1.1. Contact organisation
INSTITUTO NACIONAL DE ESTADISTICA (INE)
Avenida de Manoteras 50-52
28050 Madrid (Spain)
1.2. Contact organisation unit
Science and Technology Unit
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Avenida de Manoteras 50-52 , planta 3 despacho 326
28050 Madrid (Spain)
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 May 2024
2.2. Metadata last posted
19 December 2023
2.3. Metadata last update
31 May 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
Additional NACE2 classes included:
- Agriculture, forestry and fishing (NACE 01, 02, 03);
- Construction (NACE 41, 42, 43);
- Retail trade; repair of motor vehicles and motorcycles (NACE 45, 47);
- Accommodation and food service activities (NACE 55, 56);
- Motion picture, video and television program production, sound recording and music publishing activities; Programming and broadcasting activities (NACE 59, 60);
- Real estate activities (NACE 68);
- Professional, scientific and technical activities, except of architectural and engineering activities; technical testing and analysis (NACE 69, 70, 74-75);
- Administrative and support service activities (NACE 77-82);
- Human health and social work activities (NACE 86-88);
- Arts, entertainment and recreation (NACE 90-93);
- Repair of computers and personal and household goods, and Other personal service activities (NACE 95, 96).
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
No deviation. All enterprises with 10 employees or more are covered by the Innovation survey.
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 basic statistical unit is the Enterprise. The reporting unit, or rather, the unit from which the basic information is obtained is the Legal Unit. Given that it is perfectly defined and located and has accounting and employment data, the answer is facilitated and homogeneous information is obtained. The Legal Units can be legal persons (mercantile enterprises) or physical persons (individual entrepreneurs).
The information is obtained from each of the Legal Units that make up the enterprise, and the statistics are prepared by grouping (and where necessary, consolidating) the variables of all the Legal Units that make up the the enterprise.
The sampling was carried out at the level of the Legal Units.
Process for obtaining results at Statistical Unit Enterprise level:
- For qualitative variables:
If a Legal Unit of a complex enterprise is in the sample and responds to the questionnaire, the values of the remaining legal units of the complex enterprise that are not in the sample or do not respond to the questionnaire are imputed using the nearest neighbor method. Once the values for all the legal units of the complex enterprise have been obtained, an aggregation is carried out according to the rule: If one Legal Unit's response is Yes, the enterprise's response is considered Yes.
- For quantitative variables:
If a Legal Unit of a complex enterprise is in the sample and responds to the questionnaire, the values of the remaining legal units of the complex enterprise that are not in the sample or do not respond to the questionnaire are imputed using the average of the stratum or the business register (turnover). Once the values for all the legal units of the complex enterprise have been obtained, the values of all legal units are summed (eliminating internal flows in case of turnover)
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 survey covers the whole national territory. Main variables are disaggregated by region.
NUTS2 was used as a geographical stratification dimension for the sampling.
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 | No |
|
| CIS3 | 1998-2000 | Yes | |
| CIS light | 2002-2003* | Yes | Both of them |
| 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
The compilation and dissemination of the data are governed by the Statistical Law No. 12/1989 "Public Statistical Function" of May 9, 1989, and Law No. 4/1990 of June 29 on “National Budget of State for the year 1990" amended by Law No. 13/1996 "Fiscal, administrative and social measures" of December 30, 1996, makes compulsory all statistics included in the National Statistics Plan. It contains the statistics that must be developed in the four year period by the State General Administration's services or any other entity dependent on it. All statistics included in the National Statistics Plan are statistics for state purposes and are obligatory. The National Statistics Plan 2021-2024, approved by Royal Decree 1110/2020, of 15 December, is the Plan currently implemented. This statistical operation has governmental purposes, and it is included in the National Statistics Plan 2021-2024. (Statistics of the State Administration).
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
The main national legal regulations applicable to the protection of statistical data are:
- "Ley Orgánica 3/2018 de Protección de Datos Personales y garantía de los derechos digitales";
- "Ley 12/1989 de la Función Estadística Pública";
- "Real Decreto 428/1993, de 26 de marzo, por el que se aprueba el Estatuto de la Agencia de Protección de Datos";
- "Real Decreto 994/1999, de 11 de junio, por el que se aprueba el Reglamento de medidas de seguridad de los ficheros automatizados que contengan datos de carácter personal".
Regulation (EC) No 223/2009 on European statistics stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
7.2. Confidentiality - data treatment
INE provides information on the protection of confidentiality at all stages of the statistical process: INE questionnaires for the operations in the national statistical plan include a legal clause protecting data under statistical confidentiality. Notices prior to data collection announcing a statistical operation notify respondents that data are subject to statistical confidentiality at all stages. For data processing, INE employees have available the INE data protection handbook, which specifies the steps that should be taken at each stage of processing to ensure reporting units' individual data are protected. The microdata files provided to users are anonymised.
