1.1. Contact organisation
ISTAT -Italian National Institute of Statistics
1.2. Contact organisation unit
Directorate for Economic Statistics (DCSE)
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Via Tuscolana 1788, 00173 Rome, Italy
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
18 October 2024
2.2. Metadata last posted
31 October 2024
2.3. Metadata last update
18 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.
CIS 2022 is designed 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 CIS 2022 is covered by Regulation 2152/2019 on European Business Statistics (EBS) as well as an Implementing Act dedicated to the topic ‘business innovation’. The objectives of this Implementing Act have been anticipated by the redesign of the CIS, so that data on business innovation will be better integrated into its context of European Business Statistics. The Regulation 2152/2019 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 persons employed 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.
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
CIS covers main economic sectors according to NACE Rev.2 broken down by size class of enterprises and type of innovation activity.
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 Italian survey covered the following non Eu-core sectors: NACE Rev. 2 section F and NACE Rev. 2 divisions 45, 47, 68, 69, 70, 74.
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
In the Italian Cis2022 size classes are defined by the number of persons employed.
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
For the first time, in this edition we adopted the statistical unit Enterprise (the so-called ENT) instead of the legal unit used in the past.
The observation unit was the legal unit (LU).
Process for obtaining results at Statistical Unit Enterprise level in complex enterprises:
- For qualitative variables:
- A "representative cluster" was identified for each ENT, representing the subset of LUs that comprise each enterprise.
- Consolidation of Variables. For consolidation of non-additive qualitative variables (dichotomous or categorical), different rules were applied. For binary variables (e.g., 1=success, 0=failure), if both values were present, the ENT inherited a "1" for success. For what concerns the variables with multiple non-ordered categories, they were converted to binary indicators, where "0" represented absence and "1" presence of each category. The binary rule applied as with other binary variables. Finally, for ordinal variables (e.g., importance levels), the value was derived as the weighted average of the responses from each LU, using the number of employed persons as weights. For example, if an ENT comprises three LUs of different sizes (10, 100, and 1000 employed persons) that declared a different ‘degree of importance’ in ENV_ENREP (e.g., 3=high, 2=medium, 1=low), the ENT's final value for the variable (e.g., ENV_ENREP for "improvement of corporate reputation") was calculated as “1,” reflecting the weighted average of the responses.
- For quantitative variables:
- Identification of the Representative Cluster. A "representative cluster" was identified for each ENT, representing the subset of LUs that comprise each enterprise.
- Consolidation of Variables. Variables were categorized as additive or non-additive for consolidation. For the consolidation of additive variables, like expenditures and employed persons, they were aggregated by summing the values for the LUs within each ENT, adjusted according to each LU’s share of the ENT.
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
NUTS level 2
NUTS2 was used as a geographical stratification dimension for 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 | Comment (deviation from reference period) |
|---|---|---|---|
| CIS2 | 1994-1996 | Yes | |
| CIS3 | 1998-2000 | Yes | |
| CIS light | 2002-2003* | No | |
| 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 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).
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
Decreto del Presidente della Repubblica 11 luglio 2023, di approvazione del Programma statistico nazionale 2020-2022 - Aggiornamento 2022,degli altri atti di programmazione della statistica ufficiale e dei collegati elenchi delle rilevazioni con obbligo di risposta per i soggetti privati e dei lavori per i quali la mancata fornitura dei dati configura violazione dell’obbligo di risposta sanzionata ai sensi degli artt. 7 e 11 del decreto legislativo 6 settembre 1989, n. 322 (S.O. n. 26 alla Gazzetta Ufficiale 29 agosto 2023 - serie generale - n. 201).
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
- Regolamento (UE) 2016/679 del Parlamento europeo e del Consiglio, del 27 aprile 2016, relativo alla protezione delle persone fisiche con riguardo al trattamento dei dati personali, nonché alla libera circolazione di tali dati e che abroga la direttiva 95/46/CE (Regolamento generale sulla protezione dei dati).
