1.1. Contact organisation
Institut national de la statistique et des études économiques (STATEC)
1.2. Contact organisation unit
ENT3 - Structural Business Statistics
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
STATEC
B.P. 10
L-4401 Belvaux
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
15 November 2024
2.2. Metadata last posted
15 November 2024
2.3. Metadata last update
15 November 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
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
In accordance with COMMISSION IMPLEMENTING REGULATION (EU) 2022/1092 on innovation statistics, the following industries and services are included in the core target population. Results are made available with the following breakdowns:
All NACE – Core NACE (NACE Rev. 2 sections & divisions B-C-D-E-46-H-J-K-71-72-73 )
CORE INDUSTRY (excluding construction) (NACE Rev. 2 SECTIONS B_C_D_E)
10-12: Manufacture of food products, beverages and tobacco
13-15: Manufacture of textiles, wearing apparel, leather and related products
16-18: Manufacture of wood, paper, printing and reproduction
20: Manufacture of chemicals and chemical products
21: Manufacture of basic pharmaceutical products and pharmaceutical preparations
19-22: Manufacture of petroleum, chemical, pharmaceutical, rubber and plastic products
23: Manufacture of other non-metallic mineral products
24: Manufacture of basic metals
25: Manufacture of fabricated metal products, except machinery and equipment
26: Manufacture of computer, electronic and optical products
25-30: Manufacture of fabricated metal products (except machinery and equipment), computer, electronic and optical products, electrical equipment, motor vehicles and other transport equipment
31-33: Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment
D: ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY
E: WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION ACTIVITIES
36: Water collection, treatment and supply
37-39: Sewerage, waste management, remediation activities
CORE SERVICES (NACE Rev. 2 sections & divisions 46-H-J-K-71-72-73)(NACE code in the tables = G46-M73_INN)
46: Wholesale trade, except of motor vehicles and motorcycles
H: TRANSPORTATION AND STORAGE
49-51: Land transport and transport via pipelines, water transport and air transport
52-53: Warehousing and support activities for transportation and postal and courier activities
J: INFORMATION AND COMMUNICATION
58: Publishing activities
59: Motion picture, video and television programme production, sound recording and music publishing activities
60: Programming and broadcasting activities
61: Telecommunications
62: Computer programming, consultancy and related activities
63: Information service activities
K: FINANCIAL AND INSURANCE ACTIVITIES
64: Financial service activities, except insurance and pension funding
65: Insurance, reinsurance and pension funding, except compulsory social security
66: Activities auxiliary to financial services and insurance activities
M: PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES
71: Architectural and engineering activities; technical testing and analysis
72: Scientific research and development
73: Advertising and market research
71-73: Architectural and engineering activities; technical testing and analysis; Scientific research and development; Advertising and market research
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.
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 survey unit used is the "enterprise" as defined in the Luxembourg Business Register.
Each survey invitation sent to the enterprises, as well as each online questionnaire, contains a list of legal units that constitute the "enterprise" and that are to be taken into account when responding to the survey.
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
For Luxembourg, the regional dimension (NUTS) is not available in the national survey.
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 | |
| 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
This survey is carried out under Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics and on the basis of article 2 of the Law of 10 July 2011 establishing the Institut national de la statistique et des études économiques (STATEC).
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
Regulation (EC) No 223/2009 of the European Parliament and of the Council on the transmission of data subject to statistical confidentiality to the Statistical Office of the European Communities.
Loi du 10 juillet 2011 portant organisation de l’Institut national de la statistique et des études économiques on statistical confidentiality as it applies to the Luxembourgian Statistical System.
7.2. Confidentiality - data treatment
Primary rules
We apply the (n,k)-dominance rule, i.e. a cell is suppressed if n units separately or jointly dominate the total value of a cell by at least k%. The (n, k) parameters for Luxembourg are confidential. For any cells that are left after applying the sensitivity rule, a minimum frequency is applied. A cell is suppressed if there are less than n units in a given cell. The n parameter for Luxembourg is confidential.
The primary rules' underlying parameters are kept confidential because their disclosure could compromise the safety of the primary suppressed cells.
Secondary confidentiality rules
The secondary suppression is calculated by tau-Argus using the ‘Modular’ algorithm. Manual suppressions or cost adjustments are performed to adjust the secondary confidentiality pattern calculated by the software.
a) Secondary suppression within a table
• A cell is suppressed for secondary confidentiality if n units jointly or separately dominate the confidential subtotal by at least k%;
• special attention is paid to the impact of singletons, a risk which is in most cases directly addressed by the tau-Argus Modular algorithm;
• tau-Argus is set to minimise the cost when determining the secondary suppressed cells.