The questionnaire send to the collaborating units informs them that "The personal data that the statistical services obtain, both directly from the respondents and through administrative sources, shall be subject to protection, and covered by statistical secrecy (article 13.1 of the Law on Public Statistical Services of May 9 1989, (LFEP)). All statistical personnel shall be obligated to preserve statistical secrecy (article 17.1 of the LFEP)”.
In order to avoid data disclosure, tabulation and information at different levels are analyzed to prevent that confidential data of statistical units could be derived.
In tabulation, a cell is suppressed if there is less than 4 units representing the cell.
8.1. Release calendar
The advance release calendar that shows the precise release dates for the coming year is disseminated in the last quarter of each year.
8.2. Release calendar access
The calendar is disseminated on the INEs Internet website (Publications Calendar)
8.3. Release policy - user access
The data are released simultaneously according to the advance release calendar to all interested parties by issuing the press release. At the same time, the data are posted on the INE's Internet website (INE website) almost immediately after the press release is issued. Also some predefined tailor-made requests are sent to registered users. Some users could receive partial information under embargo as it is publicly described in the European Statistics Code of Practice
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 | X | |
| Access to public free of charge | ||
| Access to public restricted (membership/password/part of data provided, etc) |
10.2. Dissemination format - Publications
- Online database (containing all/most results).
- Analytical publication (referring to all/most results): No.
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect): No.
10.3. Dissemination format - online database
INEbase is the system the INE uses to store statistical information on the Internet. It contains all the information the INE produces in electronic formats. The primary organisation of the information follows the theme-based classification of the Inventory of Statistical Operations of the State General Administration.
The basic unit of INEbase is the statistical operation, defined as the set of activities that lead to obtaining statistical results on a determined sector or topic using data collected individually.
Access to tables and time series in INEbase, within the "Science and Technology" section in this website.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | Yes | |
| National SAFE centre | Yes | Any researcher who wants access to the microdata must sign an agreement with the National Statistics institute, for access for statistical purposes, by the research personnel, to the confidential data from the INE. The agreement describes the project, and the need to access said microdata, specifies the period during which the research time would work in the INE, provides the name of the research team and establishes the agreement clauses, including the statistical confidentiality clause.
|
| Eurostat: partially anonymised data (SUF) | Yes | |
| National: partially anonymised data | Yes |
10.5. Dissemination format - other
There is the possibility of requesting customised information from the INE User Care Department. At the time of processing said requests, this considers limitations regarding confidentiality or precision.
See this website.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
In order to facilitate the adequate comprehension and use of data, some documents are also published. These documents are:
- Standardised Methodological Report.
- General methodology (including concepts and background of the survey, objective of the survey, scope, statistical unit used, variables and its definition, sample design, collection of information, processing of information, tabulation of results).
- Model questionnaire used for the collection of the survey.
See this website.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
If a user have any special request or doubt concerning data or metadata, it is possible to contact with the Science and Technology Unit (via electronic template) in order to obtain a more extended response or clarification.
See this website: INE website.
11.1. Quality assurance
Quality assurance framework for the INE statistics is based on the ESSCoP, the European Statistics Code of Practice made by EUROSTAT. The ESSCoP is made up of 16 principles, gathered in three areas: Institutional Environment, Processes and Products. Each principle is associated with some indicators which make possible to measure it. In order to evaluate quality, EUROSTAT provides different tools: the indicators mentioned above, Self-assessment based on the DESAP model, peer review, user satisfaction surveys and other proceedings for evaluation.
In order to guarantee quality information, the information received is processing following the steps listed below:
- Control and manual filtering of the questionnaires by the units involved in the information collection, with the objective of recovering the possible lack of data, or of correcting errors in the questionnaires before they are recorded.
- Interactive recording with filtering and correction of the errors in the information obtained by the units involved in the information collection.
- Control of the information received by the unit responsible for the survey.
- Control of the scope and processing of identification errors.
- Validation of the quality of the information.
- Imputation of the partial non-response.
- Filtering and interaction correction of inconsistencies in the validated information.
- Preparation of a first phase of results analysis tables.
- Macro-publishing of the main aggregates to correct the errors not detected in the previous micro-filtering phase.
- Data analysis.
- Creation of the final data file.
- Obtaining final results tables in the unit responsible for the survey, compiled using the final data file.
11.2. Quality management - assessment
The general assessment is that the quality of the Spanish Innovation Survey is high.
