.
3.5. Statistical unit
Enterprise (Statistical Unit Enterprise).
Sampling was carried out at Statistical Unit Enterprise level. The observation unit was either the legal unit or the Statistical Unit Enterprise.
Process for obtaining results at Statistical Unit Enterprise level:
- Representative unit
- If not representative unit available, aggregating from answers from two or more legal units
- Also some case-by-case choices were made due to complex stuctures (only exceptions)
In the case there were responses from two or more legal units for one complex enterprise:
- For qualitative variables:
- main rule for example for 'yes'/'no' questions: if at least one unit reported 'yes', then it was 'yes' to complex enterprise too (for product and process innovations the 'yes' answers were evaluated separately but too much attention was not paid on that due to fact that complex enterprises are large and thus often innovative, what is the reason why 'yes' answers to introducing/implementing innovations are most often obvious).
- for responses from representative enterprises, some information, such as data for IPR, could be added from other units' responses if these other units are clearly responsible for these activities in the context of complex enterprise
- For quantitative variables:
- the legal units' turnover shares from the total turnover of complex enterprise were used (as weights)
- Innovation expenditure for complex enterprise was a sum of expenditures of legal units' expenditure if there was not available one representative or aggregated ("group level answer" concerning unit equal enough to complex enterprise) data for expenditures. It was carefully checked that there was no double counting. BERD data and previous CIS data was used as a help for identifying possible double counting.
3.6. Statistical population
Core target population is all enterprises in CORE NACE activities (see 3.3.1) with 10 or more employed persons (sum of employees and self-employed persons).
3.7. Reference area
National data for Finland: no NUTS 2 data available.
NUTS 2 data delivered to Eurostat, the CIS2022 regional data are calibrated at the Statistical Unit Enterprise level.
3.8. Coverage - Time
Several rounds of Community Innovation Survey have been conducted so far at two-year interval since the end of the 90’s.
3.8.1. Participation in the CIS waves
| CIS wave | Reference period | Participation (Yes/No) | Comment (deviation from reference period) |
|---|---|---|---|
| CIS2 | 1994-1996 | Yes | |
| CIS3 | 1998-2000 | Yes | |
| CIS light | 2002-2003* | Yes | 2000-2002 |
| CIS4 | 2002-2004 | Yes | |
| CIS2006 | 2004-2006 | Yes | |
| CIS2008 | 2006-2008 | Yes | |
| CIS2010 | 2008-2010 | Yes | |
| CIS2012 | 2010-2012 | Yes | |
| CIS2014 | 2012-2014 | Yes | |
| CIS2016 | 2014-2016 | Yes | |
| CIS2018 | 2016-2018 | Yes | |
| CIS2020 | 2018-2020 | Yes | |
| CIS2022 | 2020-2022 | Yes |
*two reference periods can be distinguished for CIS light: 2000-2002 and 2001-2003
3.9. Base period
Not relevant.
CIS indicators are available according to 3 units of measure:
- NR: Number for number of enterprises and number of persons employed.
- THS_EUR: Thousands of euros. All financial variables are provided in thousands of euros, i.e. Turnover or Innovation expenditure.
- PC: Percentage. The percentage is the ratio between the selected combinations of indicators.
For CIS 2022, the time covered by the survey is the 3-year period from the beginning of 2020 to the end of 2022.
Some questions and indicators refer to one year — 2022.
The list of indicators specifying whether they cover the 3-year period or refer to one year according to the HDC will be available in the Annex section of the European metadata (ESMS).
6.1. Institutional Mandate - legal acts and other agreements
The CIS is based on the Commission Implementing Regulation (EU) 2022/1092, implementing Regulation (EU) 2019/2152 of the European Parliament and of the Council on the production and development of Community statistics on science and technology.
This Regulation establishes innovation statistics on a statutory basis and makes the delivery of certain variables compulsory e.g. innovation activities, cooperation, development, expenditures and turnover (see the Regulation). Each survey wave may additionally include further variables.