However, we also want to provide the user with relevant data, whether it is in terms of interpretation and/or availability of time series. Consequently, the cost minimisation can be overridden for economic and/or historical reasons.
b) Secondary suppression due to linked-table disclosure risks
A link is defined to exist between a cell sharing the same cell coordinates in two tables if an estimate for that cell based on the source table can be produced within p% range of the primary confidential cell's value of the target table. Most often, estimates based on the rule of three and linear interpolation, both of which are common user scenarios, are tested. Please note that p% only refers to the relationship between the dominance and p% thresholds and not to the p% sensitivity rule.
The following linked-table risks are addressed:
• historical disclosure (time dimension): no primary historically confidential cell should be compromised by disclosing the same cell for the current reference year. As long as there is a significant link with a prior year primary confidential data, a cell may not be disclosed for the current reference year.
• links to any other table sharing at least one dimension, including SBS tables by activty.
Other SDC policy considerations
The statistical confidentiality analyses are performed on the basis of turnover (shadow variable approach). The same pattern is therefore applied to all variables, including the number of enterprises when applicable.
As a general principle, the NACE section level data, if not broken down by any other dimension than NACE and if unfiltered, are not considered confidential, except if the section has a trivial breakdown. There can be other case-by-case exceptions to this general principle.
8.1. Release calendar
Not available.
8.2. Release calendar access
Not available.
8.3. Release policy - user access
Not available.
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 | NO | |
| Access to public free of charge | X | |
| Access to public restricted (membership/password/part of data provided, etc) | NO |
10.2. Dissemination format - Publications
- Online database (containing all/most results): Yes
- Analytical publication (referring to all/most results): R&D and innovation performance of companies.
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect): Statistical portrait of Luxembourg's businesses.
10.3. Dissemination format - online database
On-line database available (for a selection of indicators) LUSTAT database.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
Any national micro-data access is governed by article 16 of the Law of 10 July 2011 on the organisation of the National Institute for Statistics and Economic Studies.
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | Yes | |
| National SAFE centre | Yes | |
| Eurostat: partially anonymised data (SUF) | No | |
| National: partially anonymised data | No |
10.5. Dissemination format - other
No other means of dissemination.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
Methodological guidelines are generally in FR (ad-hoc publications or data publications), while definitions are embedded whenever available in the relevant tables (FR and EN)
Science, technologie et innovation
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Quality documentation is generally in FR and be found in ad-hoc publications or data publications. Quality reports are only available in EN.
Science, technologie et innovation - Statistiques - Luxembourg
11.1. Quality assurance
The application of the European Statistics Code of Practice is monitored by the national quality delegate. Further documentation can be found here:
Qualité - Statistiques - Luxembourg (public.lu)
11.2. Quality management - assessment
The CIS harmonized questionnaire is used to create the national questionnaire, along with methodological guidelines to ensure consistency.
Data processing occurs both during and after data collection to maintain the highest quality of results. This includes responding to participants' questions, implementing control measures, verifying the consistency of those controls, and conducting post-collection processing.
12.1. Relevance - User Needs
STATEC's research unit is routinely asked to provide feedback on new modules in the model questionnaire, as well as to indicate any variables used in research projects that might be missing from the model questionnaire.
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 - National level | Ministry of Economy, Observatory of competitiveness | |
| 2. Researchers and students | Universities, students | |
| 3. Media |
12.2. Relevance - User Satisfaction
No user satisfaction survey conducted in this statistical area.
12.3. Completeness
All mandatory variables were collected.
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:
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 |
2.86% |
22.93% |
4.65% |
| Core industry (B_C_D_E - excluding construction) |
Total |
5.49% |
4.9% |
8.42% |
| Core Services (46-H-J-K-71-72-73) |
Total |
3.3% |
30.28% |
5.31% |
(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
Estimations of the variables of interest were performed with the calibrated weights. For a sufficiently large sample size, the calibration estimator is equivalent to the linear regression estimator and its bias tends to be minor. Consequently, the variance of the estimation is based on the residuals resulting from the relationship between the variable of interest and the ancillary variables which have been used for the calibration.
The standard error for a given survey stratum (which is normally also the stratum used for grossing-up) is calculated based on these properties. If necessary, these standard errors are then aggregated to the breakdowns requested by Eurostat.
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
Core coverage as specified in the CIS methodological recommendations, mandatory NACE Rev.2 sections and divisions of the core target population.
13.3.1.4. Coverage errors in coefficient variation
Not applicable.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones.