- Decreto legislativo 6 settembre 1989, n. 322, “Norme sul Sistema statistico nazionale e sulla riorganizzazione dell'Istituto nazionale di statistica” – art. 6 (compiti degli uffici di statistica), art. 6-bis (trattamenti di dati personali), art. 7 (obbligo di fornire dati statistici), art. 8 (segreto d'ufficio degli addetti agli uffici di statistica), art. 9 (disposizioni per la tutela del segreto statistico), art. 11 (sanzioni amministrative), art. 13 (Programma statistico nazionale).
- Decreto legislativo 30 giugno 2003, n. 196 “Codice in materia di protezione dei dati personali”.
- Decreto legislativo 10 agosto 2018, n. 101, “Disposizioni per l'adeguamento della normativa nazionale alle disposizioni del regolamento (UE) 2016/679 del Parlamento europeo e del Consiglio, del 27 aprile 2016” (Regolamento generale sulla protezione dei dati);
- Decreto legislativo 14 marzo 2013, n. 33, “Riordino della disciplina riguardante il diritto di accesso civico e gli obblighi di pubblicità, trasparenza e diffusione di informazioni da parte di Pubbliche Amministrazioni” - art. 5-ter (accesso per fini scientifici ai dati elementari raccolti per finalità statistiche).
7.2. Confidentiality - data treatment
Confidentiality flags are when just one or two enterprise dominate the data.
8.1. Release calendar
The next national release will be in November-December 2024.
8.2. Release calendar access
Not available.
8.3. Release policy - user access
Istat’s dissemination policy is oriented towards different target groups: citizens, who want data on the country, researchers, who require statistical classification and “ad hoc” analyses, students, surveys respondents and, of course, the media.
The Institute has set up different channels for each target, in order to better meet the requests for information.
Press releases are issued according to a fixed press release calendar. The press releases are simultaneously distributed by e-mail to institutions, all media and press members and research institutes, and are published on this website.
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
Regular press releases linked to the data. The last one is available on the following website: L'innovazione nelle imprese. Anni 2018-2020
10.1.1. Availability of the releases
| Dissemination and access | Availability | Comments, links, ... |
|---|---|---|
| Press release | Yes | |
| Access to public free of charge | Yes | |
| Access to public restricted (membership/password/part of data provided, etc) | No |
10.2. Dissemination format - Publications
- Online database (containing all/most results) : Istat Statistics.
- Analytical publication (referring to all/most results) : forthcoming (November-December 2024).
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect).
10.3. Dissemination format - online database
An online database is available on the following website: Istat dati
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
Microdata are disseminated through the Laboratory for Elementary Data Analysis (ADELE)- It is a “safe” environment in which researchers from universities or research institutions or bodies to which the Code of conduct and professional practice applying to processing of personal data for statistical and scientific purposes applies may conduct statistical analyses that require the use of elementary data, where information already available with other tools is not sufficient (I.Stat data warehouse). Within the Laboratory, data security and statistical confidentiality are guaranteed by the control of both the working methods and the results of the analyzes conducted by the users. Once the processing is complete, the output is evaluated in terms of statistical confidentiality by the experts of the ADELE Laboratory. Only results that positively comply with the Rules for the release of results can be issued.
Microdata are also disseminated through microdata files, that are collections of elementary data. ISTAT releases microdata files free of charge.
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | Yes | |
| National SAFE centre | Yes | |
| Eurostat: partially anonymised data (SUF) | No | |
| National : partially anonymised data | Yes |
10.5. Dissemination format - other
No other data dissemination was done
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
Information on target population, sampling design, data collection and data treatment, weights calculation method, dissemination of the data is available in the Report published at every edition of the survey's edition. The next report concerning the data obtained in CIS2022 edition is forthcoming.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Clarity is difficult to assess and relates to the quality of statistical metadata which are disseminated alongside a statistical product. In effect, it refers to the extent to which the metadata satisfy users needs. Assessment requires information from both the producer for the description of the accompanying information and from the user, for assessing the adequacy and appropriateness of such information for future use.
