1.1. Contact organisation
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH
Mannheim
(Centre for European Economic Research)
1.2. Contact organisation unit
Department Economics of Innovation and Industrial Dynamics
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
ZEW
L 7, 1
68161 Mannheim
Germany
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
31 October 2024
2.2. Metadata last posted
31 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
The German CIS 2022 also covers the following NACE groups and divisions:
69, 70.2, 74, 78, 79, 80, 81, 82
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
The German CIS 2022 is using 'person employed' as classification unit for size class of enterprises.
The survey also covers enterprises with 5 to 9 employed persons.
The sample of the German CIS 2022 was stratified by 8 size classes: 5-9, 10-19, 20-49, 50-99, 100-249,250-499, 500-999, 1000+
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
The statistical unit of the German CIS 2022 is the enterprise as defined in the EU regulation and as implemented in the Business Register of the Statistical Office of the Federal Republic of Germany.
The sampling was carried out at the level of legal units. The observation unit was the legal unit, but respondents could also report at the level of the Statistical Unit Enterprise.
The reporting unit was determined using information from previous CIS waves, and by contacting complex enterprises in person (via telephone calls).
Process for obtaining results at Statistical Unit Enterprise level, for both qualitative and quantitative variables:
The representative approach was used. In case several responses were received from legal units of the same complex enterprise, the response by the representative unit (= largest unit with the same NACE division as the complex enterprise's NACE division) was used.
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
The German CIS 2022 covers all enterprises in the statistical population that are located within the territory of the Federal Republic of Germany.
There is not breakdown of CIS 2022 results by NUTS regions available. The only breakdown available is by Western Germany vs. Eastern Germany.
The microdata nevertheless contain information on the location of an enterprise by NUTS 3-digit level.
The national classification of East Germany and West Germany was used as a stratification dimension.
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 | no |
| CIS3 | 1998-2000 | yes | no |
| CIS light | 2002-2003* | yes | no (2000-2002) |
| CIS4 | 2002-2004 | yes | no |
| CIS2006 | 2004-2006 | yes | no |
| CIS2008 | 2006-2008 | yes | no |
| CIS2010 | 2008-2010 | yes | no |
| CIS2012 | 2010-2012 | yes | no |
| CIS2014 | 2012-2014 | yes | no |
| CIS2016 | 2014-2016 | yes | no |
| CIS2018 | 2016-2018 | yes | no |
| CIS2020 | 2018-2020 | yes | no |
| CIS2022 | 2020-2022 | yes | no |
*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
European legislation applies.
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
No confidentiality policy.
7.2. Confidentiality - data treatment
Does not apply.
8.1. Release calendar
National results of the CIS 2022 were released on January 30st, 2024 (Indicator Study, see attached pdf "Indikatorenbericht").
At the same time, 47 short innovation reports on individual industries were released (see https://www.zew.de
/en/publications/zew-expertises-research-reports/research-reports/innovations/zew-sector-reports-on-innovation,
only German version available).
A technical documentation of the CIS 2022 has been provided through a background report in German, published in July 2024 (see https://ftp.zew.de/pub/zew-docs/docus/dokumentation2401.pdf).
Annexes:
Report on national results of the German CIS 2022
Technical Background Report on the German CIS 2022
8.2. Release calendar access
There is no ex-ante release calendar for the German CIS.
8.3. Release policy - user access
All reports on the German CIS 2022 results are available for free to everyone.
Result tables (Excel format) are available on the website of ZEW (German and English versions):
https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheiminnovation-
panel-the-annual-german-innovation-survey/documentations-table-appendix
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, along with the publication of the national report |
ZEW website. |
| Access to public free of charge | through ZEW website: |
Results from the Innovation Survey are also published at the Data Portal of the Federal Ministry of Education and Research: Datenportal. |
| Access to public restricted (membership/password/part of data provided, etc) | none |
10.2. Dissemination format - Publications
- Online database (containing all/most results) : https://www.zew.de/en/publications/zew-expertises-researchreports/
research-reports/innovations/mannheim-innovation-panel-the-annual-german-innovation-survey/coreindicators
https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheiminnovation-
panel-the-annual-german-innovation-survey/focus-indicators
https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheiminnovation-
panel-the-annual-german-innovation-survey/documentations-table-appendix
- Analytical publication (referring to all/most results) : https://www.zew.de/en/publications/dokumentation-zurinnovationserhebung-
2023-1
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect) :
https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheiminnovation-
panel-the-annual-german-innovation-survey/innovation-survey-academic-papers
10.3. Dissemination format - online database
https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheiminnovation-
panel-the-annual-german-innovation-survey
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
see 10.4.1.