In addition, the Regulation defines the obligatory cross-coverage of economic sectors and size class of enterprises.
6.1.1. National legislation
Statistics Act (280/2004), (Finnish/Swedish).
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
The data protection of data collected for statistical purposes is guaranteed in accordance with the requirements of the Statistics Act (280/2004), the Act on the Openness of Government Activities (621/1999), the EU's General Data Protection Regulation (EU) 2016/679 and the Data Protection Act (1050/2018). The data are protected at all stages of processing with the necessary physical and technical solutions. Statistics Finland has compiled detailed directions and instructions for confidential processing of the data. Employees have access only to the data essential for their duties. The premises where unit-level data are processed are not accessible to outsiders. Members of the personnel have signed a pledge of secrecy upon entering the service. Violation of data protection is punishable.
7.2. Confidentiality - data treatment
Statistics Finland's official guidelines on the protection of tabulated business data are applied in protecting innovation survey data. As in sample surveys, the basis for publishing the data is to not publish data on the statistical units. In terms of protection, both the threshold rule and the dominance rule is applied to data.
Industry-specific innovation data are mainly published at the 2-digit level. However, some of the most sensitive industries from the point of data protection have been combined with other industries. If there is need for protection after possible aggregations, or for other reason, the cells to be protected are hidden.
Both the primary and secondary confidentiality are taken into account and ensured.
In the tabulations submitted to Eurostat, sensitive cells are indicated as protected (also secondary protection), in which case Eurostat does not publish these data. However, the data can be used in calculating sum data at the EU level. Protection is indicated in accordance with instructions given by Eurostat.
8.1. Release calendar
National publication 25 April 2024.
8.2. Release calendar access
8.3. Release policy - user access
The data of the statistics on innovation activity are released to all data users simultaneously according to the date given in Statistics Finland's release calendar. All statistical data are available free-of-charge on the home page of the statistics https://stat.fi/en/statistics/inn
Statistical data are published as database tables in the StatFin database. The database is the primary publishing site of data.
Innovation microdata is also available for research use (Statistics Finland's Research Services Research services | Statistics Finland).
CIS is conducted and disseminated at two-year interval in pair years.
Accessibility and clarity refer to the simplicity and ease for users to access statistics using simple and user-friendly procedure, obtaining them in an expected form and within an acceptable time period, with the appropriate user information and assistance: a global context which finally enables them to make optimum use of the statistics.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Dissemination and access | Availability | Comments, links, ... |
|---|---|---|
| Press release | Yes | Product innovations generated 16 per cent of enterprises' turnover in 2022 - Statistics Finland |
| Access to public free of charge | Yes | Database tables PxWeb - Select table (stat.fi), Report on latest results only in Finnish |
| Access to public restricted (membership/password/part of data provided, etc) | Yes | Microdata for research use Research services | Statistics Finland |
10.2. Dissemination format - Publications
- Online database (containing all/most results): Yes.
- Analytical publication (referring to all/most results): Yes (in Finnish only).
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect): No.
10.3. Dissemination format - online database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
Eurostat SAFE Center and Statistics Finland's Research Services.
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | Yes | |
| National SAFE centre | Yes | |
| Eurostat: partially anonymised data (SUF) | No | |
| National: partially anonymised data | No |
10.5. Dissemination format - other
-
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
The methodology is documented on Innovation statistics homepage at Statistics Finland's website.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Documentation on methodology (see 10.6, documentation) covers also documenation for quality management (similar to the Eurostat metadata reporting).
11.1. Quality assurance
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 also compatible with the European Statistics Code of Practice.
Quality issues are critical and controlled at every stage of stastistical production process of innovation data -- in data collection, in data editing and in validating the final data.
In the data collection of innovation data, the target is to attain an adequate and sufficient response rate (target 70 per cent) for the data to be representative enough. All the illogicalities, shortcomings, faults, missing values, and other types of errors are identified and tried to be corrected. Data are corrected and validated by multiple methods, also by comparing the data to other business data (SBS, R&D). The quality of innovation data is also evaluated in the national quality report for each survey round.
