ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH Mannheim

(Centre for European Economic Research)

Department Economics of Innovation and Industrial Dynamics

ZEW

L 7, 1

68161 Mannheim

Germany

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. 

CIS 2020 is a second in a row to implement concepts and methodology of the Oslo Manual 4th Edition revised in 2018. The changes in the CIS driven by the revision of the manual and their impact on collected indicators are described in the Statistics Explained article: Community Innovation Survey – new features. 

The legal framework for CIS since 2012 is the Commission Regulation No 995/2012 that establishes the quality conditions for the data collection and transmission and identifies the obligatory cross-coverage of economic sectors, size class of enterprises and innovation indicators. The target population are enterprises with at least 10 employees 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). Please refer to the Annex section of the European metadata (ESMS) for details of the time coverage of collected indicators.

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.

CIS covers main economic sectors according to NACE Rev.2 broken down by size class of enterprises and type of innovation activity.

In accordance with Commission Regulation 995/2012 on innovation statistics, the following industries and services are included in the core target population. Results are made available with these following breakdowns :

All NACE – Core NACE (NACE Rev. 2  sections & divisions B-C-D-E-46-H-J-K-71-72-73 )

CORE INDUSTRY (excluding construction) (NACE Rev. 2 SECTIONS B_C_D_E)

10-12: Manufacture of food products, beverages and tobacco

13-15: Manufacture of textiles, wearing apparel, leather and related products

16-18: Manufacture of wood, paper, printing and reproduction

20: Manufacture of chemicals and chemical products

21: Manufacture of basic pharmaceutical products and pharmaceutical preparations

19-22: Manufacture of petroleum, chemical, pharmaceutical, rubber and plastic products

23: Manufacture of other non-metallic mineral products

24: Manufacture of basic metals

25: Manufacture of fabricated metal products, except machinery and equipment

26: Manufacture of computer, electronic and optical products

25-30: Manufacture of fabricated metal products (except machinery and equipment), computer, electronic and optical products, electrical equipment, motor vehicles and other transport equipment

31-33: Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment

D: ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY

E: WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION ACTIVITIES

36: Water collection, treatment and supply

37-39: Sewerage, waste management, remediation activities

CORE SERVICES (NACE Rev. 2 sections & divisions 46-H-J-K-71-72-73)(NACE code in the tables = G46-M73_INN)

46: Wholesale trade, except of motor vehicles and motorcycles

H: TRANSPORTATION AND STORAGE

49-51: Land transport and transport via pipelines, water transport and air transport

52-53: Warehousing and support activities for transportation and postal and courier activities

J: INFORMATION AND COMMUNICATION

58: Publishing activities

61: Telecommunications

62: Computer programming, consultancy and related activities

63: Information service activities

K: FINANCIAL AND INSURANCE ACTIVITIES

64: Financial service activities, except insurance and pension funding

65: Insurance, reinsurance and pension funding, except compulsory social security

66: Activities auxiliary to financial services and insurance activities

M: PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES

71: Architectural and engineering activities; technical testing and analysis

72: Scientific research and development

73: Advertising and market research

71-73: Architectural and engineering activities; technical testing and analysis; Scientific research and development; Advertising and market research

The German CIS 2018 also covers the following NACE groups and divisions:

69, 70.2, 74, 78, 79, 80, 81, 82

In accordance with Commission Regulation 995/2012 on innovation statistics, the following size classes of enterprises according to number of employees are included in the core target population of the CIS:

• 10 - 49 employees
• 50 - 249 employees
• 250 or more employees

The German CIS 2018 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 2018 has been stratified by 8 size classes: 5-9, 10-19, 20-49, 50-99, 100-249, 250-499, 500-999, 1000+

The description of concepts, definitions and main statistical variables is available in CIS 2020 European metadata file (ESMS) Results of the community innovation survey 2020 (CIS2020) (inn_cis12) in Eurostat database.

