1.1. Contact organisation
Turkish Statistical Institute (TurkStat)
1.2. Contact organisation unit
Sectoral Statistics Department
Science and Technology Statistics Group
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Devlet Mah.Necatibey Cad. No:114 06420 Çankaya/ANKARA
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
27 August 2025
2.1. Metadata last certified
27 August 2025
2.2. Metadata last posted
27 August 2025
2.3. Metadata last update
27 August 2025
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.
3.2. Classification system
- The distribution of principal economic activity and by product field are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Main concepts and definitions used for the production of R&D statistics are given by the Frascati Manual. |
|---|---|
| Fields of Research and Development (FORD) | There is no deviation, main concepts and definitions used for the production of R&D statistics are given by the Frascati Manual;
|
| Socioeconomic objective (SEO by NABS) | Not available |
3.3.2. Sector institutional coverage
| Business enterprise sector | All resident corporations, including not only legally incorporated enterprises, regardless of the residence of their shareholders. All resident NPIs that are market producers of goods or services or serve business. |
|---|---|
| Hospitals and clinics | Private hospitals (other than university hospitals and clinics) are included |
| Inclusion of units that primarily do not belong to BES |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviation |
|---|---|
| External R&D personnel | Not available |
| Clinical trials | Complied with Fracati Manual (FM §2.61) |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Complied with Frascati Manual. |
|---|---|
| Payments to rest of the world by sector - availability | Not available |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Not available |
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).
| Data collection on extramural R&D expenditure (Yes/No) | No |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Not available |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not available |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | 1990 and onwards |
|---|---|
| Source of funds | All elements are included |
| Type of R&D | All types are included |
| Type of costs | All elements are included |
| Economic activity of the unit | |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | The reporting unit is the enterprise. R&D expenditures are allocated to the principal economic activity of the enterprise or institute, classified according to ISIC |
| Product field | |
| Defence R&D - method for obtaining data on R&D expenditure | No specific method for the sector |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 1990 and onwards |
|---|---|
| Function | All R&D personnel in a statistical unit who engaged directly in R&D activities |
| Qualification | Compiled based on ISCED classsification |
| Age | Categorized by age groups |
| Citizenship | Not asked |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | 1990 and onwards |
|---|---|
| Function | All R&D personnel in a statistical unit who engaged directly in R&D activities |
| Qualification | Compiled based on ISCED classsification |
| Age | Categorized by age groups |
| Citizenship | Not asked |
3.4.2.3. FTE calculation
Full-time equivalent data are calculated using the survey results.
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,
In R&D Survey, business enterprise sector includes manufacturing, services enterprises and government-owned corporations. The enterprises within the scope of research;
- Enterprises known to carry out R&D activities (based on previous research results),
- Enterprises supported by The Scientific and Technological Research Council of Turkey in R&D activities or cooperated in R&D and innovation activities in the reference year,
- Enterprises supported by Small and Medium Enterprises Development Organization in the reference year,
- Enterprises in R&D centers and Technology Development Zones,
- Turkish Patent and Trademark Office database,
- Enterprises benefiting from indirect R&D supports under Law No. 5746,
- Administrative records of Revenue Administration,
- Enterprises included in the framework within the scope of Biotechnology and Innovation surveys,
- Enterprises applied for the R&D support of Turkish Technology Development Foundation
3.6. Statistical population
See below.
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.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | All business engaged in R&D activities for the reference year | |
|---|---|---|
| Estimation of the target population size | ||
| Size cut-off point | No cut-off | |
| Size classes covered (and if different for some industries/services) | 1-9,10-49, 50-249 and 250 or more employees | |
| NACE/ISIC classes covered | No deviation |
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.
| Method used to define the frame population | All R&D performers (known and potential) are included into the frame. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer |
|
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | Within the scope of R&D supports, administrative records from the Ministry of Industry and Technology were examined and all companies with R&D traces were tried to be included in the framework. This process was not carried out at regular intervals, the latest administrative records for 2023 were taken as basis. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | Included in the target population: 6934/ Dropped from the target population: 0 |
| Systematic exclusion of units from the process of updating the target population | The exclusion is made for the R&D status of the enterprises included in frame. The enterprises which are not R&D active 5 years in a row, are excluded. Moreover, administrative sources of Ministry of Treasury and Finance are utilized for the frame. Closed or liquidated enterprises are also excluded from the framework |
| Estimation of the frame population | Under the assumption of census, all R&D performers (known and potential) are included into the frame. |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested.