The following may be cited as being among the main strengths of this survey:
1) Quick collection, analysis and publication of the results at national level, in such a way that the data dissemination is carried out within the year that follows the end of the reference period.
2) As a survey that has been conducted annually since 2002, a comprehensive follow-up of the data may be performed so that possible inconsistencies therein may be detected, ensuring a time series that is consistent over time.
3) An effort has been made to collect the information online, resulting in more than 99% of the questionnaires collected this way.
4) High response rate.
12.1. Relevance - User Needs
At European level, Eurostat contributes in identifying and defining the main topics/questions to be covered. At national level, main users (e.g. Observatorio Mujeres, Ciencia e Innovación (OMCI), Ministerio de Ciencia e Innovación (MICIN), …) are consulted for their needs and are involved in the process of the development of the model questionnaires at a very early stage.
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 | The European Commission (DG ENTR) | Innovation Union Scoreboard |
| 1. Institutions – International organisations | Eurostat, OECD | Data used for different analyses or studies. |
| 1. Institutions – National Level | Ministries and Public Authorities | Data used for policy-making and assessment of Innovation phenomena. |
| 1. Institutions – National Level | National Statistical Office | Data used for annual publication on Innovation |
| 1. Institutions – Regional level | Local authorities | Data used for policy-making and assessment of Innovation phenomena |
| 2. Social actors | COTEC (Foundation for technological innovation) | Data used for different publications, notably "Annual Report: Technology and Innovation in Spain". |
| 3. Media | Journalists | Data for general and specialized publications |
| 4. Researchers and students | Universities | Data used for different analyses or studies. |
| 5. Enterprises or business | Enterprises or business | Data used for different analyses or studies and market studies. |
12.2. Relevance - User Satisfaction
The INE has carried out general user satisfaction surveys in 2007, 2010, 2013, 2016 and 2019. The purpose of these surveys is to find out what users think about the quality (in terms of relevance, accuracy and reliabilitiy, timeliness and punctuality, coherence and comparability) of the statistical outputs disseminated by INE as well as other topics such as the accessibility of the information or to which extent their needs of information are covered by the current statistics.
In the last user satisfaction survey, carried out, in 2019, the "Science and Technology" sector obtained more than 80% of positive assessment.
12.3. Completeness
The Innovation in Companies Survey meets all the requirements established in the national and international regulations related to science and technology statistics. Said statistics are governed by Commission Regulation no. 995/2012, of 26 October 2012. As a result, the rate of compulsory statistical information supplied is 100%.
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 |
1.23 % |
0.54 % |
1.35 % |
| Core industry (B_C_D_E - excluding construction) |
Total |
1.38 % |
0.47 % |
1.38 % |
| Core Services (46-H-J-K-71-72-73) |
Total |
1.98 % |
1.12 % |
2.25 % |
(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 attached document.
Annexes:
Variance estimation method
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
CIS covers every enterprise with 10 or more employees in the Core NACE economic sectors according to the regulation.
13.3.1.4. Coverage errors in coefficient variation
Coverage errors in coefficient variation are reflected by the increase in error due to:
- the decrease in sample size in the case of the over-coverage.
- stratum changes by erroneous classification, due mainly to size (employees) or economic activity.
The percentage of stratum changes is 4.40%
We don’t asses their relative weight in the total error.
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
Survey inspectors are responsible for theoretical and practical training of the staff involved in field work, and for controlling work relating to the collection of information. To this purpose, manuals and training documents are available.
13.3.3. Non response error
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.
- 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).
- 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.
- 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).
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) | 1465 | 24842 | 5.90 % | 8.38 % |
| Core industry (B_C_D_E - excluding construction) | 692 | 13373 | 5.17 % | 7.21 % |
| Core Services (46-H-J-K-71-72-73) | 773 | 11469 | 6.74 % | 9.38 % |
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
There are at least three reminders (by mail or by phone) to the non-responding enterprise, although the unit can be contacted again by different means.
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 | 0.08 % | Yes | Firstly, missing data availability is checked in a business and financial information database (containing data extracted from annual reports). Secondly, when historical data of the enterprise is available, these are used for imputation by turnover/employee ratio. Finally, if the previous steps are failed, imputation according with the strata where the enterprise is located is implemented. |
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 | 0 % | Mandatory response |
| 3.10 -- Reasons for having no innovation activities | Yes | 0 % | Mandatory response |
13.3.4. Processing error
The information collection method is a mixed system based on postal mailings and interviewer participation, with significant telephone support for the collection thereof. A postal letter is sent to the respondants informing them that their enterprise belongs to the sample of the Innovation Survey INE is carrying out. The letter also provides them with the access rights (username/password) to the on-line questionnaire hosted on the INE servers. They are also informed that, if preferred, they can apply for the paper version which they can fill in and send back to the INE through mail, fax or e-mail. Most of the respondents (99.16% in the case of the 2022 Innovation Survey) use the on-line tool so the data entry method are, essentially, the responses through online questionnaires.