Please comment on your users feedback on clarity, the available accompanying information to the data, the assistance available to users
A full technical and practical support is given to facilitate users understanding and usability of Italian CIS data. To our knowledge, users are quite satisfied with clarity of aggregated data and micro-data made available and their accompanying information.
11.1. Quality assurance
Istat quality policy is consistent with the European quality framework developed by Eurostat, and transposes its main principles and definitions. The endorsement in 2005 of the European Statistics Code of Practice (last revised in 2017) established the principles to be applied in order to ensure and strengthen both the trust and the quality of the European Statistical System. Essential points of Istat quality policy are:
- Process quality: consisting in the production of accurate statistical information efficiently and effectively;
- Product quality: consisting in the dissemination of high-quality timely statistical data which are relevant for the users, also the potential ones;
- Documentation: consisting in the storage and availability of information necessary not only for a proper use of data but also to ensure transparency in all the production activities of statistical data;
- Respect for respondents: consisting in the reduction of response burden and in the respect of respondent’s privacy;
- Strengthening of statistical literacy: consisting in promoting a proper use of statistical information in policy-making to better support decisions and policies;
- Users’ orientation: consisting in making statistical information easily accessible and understandable and in satisfying user needs as much as possible.
11.2. Quality management - assessment
By this edition, the statistical unit of 'enterprise' has been adopted as unit of analysis rather than the 'legal unit', used in the past edition. This means that the CIS2022 data are not fully comparable with the previous ones obtained from the past edition.
This edition of Cis was fully conducted via Web through a on line questionnaire. The electronic data capture is a generalized system for aided development and monitoring of web surveys called GINO++ (much more than Gathering INformation Online). It allows the survey manager himself (that is without software developers) to perform three key phases of a survey: designing, capturing and monitoring.
About the response rate, we reach a 70.0% considering the EU core target population. As a whole (considering the Nace section F and other non Eu-core service sectors), after cleaning the initial sample from the frame and coverage errors, especially due to the out-of-scope and dead units, we got a total response rate of 67,5%.
About the accuracy of the data, the main perceived problems in CIS2022 are confirmed in the quantification of the turnover from innovative products (Question 3.3), the innovation expenditures (Question 3.8), other enterprise's enpenditures (Question 7.6).
For what concerns the question with the highest non response rate (innovation expenditures), direct contacts with respondents confirmed, as in the previous editions, that some respondents consider the collection of such detailed information a very difficult and time-consuming activity.
12.1. Relevance - User Needs
Since one of our main purposes is to fulfil the needs defined at European level (by adopting a questionnaire as consistent as possible with the Eu one) and since one of the main priorities of our institute is to minimize as much as possible the statistical burden on respondents, we chose to be in line with the Eu questionnaire and to not introduce relevant changes if not really necessary for our various institutions or communities.
12.1.1. Needs at national level
Italian National Institute of Statistics
| User group | Short description of user group | Main needs for CIS data of the user group Users’ needs |
|---|---|---|
| 1. Institutions - International level | OECD | Micro-data used for analyses on innovation and economic performances |
| 1. Institutions - National level | Italian National Institute of Statistics | Publication of CIS data in the Annual Report, the Italian Statistical Yearbook, Noi Italia, Report on the equitable and sustainable well-being, Report on SDGs and other publications |
| 1. Institutions - Regional level | Regional statistics agencies | Development of region-specific innovation indicators for designing more customised policy measures and evaluating innovation policy tools already applied at regional level |
| 2. Social actors | Industry associations | Firm-level analyses on innovation |
| 3. Reserchers and students | Researchers from universities, research institutions | Firm-level analyses on innovation |
12.2. Relevance - User Satisfaction
No user satisfaction survey was undertaken.