13.3.2.1. Measures for reducing measurement errors
Error detection is an integral part of data collection and processing activities. Automated checks are applied to data records during the collection to identify reporting and entry errors. These audits identify potential errors in key totals and ratios that exceed tolerance thresholds, as well as problems with the consistency of the data collected.
13.3.3. Non response error
Conduct an analysis of relevant data available for nonresponding units. If applicable, use auxiliary data sources, such as administrative records, to profile these units and identify common characteristics. This profiling can then inform strategies, such as targeted follow-ups, to address the specific needs or concerns of likely non-respondents.
Analyze item nonresponse rates across all survey variables. Look for patterns of missing data associated with sensitive topics.
Apply weighting adjustments to account for nonresponse, ensuring the final estimates are representative.
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) | 135 | 1099 | 12.3 % | |
| Core industry (B_C_D_E - excluding construction) | 22 | 227 | 9.7 % | |
| Core Services (46-H-J-K-71-72-73) | 113 | 872 | 12.9 % |
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
In order to reduce the non-response, 3 reminders are sent (with a registered letter to high-impact enterprises, on the 3rd reminder).
13.3.3.2. Item non-response - rate
See below.
13.3.3.2.1. Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employed persons)
Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employed persons).
| Item non-response rate (un-weighted) (%) |
Imputation (Yes/No) |
If imputed, describe method used, mentioning which auxiliary information or stratification is used | |
|---|---|---|---|
| Turnover | 0 % | No |
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 question |
| 3.10 -- Reasons for having no innovation activities | Yes | 0% | mandatory question |
13.3.4. Processing error
The data entry method was the online questionnaire for 99% of the responses; the remaining 1% of paper questionnaires were entered using a data entry application developed in-house.
An extensive validation process of the data is carried out. One part of the validations is integrated in the data collection in the dynamic web-questionnaire; another part is carried out after the data collection using micro- and macro validation techniques. The individual reports from the enterprises are compared to former years reports and the registered information on the number of employees and turnover. Outlier detection is also used as a validation process.
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: 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
Restricted from publication
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
Not applicable.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. National questionnaire – compliance with Eurostat model questionnaire
Methodological deviations from the CIS Harmonised Data Collection (HDC)
| Questions not included in national questionnaire compared to HDC | Comment |
|---|---|
| 2.1 During the three years 2020 to 2022, to what extent do the following characteristics describe the conditions faced by your enterprise ? |
|
| 2.3 During the three years 2020 to 2022, did your enterprise | |
| 2.4 During the three years 2020 to 2022, did your enterprise: | |
| 5.2 During the three years 2020 to 2022, did your enterprise purchase machinery, equipment or software based on |
|
| 5.3 During the three years from 2020 to 2022, did your enterprise offer any of the following types of goods or services to meet user requirements? |
|
| 5.4 Since 2020, did your enterprise implement any of the following fundamental changes to its business model? |
|
| 7.6 How much did your enterprise spend in 2022 on the following items ? | |
| 7.8 During the three years from 2020 to 2022, did your enterprise engage in any of the following activities with one or more enterprises of your enterprise group? |
|
| 7.9 During the three years from 2020 to 2022, did your enterprise try to obtain funding in the form of intra-group loans? |
| Changes in the filtering compared to HDC | Comment |
|---|---|
| No |
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 |
|---|---|
| During the three years 2020 to 2022, how would you describe the competitive environment on the main market you were operating in? |
|
| To what extent do the following factors describe the competitive environment on the main market? | |
| During the three years 2020 to 2022, how important to the management of your business were the following methods of organising work? |
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 | 101% | 100 % | 102% |
| Core industry (B_C_D_E - excluding construction) | Total | 100% | 100 % | 109% |
| Core Services (46-H-J-K-71-72-73) | Total | 101% | 101 % | 101% |
* 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
All indicators presented in the CIS are collected directly from enterprises via CIS e-questionnaire.
18.1.1. Sampling frame (or census frame)
The frame population used for the sample differs from the one used in SBS. It is based on the most recent SBS preliminary data available for sampling purposes.
However, the frame population for the final data production uses the same snapshot as the SBS final results for the same reference year.
18.1.2. Sampling design
The frame population was stratified using the following criteria:
- 3 size classes (i.e. 10-49, 50-249, 250+ employees)
- 26 NACE categories (B05_09, C10_12, C13_15, C16_18, C19_22, C23_23, C24_24, C25_30, C31_33, D35, E36_39, G46, H49_51, H52_53, J58, J59, J60, J61, J62, J63, K64, K65, K66, M71, M72, M73).
- A dummy variable on whether the unit had applied for LU public grants for R&D.
This lead to the creation of 126 strata.