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: 19 December 2023.
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) : 26 June 2024 (-4 days).
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
We follow the international standards, concepts and definitions provided by Oslo manual and Eurostat guidelines at the whole territory and there is not discrepancy at nacional level and in comparison to remaining EU countries.
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 |
|---|---|
| Q2.1 & Q2.2 | Not implemented |
| Q4.3 | Not implemented |
| Q5.1 & Q5.4 | Not implemented |
| Q6.2 | Not implemented |
| Q7.2 & Q7.8 & Q7.9 | Not implemented |
| Changes in the filtering compared to HDC | Comment |
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 |
|---|---|
| A.5. Is your company located in a Scientist or Technological Park? If yes, what is the full name of the Scientist or Technological Park? What is the year of the incorporation to the Scientist or Technological Park? | |
| A.9. Indicate the number of owners of the company by sex and age | |
| A.10. Indicate the number of owners of the company by title | |
| D.1.2. During the period 2020-2022, what was the last year your enterprise introduced any product innovation? | |
| D.2.2. During the period 2020-2022, what was the last year your enterprise introduced any business process innovation? | |
| D.3.3. Expenses on internal R&D and other innovation activities by Autonomous Community in 2022 | |
| D.3.4.1. Personnel employed by the company dedicated to innovative activities in 2022 by sex and age group | |
| D.3.4.2. Personnel employed by the company dedicated to innovative activities in 2022 by title, in FTE | |
| F.3. During the period 2020-2022, did your company purchase or acquire IN licenses for patents or other intellectual and industrial property rights? | |
| Q3.4 from HDC | We add options in "Your enterprise together with other enterprises or organisations": Universities, Technology Centers and Enterprises and others. |
| Q3.6 from HDC | We add options in "Your enterprise together with other enterprises or organisations": Universities, Technology Centers and Enterprises and others. |
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 | 102.48 % | 101.03 % | 112.13 % |
| Core industry (B_C_D_E - excluding construction) | Total | 95.63 % | 101.72 % | 101.83 % |
| Core Services (46-H-J-K-71-72-73) | Total | 108.66 % | 100.40 % | 128.11 % |
* 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 Central Businesses Directory (DIRCE) collects all Spanish businesses in a single directory. Its basic objective is to enable business-targeted sample surveys to be conducted, and consequently, it registers information such as identity data, location, main activity or number of employees. This information is obtained from administrative sources (Inland Revenue and Social Security) and complemented with data from common statistical operations. This directory is annually updated.
Similarly, due to the coordination of the R&D and the Innovation Survey, the Directory of Enterprises that performed R&D or are potential R&D performers (DIRID) is also used. This register is annually updated by the following data:
- Enterprises receiving public support or grants for R&D activities (including not only Central Government but also almost all Autonomous Communities Governments).
- terprises performing R&D activities in previous surveys.
18.1.2. Sampling design
The total number of initial strata for 2020 data is 3,363 (3*59*19), including empty strata.
Due to the coordination of the R&D and the Innovation Survey mentioned before, the final sample is obtained by adding up two sets of enterprises:
1) First, the set of all enterprises that have potentially carried out R&D activities in the reference year (i.e the enterprises contained in the DIRID).
2) Second, a stratified-sample of enterprises from the Central Businesses Directory (DIRCE) (where the enterprises in the 1) set have been previously removed) obtained from the crossing the following variables:
a) Size of the enterprise: The following intervals are considered, depending on the number of employees:
10 to 49
50 to 199
200 or more employees
The strata constituted by companies with 200 or more employees has been analyzed exhaustively.
b) Main activity according to CNAE-2009: The 59 divisions or activity groups enumerated in Table 1.
c) Autonomous Community where company headquarter is located.
Annexes:
Table 1
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
|---|---|
| Target population (A) (*) | 74737 |
| Sample (B = C+D) | 25870 |
| In case of combination sample/census: | |
| Sampled units (C) | 14904 |
| Enumerated units/census (D) | 10966 |
| Overall sample rate (E = 100*B/A) | 34.61% |
(*) 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 |
|---|---|---|
| Main activity | Central Businesses Directory (DIRCE) | 2022 |
| Total turnover | CIS2020 | 2020 |
| Average number of persons employed | CIS2020 | 2020 |
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
|---|---|
| Data source used for deriving population totals | The Central Businesses Directory (DIRCE) collects all Spanish businesses in a single up-to-date directory. |
| Variables used for weighting | Size of the enterprise, main activity according to CNAE-2009 and NUTS region at level 2. |
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
See below
18.3.1. Survey participation
Due to the inclusion of the Innovation Survey in the National Statistical Plan, it is considered as a statistic of obligatory compliance, and as a result, non-responding enterprises can be economically fined.