12.3. Completeness
The data are available for all the compulsory cells in the standard CIS 2022 output tabulation, except for the Nace 12 (included in the Nace 11). The voluntary cells that were missing refer to Non-Eu core Nace division that were not covered in the Italian survey (such as agriculture and some services) or to non-compulsory questions.
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).
Coefficient of Variation= (Square root of the estimate of the sampling variance) / (Estimated value)
Formula:
where
13.2.1.1. Coefficient of variations for key variables
Coefficient of variation (%) for key variables by NACE categories and for enterprises with 10 and more employees
| NACE |
Size class |
(1) |
(2) |
(3) |
|---|---|---|---|---|
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) |
Total |
0.9 |
8.0 |
3.9 |
| Core industry (B_C_D_E - excluding construction) |
Total |
1.3 |
8.0 |
5.3 |
| Core Services (46-H-J-K-71-72-73) |
Total |
1.2 |
19.1 |
5.2 |
[1] = Coefficient of variation for the percentage of innovative enterprises (INN) in the total population of enterprises (ENT22)
[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 [TUR22,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
The coefficient of variation is the ratio of the square root of the variance of the estimator to the expected value. It is estimated by the ratio of the square root of the estimation of the sampling variance to the estimated value. Sampling design, weighting and changes of strata of sampling units are taken into account in the estimation of the sampling variance.
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 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.
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
Coverage errors arise mainly from over-coverage. The main discrepancies between target and frame population include: out-of-scope units, dead units, changes of strata, changes in address.
13.3.1.4. Coverage errors in coefficient variation
The estimation of the CVs have taken into account the sampling design and the changes of strata of sampling units.
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
No measure for reducing measurement errors.
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.
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)
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 persons employed
| 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) | 7,192 | 24,828 | 29.0 | 34.2 |
| Core industry (B_C_D_E - excluding construction) | 3,257 | 11,856 | 27.5 | 33.1 |
| Core Services (46-H-J-K-71-72-73) | 3,935 | 12,972 | 30.3 | 35.8 |
13.3.3.1.2. Maximum number of recalls/reminders before coding
Two reminders are undertaken before coding an enterprise as non-responding.
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)
Restricted from publication
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 | 0.7% | |
| 3.10 -- Reasons for having no innovation activities | YES | 0.1% |
13.3.4. Processing error
Web techniques were used for CIS 2022 data capturing. Respondents - through their browsers - can access an electronic questionnaire, put on the Istat web site and fill in it online. The data capture technique used in this edition is a generalised system called GINO++ [much more than Gathering INformation Online] that allowed - without software developers - to design, capture and check on the data entered and monitor the progress of the survey in real-time. It allowed thus to insert some types of hard or soft rules associated with some variables to help in preventing from non sampling errors. With regard to the checking rules which were activated in the electronic questionnaire, we decided to contain them and to limit the hard checks (which prevent the respondent from going on without correcting his errors) so to minimise the risk that respondents give up to fill in the form. Further, we introduced interactive edits so to ask respondents for checking the entered data (and correcting them if necessary) in order to minimise the follow up from the operators. In particular, the system enabled/disabled the possibility of filling in some fields and activated some blocking checks on the questions of the Section 3 and some non-blocking consistency checks on Sections 2 and 4.
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 : forthcoming (November 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: 30 June 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.
