1.1. Contact organisation
State Data Agency (Statistics Lithuania)
1.2. Contact organisation unit
Knowledge Economy and Special Survey Statistics Division
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
29 Gedimino Ave, LT-01500 Vilnius, Lithuania
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
8 October 2024
2.2. Metadata last posted
8 October 2024
2.3. Metadata last update
8 October 2024
3.1. Data description
The Community Innovation Survey (CIS) is a survey about innovation activities in enterprises. The survey is designed to collect the information on types of innovation, processes of development of innovation like cooperation patterns, financing and expenditure, objectives of innovation activities or barriers for initiating or implementing innovation.
The CIS provides statistics by type of innovators, economic activity and size class of enterprises. The survey is currently carried out every two years across the EU Member States, EFTA countries and EU candidate countries.
In order to ensure comparability across countries, Eurostat together with the countries develops a Harmonised Data Collection (HDC) questionnaire and drafts the methodological recommendations for implementation of each survey round.
The CIS 2022 implements the concepts and methodology of the Oslo Manual 4th Edition revised in 2018. The changes that the CIS has undergone due to the revision of the manual and their impact on the indicators collected are described in the Statistics Explained article: Community Innovation Survey – new features.
The legal framework for CIS 2022 is the Commission Implementing Regulation (EU) 2022/1092, which sets out the quality conditions and identifies the obligatory cross-coverage of economic sectors, size class of enterprises and innovation indicators. The target population is enterprises with at least 10 employed persons (sum of employees and self-employed persons) classified in the core NACE economic sectors (see 3.3). Further activities may be covered on a voluntary basis in national datasets. Most statistics are based on the 3-year reference period (t, t-1, t-2), but some use only one calendar year (t or t-2).
3.2. Classification system
Indicators related to the enterprises are classified by country, economic activity (NACE Rev. 2), size class of enterprises and type of innovation.
The main typology of classification of enterprises in reference to innovation is the distinction between innovation-active enterprises (INN) and not innovation-active enterprises (NINN).
The enterprise is considered as innovative (INN) if during the reference period it successfully introduced a a) product or a) business process innovation, c) completed but not yet implemented the innovation, d) had ongoing innovation activities, e) abandoned innovation activities or was f) engaged in in-house R&D or R&D contracted out. Non-innovative (NINN) enterprises had no innovation activity mentioned above whatsoever during the reference period.
3.3. Coverage - sector
3.3.1. Main economic sectors covered - NACE Rev.2
In accordance with the Commission Implementing Regulation (EU) 2022/1092 on innovation statistics, the following sectors of the economic activity are included in the core target population: NACE Sections B, C, D, E, H, J, K, and Divisions 46, 71, 72 and 73.
3.3.1.1. Main economic sectors covered - NACE Rev.2 - national particularities
No additional coverage.
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 deviations.
3.4. Statistical concepts and definitions
The description of concepts, definitions and main statistical variables will be available in CIS 2022 European metadata file (ESMS) Results of the community innovation survey 2022 (CIS2022) (inn_cis13) in Eurostat database.
3.5. Statistical unit
The statistical unit – the enterprise.
Enterprise – the smallest combination of legal units that is an organizational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocation of its current resources. An enterprise carries out one or more activities at one or more locations. An enterprise may be a sole legal unit.
The observation unit was the legal unit, while sampling was carried out at Statistical Unit Enterprise level.
Process for obtaining results at Statistical Unit Enterprise level in complex enterprises:
- For qualitative variables: If one observation legal unit was selected in the enterprise, then its qualitative answers are provided as for the statistical unit 'enterprise'. Most often, one 'largest product producing unit' as observation unit (legal unit) was selected. If two or more observation legal units was selected in the enterprise, we consolidated their responses and present them as responses of enterprise. If at least one unit reported ‘yes’, the value for the enterprise is set on 'yes'. Variables measured on an importance scale (categorical variables): in case that observations for two ore more than two observation units that belong to the CIS target population are available, the highest value among all observation units was used, but in exceptional cases the case-by-case choices made.