The method used for sampling was a stratified random sample, with varying sampling rates depending on size class:
- For the two size classes 50-249 and 250+, the sampling rate was 100% (i.e. a census);
- For small enterprises (10-49 employees), there are two possibilities: if in the stratum are at least 3 enterprises, then a random stratified sampling is applied, otherwise, the entire stratum is preserved.
Units that received or applied for an R&D grant were sampled at 100%.
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
|---|---|
| Target population (A) (*) | 2039 |
| Sample (B = C+D) | 1140 |
| In case of combination sample/census: | |
| Sampled units (C) | 585 |
| Enumerated units/census (D) | 555 |
| Overall sample rate (E = 100*B/A) | 56% |
(*) 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 |
|---|---|---|
| Turnover | SBS | 2022 |
| Employment | SBS | 2022 |
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
|---|---|
| Data source used for deriving population totals | SBS |
| Variables used for weighting | Employment |
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
Systematic process of gathering data for official statistics.
18.3.1. Survey participation
Mandatory
18.3.2. Survey type
Data are collected from a combination of a sample survey and a census.
18.3.3. Combination of sample survey and census data
The method used for sampling was a stratified random sample, with varying sampling rates depending on size class:
- For the two size classes 50-249 and 250+, the sampling rate was 100% (i.e. a census);
- For the size class 10-49, the sampling rate was generally fixed at 40%, the only exceptions being G46 and H49_51 with a sampling rate of 30%;
- Units that received or applied for an R&D grant were sampled at 100%;
Strata containing less than 10 units were sampled at 100%.
18.3.4. Census criteria
• Enterprises with >50 employees;
• Enterprises that received or applied for an R&D grant were sampled at 100%;
• Strata containing less than 10 units were sampled at 100%.
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 | No paper questionnaire was sent out, however, they had the possibility to download and print out a PDF version of the questionnaire in case they did not want to respond online. |
| Other | No |
18.4. Data validation
Not requested.
18.5. Data compilation
Operations performed on data to derive new information according to a given set of rules.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition of imputation rate:
Imputation rate (for the variable x) (%) = 100 * (Number of replaced values) / (Total number of values for a given variable)
Definition of weighted imputation rate:
Weighted imputation rate= 100 * (Number of total weighted replaced values) / (Total number of weighted values for a given variable)
18.5.1.1. Imputation rate for metric variables
Imputation rate (%) for metric variables by NACE categories and for enterprises with 10 or more employed persons:
| NACE | Size class | Total Turnover (1) | Turnover from products new to the market (2) | R&D expenditure in-house (3) | |||
|---|---|---|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | Unweighted | Weighted | ||
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) | Total | 0% | 0% | 0% | 0% | 0% | 0% |
| Core industry (B_C_D_E - excluding construction) | Total | 0% | 0% | 0% | 0% | 0% | 0% |
| Core Services (46-H-J-K-71-72-73) | Total | 0% | 0% | 0% | 0% | 0% | 0% |
(1) = Imputation rate (%) for the total turnover in the last year of the reference period (t) (TUR)
(2) = Imputation rate (%) for the share of the turnover in the last year of the reference period (t) due to new or improved product new to the market in the total turnover for product innovative enterprises TUR_PRD_NEW_MKT/TOVT(INNO_PRD)
(3) = Imputation rate (%) for the R&D expenditure performed in-house (EXP_INNO_RND_IH)
18.5.2. Weights calculation
Weights calculation method for sample surveys
| Method | Selected applied method | Comments |
|---|---|---|
| Inverse sampling fraction | ||
| Non-respondent adjustments | ||
| Other | X | The initial weights are based on the inverse sampling fraction. The final weights were calculated as follows: • First, the original sampling weights for each stratum were adjusted to compensate for unit non-response (original weight divided by response rate for the survey stratum). The auxiliary variable used was the number of persons employed; the strata used were the same as those used for sampling, when possible. Calibration also took into account whether enterprises applied for or received a national public grant for R&D. |
18.6. Adjustment
In order to obtain reliable results for quantitative variables (that are in line with SBS totals) the weights are calibrated using to the number of units and employment per stratum as auxiliary information. Calibration is carried out in R, using the calib method of the sampling package with a logit distance function.
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).
15 November 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 survey unit used is the "enterprise" as defined in the Luxembourg Business Register.
Each survey invitation sent to the enterprises, as well as each online questionnaire, contains a list of legal units that constitute the "enterprise" and that are to be taken into account when responding to the survey.
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).
For Luxembourg, the regional dimension (NUTS) is not available in the national survey.
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.
All indicators presented in the CIS are collected directly from enterprises via CIS e-questionnaire.
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.
Not applicable.
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.