The statistical unit of the German CIS 2018 is the legal unit according to the definition of legal unit in the Business Register of the Statistical Office of the Federal Republic of Germany.

There are some deviations to this rule in case of large, complex enterprises that are active in different NACE divisions. For some of these large, complex enterprises, the statistical unit is the business unit that best matches the activities of the enterprise that fall under a single NACE division.

Core target population are all enterprises in CORE NACE activities (see 3.3.1) with 10 or more persons employed.

The German CIS 2018 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 2018 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.

Several rounds of Community Innovation Survey have been conducted so far at two-year interval since end of 90’s.

*two reference periods can be distinguished for CIS light: 2000-2002 and 2001-2003

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 2020, the time covered by the survey is the 3-year period from the beginning of 2018 to the end of 2020.

Some questions and indicators refer to one year — 2020.

The list of indicators covering the 3-year period and referring to one year according to the HDC is available in the Annex section of the European metadata (ESMS). 

CIS surveys are based on the Commission Regulation No 995/2012, implementing Decision No 1608/2003/EC 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.

European legislation applies.

Not requested.

CIS data are transmitted to Eurostat via EDAMIS using the secured transmission system.

No confidentiality policy.

Does not apply.

National results of the CIS 2020 were released on February 1st, 2022 (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 2020 will be publised in November 2022 (see https://www.zew.de/publikationen/dokumentation-zur-innovationserhebung-2021, only German version available).

There is no ex-ante release calendar for the German CIS.

All reports on the German CIS 2020 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/mannheim-innovation-panel-the-annual-german-innovation-survey/documentations-table-appendix

Differently to the CIS in most other EU member states, the German CIS is conducted every year. Results are therefore published every year. Results of an innovation survey with the reference year t are usually released at the end of January of year t+2.

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.

See below.

- Online database (containing all/most results) : https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheim-innovation-panel-the-annual-german-innovation-survey/core-indicators

https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheim-innovation-panel-the-annual-german-innovation-survey/focus-indicators

https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheim-innovation-panel-the-annual-german-innovation-survey/documentations-table-appendix

- Analytical publication (referring to all/most results) : https://www.zew.de/en/publications/dokumentation-zur-innovationserhebung-2021-1

- 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/mannheim-innovation-panel-the-annual-german-innovation-survey/innovation-survey-academic-papers

https://www.zew.de/en/publications/zew-expertises-research-reports/research-reports/innovations/mannheim-innovation-panel-the-annual-german-innovation-survey

Not requested.

see 10.4.1.

No other means of dissemination.

Not requested.

The methodology is documented in a separate report: Rammer, Christian and Torben Schubert (2022), Dokumentation zu den Innovationserhebungen 2017 bis 2021, Mannheim.

Not requested.

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 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.

No quality report at the national level has been produced for CIS 2020 in Germany.

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 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.

Relevance is the degree to which statistics meet current and potential users’ needs. It includes the production of all needed statistics and the extent to which concepts used (definitions, classifications etc.) reflect user needs. The aim is to describe the extent to which the statistics are useful to, and used by, the broadest array of users. For this purpose, statisticians need to compile information, firstly about their users and their needs.

The CIS is based on a common questionnaire and a common survey methodology in order to achieve comparable, harmonised and high quality results for EU Member States, EFTA countries, Candidates and Associated countries.

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).

Satisfaction of users of CIS 2020 is assessed to be high based on the number of individuals and users that use tabulated results, reports and micro-data.

There are no specific issues with completeness of the German CIS 2020.

The tabulated results of the German CIS 2020 include all mandatory variables.

Not requested.

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).

That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.

The main indicator used to measure sampling errors for CIS data is the coefficient of variation (CV).