National currency, National currency per inhabitant, Purchasing Power Standard per inhabitant, Percentage of gross domestic product, Percentage of total R&D expenditure and Import Weighted Exchange Rate.
R&D personnel data is available in full-time equivalent (FTE), in head count (HC).
01 January 2023 - 31 December 2023.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements | 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 laying down technical specifications and arrangements pursuant to 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. Regulation No 2020/1197 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. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
|---|---|
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | not available |
|---|---|
| Legal acts | Law No. 5429. |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Law No. 5429. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Law No. 5429. |
| Planned changes of legislation | no plans |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development.
- EBS Methodological Manual on R&D Statistics.
6.2. Institutional Mandate - data sharing
Not requested.
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: According to Articles 13,14 and 15 of Law No. 5429
b) Confidentiality commitments of survey staff: Law No. 5429
7.2. Confidentiality - data treatment
The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute.
8.1. Release calendar
From 1990 and onwards, R&D data are published annually.
8.2. Release calendar access
8.3. Release policy - user access
It can be reached the contents of "Press Release" "Statistical Tables", "Databases", "Reports" and "Metadata" via "Reports and Metadata" after you choose the related topic in "Statistics" menu.
Moreover, it can be reached many information available in international or local level via "Regional Statistics", "Province Indicators", "International Selected Indicators" applications take place in "E-Services" menu using TurkStat Website.
Annual.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | Regular releases |
| Ad-hoc releases | Not applicable |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
|---|---|---|
| General publication/article (paper, online) |
Y | |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
The statistical tables published online are available on the website to observe the time series. Moreover, Central Dissemination System is used for the survey results. Central Data Dissemination System (MEDAS) is a software system that serves data belonging to different statistical subjects through a single dissemination channel and in a comparable way. Until MEDAS, there were more than 60 different databases and web interfaces in Turkstat’s dissemination database. In this database, data is shared in the R&D expenditure, R&D source of funds, Human resource on R&D categories until 2003 within the scope of Business Enterprises Sector statistics.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | not available |
|---|---|
| Access cost policy | not available |
| Micro-data anonymisation rules |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1 | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | ||
| Data prepared for individual ad hoc requests | N | ||
| Other | Y | It is prepared providing a protocol made with the related stakeholders. The confidentially is also saved in any case. It is not valid for the individual requests. The available data is open in Data Research Centre for researchers. |
1) Y – Yes, N - No
10.6. Documentation on methodology
It can be reach detailed information via Documentation on Methodology
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | R&D data is published on web with national metadata file |
|---|---|
| Request on further clarification, most problematic issues | All the required explanations are available on metadata file |
| Measures to increase clarity | No |
| Impression of users on the clarity of the accompanying information to the data | not available |
11.1. Quality assurance
TurkStat is ensuring that the statistical practices used to compile national R&D data are in compliance with Frascati Manual recommendations.
Quality evaluation of R&D statistics is carried out based on the information provided in the national and international quality reports.
11.2. Quality management - assessment
During Frascati Manual revision process, TurkStat was in close cooperation with EU Member States and Eurostat. The questionnaires sent by OECD and Eurostat were examined, the breakdowns that were not collecting were specified. Afterwards, the variables / breakdowns were included in the revised questionnaires.
