1.1. Contact organisation
ROMANIA
NATIONAL INSTITUTE OF STATISTICS
1.2. Contact organisation unit
DEPARTMENT OF SHORT TERM ECONOMIC INDICATORS STATISTICS
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
16 Libertatii Bvd, Bucharest 5, Romania
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
27 October 2022
2.2. Metadata last posted
27 October 2022
2.3. Metadata last update
27 October 2022
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
No, national particularities; the core NACE activities only
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 national particularities.
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
Statistical unit is an enterprise.
The enterprise is a group of legal units constituted as an organizational entity of goods production, trade services or social interest services, which benefits of a decisional autonomy, especially in view to ensure its current resources. An enterprise is carrying out one or several activities, on one or several places (headquarters - local units of enterprises).
Sampling was carried out at the level of the legal units. The observation unit was the legal unit.
See also 'Data compilation'.
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
According NUTS 2016 classification, CIS collect and disseminate regional information at NUTS 2 level (Romanian 8 basic regions for the application of regional policies).
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 | yes | |
| CIS3 | 1998-2000 | yes | |
| CIS light | 2002-2003* | yes | 2001-2003 |
| 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
- Law on the organization and functioning of official statistics in Romania no. 226/2009.
- Government Decision no. 161/2023 on the approval of the National Annual Statistical Program 2023.
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
No deviations from secure procedure.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46 / EC (General Regulation on data protection)
Law no. 190 of 18 July 2018 on measures to implement Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of these data and repealing Directive 95/46 / EC (General Data Protection Regulation). See information at this website.
Commission Implementing Regulation (EU) 2022/1092 on innovation statistics.
LAW no. 363 of December 28, 2018 on protection natural persons regarding the processing of personal data by the competent authorities for the purpose of preventing, detecting, investigating, prosecuting and combating crime or the execution of punishments, educational and security measures, and regarding the free movement of such data
Law no. 102/2005 on the establishment, organization and functioning of the National Authority for the Supervision of Personal Data Processing, with subsequent amendments and completions.
Law on the organization and functioning of official statistics in Romania no. 226/2009. See information at this website.
7.2. Confidentiality - data treatment
The rules that have been applied for aggretate data were the following: the rule of three, the dominance and the precision.
8.1. Release calendar
On the NIS website there are two calendars one for the press releases and the other for publications; both of them are accessible to the general public.
8.2. Release calendar access
- INSSE website - for publications.
- INSSE website for press release.
8.3. Release policy - user access
The NIS has on the web page a section “Calendar of press releases”, with links to the monthly lists of publications planned for the current year. Each monthly list is sorted by date of publication and contains a brief description of the statistics to be provided. The monthly calendar is established for the following year in December of the previous year. NIS publishes annually on the site the calendar of press releases, calendar based on the terms of the Annual National Statistical Program and contains: title of the press release, reference period, date of issue. The monthly calendar is established and posted on the NIS website from December of the previous year. The calendar of press releases on the NIS website covers only the statistics published by the NIS
In the event of a change in the broadcast date, this is announced 24 hours before the calendar date, specifying the new broadcast date.
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 | yes | The NIS press releases are sent directly by the NIS Press Office to the accredited media and ministries and are posted online at 9:00 a.m. in the dedicated section. Also, on the site there is the archive of these press releases, structured on statistical topics. |
| Access to public free of charge | yes | Access to statistical data takes place simultaneously for all categories of users. |
| Access to public restricted (membership/password/part of data provided, etc) | no |
10.2. Dissemination format - Publications
- Online database (containing all/most results): "Innovation in business enterprises” -
- Analytical publication (referring to all/most results): General paper publication -Availlable only for national users.
- Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect).