Coefficient of Variation= (Square root of the estimate of the sampling variance) / (Estimated value)

Formula:

where

Coefficient of variation (%) for key variables by NACE categories and for enterprises with 10 and more employees

[1] = Coefficient of variation for the percentage of innovative enterprises (INN) in the total population of enterprises (ENT20)
[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 [TUR20,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).

see 13.2.1

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.

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 has 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.

Not requested.

Not requested.

No under covered groups.

None.

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.

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.

Non response occurs when a survey fails to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.                                                                                                 

There are two types of non-response:                                                                                                

1) Unit non-response, which occurs when no data (or so little as to be unusable) are collected about a population unit designated for data collection.                                                                                                                                                                                                       

a) Un-weighted unit non-response rate (%) = 100*(Number of units with no response or not usable response) / (Total number of in-scope (eligible) units in the sample)                                                                                                                                                                                                                 

b) Weighted unit non-response rate (%) = 100*(Number of weighted units with no response or not usable response) / (Total number of in-scope (eligible) units in the sample)                                                                                                            

2) Item non-response, which occurs when only data on some, but not all survey data items are collected about a population unit designated for data collection.                                                                                                                                                                                                   

a) Un-weighted item non-response rate (%) = 100*(Number of units with no response at all for the item) / (Total number of eligible, for the item, units in the sample i.e. filters have to be taken into account)    

See below.

Un-weighted and weighted unit non-response rate by NACE categories and for enterprises with 10 or more persons employed

The number of eligible units is the number of sample units, which indeed belong to the target population.

Up to four reminders were made, combining written, e-mail and telephone reminders.

See below.

Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or persons employed).

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 employees)
 

 none

Not requested.

Timeliness and punctuality refer to time and dates, but in a different manner.

The timeliness of statistics reflects the length of time between data availability and the event or phenomenon they describe.

Timeliness of national data – date of first release of national level : February 1st, 2022

Not requested.

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.

Date of transmission of complete and validated data to Eurostat (Number of days between that data and 30 June 2022) : 14 days (delivery of final, validated data at July 14th, 2022)

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.

The same international standards, concepts and definitions (Oslo manual or Eurostat guidelines) have been applied for all geographical areas of Germany.

Not requested.

Methodological deviations from the CIS Harmonised Data Collection (HDC)

Methodological deviations from the CIS Harmonised Data Collection (HDC)

Due to important methodological changes driven by Oslo Manual 2018, CIS 2018 and CIS 2020 cannot be directly compared with previous CIS waves.

Not requested.

See the comparison between SBS and CIS data in the section 15.3.3 below.

Not requested.

Not requested.

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 persons employed

* Numbers are to be provided for the last year of the reference period (t)

Comment: Higher total turnover in CIS is due to financial services (NACE K) which is based on CIS definition (gross interest received, gross premium written) while SBS includes only turnover on which VAT is applied. Higher number of employed persons due to inlcusion of self-employed persons and civil servants in CIS data, which are not included in German business register data.

Not requested.

Confidential information on the production cost of the CIS.

Not requested.

Not requested.

Not requested.

See below

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.

The German CIS 2020 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.

Variables and indicators filled or prefilled from other sources.

According to the Commission Regulation (UE) 995/2012, the innovation statistics shall be provided to Eurostat every two years in each even year t+18. 

In Germany, the innovation survey is conducted every year, and national innovation statistics are produced for each calendar year.

See below

Participation in the survey is voluntary.

Stratified random sample survey with a census for enterprises with 500 or more employed persons, combination of paper and online survey.

Combination of both sample survey and census.

500 or more employed persons.

Data collection method

Not requested.

Operations performed on data to derive new information according to a given set of rules.

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)

Imputation rate for metric variables by NACE categories and for enterprises with 10 or more persons employed:

(1) = Total turnover in the last year of the reference period (t) (TUR)

(2) = 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/TUR(INNO_PRD)

(3) = R&D expenditure performed in-house (EXP_INNO_RND_IH)

Weights calculation method for sample surveys

No adjustment was applied to the data.

Not requested.