15.1. Comparability - geographical
The Community Innovation Survey still suffers from some critical methodological drawbacks when it is used for regional analyses. One of the key question has to do with the adoption of the ‘enterprise’ used as statistical unit for data collection. The problem here is to what extent this methodological choice can represent ts the best one for measuring the regional dimension of innovation. The CIS gives the status of innovative to the enterprise as a whole and the criterion used for the regionalisation of the CIS data consists of assigning the overall innovation activity to the region where enterprises’ headquarters are located. This introduces a significant regional bias. Such a problem is especially found in multi-plant enterprises, whose innovation activities can be spread across different regions. More precisely, if regionalization is straightforward for enterprises with only one local unit, it becomes problematic for those with several local units in different regions. According to the traditional regionalisation approach, the enterprises with establishments in more than one region are considered as performing all their innovation activities in the region of the head office. It means that the local units involved in innovation activity and placed in other regions turn out undetectable with the consequence that the survey fails to take into account where innovation activities really take place. In other terms, the conventional CIS regional attribution of innovation activities thus might lead to biased results of the actual spatial distribution of innovation and, namely, lead to an underestimation of the innovation activities of those regions which host productive units of enterprises whose head offices are located elsewhere. A case in point is in out country, where many firms located in the Northern regions have production facilities in the South: this approach would not allow the innovation capabilities in the South to emerge.
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 |
|---|---|
| All mandatory and voluntary variables were collected. |
| Changes in the filtering compared to HDC | Comment |
|---|---|
| No deviation |
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 |
|---|---|
| Questions on the impact that the Covid-19 epidemic had on the innovation propensity and intensity. |
15.2. Comparability - over time
Due to important methodological changes introduced in the statistical unit chosen for sampling and data analysis, moving from 'legal unit' to 'enterprise', CIS 2022 estimates are not fully comparable with the previous CIS data.
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 persons employed
| NACE | Size class | Number of enterprises (SBS/CIS)* | Number of persons employed (SBS/CIS)* | Total Turnover (SBS/CIS)* |
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) | Total | Not available. | Not available. | Not available. |
| Core industry (B_C_D_E - excluding construction) | Total | Not available. | Not available. | Not available. |
| Core Services (46-H-J-K-71-72-73) | Total | Not available. | Not available. | Not available. |
* 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 sampling frame is the official statistical business register, called ASIA-ENT (Archivio Statistico delle Imprese Attive - statistical business register of active enterprises). ASIA provides both the key variables for the stratification (number of employed persons, NACE economic activity, NUTS geographical information) and the identification characters (enterprise name, address, etc.). Since the most updated version of Asia (reference year: 2021) was used, the CIS2022 survey universe consisted of all the profit enterprises and independent professional units active in 2021.
18.1.2. Sampling design
The sampling design is a stratified cluster sampling. The clusters are the enterprises (ENT), which are stratified based on the economic activity sector (Ateco 2007 classification), size class (three categories: 10-49 employed persons, 50-249 e.p., and 250 e.p. and more), and region (21 categories - level 2 of the European NUTS classification), resulting in a total of 2,541 strata. Within each enterprise, all the Legal Units (UG) that comprise it are selected, excluding those that do not fall within the scope of the survey (i.e., non-resident in Italy, ceased operations before July 1, 2022, with fewer than 10 employed persons, or engaged in economic activities outside the relevant sections of interest).
The theoretical sample consists of 24,963 ENT and 38,993 UGs. The sample allocation was also studied in terms of enterprises. The number of enterprises to be surveyed in each stratum was determined by adopting the multivariate and multidomain optimal allocation methodology implemented in the R2BEAT software, developed in the R environment (Barcaroli G., Fasulo A., Guandalini A., Terribili M.D., 2023, Two-Stage Sampling Design and Sample Selection with the R Package R2BEAT. The R Journal, 15(3):191-213).
The sample size and allocation within the strata were defined so that the estimates of the number of employed persons and revenue—variables closely related to the variables of interest in the survey—did not exceed, for each of the domains of interest, a maximum error expressed in terms of the Coefficient of Variation (CV). In each stratum, enterprsies were selected with equal probability, and within each business, information on all the UGs that fall within the scope of the survey was collected.