11.2. Quality management - assessment
The overall quality of innovation data is considered good.
Production of innovation statistics follows EBS Regulation, Eurostat's methodological recommendations and the measurement guidelines given by the Oslo Manual. Multiple methods of controlling and correcting the data are used to quarantee data quality.
For the next survey, the frame will be renewed at t instead of t-1. Also editing and imputation methods will be evaluated ja renewed where applicable.
12.1. Relevance - User Needs
User needs and importance of different survey questions and different topics are mapped among main users of innovation data and innovation statistics (ministries, research institutes, associations and other organisations working with innovation) before every survey round.
12.1.1. Needs at national level
| User group | Short description of user group | Main needs for CIS data of the user group Users’ needs |
|---|---|---|
| 1 Institutions - European level | Comission e.g. Eurostat, DG ENTR | Statistics, analysis, scoreboards |
| 1 Institutions - International organisations | OECD | Statistics, analysis, comparisons, publications |
| 1 Institutions - National level | Ministry of Employment and the Economy and other ministries involved in innovation policy |
Follow up of development, analysis on innovation and on its effect on economy |
| 1 Institutions - National level | Organisations working with issues relating to innovation, e.g. Business Finland |
E.g. for promoting innovation environment |
| 2 Social actors | Employers associations, trade unions | |
| 3 Media | TV, newspapers, magazines | |
| 4 Research and students | Reseach organisations (e.g. universities, private and public research institutes) and students | E.g. Microdata for analysis and research, thesis |
| 5 Enterprises or businesses | Enterprises |
12.2. Relevance - User Satisfaction
User satisfaction surveys for CIS havent been undertaken. Statistics Finland has mapped user needs before each survey round and the Finnish CIS survey has covered national questions on topical themes important for users. Also other feedback received is taken into account when planning the survey and publishing.
12.3. Completeness
All the mandatory data according to the EBS legislation is published and transmitted to Eurostat.
12.3.1. Data completeness - rate
Not requested.
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
13.2. Sampling error
Restricted from publication
13.2.1. Sampling error - indicators
The main indicator used to measure sampling errors for CIS data is the coefficient of variation (CV).
CV= Coefficient of variation (%) = 100 * (Square root of the estimate of the sampling variance) / (Estimated value)
Formula:
where
and
13.2.1.1. Coefficient of variations for key variables
Coefficient of variation (%) for key variables by NACE categories and for enterprises with 10 or more employed persons
| NACE | Size class |
(1) |
(2) |
(3) |
|---|---|---|---|---|
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) | Total |
1.5 |
2,5 |
2.7 |
| Core industry (B_C_D_E - excluding construction) | Total |
2.0 |
3.6 |
3.7 |
| Core Services (46-H-J-K-71-72-73) | Total |
2.1 |
3.4 |
4.0 |
(1) = Coefficient of variation for the percentage of innovative enterprises (INN) in the total population of enterprises (ENT)
(2) = Coefficient of variation for the turnover of product innovative enterprises with new or improved products (TUR_PRD_NEW_MKT), as a percentage of total turnover of product innovative enterprises [TOVT,INNO_PRD].
(3) = Coefficient of variation for percentage of product and/or process innovative enterprises (incl. enterprises with abandoned and or on-going activities) involved in any innovation co-operation arrangement [COOP_ALL,INN], as a percentage of innovative enterprises (INN).
13.2.1.2. Variance estimation method
See 13.2.1, SAS.
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that have a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under covered groups of the target population
Frame is defined based on t-1 and that is why enterprises established after that (i.e. during t) are not covered.