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | yes | CIS 2022 data will be provided to Eurostat as soon as a data request from Eurostat has been received |
| National SAFE centre | yes | See Kooperationen. |
| Eurostat: partially anonymised data (SUF) | yes | CIS 2022 data will be provided to Eurostat as soon as a data request from Eurostat has been received |
| National: partially anonymised data | yes | See Kooperationen. |
10.5. Dissemination format - other
No other means of dissemination.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
The methodology is documented in a technical background report: Rammer, Christian and Torben Schubert (2024), Dokumentation zur Innovationserhebung 2023, Mannheim. (https://ftp.zew.de/pub/zew-docs/docus/dokumentation2401.pdf)
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
For data collection, data processing and data analysis, the standard quality procedures for enterprise surveys are applied, including double entry of data from paper questionnaires and codes for detecting likely data errors and inconsistencies. The quality of the German CIS 2022 is assessed to be very good. Owing to the fact that innovation surveys are conducted annually in Germany in the form of a panel survey, enterprises do fully understand the underlying concepts and questions and are prepared to provide the demanded information in high quality, including information on innovation expenditures. The panel nature of the survey furthermore offers the opportunity to cross-check the information provided by enterprises with information from previous years for the same enterprise, which eases the identification of potential errors in the data. No quality report at the national level has been produced for CIS 2022 in Germany.
11.1. Quality assurance
For data collection, data processing and data analysis, the standard quality procedures for enterprise surveys are applied, including double entry of data from paper questionnaires and codes for detecting likely data errors and inconsistencies.
11.2. Quality management - assessment
The quality of the German CIS 2020 is assessed to be very good. Owing to the fact that innovation surveys are conducted annually in Germany in the form of a panel survey, enterprises do fully understand the underlying concepts and questions and are prepared to provide the demanded information in high quality, including information on innovation expenditures. The panel nature of the survey furthermore offers the opportunity to cross-check the information provided by enterprises with information from previous years for the same enterprise, which eases the identification of potential errors in the data.
12.1. Relevance - User Needs
User needs are identified through the scientific advisory board of the German CIS. The advisory board includes representatives from academia (universities, research institutes), industry (industry associations), policy (Federal Ministries, Expert Commission on Research and Innovation) as well as from statistical bodies (Federal Statistical Office, organisation that conducts the R&D survey for the enterprise sector).
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 - Federal government |
Policy officers in Federal Ministries who design and deliver government policies |
Up-to-date information on innovation activities in the enterprise sector, broken down by size, sector, type of innovation, incl. international comparison |
| 2. Institutions - State governments (reigons) |
Policy officers in Ministries of Federal States who design and deliver state government policies |
Up-to-date information on innovation activities in the enterprise sector in the respective Federal State, broken down by size, sector, type of innovation, incl. comparison to other regions (both national and international) |
| 3. Social actors - Industry associations, unions, other stakeholders |
Experts in industry association, unions and other stakeholders who are engaged in designing and evaluating innovation policies. |
Up-to-date information on innovation activities in the relevant industry, broken down by size, type of innovation, incl. international comparison for the same industry |
| 4. Researchers and students |
Researchers at |
Micro-data, preferable panel data |
12.2. Relevance - User Satisfaction
Satisfaction of users of CIS 2022 is assessed to be high based on the number of individuals and users that use
tabulated results, reports and micro-data.
12.3. Completeness
There are no specific issues with completeness of the German CIS 2022.
The tabulated results of the German CIS 2022 include all mandatory variables.
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
Restricted from publication
13.2.1.2. Variance estimation method
see 13.2.1
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
No under-covered groups.
13.3.1.4. Coverage errors in coefficient variation
None.
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
The main approach to detect measurement errors are comparison of enterprise responses with responses from previous years for each variable. If no such information is available, outliers (based on the mean and standard deviation within a stratum for the respective variable) are identified and analysed.
13.3.3. Non response error
see below
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) | 7127 | 14815 | 48.1% | 47.9% |
| Core industry (B_C_D_E - excluding construction) | 4461 | 9741 | 45.8% | 45.7% |
| Core Services (46-H-J-K-71-72-73) | 2666 | 5074 | 52.5% | 49.8% |
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
Up to five reminders were made, combining postal, e-mail and telephone reminders.
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 | 0.0% |
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 | 5.5% | |
| 3.10 -- Reasons for having no innovation activities | yes | 7.1% |
13.3.4. Processing error
None.
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: January 30th, 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) : 18 days (delivery of final, validated data at July 18th, 2024)
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
The same international standards, concepts and definitions (Oslo manual or Eurostat guidelines) have been applied for all geographical areas of Germany.