- For quantitative variables: Sum of all observation legal units. We obtain the total number of employed and consolidated turnover of the complex enterprises from the SBS survey. If two or more observation legal units was selected in the enterprise, we consolidated their responses and present them as responses of enterprise. Innovation expenditure: sum of innovation expenditure of all observations units. Percentages for share of turnover due to products new for the market, were first converted into absolute numbers (by multiplying the percentages with legal unit turnover) and then summed, and then again written as percentages for the enterprise as a whole.
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
Reference area – country.
11 main indicators – by regions, counties.
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 (Yes/No) | Comment (deviation from reference period) |
|---|---|---|---|
| CIS2 | 1994-1996 | No | The first CIS survey was about 1997-1998 innovations. |
| CIS3 | 1998-2000 | No | The second CIS survey was about 1998-2001 innovations. |
| 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 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
EU legislation and national Official statistics programme Part I.
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
In the process of statistical data collection, processing and analysis and dissemination of statistical information, Statistics Lithuania fully guarantees confidentiality of the data submitted by respondents (households, enterprises, institutions, organisations and other statistical units), as defined in the Confidentiality policy guidelines of Statistics Lithuania.
7.2. Confidentiality - data treatment
Statistical Disclosure Control Manual, approved by Order No DĮ-26 of 19 January 2024 of the Director General of Statistics Lithuania;
The State Data Governance Information System Data Security Regulations and Rules for the Secure Management of Electronic Information in the State Data Governance Information System, approved by Order No DĮ-202 of 27 August 2021 of the Director General of Statistics Lithuania.
By national policy on aggregated data confidentiality the minimum number of enterprises in breakdowns must to be 3 enterprises.
8.1. Release calendar
Statistical information is published on the Official Statistics Portal according to the Official Statistics Calendar.
8.2. Release calendar access
8.3. Release policy - user access
Statistical information is prepared and disseminated under the principle of impartiality and objectivity, i.e. in a systematic, reliable and unbiased manner, following professional and ethical standards (the European Statistics Code of Practice), and the policies and practices followed are transparent to users and survey respondents.
All users have equal access to statistical information. All statistical information is published at the same time – at 9 a.m. on the day of publication of statistical information as indicated in the calendar on the Official Statistics Portal. Relevant statistical information is sent automatically to news subscribers.
Statistical information is published following the Official Statistics Dissemination Policy Guidelines and Statistical Information Dissemination and Communication Rules of Statistics Lithuania approved by Order No DĮ-176 of 2 July 2021 of the Director General of Statistics Lithuania.
CIS is conducted and disseminated at two-year interval in pair years.
Accessibility and clarity refer to the simplicity and ease for users to access statistics using simple and user-friendly procedure, obtaining them in an expected form and within an acceptable time period, with the appropriate user information and assistance: a global context which finally enables them to make optimum use of the statistics.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Dissemination and access | Availability | Comments, links, ... |
|---|---|---|
| Press release | X | 20 December 2023 Innovation activities |
| Access to public free of charge | X | Free of charge |
| Access to public restricted (membership/password/part of data provided, etc) | - |
10.2. Dissemination format - Publications
- Online database (containing all/most results): All main results are published in the Database of Indicators (free of charge).
- Analytical publication (referring to all/most results): Statistical information is not published in publications.
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect): Statistical information is not published in publications.
10.3. Dissemination format - online database
Statistical indicators are published in the Database of Indicators (Science and technology -> Innovation activities).
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
No microdata access
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | No | |
| National SAFE centre | No | |
| 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 documents are published in the Official Statistics Portal section Innovation activites.
The survey meta-information post together with database. Meta-information Innovation activities is available from 28 February 2024.
The process of the preparation of statistical information is described in the Methodology of statistical survey on innovation activities.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
A quality report is sent to Eurostat for each period of the innovation survey.
The national quality report – metadata – is produced and post together with database. Metadata Innovation activities is available from 28 February 2024.
11.1. Quality assurance
Quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice and ESS Quality Assurance Framework.
In 2007, a quality management system, conforming to the requirements of the international quality management system standard ISO 9001, was introduced at Statistics Lithuania. Main trends in activity of Statistics Lithuania aimed at quality management and continuous development in the institution are established in the Quality Policy.