10.3. Dissemination format - online database
INSSE website - table. Available also in Romanian language format.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Dissemination of microdata
| Mean of dissemination | Availability of microdata | Comments, links, ... |
|---|---|---|
| Eurostat SAFE centre | yes | |
| National SAFE centre | NIS does not have a "Safe center" for access to microdata. Due to the confidential nature of microdata, direct access to anonymized data is offered only for scientific research projects according to European and national legislation in the field, through an access contract. The access is, in principle, limited to universities, research institutes, national statistical institutes, central banks within the EU and euro area countries, as well as to the European Central Bank. Individuals cannot be granted direct access to microdata. The access to microdata is allowed only to research projects carried out on behalf of an accredited organization for scientific research, and exclusively for its staff, which signs a contract with NIS. Requests for changes shall be made by the contractor before the expiry of the contract by means of an amendment to the contract. |
|
| Eurostat: partially anonymised data (SUF) | yes | |
| National: partially anonymised data |
10.5. Dissemination format - other
Sectoral indicators, more detailed NACE activities, SMEs regional data, high-tech innovative enterprises, competitiveness indicators, sustenable indicators, logistics indicators.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
Data are accompanied of metadata describing the indicators and the calculation thereof.
To all other questions regarding the methodology or the manner of designing the tables and the data we respond whenever necessary.
In the TEMPO online database, each indicator is accompanied by the related metadata.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Romanian NIS has a special department for dissemination of data. All requirements of the users are solved by this department in cooperation with the production and methodological departments.
Data are accompanied by detailed metadata. To all other questions regarding the methodology or the manner of designing the tables and the data we respond whenever necessary.
CIS data quality reports are prepared at the end of each survey wave, once every 2 years, in even years. They are transmitted to Eurostat and posted on the NIS website.
11.1. Quality assurance
The quality quantifies how well the statistics are fit for their purpose. The criteria to judge statistical quality correspond to: relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity, comparability, coherence and cost and burden. The aim is to reduce the errors of coverage, the non-response, the measurements errors, the processing errors. Sampling errors occur when the survey results are obtained from a sample rather than the population as a whole. They may also include estimation errors due to estimators that, by design or otherwise, create bias. Sampling errors are measured by the values of the coefficients of variation.
| Legislation concerning quality assurance, Task Forces or Working Groups, etc. | Law No. 226/2009 on the organisation and functioning of official statistics in Romania |
|---|---|
| Internal procedures | |
| Methodological standards and guidelines assuring the quality of the production process and the output | European Statistics Code of Practice |
| Quality Guidelines for Romanian Official Statistics | |
| Oslo Manual |
11.2. Quality management - assessment
The CIS survey is conducted to provide knowledge about innovation on business enterprises and to allow comparisons with other European countries. The national methodology is based on the Eurostat methodology prepared for CIS 2022 survey and meets the requirements of the six quality dimensions according with European Statistical System and other important characteristics.
Strengths:
- Use of a common questionnaire and a methodology for all countries;
- Experiences and knowledge got from the other waves of CIS survey;
- Compliance with user requirements and providing all necessary data;
- Data collection through online portal questionnaires which decrease the number of errors in data;
- Comparability of data with other two important surveys: SBS survey and R&D survey;
- Punctuality of time schedule of effective publication.
Weaknesses
- Absence of a common IT program in order to process CIS data at micro-level in a harmonised fashion for all Member States.
- A detailed methodology for computing the weighting coefficients.
12.1. Relevance - User Needs
At every CIS wave, before the finalisation of the national questionnaire, the main national users and the representatives of regional NIS departments are invited to a discussion, by Romanian NIS, to express their opinions and suggestions about the final national questionnaire (clarity, difficulties, understanding and perception of the questions and about other national statistical needs).