18.3.2. Survey type
The survey is collected by a combination of census and sample survey, as the Innovation surveys is collected combined with the R&D survey for Business sector.
18.3.3. Combination of sample survey and census data
As the Innovation and the R&D survey are carried out together, enterprises that are part of the DIRID (register of national enterprises with R&D activities or potentially R&D performers) as well as part of the frame population of the Innovation survey, are included in the sample. Those enterprises included in the DIRID are studied exhaustively. Taking account of this group of enterprises already selected and the frame population, a sample is extracted from the DIRCE (official, up-to-date, statistical business register) by crossing the following variables: size, economic activity and NUTS.
In every stratum, a systematic selection randomly started is executed, sorting out the enterprises by size and location. The categories of the variables used to stratify are:
- size: 10 to 49, 50 to 199, 200 or more employees. Enterprises with 200 or more employees are analyzed exhaustively.
- main activity at NACE2 (2-digit level and aggregations).
- NUTS region at level 2.
18.3.4. Census criteria
Enterprises that are part of the DIRID (register of national enterprises with R&D activities in previous surveys or are potentially R&D performers) are studied exhaustively. Besides, enterprises with 200 or more employees are studied through a complete enumeration.
18.3.5. Data collection method
Data collection method
| Survey method | Yes/No | Comment |
|---|---|---|
| Face-to-face interview | No | |
| Telephone interview | Yes | It is used the telephone interview in order to strengthen data collection. The interviewer uses the web platform |
| Postal questionnaire | Yes | Some units prefer to send the survey by post. |
| Electronic questionnaire (format Word or PDF to send back by email) | No | |
| Web survey (online survey available on the platform via URL) | Yes | Most of the units prefer this method. |
| Other |
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 | 0.11 % | 0.04 % | 0.12 % | 0.08 % | 0.19 % | 0.10 % |
| Core industry (B_C_D_E - excluding construction) | Total | 0.04 % | 0.02 % | 0.12 % | 0.08 % | 0.13 % | 0.08 % |
| Core Services (46-H-J-K-71-72-73) | Total | 0.19 % | 0.05 % | 0.12 % | 0.07 % | 0.25 % | 0.12 % |
Legal Units before consolidating as a statistical enterprise
(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 the annexed document. |
| Non-respondent adjustments | x | See the annexed document. |
| Other |
Annexes:
Estimators
18.6. Adjustment
No calibration method has been used.
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 May 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 basic statistical unit is the Enterprise. The reporting unit, or rather, the unit from which the basic information is obtained is the Legal Unit. Given that it is perfectly defined and located and has accounting and employment data, the answer is facilitated and homogeneous information is obtained. The Legal Units can be legal persons (mercantile enterprises) or physical persons (individual entrepreneurs).
The information is obtained from each of the Legal Units that make up the enterprise, and the statistics are prepared by grouping (and where necessary, consolidating) the variables of all the Legal Units that make up the the enterprise.
The sampling was carried out at the level of the Legal Units.
Process for obtaining results at Statistical Unit Enterprise level:
- For qualitative variables:
If a Legal Unit of a complex enterprise is in the sample and responds to the questionnaire, the values of the remaining legal units of the complex enterprise that are not in the sample or do not respond to the questionnaire are imputed using the nearest neighbor method. Once the values for all the legal units of the complex enterprise have been obtained, an aggregation is carried out according to the rule: If one Legal Unit's response is Yes, the enterprise's response is considered Yes.
- For quantitative variables:
If a Legal Unit of a complex enterprise is in the sample and responds to the questionnaire, the values of the remaining legal units of the complex enterprise that are not in the sample or do not respond to the questionnaire are imputed using the average of the stratum or the business register (turnover). Once the values for all the legal units of the complex enterprise have been obtained, the values of all legal units are summed (eliminating internal flows in case of turnover)
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 survey covers the whole national territory. Main variables are disaggregated by region.
NUTS2 was used as a geographical stratification dimension for the sampling.
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.
We follow the international standards, concepts and definitions provided by Oslo manual and Eurostat guidelines at the whole territory and there is not discrepancy at nacional level and in comparison to remaining EU countries.
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.