Considering the updated manual and additional data requirements, data collection methods and reporting process were enhanced in a similar way to the other national statistical offices.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| Institutions |
|
Strategic goals, grant schemes, research project, government allocations for R&D activities |
|---|---|---|
| Institutions | OECD and Eurostat | International comparison. |
| Researchers and students | Researchers and students need statistics, analyses, ad hoc services, access to specific data | Statistics, analyses, access to specific data in Data Research Centre |
| Media | National and regional media | Press release results. |
1) Users' class codification
1- Institutions:
- European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
- in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
- International organisations: OECD, UN, IMF, ILO, etc.
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. )
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No |
|---|---|
| User satisfaction survey specific for R&D statistics | Not applicable. |
| Short description of the feedback received | Not applicable. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Completeness of statistics is good.
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | Not applicable | |||||
| Obligatory data on R&D expenditure | x | |||||
| Optional data on R&D expenditure | x | |||||
| Obligatory data on R&D personnel | x | |||||
| Optional data on R&D personnel | x | |||||
| Regional data on R&D expenditure and R&D personnel | x |
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%.
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y-1990 | |||||
| Type of R&D | Y-1990 | |||||
| Type of costs | Y-1990 | |||||
| Socioeconomic objective | N | |||||
| Region | Y-2010 (NUTS 1) Y-2018 (NUTS2) | |||||
| FORD | Y-1990 | |||||
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1990 | |||||
| Function | Y-1990 | |||||
| Qualification | Y-1990 | |||||
| Age | Y-1990 | |||||
| Citizenship | N | |||||
| Region | Y-2010 | |||||
| FORD | Y-1990 | |||||
| Type of institution | N | |||||
| Economic activity | Y-1990 | |||||
| Product field | Y-1990 | |||||
| Employment size class | Y-1990 |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1990 | |||||
| Function | Y-1990 | |||||
| Qualification | Y-1990 | |||||
| Age | Y-1990 | |||||
| Citizenship | N | |||||
| Region | Y-2010 | |||||
| FORD | Y-1990 | |||||
| Type of institution | N | |||||
| Economic activity | Y-1990 | |||||
| Product field | Y-1990 | |||||
| Employment size class | Y-1990 |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables |
Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| There is no other dimension/variable available at national level. | |||||
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
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Researchers – PhD / Master / Bachelor – Male & Female | [FTE value] / [HC value] | Annual |
| Technicians – PhD / Master / Bachelor – Male & Female | [FTE value] / [HC value] | Annual |
| Other support staff – All qualifications – Male & Female | [FTE value] / [HC value] | Annual |
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.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | - | - | - | - | - | - | - |
|---|---|---|---|---|---|---|---|
| Total R&D personnel in FTE | - | - | - | - | - | - | - |
| Researchers in FTE | - | - | - | - | - | - | - |
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.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1 |
4 (Good)2 |
3 (Satisfactory)3 |
2 (Poor)4 |
1 (Very poor)5 |
| Total intramural R&D expenditure | x | ||||
|---|---|---|---|---|---|
| Total R&D personnel in FTE | x | ||||
| Researchers in FTE | x |
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.
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.
13.2.1. Sampling error - indicators
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.2.1.1. Variance Estimation Method
Does not apply. Census survey.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1 | Services sector2 | TOTAL | |
| R&D expenditure | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. |
| R&D personnel (FTE) | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. |
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)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
| R&D expenditure | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. |
| R&D personnel (FTE) | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. | Does not apply. Census survey. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors: No such errors known.
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
Magnitude of error (%) = (Observed Value-True Value)/ True Value (%)
No such errors known. (Census)
13.3.1.2. Common units - proportion
Not requested.
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.
| By size class for the Industry Sector | |||||
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| Number or surveyed enterprises in the stratum (according to frame) | Does not apply | ||||
|---|---|---|---|---|---|
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | |||||
| Misclassification rate | |||||
| By size class for the Services Sector | |||||
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| Number or surveyed enterprises in the stratum (according to frame) | Does not apply | ||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | |||||
| Misclassification rate | |||||
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: No such errors known.
b) Measures taken to reduce their effect:
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.