12.1.1. Needs at national level
| User group | Short description of user group | Main needs for CIS data of the user group Users’ needs |
|---|---|---|
| 1. Institutions - European level | The European Commission | Innovation Union Scoreboard |
| 1. Institutions - European level | The European Commission (DGs,European Council,European Parliament, ECB, other European agencies. | Data are used for planning, policy and monitoring purposes and for the calculation of indicators of some publications, for example Innovation Union Scoreboard, Regional Innovation Scoreboard and for other publications (Science, Technology and Innovation in Europe, Statistics in Focus, Pocket book, Statistics explained) which have need of comparability between data of Member States and other European countries and world countries concerning innovation statistics or, for preparing regulations and laws for science and technology field. |
| 1. Institutions - national level | President Administration | Development of economic and social policies |
| 1. Institutions - national level | Government departments | Planning, policy and preparing laws, norms and regulations. |
| 1. Institutions - national level | Ministry of Economy | Industrial policies, competitiveness of enterprises and to promote commerce and foreign investments. |
| 1. Institutions - national level | Ministry of Education | Set up the strategy and policies for R&D and innovation field. |
| 1. Institutions - national level | Ministry of European Investments and Projects | Operational programs, structural and cohesion funds. |
| 1. Institutions - national level | National Forecast Commission | Calculate forecasts |
| 1. Institutions - national level | Other ministries and regional agencies | Analysis and sectorial comparisons |
| 1. Institutions - national level | Romanian National Institute of Statistics and territorial departments | Data are used for studies and comparisons with other statistical data |
| 1. Institutions - International organisations | OECD | Improvement methodologies and for other studies and analysis |
| 2. Social actors | Chamber of Deputies | Commission for Industry and Services, Commission for work and social protection, Commission on Science and Technology to set up specific policies |
| 2. Social actors | Libraries (National Library, Metropolitan Library of Bucharest, Senate Library, Chamber of Deputies Library, University Central Library, Academy of Economic Studies Library) | Information, documentation and development of collections |
| 2. Social actors | United Nation Library in Romania | Information and documentation |
| 2. Social actors | Trade Unions, Employers’ Associations | Studies |
| 3. Media | International, national and regional media | Interested in press releases, analysis and comments. |
| 4. Researchers and students | Romanian Academy, Universities, Higher Education Institutes | Studies and analysis |
| 4. Researchers and students | Analysis and access to specific innovation data | |
| 5. Enterprises or businesses | Marketing and organizational strategies, consultancy services |
12.2. Relevance - User Satisfaction
NIS Romania conducted a general user satisfaction survey on every 3 years, where CIS and R&D domain is mention.
The users request more detailed data for SMEs, regional data, new ad-hoc module and also accessibility to micro data.
12.3. Completeness
National survey includes all indicators of European questionnaire, CIS 2022. We have no missingness issues.
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 |
3.93% | 4.90% |
1.70% |
| Core industry (B_C_D_E - excluding construction) |
Total |
4.76% |
7.32% |
2.48% |
| Core Services (46-H-J-K-71-72-73) |
Total |
6.09% |
6.25% |
2.30% |
(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
- Varianceh = (Ph*(1-Ph)*(Nh-mh))/( Nh *( mh -1))
- Varianceh= variance of the stratum h
- Where: Nh = target population in stratum h
- mh= number of responding units in the realised sample, in stratum h
- Ph= proportion of stratum h, meaning the ratio between the sum of weightnr, for each indicator, and the target population of the stratum (Nh).
- Then we have computed the Sh (Standard Deviation for each stratum), the coefficient of variance and the aggregates.
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
Estimation of coverage was computed with the initial coefficient of extraction using the turnover.
Under covered groups (<100%) – section B (99.7%), section D (99.6%), section E (98.6%), section H (97.7%), in Core NACE 46 (99.0%), in Core NACE 58 (98.6%), in Core NACE 62 (97.2%), in Core NACE 73 (96.0%).
Over covered groups (>100%) – section K (101.1%), in Core NACE 60, 61 (100.8%), in Core in Core NACE 63 (102.4%), in Core NACE 71 (100.7%).
100% covered groups: section C and in Core NACE 72.
13.3.1.4. Coverage errors in coefficient variation
CVs incorporate the effects of coverage errors.
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
The measures for reducing errors consisted in selection of staff with knowledge in CIS methodology and experience in data entry and validation checks for online questionnaires. Also, we developed detailed methodological notes regarding the new terms and their definition. We re-contact the respondents for supplementary clarifications.
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:
- 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.
- 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.