Monitoring of the quality indicators of statistical processes and their results and self-evaluation of statistical survey managers is regularly carried out in order to identify areas which need improvement and to promptly eliminate shortcomings.
More information on assurance of quality of statistical information and its preparation is published in the Quality Management section on the Statistics Lithuania website.
11.2. Quality management - assessment
The survey response rate was 99.9 per cent. The survey are mandatory and only fully completed e-questionnaires are accepted. Respondents must answer to all questions, which are necessary to answer for them. Electronic survey is with strict validation rules.
Data for the survey were collected at regional statistical offices. The specialists of these offices were responsible for statistical data collection, sending reminders to the respondents, consulting them, checking and correction by the protocols of logical errors. The entry errors were detected both by manual comparisons and during running logical controls. But their number was relatively small. Logical errors were eliminated by the contacting with respondents.
After data collection the quality of the obtained information was analyzed. The results of the calculation was compared with the results of the previous year, the corresponding indicators of other statistical surveys and administrative sources. Outstanding values of indicators was identified and analyzed. In the event of significant deviations, the data provider shall be contacted and the reasons for the deviation explained.
Verification of data according to additional control conditions, determination of exceptions of quantitative indicators, comparison of the data set with the data of the previous period, inaccuracies, trends of changes were performed. In case of deviations, the reasons for them were explained and, if necessary, the respondents contacted. Data were corrected if inaccuracies were identified.
The weaknesses were in the data collection. Often enterprises accounting departments completed questionnaires with a less knowledge about innovation, R&D and management. By them, the innovation is invention of absolutely a new product but not it‘s improvement.
The coefficient of variation for the percentage of innovative enterprises are good. Data calculated with the possibly accuracy.
12.1. Relevance - User Needs
The main users of statistical information are State authorities and agencies, international organisations, the media, research and business communities, students, whose needs are satisfied without a breach of the confidentiality principle.
The statistical information of the Innovation activities survey is used to analyze the state of the country's economy, forecast market developments and growth changes.
The national questionnaire was harmonised with the national public institutions (Ministry of the Economy and Innovation, Ministry Education, Science and Sport, Innovation Agency Lithuania).
User satisfaction in Statistics Lithuania:
From 2005, general user opinion surveys have been conducted on a regular basis. Official Statistics Portal traffic is monitored, website visitor opinion polls, general opinion poll on the products and services of Statistics Lithuania, target user group opinion polls and other surveys are conducted. In 2007, the compilation of a user satisfaction index was launched. The said surveys are aimed at the assessment of the overall demand for and necessity of statistical information in general and specific statistical indicators in particular.
More information on user surveys and their results is available in section User surveys on the Statistics Lithuania website.
12.1.1. Needs at national level
| User group | Short description of user group | Main needs for CIS data of the user group Users’ needs |
|---|---|---|
| 1. Institutions - European level | Eurostat, DG RTD, DG REGIO | To produce innovation STI statistics and format innovation policy |
| 1. Institutions - International organisations | OECD | To produce innovation STI statistics for OECD Science, Technology and Industry Scoreboard |
| 1. Institutions - National level | Ministry of Economy and Innovation, Ministry of Education and Science | Data used for the market analysis and format innovation policy |
| 1. Institutions - National level | State Data Agency (Statistics Lithuania) | To produce STI statistics |
| 1. Institutions - National level | Innovation Agency, Innovation centre | Data used for the own market analysis, their marketing strategy, they offer consultancy services |
| 3 - Media | National media: TV, newspapers | National media: TV, newspapers |
| 4 - Researchers and students | Universities, students etc | Data used for the science works, analysis |
| 5 - Enterprises or businesses | Data used for the own market analysis, their marketing strategy |
12.2. Relevance - User Satisfaction
User satisfaction survey related to CIS data was not carry out.
12.3. Completeness
All mandatory indicators where included to survey questionnaire and all data was sent to Eurostat.
All mandatory indicators are published and are avelable in the national Database of Indicators (free of charge).
12.3.1. Data completeness - rate
Not requested.
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
13.2. Sampling error
Restricted from publication
13.2.1. Sampling error - indicators
The main indicator used to measure sampling errors for CIS data is the coefficient of variation (CV).