13.3.1.4. Coverage errors in coefficient variation
Over coverage units are eliminated from the estimation and from the calculation of coefficient of variation.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
13.3.2.1. Measures for reducing measurement errors
Training of personnel working with the data collection and editing. Helping respondents with their questions. Checks and controls in online questionnaire. Following the question order of HDC. Large variety of editing and validating rules that identify and localize possible errors or illogical values in the data. Using other data (e.g. R&D survey data, other business data, financing data, previous CIS data) with which the correctness and consistency of a data can be checked and verified.
13.3.3. Non response error
Unit non-response and declining response rate challenge innovation data. Innovation as a phenomenon is complex and rich in nuances and that is why the data should be with good coverage.
Statistics Finland carried out several steps and measures to achieve the target response rate of 70 per cent. The final response rate however did not reach the target. Non-response survey was not carried out due to weak responding and due to lack of time.
Item non-response instead seem to affect less on the data. Rates of item non-response are quite low in the Finnish CIS data.
13.3.3.1. Unit non-response - rate
See below.
13.3.3.1.1. Un-weighted and weighted unit non-response rate by NACE categories and for enterprises with 10 or more employed persons
Un-weighted and weighted unit non-response rate by NACE categories and for enterprises with 10 or more employed persons
| NACE | Number of eligible units with no response | Total number of eligible units in the sample | Un-weighted unit non-response rate (%) | Weighted unit non-response rate (%) |
|---|---|---|---|---|
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) | 1418 | 3904 | 36.3 % | 38.5 % |
| Core industry (B_C_D_E - excluding construction) | 603 | 1736 | 34.7 % | 37.4 % |
| Core Services (46-H-J-K-71-72-73) | 815 | 2168 | 37.6 % | 39.3 % |
The number of eligible units is the number of sample units, that ultimately indeed belong to the target population.
13.3.3.1.2. Maximum number of recalls/reminders before coding
Four reminder letters, two email reminders (plus in addition some single contacts) and one round of robot calls to remind respondents.
13.3.3.2. Item non-response - rate
See below.
13.3.3.2.1. Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employed persons)
Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employed persons).
| Item non-response rate (un-weighted) (%) |
Imputation (Yes/No) |
If imputed, describe method used, mentioning which auxiliary information or stratification is used | |
| Turnover | 0 % | No |
BR/SBS data used.
13.3.3.2.2. Item non response rate for new questions
Item non-response rate for new questions in CIS t (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employed persons)
| NEW QUESTIONS IN CIS 2022 | Inclusion in national questionnaire (Yes/No) | Item non response rate (un-weighted) (%) | Comments |
|---|---|---|---|
| 3.9 -- Reasons for not having more innovation activities | Yes | 2.5 % | |
| 3.10 -- Reasons for having no innovation activities | Yes | 6.7 % |
13.3.4. Processing error
No processing errors detected. By using several types of editing and especially validation checks we try to avoid processing errors.
13.3.5. Model assumption error
Not requested.
Timeliness and punctuality refer to time and dates, but in a different manner.
14.1. Timeliness
The timeliness of statistics reflects the length of time between data availability and the event or phenomenon they describe.
14.1.1. Time lag - first result
Timeliness of national data – date of first release of national level: 25 April 2024.
14.1.2. Time lag - final result
Not requested.
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Date of transmission of complete and validated data to Eurostat (Number of days between that date and 30 June 2024) : -6.
Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains, or over time.
The coherence of statistical outputs refers to the degree to which the statistical processes by which they were generated used the same concepts (classifications, definitions, and target populations) and harmonised methods. Coherent statistical outputs have the potential to be validly combined and used jointly.