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) | 1169 | 9235 | 12.66 % | 11.99 % |
| Core industry (B_C_D_E - excluding construction) | 545 | 5193 | 10.49 % | 10.01 % |
| Core Services (46-H-J-K-71-72-73) | 624 | 4042 | 15.44 % | 13.68 % |
The number of eligible units is the number of sample units, that ultimately indeed belong to the target population.
When the sample is drawn a design-weight is calculated. When the data collection is closed a final calibrated weight is calculated, compensating the non-response.
A number of 638 units were removed from the sample because they don't belong to target population after inspection of their characteristics.
13.3.3.1.2. Maximum number of recalls/reminders before coding
3 recalls.
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 for all enterprises. |
| 3.10 -- Reasons for having no innovation activities | Yes | 0 % | Mandatory question for all enterprises. |
13.3.4. Processing error
The methods used for data entry were data keying and responses through online questionnaires
To check variables, correlations between chapters and items were applied. An edit rate is not available.
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: April 29, 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 (Number of days between that date and 30 June 2024) : 0.
15.1. Comparability - geographical
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.
In Romania, there are no problems of comparability between the regions of the country, because all regions are subject to the same assessment and the same questions from the CIS questionnaire, without other additional questions.
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 optional variables were collected. | The national questionnaire is identical to HDC. |
| Changes in the filtering compared to HDC | Comment |
|---|---|
| There are no filtering changes compared to HDC. | The national questionnaire is identical to HDC. |
Annexes:
National questionnaire for CIS 2022
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 |
|---|---|
| No | The national questionnaire is identical to HDC. |
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 | 100% | 100% | 100% |
| Core industry (B_C_D_E - excluding construction) | Total | 100% | 100% | 100% |
| Core Services (46-H-J-K-71-72-73) | Total | 100% | 100% | 100% |
* 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 national business register, named REGIS was used.
18.1.2. Sampling design
The sampling design used was the stratified sampling with simple random sampling within the strata. The strata were defined according to the activity, enterprise size by the number of employees and the geographical region. For the sample allocation, Neymann allocation method was used.
Number of strata used was 945.
- Sampled units: all enterprises with <250 persons employed;
- Census : all enterprises with 250 or more persons employed.
18.1.3. Target population and sample size
| Sample/census indicator | Number of enterprises |
| Target population (A) (*) | 27535 |
| Sample (B = C+D) | 9873 |
| In case of combination sample/census: | |
| Sampled units (C) | 3991 |
| Enumerated units/census (D) | 5882 |
| Overall sample rate (E = 100*B/A) | 35.1% |
(*) 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 | BR | 2022 |
| number of employees | BR | 2022 |
18.1.5. Data source and variables used for derivation and weighting
| Item | Response |
|---|---|
| Data source used for deriving population totals | The universe is formed of the enterprises belonging to the whole industry and a part of services selected from national business register. The data source for the totals is represented by the population of the enterprises used in Structural Business Survey (SBS) 2022 only for the covering of CIS 2022. (We compared the totals from SBS with the totals of CIS 2022 regarding the following indicators: number of enterprises, turnover and number of employees) The level was: NACE 2 digits and size class according to average number of employees. |
| Variables used for weighting | The variables used for weighting were the following: turnover, the number of enterprises and 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
See below
18.3.1. Survey participation
The survey is mandatory.
18.3.2. Survey type
Data are collected through a combination of a census and a sample survey.
18.3.3. Combination of sample survey and census data
The population classes covered by sampling are the following: 10-49; 50-249.
The population class covered by census was: 250+.
18.3.4. Census criteria
The criterion for census was the size class: all units with 250 and more employees were surveyed.
18.3.5. Data collection method
Data collection method
| Survey method | Yes/No | Comment |
|---|---|---|
| Face-to-face interview | ||
| Telephone interview | ||
| Postal questionnaire | ||
| Electronic questionnaire (format Word or PDF to send back by email) | Yes | |
| Web survey (online survey available on the platform via URL) | Yes | |
| Other |
18.4. Data validation
Not requested.