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.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)
Weighted Unit Non- Response Rate = 1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons |
50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| Number of units with a response in the realised sample | 0 | 0 | 0 | 0 | 0 |
| Total number of units in the sample | 0 | 0 | 0 | 0 | 0 |
| Unit Non-response rate (un-weighted) | 0 | 0 | 0 | 0 | 0 |
| Unit Non-response rate (weighted) | 0 | 0 | 0 | 0 | 0 |
According to the law 5429, it is obligatory to answer the questionnaire.
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
| Number of units with a response in the realised sample | 0 | 0 | 0 |
| Total number of units in the sample | 0 | 0 | 0 |
| Unit Non-response rate (un-weighted) | 0 | 0 | 0 |
| Unit Non-response rate (weighted) | 0 | 0 | 0 |
According to the law 5429, it is obligatory to answer the questionnaire.
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)
13.3.3.1.3. Recalls/Reminders description
The regional offices get in contact if necessary.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | Not applicable |
|---|---|
| Selection of the sample of non-respondents | Not applicable |
| Data collection method employed | Web based questionnaire |
| Response rate of this type of survey | |
| The main reasons of non-response identified |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item non-Response Rate (%) = 1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample) * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
| Item non-response rate (un-weighted) (%) | 0 | 0 | 0 |
| Imputation (Y/N) | N | N |
N |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | |
|
|
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
| Total intramural R&D expenditure | Sampling method is not used. The calculation based on census. |
|---|---|
| Total R&D personnel in FTE | Sampling method is not used. The calculation based on census. |
| Researchers in FTE | Sampling method is not used. The calculation based on census. |
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, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Web questionnaire |
|---|---|
| Estimates of data entry errors | Not possible to estimate the ratio. |
| Variables for which coding was performed | Not possible |
| Estimates of coding errors | Not possible to estimate the ratio. |
| Editing process and method | There is a control and analyze process called analyze perspective. It is defined for central organization and regional offices. Central organization give the regional offices some specific dates determined in schedule before. Both central organization and regional offices have chance to observe for possible errors and suspicious situations via programme. The regional offices can confirm or explain the situation or rectify it. |
| Procedure used to correct errors | The regional offices get in contact with respondents if necessary. |
13.3.5. Model assumption error
Not requested.
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.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
a) End of reference period: There are no first results
b) Date of first release of national data:
c) Lag (days):
14.1.2. Time lag - final result
- End of reference period: 2023.
- Date of first release of national data: 06 November 2024.
- Lag (days): 306.
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
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
There is no lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 12 | 12 |
| Actual date of transmission of the data (T+x months) | 11 | 11 |
| Delay (days) | None | None |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
Main concepts and definitions are used for the production of R&D statistics are given by the Frascati Manual. However, data on external R&D personnel and external R&D expenditure are not compiled.
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.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly paragraph 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly paragraph 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly paragraph 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly paragraph 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly § 3.51-3.59) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
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.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|
| Data collection preparation activities | No deviation | |
| Data collection method | No deviation | |
| Cooperation with respondents | No deviation | |
| Follow-up of non-respondents | No deviation | |
| Data processing methods | No deviation | |
| Treatment of non-response | No deviation | |
| Data weighting | No deviation | |
| Variance estimation | No deviation | |
| Data compilation of final and preliminary data | No deviation | |
| Survey type | No deviation | |
| Sample design | No deviation | |
| Survey questionnaire | No deviation |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | 1993 | The survey was extended to include Financial Intermediation, Software consultancy and Other computer services. Consequently, 1993 data for Sub-Total Services are not comparable with those for previous years. | |
| Function | |||
| Qualification | |||
| R&D personnel (FTE) | 1993 | The survey was extended to include Financial Intermediation, Software consultancy and Other computer services. Consequently, 1993 data for Sub-Total Services are not comparable with those for previous years. | |
| Function | |||
| Qualification | |||
| R&D expenditure | 1994,1993,2022 | The survey was extended to include Financial Intermediation, Software consultancy and Other computer services. Consequently, 1993 data for Sub-Total Services are not comparable with those for previous years. Other current cost revised for 2015-2022 | |
| Source of funds | |||
| Type of costs | |||
| Type of R&D | |||
| Other |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Data produced in the same way in the odd and even years.
