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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | NATIONAL INSTITUTE OF STATISTICS ROMANIA |
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1.2. Contact organisation unit | DEPARTMENT OF SHORT TERM ECONOMIC INDICATORS STATISTICS |
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1.5. Contact mail address |
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2.1. Metadata last certified | 06/10/2023 | ||
2.2. Metadata last posted | 06/10/2023 | ||
2.3. Metadata last update | 06/10/2023 |
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3.1. Data description | ||||||||||||||||||
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the business enterprise sector consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics. (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology. |
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3.2. Classification system | ||||||||||||||||||
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3.2.1. Additional classifications | ||||||||||||||||||
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3.3. Coverage - sector | ||||||||||||||||||
See below. |
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3.3.1. General coverage | ||||||||||||||||||
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3.3.2. Sector institutional coverage | ||||||||||||||||||
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3.3.3. R&D variable coverage | ||||||||||||||||||
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3.3.4. International R&D transactions | ||||||||||||||||||
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3.3.5. Extramural R&D expenditures | ||||||||||||||||||
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
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3.4. Statistical concepts and definitions | ||||||||||||||||||
See below. |
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3.4.1. R&D expenditure | ||||||||||||||||||
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3.4.2. R&D personnel | ||||||||||||||||||
See below. |
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3.4.2.1. R&D personnel – Head Counts (HC) | ||||||||||||||||||
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3.4.2.2. R&D personnel – Full Time Equivalent (FTE) | ||||||||||||||||||
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3.4.2.3. FTE calculation | ||||||||||||||||||
The respondent unit calculates the hours worked in research projects and computes in full time equivalent. |
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3.4.2.4. R&D personnel - Cross-classification by function and qualification | ||||||||||||||||||
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3.5. Statistical unit | ||||||||||||||||||
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, – if there are deviations please explain. |
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3.6. Statistical population | ||||||||||||||||||
See below. |
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3.6.1. National target population | ||||||||||||||||||
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
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3.6.2. Frame population – Description | ||||||||||||||||||
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
1) i.e. enterprises previously not known or not supposed to perform R&D |
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3.7. Reference area | ||||||||||||||||||
Not requested. R&D statistics cover national and regional data. |
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3.8. Coverage - Time | ||||||||||||||||||
Not requested. See point 3.4. |
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3.9. Base period | ||||||||||||||||||
Not requested. |
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R&D indicators are available according to 3 units of measure: - R&D Expenditure is available in National currency; - R&D Personnel data is available in full-time equivalent (FTE); - R&D Personnel data is available in headcount (HC).
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Reference period is the calendar previous year. |
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6.1. Institutional Mandate - legal acts and other agreements | ||||||||||||||
See below. |
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6.1.1. European legislation | ||||||||||||||
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6.1.2. National legislation | ||||||||||||||
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6.1.3. Standards and manuals | ||||||||||||||
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development |
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6.2. Institutional Mandate - data sharing | ||||||||||||||
Not requested. |
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7.1. Confidentiality - policy | |||
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes. A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties. a) Confidentiality protection required by law: 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) https://insse.ro/cms/ro/content/norme-de-confiden%C8%9Bialitate 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) https://insse.ro/cms/ro/content/norme-de-confiden%C8%9Bialitate Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business 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. b) Confidentiality commitments of survey staff: A confidentiality certificate agreement is signed upon employment, where the official terms of confidentiality are established |
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7.2. Confidentiality - data treatment | |||
Primary confidentiality: |
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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. The final data are target to be published in press release and also in national publication to 11 months after the end of the reference year (in November). |
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8.2. Release calendar access | |||
https://insse.ro/cms/files/catalog/Catalogul_publicatiilor_INS_2022.pdf - for publications https://insse.ro/cms/ro/comunicate-de-presa-view for press release |
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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. |
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The frequency of dissemination is annual. |
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10.1. Dissemination format - News release | ||||||||||||||||
See below. |
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10.1.1. Availability of the releases | ||||||||||||||||
1) Y - Yes, N – No |
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10.2. Dissemination format - Publications | ||||||||||||||||
See below. |
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10.2.1. Availability of means of dissemination | ||||||||||||||||
1) Y – Yes, N - No |
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10.3. Dissemination format - online database | ||||||||||||||||
Data for BES sector of performance are available in database TEMPO ONLINE: http://statistici.insse.ro:8077/tempo-online/#/pages/tables/insse-table |
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10.3.1. Data tables - consultations | ||||||||||||||||
Not requested. |
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10.4. Dissemination format - microdata access | ||||||||||||||||
See below. |
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10.4.1. Provisions affecting the access | ||||||||||||||||
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10.5. Dissemination format - other | ||||||||||||||||
See below. |
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10.5.1. Metadata - consultations | ||||||||||||||||
Not requested. |
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10.5.2. Availability of other dissemination means | ||||||||||||||||
1) Y – Yes, N - No Y |
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10.6. Documentation on methodology | ||||||||||||||||
A confidentiality certificate agreement is signed upon employment, where the official terms of confidentiality are established. 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. |
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10.6.1. Metadata completeness - rate | ||||||||||||||||
Not requested. |
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10.7. Quality management - documentation | ||||||||||||||||
See below. |