18.5. Data compilation
Operations performed on data to derive new information according to a given set of rules.
Process for obtaining results at Statistical Unit Enterprise level in complex enterprises:
- For qualitative variables: In case of enterprises (ENT) with two or more legal units (LEU), a representative unit established by the Statistical Business Register was used. For qualitative dichotomous questions (yes/no questions) it was assumed that if for one LEU the answer is 'yes', then the answer should be 'yes' for the whole ENT. For the several variables that the CIS collects data at a categorical scale data from the representative unit only was used, for the whole ENT.
- For quantitative variables: aggregated data from the Structural Business Statistics were used, if available ; and if not, all legal units of the enterprise were summed. Variables, like R&D expenditure, are non-additive in the general case. Because the information available, in the administrative sources or statistical surveys, that can be used for profiling does not allow to indicate clear hypotheses for a better consolidation it is recommended to treat them as being additive, for practical reasons. However, if further information is available, from surveys or manual profiling, transactions within the same enterprise must be eliminated.
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 | X | The initial weights are computed based on the inverse of the sampling fraction. Weighti = Nh/nh where Nh is the total number of enterprises/employees in stratum h of the population and nh is the realised sample in stratum h of the population, assuming that each unit in the stratum had the same probability of inclusion. |
| Non-respondent adjustments | X | After collection of data, the initial weights are adjusted aiming to compensate the non-response rate. Initially is computed the ratio Nh / mh where Nh is the total number of enterprises in stratum h of the population and mh is the number of enterprises in the realised sample in stratum h of the population, with response. Starting from Nh / mh as basic final weights, we got calibrated weights using a specific statistical software. The auxiliary source for the totals (calibration files) is represented by the population of the enterprises from Structural Business Survey (SBS) 2022, only for the covering of CIS 2022. The level of aggregation was NACE 2 digits and size class according to average number of employees. We compared the totals from SBS 2022 with the totals of CIS 2022, regarding the following indicators: number of enterprises, turnover and number of employees. There were computed 3 coefficients: for turnover, number of enterprises and number of employees. |
| Other |
18.6. Adjustment
In order to get the calibrated weights we used CLAN software. The level was: NACE 2 digits, size class according to average number of employees.
There were computed 3 coefficients for turnover, number of enterprises and number of employees.
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).
27 October 2022
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.
Statistical unit is an enterprise.
The enterprise is a group of legal units constituted as an organizational entity of goods production, trade services or social interest services, which benefits of a decisional autonomy, especially in view to ensure its current resources. An enterprise is carrying out one or several activities, on one or several places (headquarters - local units of enterprises).
Sampling was carried out at the level of the legal units. The observation unit was the legal unit.
See also 'Data compilation'.
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).
According NUTS 2016 classification, CIS collect and disseminate regional information at NUTS 2 level (Romanian 8 basic regions for the application of regional policies).
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.
Process for obtaining results at Statistical Unit Enterprise level in complex enterprises:
- For qualitative variables: In case of enterprises (ENT) with two or more legal units (LEU), a representative unit established by the Statistical Business Register was used. For qualitative dichotomous questions (yes/no questions) it was assumed that if for one LEU the answer is 'yes', then the answer should be 'yes' for the whole ENT. For the several variables that the CIS collects data at a categorical scale data from the representative unit only was used, for the whole ENT.
- For quantitative variables: aggregated data from the Structural Business Statistics were used, if available ; and if not, all legal units of the enterprise were summed. Variables, like R&D expenditure, are non-additive in the general case. Because the information available, in the administrative sources or statistical surveys, that can be used for profiling does not allow to indicate clear hypotheses for a better consolidation it is recommended to treat them as being additive, for practical reasons. However, if further information is available, from surveys or manual profiling, transactions within the same enterprise must be eliminated.
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.
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.
In Romania, there are no problems of comparability between the regions of the country, because all regions are subject to the same assessment and the same questions from the CIS questionnaire, without other additional questions.
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.