15.1. Comparability - geographical
Regional data are not published as a part of basic statistics.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. National questionnaire – compliance with Eurostat model questionnaire
Methodological deviations from the CIS Harmonised Data Collection (HDC)
| Questions not included in national questionnaire compared to HDC | Comment |
|---|---|
| 2.1 Market conditions | |
| 2.2 Strategies | |
| Voluntary subcategories for other innovation expenditure under the question 3.8 on innovation | |
| 5.1 Climage change | |
| 5.2 Purchase of machinery, equipment or software | |
| 5.3 User requirements | |
| 5.4 Business model | |
| 6.2 Factors driving introduction of innovations with environmental benefits | |
| 7.2 Personnel with tertiary degree | |
| 7.4 Region for turnover | |
| 7.6 Spending | |
| 7.8 Activities and flows between group enterprises | |
| 7.9 Intra-group loans |
| Changes in the filtering compared to HDC | Comment |
| 6.1 Introduction of innovations with environmental benefits | The question was asked only for enterprises with innovation activity |
15.1.3. National questionnaire – additional questions
Methodological deviations from the CIS Harmonised Data Collection (HDC)
| Additional questions in national questionnaire (not included in HDC) | Comment |
|---|---|
| There were three questions on collaboration and connections between enterprises and research organisations (universities, universities of applied sciences and research institutes):
|
Collaboration between enterprises and research organisations was defined by following: it refers to organised, active cooperation, as well as other transfer of know-how, collaboration and goal-oriented interaction or communication. |
| A question on skills and competences required by enterprises (which competence areas were important for enterprise’s innovation activity in 2020 to 2022 and did an enterprise have sufficient competence in these areas?) |
15.2. Comparability - over time
Due to important methodological changes introduced by Oslo Manual 2018, the data from 2018 onwards cannot be directly compared with CIS waves prior to 2018.
15.2.1. Length of comparable time series
Not requested.
15.3. Coherence - cross domain
See the comparison between SBS and CIS data in the section 15.3.3 below.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not requested.
15.3.3. Coherence – Structural Business Statistics (SBS)
This part compares key variables for aggregated CIS data with SBS data
Definition of relative difference between CIS and SBS data: DIFF = (SBS/CIS)*100
Comparison between SBS and CIS data (relative difference) by NACE categories and for enterprises with 10 or more employed persons
| NACE | Size class | Number of enterprises (SBS/CIS)* | Number of employed persons (SBS/CIS)* | Total Turnover (SBS/CIS)* |
|---|---|---|---|---|
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) | Total | 102 % | 101 % | 99 % |
| Core industry (B_C_D_E - excluding construction) | Total | 102 % | 100 % | 100 % |
| Core Services (46-H-J-K-71-72-73) | Total | 102 % | 102 % | 98 % |
* Numbers are to be provided for the last year of the reference period (t)
15.4. Coherence - internal
Not requested.
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See below.
18.1.1. Sampling frame (or census frame)
National Business Register (BR).
18.1.2. Sampling design
Sample for enterprises with 10 to 249 persons employed. Census for enterprises with 250 persons employed or more.
Sample was based on simple random sampling, NACE (2-digit) and size class (10-49, 50-249) as stratifying variables. Sample size for enterprises with 10 to 49 persons employed was 33 % from the frame population and for enterprises with 50 to 249 persons employed 58 %.
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
| Target population (A) (*) | 9924 |
| Sample (B = C+D) | 3984 |
| In case of combination sample/census: | |
| Sampled units (C) | 3593 |
| Enumerated units/census (D) | 391 |
| Overall sample rate (E = 100*B/A) | 40.1% |
(*) CIS core population, i.e. NACE Rev.2 B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employed persons.
18.1.4. Data source for pre-filled variables
Variables and indicators filled or prefilled from other sources.
| Variables/Indicators | Source | Reference year |
|---|---|---|
| - |
No filled or pre-filled data or indicators on the Finnish CIS2022 questionnaire.
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
|---|---|
| Data source used for deriving population totals | National Business Register |
| Variables used for weighting | Number of enterprises, turnover |
18.2. Frequency of data collection
According to the Commission Implementing Regulation (EU) 2022/1092, the innovation statistics shall be provided to Eurostat every two years in each even year. The data collection takes place every second year in year t-2 preceding the data provision.
18.3. Data collection
The CIS2022 survey was carried out by Statistics Finland.