CV= Coefficient of variation (%) = 100 * (Square root of the estimate of the sampling variance) / (Estimated value)
Formula:
where
and
13.2.1.1. Coefficient of variations for key variables
Coefficient of variation (%) for key variables by NACE categories and for enterprises with 10 or more employed persons
| NACE |
Size class |
(1) |
(2) |
(3) |
|---|---|---|---|---|
| Core NACE (B-C-D-E-46-H-J-K-71-72-73) |
Total |
2.3 % |
5.5 % |
4.0 % |
| Core industry (B_C_D_E - excluding construction) |
Total |
2.7 % |
3.3 % |
5.2 % |
| Core Services (46-H-J-K-71-72-73) |
Total |
3.5 % |
12.6 % |
5.8 % |
(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
The variance estimator of the direct population total estimator (or the Horvitz-Thomson) was used.
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
There were some divergences between the target population and the frame population due of economically not active enterprises. These errors were estimated according to the sample design.
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. 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
Electronic Statistical Business Data Preparation and Transmission system e-Statistics is used for collection of statistical data. Statistical data are checked during entry and processing. The requirements for statistical data control are presented in the technical task of the Survey programming work. The questionnaire cannot be approved until the questions have been answered. Arithmetic, logical and compatibility control of statistical data, comparison of data with data of the previous period are foreseen.
Statistical data are corrected by taking into account such types of errors: those that should be corrected and those that might be ignored (these are warnings to respondents about possible errors).
According to the format of the statistical survey, enterprises are considered to submit a fully completed report on innovation activities survey INV-01. If the value of the indicator is not filled in, it is considered that such a phenomenon does not exist in the company and it is not a non-response of the indicator.
13.3.3. Non response error
The survey was carried out mandatory. The unit non-response rate stood at 0.1 percent.
Respondents must answer to all necessary questions. Electronic survey was with strict validation rules.
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) | 3 | 2257 | 0.13 % | <1 % |
| Core industry (B_C_D_E - excluding construction) | 0 | 1069 | 0 % | <1 % |
| Core Services (46-H-J-K-71-72-73) | 3 | 1188 | 0.25 % | <1 % |
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
3 automatic reminders (3 days to deadline, deadline day and 1 day after) were sent via the e-system. Then specialists of the Regional Statistical Offices sent / maked a call on average 3 times to enterprises before enterprises were coded 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)
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 |
There was no item non-response. No imputation done.
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 % | |
| 3.10 -- Reasons for having no innovation activities | Yes | 0 % |
13.3.4. Processing error
The number of processing errors was relatively small. Logical errors were eliminated by the contacting with respondents. In general, the problem was in the respondent's understanding and interpretation of questions.
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: 20 December 2023
14.1.2. Time lag - final result
Not requested.
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Date of transmission of complete and validated data to Eurostat (Number of days between that date and 30 June 2024) : 26 June 2022 (4 days).
Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains, or over time.
The coherence of statistical outputs refers to the degree to which the statistical processes by which they were generated used the same concepts (classifications, definitions, and target populations) and harmonised methods. Coherent statistical outputs have the potential to be validly combined and used jointly.