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
|---|---|
| Target population | 156,624 ENT (193,026 Legal Units) |
| Sample | 24,963 ENT (38,993 LU) |
| In case of combination sample/census: | |
| Sampled units | 15,012 ENT |
| Enumerated units/census | 9,951 ENT |
| Overall sample rate (overall sample/target population) | 15.9% in terms of ENT (20.2% in terms of LU) |
18.1.4. Data source for pre-filled variables
Variables and indicators filled or prefilled from other sources.
| Variables/Indicators | Source | Reference year |
|---|---|---|
| None |
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
|---|---|
| Data source used for deriving population totals | ASIA-ENT (Archivio Statistico delle Imprese Attive - statistical business register of active enterprises) and ASIA-UG (Archivio Statistico delle Unitù giuridiche Attive - statistical business register of active legal units) |
| Variables used for weighting | Number of enterprises, number of persons employed |
18.2. Frequency of data collection
According to the Regulation 2152/2019 on European Business Statistics (EBS), the innovation statistics shall be provided to Eurostat every two years in each even year t+18.
18.3. Data collection
See below:
18.3.1. Survey participation
It is a mandatory survey included in the National Statistical Programme that regulates the production of official statistical information.
18.3.2. Survey type
It is a combination of sample and census survey.
18.3.3. Combination of sample survey and census data
The census refers to the enterprises with 250 + persons employed. For the rest of population, a stratified random sample has been built.
18.3.4. Census criteria
The census refers to the enterprises with 250 + persons employed. For the rest of population, a stratified random sample has been built.
18.3.5. Data collection method
Data collection method
| Survey method | Yes/No | Comment |
|---|---|---|
| Face-to-face interview | No | |
| Telephone interview | No | |
| Postal questionnaire | No | |
| Electronic questionnaire (format Word or PDF to send back by email) | No | |
| Web survey (online survey available on the platform via URL) | Yes | |
| Other | No |
18.4. Data validation
Not requested.
18.5. Data compilation
Operations performed on data to derive new information according to a given set of rules.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition of imputation rate:
Imputation rate (for the variable x) (%) = 100*(Number of replaced values) / (Total number of values for a given variable)
Definition of weighted imputation rate:
Weighted imputation rate= 100*(Number of total weighted replaced values) / (Total number of weighted values for a given variable)
18.5.1.1. Imputation rate for metric variables
Imputation rate for metric variables by NACE categories and for enterprises with 10 or more persons employed:
| 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 | 3.1% | 3.1% | 0.0% | 0.0% | 7.7% | 6.7% |
| Core industry (B_C_D_E - excluding construction) | Total | 2.2% | 2.1% | 0.0% | 0.0% | 7.2% | 6.3% |
| Core Services (46-H-J-K-71-72-73) | Total | 2.7% | 2.7% | 0.0% | 0.0% | 7.5% | 6.6% |
(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)
18.5.2. Weights calculation
Weights calculation method for sample surveys
| Method | Selected applied method | Comments |
|---|---|---|
| Inverse sampling fraction | ||
| Non-respondent adjustments | X | Calibration estimators methodology was used for the estimation process. To address non-response and ensure that estimates align with the totals from the Register of LUs and the Register of Enterprise (ENT) for 2021, sample weights were adjusted through a calibration process. This adjustment ensured consistency across each of the survey's domains of interest by meeting the following
A total of 483 constraints were applied in the calibration. Although the number of employed persons per LU was not directly considered, this approach controlled the estimate of the national LU employed persons’ |
| Other |
18.6. Adjustment
To address the non response and to make the estimates consistent with the totals from the ASIA UG and ASIA ENT Registers for 2021, the sampling weights of the enterprises were adjusted through a calibration step. In the calibration step, it was ensured that the sampling weight system maintained coherence, in each of the domains of interest, with the number of enterprises, the number of Legal Units (UG) and the number of employed persons per enterprise, resulting in a total of 483 constraints. The calibration was achieved using a linear distance function with a lower bound of 0.1 to prevent negative weights. The software used for calibration and the calculation of estimates and errors is ReGenesees, a system developed in the R environment for design-based and model-assisted analysis of complex sample surveys (Zardetto, 2015). The resulting weights are used to produce estimates at the enterprise level. To produce estimates at the UG level, a weight system consistent with the ‘enterprise weight’ was also determined. Specifically, each UG was assigned the weight of the enterprise it belongs to. For UGs belonging to multiple enterprises, the weight was obtained by summing the weights of all the enterprises to which the UG belongs.