The survey was started (letters to survey target enterprises) in the beginning of September 2023 with the deadline in the end of September. After that, several reminders (letters and emails) to non-respondents were sent. Also other measures (robot calls) to increase responding were utilized.
The data was collected via online (web) questionnaire.
18.3.1. Survey participation
Mandatory.
18.3.2. Survey type
Combination of census and sample survey.
18.3.3. Combination of sample survey and census data
Sample for enterprises with 10 to 249 persons employed, census for enterprises with 250 or more persons employed.
18.3.4. Census criteria
250 persons employed.
18.3.5. Data collection method
Data collection method
| Survey method | Yes/No | Comment |
|---|---|---|
| Face-to-face interview | No | |
| Telephone interview | No | |
| Postal questionnaire | No | Printing paper questionnaire out from Statistics Finland's webpage is possible -> responding by mail |
| Electronic questionnaire (format Word or PDF to send back by email) | No |
|
| Web survey (online survey available on the platform via URL) | Yes | |
| Other | No |
18.4. Data validation
Not requested.
18.5. Data compilation
Operations performed on data to derive new information according to a given set of rules.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition of imputation rate: Imputation rate (for the variable x) (%) = 100 * (Number of replaced values) / (Total number of values for a given variable).
Definition of weighted imputation rate: Weighted imputation rate= 100 * (Number of total weighted replaced values) / (Total number of weighted values for a given variable).
18.5.1.1. Imputation rate for metric variables
Imputation rate (%) for metric variables by NACE categories and for enterprises with 10 or more employed persons:
| NACE | Size class | Total Turnover (1) | Turnover from products new to the market (2) | R&D expenditure in-house (3) | |||
|---|---|---|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | Unweighted | Weighted | ||
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) | Total | 0.0 % | 0.0 % | 2.6 % | 3.1 % | 2.9 % | 3.3 % |
| Core industry (B_C_D_E - excluding construction) | Total | 0.0 % | 0.0 % | 2.3 % | 3.4 % | 2.6 % | 2.5 % |
| Core Services (46-H-J-K-71-72-73) | Total | 0.0 % | 0.0 % | 3.0 % | 2.9 % | 3.3 % | 4.1 % |
(1) = Imputation rate (%) for the total turnover in the last year of the reference period (t) (TUR)
(2) = Imputation rate (%) for the share of the turnover in the last year of the reference period (t) due to new or improved product new to the market in the total turnover for product innovative enterprises TUR_PRD_NEW_MKT/TOVT(INNO_PRD)
(3) = Imputation rate (%) for the R&D expenditure performed in-house (EXP_INNO_RND_IH)
18.5.2. Weights calculation
Weights calculation method for sample surveys
| Method | Selected applied method | Comments |
|---|---|---|
| Inverse sampling fraction | x | Yes, covered by Nh/nh, where Nh is the total number of enterprises in the stratum h of total population and nh is the number of enterprises responding and in the data in the stratum h. For euro variables corresponding weights based on turnover (total turnover of enterprises in the population per stratum / turnover of enterprises in the data per stratum). |
| Non-respondent adjustments | x | Yes, covered by Nh/nh, where Nh is the total number of enterprises in the stratum h of total population and nh is the number of enterprises responding and in the data in the stratum h. or euro variables corresponding weights based on turnover (total turnover of enterprises in the population per stratum / turnover of enterprises in the data per stratum). |
| Other |
18.6. Adjustment
No further calibration but in addition to weights based on number of enterprises also weights based on turnover used (for monetary variables). SAS procedures.
18.6.1. Seasonal adjustment
Not requested.
No further comments.
The Community Innovation Survey (CIS) is a survey about innovation activities in enterprises. The survey is designed to collect the information on types of innovation, processes of development of innovation like cooperation patterns, financing and expenditure, objectives of innovation activities or barriers for initiating or implementing innovation.