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 |
|---|---|
| 5.2, 5.3, 5.4, 6.2, 7.8, 7.9 | |
| Not all items: 3.8, 4.3, 7.6 | 3.8: differentiation of all other innovation expenditure not included 4.3: Tax credits or allowances for all other types of activities not included 7.6: expenditure on product design and on IPRs not oncluded |
| Changes in the filtering compared to HDC | Comment |
|---|---|
| none |
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 |
|---|---|
| Number of part-time employed persons; Share of exports to the UK; Share of turnover of main group of products/services; Market share of main group of products/services; World-first product innovations; Cost reduction owing to process innovation; Number of R&D personnel; Expenditure for own R&D personnel; Capital expenditure for innovation; Planned innovation activities in 2023 and 2024; Total innovation expenditure planned for 2023 and 2024; Use of artificial intelligence in the enterprise; Use of Energy by source; Energy-related measures; Social innovations;Total expenditure on personnel; Total expenditure on materials, services, energy and other operating costs; Amount of fixed capital (property, plant & equipment); Profit margin |
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 | 100.0% | 100.0% | 100.0% |
| Core industry (B_C_D_E - excluding construction) | Total | 100.0% | 100.0% | 100.0% |
| Core Services (46-H-J-K-71-72-73) | Total | 100.0% | 100.0% | 100.0% |
* 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)
The statistical business register of the German national statistical office (Destatis) is not accessible for third parties for purposes of sampling. Instead, a database of the largest German credit rating agency (CREDITREFORM) was used. This database contains information on virtually all enterprises with more than 5 employees in Germany. This database is processed by ZEW in order to use it for sampling purposes. The data base contains about 3.3 million economically active enterprises, which corresponds to the number of economically active enterprises reported by Destatis based on the business register.
18.1.2. Sampling design
The German CIS 2022 had a total number of 896 strata (56 NACE divisions, 8 size classes, 2 regions). Random sampling with known and disproportional selection probabilities based on the variance of innovation activities per stratum observed in prior and the number of enterprises in a stratum’s total population was applied.
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
| Target population (A) (*) | 169279 |
| Sample (B = C+D) | 21309 |
| In case of combination sample/census: | |
| Sampled units (C) | 17939 |
| Enumerated units/census (D) | 3370 |
| Overall sample rate (E = 100*B/A) | 12.6 % |
(*) 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 |
|---|---|---|
| Age | Mannheim Enterprise Panel | 2022 |
| Turnover, no. of employed persons | Mannheim Enterprise Panel | 2022 |
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
| Data source used for deriving population totals | National business register of the German national statistical office (Destatis) |
| Variables used for weighting | Three different weights are used for qualitative and quantitative variables:
|
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.
In Germany, the innovation survey is conducted every year, and national innovation statistics are produced for each calendar year.
18.3. Data collection
See below
18.3.1. Survey participation
Participation in the survey is voluntary.
18.3.2. Survey type
Stratified random sample survey with a census for enterprises with 500 or more employed persons, combination of paper and online survey.
18.3.3. Combination of sample survey and census data
Combination of both sample survey and census.
18.3.4. Census criteria
500 or more employed persons.
18.3.5. Data collection method
Data collection method
| Survey method | Yes/No | Comment |
|---|---|---|
| Face-to-face interview | no | |
| Telephone interview | yes | for non-response survey |
| Postal questionnaire | yes | |
| Electronic questionnaire (format Word or PDF to send back by email) | yes | only in a very few cases if the respondent asked for a pdf version of the questionnaire |
| 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% | 14.4% | 59.1% | 11.6% | 31.6% |
| Core industry (B_C_D_E - excluding construction) | Total | 0% | 0% | 15.2% | 49.5% | 12.6 % | 9.1% |
| Core Services (46-H-J-K-71-72-73) | Total | 0% | 0% | 12.7% | 45.5% | 9.5% | 21.0% |
(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 | see attached file, chapter 2.3 |
| Non-respondent adjustments | x | see attached file, chapter 2.3 |
| Other |
18.6. Adjustment
No adjustment was applied to the data.
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.
The statistical unit of the German CIS 2022 is the enterprise as defined in the EU regulation and as implemented in the Business Register of the Statistical Office of the Federal Republic of Germany.
The sampling was carried out at the level of legal units. The observation unit was the legal unit, but respondents could also report at the level of the Statistical Unit Enterprise.
The reporting unit was determined using information from previous CIS waves, and by contacting complex enterprises in person (via telephone calls).
Process for obtaining results at Statistical Unit Enterprise level, for both qualitative and quantitative variables:
The representative approach was used. In case several responses were received from legal units of the same complex enterprise, the response by the representative unit (= largest unit with the same NACE division as the complex enterprise's NACE division) was used.
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).
The German CIS 2022 covers all enterprises in the statistical population that are located within the territory of the Federal Republic of Germany.
There is not breakdown of CIS 2022 results by NUTS regions available. The only breakdown available is by Western Germany vs. Eastern Germany.
The microdata nevertheless contain information on the location of an enterprise by NUTS 3-digit level.
The national classification of East Germany and West Germany was used as a stratification dimension.
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.
The same international standards, concepts and definitions (Oslo manual or Eurostat guidelines) have been applied for all geographical areas of Germany.
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.