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 2022 (CIS2022) (inn_cis13) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2022 survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D statistics are produced according to System of National Accounts (SNA) and Frascati Manual 2015. Fallowing provisional data on R&D expenditure are provided to National Accounts Unit :
- Gross domestic expenditure on R&D by sector and type of cost.
- Gross domestic expenditure on R&D by sector of performance and by source of funds.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
|---|---|---|---|---|---|
| R&D Status (YES) |
11478 | 9939 | 1539 | When we compare BES with CIS, the number of enterprises stating that they carry out R&D activities in BES is 11478. In CIS, this value is 9939. Also, the total R&D expenditure amount in BES is calculated as 245,966,161,160 TRY. In CIS, this amount is 118,622,623,996 TRY. However, it should be noted that CIS data covers a two-year reference period. The number of variables calculated for R&D activities in BES is high, and it is expected that this survey will obtain more accurate results regarding R&D expenditure amounts. | |
| Total R&D Expenditure | 245,966,161,160 TRY | 118,622,623,996 TRY | 127,343,537,164 TRY | When we compare BES with CIS, the number of enterprises stating that they carry out R&D activities in BES is 11478. In CIS, this value is 9939. Also, the total R&D expenditure amount in BES is calculated as 245,966,161,160 TRY. In CIS, this amount is 118,622,623,996 TRY. However, it should be noted that CIS data covers a two-year reference period. The number of variables calculated for R&D activities in BES is high, and it is expected that this survey will obtain more accurate results regarding R&D expenditure amounts. | |
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | Not applicable | Not applicable | Not applicable |
| Final data (delivered T+18) | T+11 | T+11 | T+11 |
| Difference (of final data) |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 675.866 TRY R&D labor costs per FTE ( 128.645.101.262 total RD personnel costs / 190.341 total FTE RD personnel in R&D ). |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | No distinction between internal and external R&D personnel available. |
(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).
See below.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | 4,772,752 | |
|---|---|---|
| Data collection costs | 35,439 | |
| Other costs | 361 | |
| Total costs | 4,808,552 | |
| Comments on costs | ||
| There is no any subcontracting. | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | 32931 | |
|---|---|---|
| Average Time required to complete the questionnaire in hours (T)1 | Not available. | |
| Hourly cost (in national currency) of a respondent (C) | Not available. | |
| Total cost | Not available. |
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’)
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
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.