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10.7.1. Information and clarity | ||||||||||||||||
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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. The legal acts and other document related quality assurance are: Legislation concerning quality assurance, Task Forces or Working Groups, Law No. 226/2009 on the organisation and functioning of official statistics in Romania, Internal procedures, European Statistics Code of Practice, Quality Guidelines for Romanian Official Statistics Statistical practices used to compile national R&D data for government sector of performance are in compliance with Frascati Manual recommendations. |
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11.2. Quality management - assessment | |||
National methodology applies harmonized concepts and definitions according with Frascati Manual. As it is recommended, we include in the national R&D survey on the BES sector all enterprises known or supposed to perform R&D. The methodology was improved through the identification of units belonging BES sector of performance. The R&D survey for BES sector of performance is conducted to provide knowledge about R&D indicators (mandatory and optional) and to allow comparisons with other European countries. At every R&D survey for BES sector of performance , 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).
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12.1. Relevance - User Needs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.1.1. Needs at national level | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Users' class codification 1- Institutions: 2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level. 3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public. 4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.) 5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services) 6- Other (User class defined for national purposes, different from the previous classes. ) |
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12.2. Relevance - User Satisfaction | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys. |
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12.2.1. National Surveys and feedback | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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12.3. Completeness | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.1. Data completeness - rate | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data completeness of final mandatory data are very good and good. National questionnaire survey for BES sector of performance included also mandatory and optional R&D indicators. Starting with 2019 year of reference, we stopped collecting data by NACE industry orientation indicator. |
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12.3.2. Completeness - overview | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
Criteria: A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply. B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%. |
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12.3.3. Data availability | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.3.1. Data availability - R&D Expenditure | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.2. Data availability - R&D Personnel (HC) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.3. Data availability - R&D Personnel (FTE) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.4. Data availability - other | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested. 2) Y-start year |
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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). Several types of statistical errors occur during the survey process. The following typology of errors has been adopted: 1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated. 2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise: a) Coverage errors, b) Measurement errors, c) Non response errors and d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce. |
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13.1.1. Accuracy - Overall by 'Types of Error' | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘. 2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D. |
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13.1.2. Assessment of the accuracy with regard to the main indicators | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat. 2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met. 3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria. 4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met. 5) 'Very Poor' = If all the three criteria are not met. |
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13.2. Sampling error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated. |
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13.2.1. Sampling error - indicators | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The main indicator used to measure sampling errors is the coefficient of variation (CV). |
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13.2.1.1. Variance Estimation Method | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PROC SURVEYMEANS-SAS 9.1 |
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13.2.1.2. Coefficient of variation for key variables by NACE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) 2) Services sector (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99) |
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13.2.1.3. Coefficient of variation for key variables by Size Class | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. |
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13.3.1. Coverage error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
a) Description/assessment of coverage errors: Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling. b) Measures taken to reduce their effect:F The magnitude of the error is computed as a percentage of the relative difference between the indicator's expected "true values" based on the target population and the indicators observed values in the frame population. Magnitude of error(%)=(Observed Value-True Value)/True Value (%) Note:non-responding units are not considered omitted |
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13.3.1.1. Over-coverage - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Magnitude of error (%) = (Observed Value-True Value)/ True Value (%) |
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13.3.1.1.1. Over-coverage rate - groups | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.1.2. Common units - proportion | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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13.3.1.3. Frame misclassification rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
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13.3.2. Measurement error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias. a) Description/assessment of measurement errors: Few processing or measurement errors b) Measures taken to reduce their effect: The measures for reducing errors consisted in selection of staff with knowledge in R&D 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 recontact the respondents for supplementary clarifications. |
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13.3.3. Non response error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Non-response occurs when a survey failed 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 elements of non-response: - Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit. - Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit. The extent of response (and accordingly of non response) is also measured with response rates. |
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13.3.3.1. Unit non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate. |
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13.3.3.1.1. Unit non-response rates by Size Class | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.1.2. Unit non-response rates by NACE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43) 2) Services (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99) |
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13.3.3.1.3. Recalls/Reminders description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Two reminders. |
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13.3.3.1.4. Unit non-response survey | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.2. Item non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Definition: |