15.1. Comparability - geographical
Statistical data are comparable among the regions, counties of Lithuania and among EU countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. National questionnaire – compliance with Eurostat model questionnaire
Methodological deviations from the CIS Harmonised Data Collection (HDC)
| Questions not included in national questionnaire compared to HDC | Comment |
|---|---|
| 2.3, 2.4 Used patents and IRPs | In order to reduce the burden on respondents, Lithuania did not include some voluntary questions (from HDC) in the national questionnaire |
| 4.1 What was the average number of employed persons by your enterprise in 2020 and 2022? | For information on staff, structural business statistics survey data and administrative data sources (State Social Insurance Fund Board (Sodra) are used additionally. |
| 4.2 Approximately what percentage of the persons employed in your enterprise in 2022 had a tertiary degree? | In order to reduce the burden on respondents, Lithuania did not include some voluntary questions (from HDC) in the national questionnaire |
| 4.3 What was your enterprise’s total turnover in 2020 and 2022? | For information on turnover, structural Business statistics survey data are used. |
| 4.5 Age of enterprise | For information on age of enterprise, business Statistical Business Register data are used. |
| 4.6 Total expenditure in 2022 | In order to reduce the burden on respondents, Lithuania did not include some voluntary questions (from HDC) in the national questionnaire |
| 4.7 In 2022, was your enterprise part of enterprise group | For information on age of enterprise, business Statistical Business Register data are used. |
| 4.8 Enterprise group: inflows and outflows | In order to reduce the burden on respondents, Lithuania did not include some voluntary questions (from HDC) in the national questionnaire |
| Changes in the filtering compared to HDC | Comment |
|---|---|
| No deviations. |
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 |
|---|---|
| 4.2 Other financial support from a not European Union | 1 additional question was included in the national questionnaire to seek full situation on outside support |
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 | 99.7 % | 95.9 % | 100.9 % |
| Core industry (B_C_D_E - excluding construction) | Total | 100.2 % | 98.1 % | 100.8 % |
| Core Services (46-H-J-K-71-72-73) | Total | 99.4 % | 93.8 % | 101.1 % |
* Numbers are to be provided for the last year of the reference period (t)
Comparison between SBS and CIS data (relative difference) by NACE categories calculated excluding section K (FINANCIAL AND INSURANCE ACTIVITIES).
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
The main source of statistical data is a sample survey of innovation activities (sample 2 257 enterprises or 28.5 per cent of the total survey population).
For the collection of statistical data a statistical report form has been prepared - the statistical report on innovation activities INV-01 (every 2 years).
18.1.1. Sampling frame (or census frame)
The source of the frame - the Statistical Business Register.
18.1.2. Sampling design
In accordance with section 2 of the annex of the Commission Regulation on Community statistics on innovation, enterprises in the NACE Rev. 2 sections B, C, D, E, H, J, K and in the NACE Rev. 2 divisions 46 and divisions 71, 72 and 73 are covered.
The official up-to-date statistical Business Register of the country was used as a sampling frame. The target population were stratified into similar groups; the characteristics for the breakdown were: economic activities (in accordance with NACE Rev. 2) at least at a 2-digit level and enterprise size according to the number persons of employed (10–49, 50–249, 250+, excluding enterprises with 0-9 of employed persons).
The enterprises with 10-49 and 50-249 persons of employed are covered by sampling.
The enterprises size class (250+) persons of employed are covered by complete enumeration.
Within every NACE Rev 2 group the strata of enterprises were formed by number of employed persons groups. If in the strata are 7 or less enterprises, they are all involved in the statistical survey. The Neyman optimal allocation (with variable number of persons employed) was used for determination of the sample size for each stratum specified.
In each stratum simple random sampling was used. The final number of strata was 139.
The sample was designed with no reference to other surveys. Sample rate is about 29 per cent of the population.
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
|---|---|
| Target population (A) (*) | 8003 |
| Sample (B = C+D) | 2293 |
| In case of combination sample/census: | |
| Sampled units (C) | 1815 |
| Enumerated units/census (D) | 478 |
| Overall sample rate (E = 100*B/A) | 28.7 % |
(*) 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.
Gross sample – 2293. Net sample – 2257. Number of units with a response in the realised sample – 2254.
18.1.4. Data source for pre-filled variables
Variables and indicators filled or prefilled from other sources.
| Variables/Indicators | Source | Reference year |
|---|---|---|
| Turnover | Main sourse – Structural business statistics (SBS) survey. | 2022 |
| Number of employed persons | Structural business statistics (SBS) survey data and administrative data sources (State Social Insurance Fund Board (Sodra) | 2022 |
| Age of enterprise | The Statistical Business Register | 2022 |
| Belonging to an enterprise group | The Statistical Business Register | 2022 |
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
|---|---|
| Data source used for deriving population totals | Data source is the Statistical Business Register. |
| Variables used for weighting | The variables used for weighting were the same as for stratification, i. e. the economic activities and enterprise size according to the number of employees. |
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
The data are collected via the electronic statistical data preparation and transfer system e-Statistics. For the collection of statistical data a statistical report form has been prepared - the statistical report on innovation activities INV-01 (every 2 years). The national statistical report of the survey is prepared every 2 years according to a standard questionnaire prepared by Eurostat.