18.1.1. Data source – general information
| Survey name | Survey on R&D Activities in Financial and Non-Financial Corporations |
|---|---|
| Type of survey | Census. The survey is a web based questionnaire. |
| Combination of sample survey and census data | No sampling for the survey. |
| Combination of dedicated R&D and other survey(s) | |
| Sub-population A (covered by sampling) | No sampling for the survey. |
| Sub-population B (covered by census) | No sampling for the survey. |
| Variables the survey contributes to | Not available |
| Survey timetable-most recent implementation | Not available |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Only Census:
|
||
|---|---|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable, as all R&D data are census. | ||
| Stratification variable classes | Not applicable, as all R&D data are census. | ||
| Population size | Not applicable, as all R&D data are census. | ||
| Planned sample size | Not applicable, as all R&D data are census. |
||
| Sample selection mechanism (for sample surveys only) | Not applicable, as all R&D data are census. | ||
| Survey frame | Census Frame: 32931 | ||
| Sample design | Not applicable, as all R&D data are census. | ||
| Sample size | Not applicable, as all R&D data are census. | ||
| Survey frame quality | Not applicable, as all R&D data are census. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source |
|
|---|---|
| Description of collected data / statistics | The data sets mentioned above consist of administrative records received from various institutions. |
| Reference period, in relation to the variables the survey contributes to | 2021 |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Realised sample size (per stratum) | Census |
|---|---|
| Mode of data collection | Web survey |
| Incentives used for increasing response | Using administrative records for business registration system. The new approach has diminished the non-response rate over the last 5 years. |
| Follow-up of non-respondents | Organized by regional offices |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No replacement due to census |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | High response rate thanks to Law No. 5429. |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Non-response analysis is performed at the end of the process. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Research and Development Activities Survey, 2023 Research And Development Actıvıtıes Survey For Fınancıal And Non-Fınancıal Corporatıons, 2023 |
| R&D national questionnaire and explanatory notes in the national language: | Araştırma-Geliştirme Faaliyetleri Araştırması, 2023 Mali Ve Mali Olmayan Şirketler Araştırma Geliştirme Faaliyetleri İstatistikleri Soru Formu, 2023 |
| Other relevant documentation of national methodology in English: | Turkish Statistical Institute (TurkStat) R&D Survey Methodology Manual; Turkish Statistical Law (No. 5429) |
| Other relevant documentation of national methodology in the national language: | Turkish Statistical Institute (TurkStat) R&D Survey Methodology Manual; Turkish Statistical Law (No. 5429) |
18.4. Data validation
R&D data is checked for consistency and compared with previously calculated data before publication. Suspected errors are questioned and reported to the authorities.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) / (Total number of possible records for x)*100
There is no imputation due to the census structure.
18.5.1.1. Imputation rate by Size class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| R&D expenditure | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. |
| R&D personnel (FTE) | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. |
18.5.1.2. Imputation rate by NACE
| Industry1 | Services2 | TOTAL | |
| R&D expenditure | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. |
| R&D personnel (FTE) | There is no imputation due to the census structure. | There is no imputation due to the census structure. | There is no imputation due to the census structure. |
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)
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The final data transmission is T+12. |
|---|---|
| Data compilation method - Preliminary data | No preliminary data |
18.5.3. Measurement issues
| Method of derivation of regional data | Not available |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Not available |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | Not available |
18.5.4. Weighting and estimation methods
| Weight calculation method | Not applicable |
|---|---|
| Data source used for deriving population totals (universe description) | Not applicable |
| Variables used for weighting | Not applicable |
| Calibration method and the software used | Not applicable |
| Estimation | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
R&D survey is census and there is no weight for units. NACE Rev. 2 classification and number of employees are compiled for only R&D active enterprises from administrative sources. Therefore, the detailed breakdowns (NACE and number of employees) for number of units with a response in the realised sample could not be reported.
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.
27 August 2025
See below.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993,
In R&D Survey, business enterprise sector includes manufacturing, services enterprises and government-owned corporations. The enterprises within the scope of research;
- Enterprises known to carry out R&D activities (based on previous research results),
- Enterprises supported by The Scientific and Technological Research Council of Turkey in R&D activities or cooperated in R&D and innovation activities in the reference year,
- Enterprises supported by Small and Medium Enterprises Development Organization in the reference year,
- Enterprises in R&D centers and Technology Development Zones,
- Turkish Patent and Trademark Office database,
- Enterprises benefiting from indirect R&D supports under Law No. 5746,
- Administrative records of Revenue Administration,
- Enterprises included in the framework within the scope of Biotechnology and Innovation surveys,
- Enterprises applied for the R&D support of Turkish Technology Development Foundation
See below.
Not requested. R&D statistics cover national and regional data.
01 January 2023 - 31 December 2023.
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.
National currency, National currency per inhabitant, Purchasing Power Standard per inhabitant, Percentage of gross domestic product, Percentage of total R&D expenditure and Import Weighted Exchange Rate.
R&D personnel data is available in full-time equivalent (FTE), in head count (HC).
See below.
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.
Annual.
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.
See below.
See below.