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13.3.3.2.1. Un-weighted item non-response rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.3. Magnitude of errors due to non-response | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.4. Processing error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data. |
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13.3.4.1. Identification of the main processing errors | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.5. Model assumption error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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14.1. Timeliness | |||||||||||||||
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar. |
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14.1.1. Time lag - first result | |||||||||||||||
Time lag between the end of reference period and the release date of the results:
a) End of reference period:31.12.2021 b) Date of first release of national data:16.11.2022 c) Lag (days):320 |
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14.1.2. Time lag - final result | |||||||||||||||
a) End of reference period:31.12.2021 b) Date of first release of national data:25.11.2022 c) Lag (days):329 |
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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. |
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14.2.1. Punctuality - delivery and publication | |||||||||||||||
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release). |
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14.2.1.1. Deadline and date of data transmission | |||||||||||||||
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15.1. Comparability - geographical | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.1.2. General issues of comparability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Previous 1993 R&D data could not be recomputed according with Frascati Manual due to the inclusion of other activities that did not belonged to Frascati Manual; |
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15.1.3. Survey Concepts Issues | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
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15.1.4. Deviations from recommendations | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
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15.2. Comparability - over time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.1. Length of comparable time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.2. Breaks in time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Breaks years are years for which data are not fully comparable to the previous period. |
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15.2.3. Collection of data in the even years | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. |
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15.3. Coherence - cross domain | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS). The Community innovation survey 2020 (CIS2020) (inn_cis12) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2020 survey. |
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15.3.1. Coherence - sub annual and annual statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.3.2. Coherence - National Accounts | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R&D statistics for BES sector of performance are compiled in according with institutional BES sector as defined based on the System of National Account (SNA). |
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15.3.3. National Coherence Assessments | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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15.3.4. Coherence – Foreign-controlled EU enterprises – inward FATS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
For R& D Expenditure in FATS, the same confidentiality rules are apply as in SBS statistics. Data for R& D Expenditure in FATS are provided from R&D Expenditure in R &D statistics. |
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15.4. Coherence - internal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.4.1. Comparison between preliminary and final data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This part compares key R&D variables as preliminary and final data.
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15.4.2. Consistency between R&D personnel and expenditure | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.). (2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel). |
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The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. |
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16.1. Costs summary | |||||||||||||||||||||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies. |
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16.2. Components of burden and description of how these estimates were reached | |||||||||||||||||||||
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’) |
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17.1. Data revision - policy | |||
Not requested. |
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17.2. Data revision - practice | |||
Not requested. |
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17.2.1. Data revision - average size | |||
Not requested. |
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18.1. Source data | ||||||||||||||||||||||||||||||||||||||||||||
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in. |
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18.1.1. Data source – general information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.2. Sample/census survey information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.3. Information on collection of administrative data or of pre-compiled statistics | ||||||||||||||||||||||||||||||||||||||||||||
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18.2. Frequency of data collection | ||||||||||||||||||||||||||||||||||||||||||||
See 12.3.3. |
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18.3. Data collection | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.3.1. Data collection overview | ||||||||||||||||||||||||||||||||||||||||||||
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18.3.2. Questionnaire and other documents | ||||||||||||||||||||||||||||||||||||||||||||
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18.4. Data validation | ||||||||||||||||||||||||||||||||||||||||||||
For survey, data are collected online using the Portal WEB application and self - administrated method. We have an IT solution developed to find out measurement and processing errors occurred in different stages of the survey. The application was designed for online data collection and validation. The IT solution allowed to perform online data entry and validation at unit level. Also, solution allowed to perform data entry and validation questionnaires received on paper by post/email from all our 42 counties. The IT solution contained the following categories of logical sets to check: - the primary data from the questionnaires - the logical flows among the questionnaire chapters - the data integrity and correctness - the data comparability between indicators related personnel and expenditures |
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18.5. Data compilation | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||||||||||||||||||
Imputation is the method of creating plausible (but artificial) substitute values for all those missing. |
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18.5.1.1. Imputation rate (un-weighted) (%) by Size class | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.1.2. Imputation rate (un-weighted) (%) by NACE | ||||||||||||||||||||||||||||||||||||||||||||
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43) 2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
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18.5.2. Data compilation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.3. Measurement issues | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.4. Weighting and estimation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.6. Adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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Annexes: CHESTIONAR_CD_BES_2021 |
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