The CIS provides statistics by type of innovators, economic activity and size class of enterprises. The survey is currently carried out every two years across the EU Member States, EFTA countries and EU candidate countries.
In order to ensure comparability across countries, Eurostat together with the countries develops a Harmonised Data Collection (HDC) questionnaire and drafts the methodological recommendations for implementation of each survey round.
The CIS 2022 implements the concepts and methodology of the Oslo Manual 4th Edition revised in 2018. The changes that the CIS has undergone due to the revision of the manual and their impact on the indicators collected are described in the Statistics Explained article: Community Innovation Survey – new features.
The legal framework for CIS 2022 is the Commission Implementing Regulation (EU) 2022/1092, which sets out the quality conditions and identifies the obligatory cross-coverage of economic sectors, size class of enterprises and innovation indicators. The target population is enterprises with at least 10 employed persons (sum of employees and self-employed persons) classified in the core NACE economic sectors (see 3.3). Further activities may be covered on a voluntary basis in national datasets. Most statistics are based on the 3-year reference period (t, t-1, t-2), but some use only one calendar year (t or t-2).
31 October 2024
The description of concepts, definitions and main statistical variables will be available in CIS 2022 European metadata file (ESMS) Results of the community innovation survey 2022 (CIS2022) (inn_cis13) in Eurostat database.
Enterprise (Statistical Unit Enterprise).
Sampling was carried out at Statistical Unit Enterprise level. The observation unit was either the legal unit or the Statistical Unit Enterprise.
Process for obtaining results at Statistical Unit Enterprise level:
- Representative unit
- If not representative unit available, aggregating from answers from two or more legal units
- Also some case-by-case choices were made due to complex stuctures (only exceptions)
In the case there were responses from two or more legal units for one complex enterprise:
- For qualitative variables:
- main rule for example for 'yes'/'no' questions: if at least one unit reported 'yes', then it was 'yes' to complex enterprise too (for product and process innovations the 'yes' answers were evaluated separately but too much attention was not paid on that due to fact that complex enterprises are large and thus often innovative, what is the reason why 'yes' answers to introducing/implementing innovations are most often obvious).
- for responses from representative enterprises, some information, such as data for IPR, could be added from other units' responses if these other units are clearly responsible for these activities in the context of complex enterprise
- For quantitative variables:
- the legal units' turnover shares from the total turnover of complex enterprise were used (as weights)
- Innovation expenditure for complex enterprise was a sum of expenditures of legal units' expenditure if there was not available one representative or aggregated ("group level answer" concerning unit equal enough to complex enterprise) data for expenditures. It was carefully checked that there was no double counting. BERD data and previous CIS data was used as a help for identifying possible double counting.
Core target population is all enterprises in CORE NACE activities (see 3.3.1) with 10 or more employed persons (sum of employees and self-employed persons).
National data for Finland: no NUTS 2 data available.
NUTS 2 data delivered to Eurostat, the CIS2022 regional data are calibrated at the Statistical Unit Enterprise level.
For CIS 2022, the time covered by the survey is the 3-year period from the beginning of 2020 to the end of 2022.
Some questions and indicators refer to one year — 2022.
The list of indicators specifying whether they cover the 3-year period or refer to one year according to the HDC will be available in the Annex section of the European metadata (ESMS).
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
CIS indicators are available according to 3 units of measure:
- NR: Number for number of enterprises and number of persons employed.
- THS_EUR: Thousands of euros. All financial variables are provided in thousands of euros, i.e. Turnover or Innovation expenditure.
- PC: Percentage. The percentage is the ratio between the selected combinations of indicators.
Operations performed on data to derive new information according to a given set of rules.
See below.
CIS is conducted and disseminated at two-year interval in pair years.
The timeliness of statistics reflects the length of time between data availability and the event or phenomenon they describe.
Regional data are not published as a part of basic statistics.
Due to important methodological changes introduced by Oslo Manual 2018, the data from 2018 onwards cannot be directly compared with CIS waves prior to 2018.