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.
CIS 2022 is designed 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 CIS 2022 is covered by Regulation 2152/2019 on European Business Statistics (EBS) as well as an Implementing Act dedicated to the topic ‘business innovation’. The objectives of this Implementing Act have been anticipated by the redesign of the CIS, so that data on business innovation will be better integrated into its context of European Business Statistics. The Regulation 2152/2019 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 persons employed 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.
18 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.
For the first time, in this edition we adopted the statistical unit Enterprise (the so-called ENT) instead of the legal unit used in the past.
The observation unit was the legal unit (LU).
Process for obtaining results at Statistical Unit Enterprise level in complex enterprises:
- For qualitative variables:
- A "representative cluster" was identified for each ENT, representing the subset of LUs that comprise each enterprise.
- Consolidation of Variables. For consolidation of non-additive qualitative variables (dichotomous or categorical), different rules were applied. For binary variables (e.g., 1=success, 0=failure), if both values were present, the ENT inherited a "1" for success. For what concerns the variables with multiple non-ordered categories, they were converted to binary indicators, where "0" represented absence and "1" presence of each category. The binary rule applied as with other binary variables. Finally, for ordinal variables (e.g., importance levels), the value was derived as the weighted average of the responses from each LU, using the number of employed persons as weights. For example, if an ENT comprises three LUs of different sizes (10, 100, and 1000 employed persons) that declared a different ‘degree of importance’ in ENV_ENREP (e.g., 3=high, 2=medium, 1=low), the ENT's final value for the variable (e.g., ENV_ENREP for "improvement of corporate reputation") was calculated as “1,” reflecting the weighted average of the responses.
- For quantitative variables:
- Identification of the Representative Cluster. A "representative cluster" was identified for each ENT, representing the subset of LUs that comprise each enterprise.
- Consolidation of Variables. Variables were categorized as additive or non-additive for consolidation. For the consolidation of additive variables, like expenditures and employed persons, they were aggregated by summing the values for the LUs within each ENT, adjusted according to each LU’s share of the ENT.
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).
NUTS level 2
NUTS2 was used as a geographical stratification dimension for 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 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).
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 Community Innovation Survey still suffers from some critical methodological drawbacks when it is used for regional analyses. One of the key question has to do with the adoption of the ‘enterprise’ used as statistical unit for data collection. The problem here is to what extent this methodological choice can represent ts the best one for measuring the regional dimension of innovation. The CIS gives the status of innovative to the enterprise as a whole and the criterion used for the regionalisation of the CIS data consists of assigning the overall innovation activity to the region where enterprises’ headquarters are located. This introduces a significant regional bias. Such a problem is especially found in multi-plant enterprises, whose innovation activities can be spread across different regions. More precisely, if regionalization is straightforward for enterprises with only one local unit, it becomes problematic for those with several local units in different regions. According to the traditional regionalisation approach, the enterprises with establishments in more than one region are considered as performing all their innovation activities in the region of the head office. It means that the local units involved in innovation activity and placed in other regions turn out undetectable with the consequence that the survey fails to take into account where innovation activities really take place. In other terms, the conventional CIS regional attribution of innovation activities thus might lead to biased results of the actual spatial distribution of innovation and, namely, lead to an underestimation of the innovation activities of those regions which host productive units of enterprises whose head offices are located elsewhere. A case in point is in out country, where many firms located in the Northern regions have production facilities in the South: this approach would not allow the innovation capabilities in the South to emerge.
Due to important methodological changes introduced in the statistical unit chosen for sampling and data analysis, moving from 'legal unit' to 'enterprise', CIS 2022 estimates are not fully comparable with the previous CIS data.