Data for the survey were collected at regional statistical offices. The specialists of these offices were responsible for statistical data collection, sending reminders to the respondents, consulting them, micro data editing, checking and correction by the protocols of logical errors.
18.3.1. Survey participation
The survey was carried out mandatory, it was included in the Official statistics programme.
18.3.2. Survey type
The survey type – combination census/sample survey.
18.3.3. Combination of sample survey and census data
The survey was realised as a combination of sample survey and census.
The enterprises with 10-49 and 50-249 employed are covered by sampling.
The enterprises size class (250+) employed are covered by complete enumeration.
As well as all enterprises from the list of the Lithuanian Business Support Agency, which received funds from the European Union for R&D or innovation activities during the three-year period under investigation.
18.3.4. Census criteria
The enterprises size class (250+) employed are covered by complete enumeration.
As well as all enterprises from the list of the Lithuanian Business Support Agency, which received funds from the European Union for R&D or innovation activities during the three-year period under investigation.
18.3.5. Data collection method
Data collection method
| Survey method | Yes/No | Comment |
|---|---|---|
| Face-to-face interview | No | |
| Telephone interview | No | Only for contact with respondents for correct various errors in online survey questionnaires. |
| 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.
Estimates of statistical indicators in the assessment areas and whole population are calculated using the Horvitz-Thompson estimate for stratified samples.
Enterprises which refused to participate in the survey, weight conversion is performed.
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 | x | Economically non active enterprises was excluded from target population. |
| Other | x | All results were weighted against the total population by means of sample weights. The weights were established by using the probability of each enterprise to be included in the sample. The primary strata were not changed and the primary weights were used for actual estimation domains. |
18.6. Adjustment
The calibration method was not used.
18.6.1. Seasonal adjustment
Not requested.
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).
8 October 2024
The description of concepts, definitions and main statistical variables will be available in CIS 2022 European metadata file (ESMS) Results of the community innovation survey 2022 (CIS2022) (inn_cis13) in Eurostat database.
The statistical unit – the enterprise.
Enterprise – the smallest combination of legal units that is an organizational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocation of its current resources. An enterprise carries out one or more activities at one or more locations. An enterprise may be a sole legal unit.
The observation unit was the legal unit, while sampling was carried out at Statistical Unit Enterprise level.
Process for obtaining results at Statistical Unit Enterprise level in complex enterprises:
- For qualitative variables: If one observation legal unit was selected in the enterprise, then its qualitative answers are provided as for the statistical unit 'enterprise'. Most often, one 'largest product producing unit' as observation unit (legal unit) was selected. If two or more observation legal units was selected in the enterprise, we consolidated their responses and present them as responses of enterprise. If at least one unit reported ‘yes’, the value for the enterprise is set on 'yes'. Variables measured on an importance scale (categorical variables): in case that observations for two ore more than two observation units that belong to the CIS target population are available, the highest value among all observation units was used, but in exceptional cases the case-by-case choices made.
- For quantitative variables: Sum of all observation legal units. We obtain the total number of employed and consolidated turnover of the complex enterprises from the SBS survey. If two or more observation legal units was selected in the enterprise, we consolidated their responses and present them as responses of enterprise. Innovation expenditure: sum of innovation expenditure of all observations units. Percentages for share of turnover due to products new for the market, were first converted into absolute numbers (by multiplying the percentages with legal unit turnover) and then summed, and then again written as percentages for the enterprise as a whole.
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).
Reference area – country.
11 main indicators – by regions, counties.
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 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.
Estimates of statistical indicators in the assessment areas and whole population are calculated using the Horvitz-Thompson estimate for stratified samples.
Enterprises which refused to participate in the survey, weight conversion is performed.
The main source of statistical data is a sample survey of innovation activities (sample 2 257 enterprises or 28.5 per cent of the total survey population).
For the collection of statistical data a statistical report form has been prepared - the statistical report on innovation activities INV-01 (every 2 years).
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.
Statistical data are comparable among the regions, counties of Lithuania and among EU countries.
Due to important methodological changes introduced by Oslo Manual 2018, the data from 2018 onwards cannot be directly compared with CIS waves prior to 2018.
