1.1. Contact organisation
Hungarian Central Statistical Office
1.2. Contact organisation unit
Business Statistics Department, Internal Trade Information and Research and Development Statistics Section
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
1024 Budapest, Keleti Károly utca 5-7, Hungary
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
30 October 2023
2.2. Metadata last posted
30 October 2023
2.3. Metadata last update
30 October 2023
3.1. Data description
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the 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 the 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 is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | No deviation. |
| Fields of Research and Development (FORD) | |
| Socioeconomic objective (SEO) | |
3.3.2. Sector institutional coverage
| Private non-profit sector | Data for this sector are not available separately but included in the BES and GOV sectors' data. PNP units are defined in accordance with SNA. PNP units are surveyed within the BES and GOV sectors. The number of PNP units surveyed is very low, and their contribution to the overall R&D performance is insignificant. To avoid data protection issues PNP sector data is not published separately. |
| Inclusion of units that primarily do not belong to GOV |
3.3.3. R&D variable coverage
| R&D administration and other support activities | |
| External R&D personnel | |
| Clinical trials |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | |
| Payments to rest of the world by sector - availability |
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) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | See information in BES and GOV sectors’ metadata. |
| Source of funds | |
| Type of R&D | |
| Type of costs | |
| Defence R&D - method for obtaining data on R&D expenditure |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | See information in BES and GOV sectors’ metadata. |
| Function | |
| Qualification | |
| Age | |
| Citizenship |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | See information in BES and GOV sectors’ metadata. |
| Function | |
| Qualification | |
| Age | |
| Citizenship | |
3.4.2.3. FTE calculation
Reporting units make the calculations of FTE for RSE and technicians and other personnel.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Not available. | ||
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
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 of institutional units.
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 PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| 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 | ||
| Estimation of the target population size |
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. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D personnel: number of persons
R&D expenditure: thousand Euro, thousand HUF
January 1 - December 31, 2021
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. 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. 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 | Mandatory |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | R&D data collection was carried out according to the national Government Decree on The National Statistical Data Collection Programme enacting the surveys of the reference period |
| Legal acts | Hungarian Act CLV. of 2016. on Statistics |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | |
| Planned changes of legislation |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D
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:
Legislation and policy at national level:
- The Act CLV of 2016 on Official Statistics (the Hungarian Statistical Law);
- Act CXII of 2011 on Informational self-administration and freedom of information.
- The confidentiality policy of HCSO is available here
- Additional information in English is available on the website
HCSO ensures confidentiality for all the data reported by data providers and the exclusive use of the data for statistical purposes. We disseminate only aggregated data in full compliance with the rules of confidentiality. Individual data, as well as aggregated data consisting of fewer than 3 enterprises are regarded as confidential. Researchers have access to de-identified data sets and to anonymised micro data for scientific purposes with appropriate legal and methodological guaranties in place.
b) Confidentiality commitments of survey staff:
Employees can work with datasets in their competence with registered and controlled access rights, and need to work in line with the confidentiality policies and protocols.
7.2. Confidentiality - data treatment
According to the Hungarian Act on Statistics those aggregates which come from less than 3 data providers are deemed to be confidential. To publish these values we need a permission from each affected data provider.
8.1. Release calendar
There is a release policy in place for the R&D data set. The release calendar is publicly available on the website.
PNP sector specific R&D data is not published.
8.2. Release calendar access
HCSO's publication and revision calendar is publicly available on the website:
https://www.ksh.hu/katalogus/#/en
https://www.ksh.hu/katalogus/#/
8.3. Release policy - user access
PNP sector specific R&D data is not published separately.
R&D data of the PNP sector is included in the BES and GOV sectors' data.
Data is disseminated to the public according to the release policy and release calendar. At t+M6, some key, preliminary results were published in the national online summary tables. Dissemination of the final data in the national database took place at t+M9.
Yearly.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | ||
| Ad-hoc releases |
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) |
||
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
1) Y – Yes, N - No
10.3. Dissemination format - online database
http://statinfo.ksh.hu/Statinfo/themeSelector.jsp?page=2&szst=OHK&lang=en
https://www.ksh.hu/stadat_eng?lang=en&theme=tte
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 | |
| Access cost policy | |
| 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 | |||
| Data prepared for individual ad hoc requests | |||
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Each publication with R&D data contains the main definitions, concepts and a section on brief methodological summary of the R&D data collection.
E.g.: Summary Tables - Methodology Summary:
https://www.ksh.hu/apps/meta.objektum?p_lang=EN&p_menu_id=110&p_ot_id=100&p_obj_id=BHAA
https://www.ksh.hu/docs/eng/modsz/tte_meth.html
Detailed R&D metadata are available on the website of Hungarian Central Statistical Office, both in English and Hungarian:
http://www.ksh.hu/apps/meta.menu?p_lang=EN&p_menu_id=210&p_session_id=38254329
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.) | |
| Request on further clarification, most problematic issues | |
| Measure to increase clarity | |
| Impression of users on the clarity of the accompanying information to the data |
11.1. Quality assurance
The HCSO Quality Policy lays out the principles and commitments related to the quality of statistics. The documentis consistent with the goals set out in the Mission and Vision statements andwith the principles of the European Statistics Code of Practice and is publicly available on the HCSO website.
The European Statistics Code of Practice is available on the website of the HCSO. Also, HCSO together with the member-organisations of the Hungarian Official Statistical Service created a National Statistics Code of Practice based on the European Statistics Code of Practice.
Quality Guidelines are meant to ensure the quality of the statistical processes. The document has been in place since 2007 (1st revision in 2009, 2nd revision in 2014 and 3rd revision is currently ongoing). The latest version (2014) is available on the HCSO website.
At HCSO, special attention is given to quality measurement, monitoring and documentation. Procedures are in place in order to ensure updated documentation on product quality. Apart from the internal reports, quality reports are regularly provided to Eurostat as well.
All statistical processes of the national R&D survey were carried out in accordance with HCSO’s Quality Policy, Quality Guidelines and in line with the National Statistics Code of Practice that is consistent with the principles of the European Statistics Code of Practice.
In the R&D data collection, principles relevant for the institutional environment, the statistical procedures and statistical output were observed.
11.2. Quality management - assessment
As primary aspect, Commission Regulation was taken into account beside data requests of the other national and international users. All data providers receive a detailed guideline as an annex of the questionnaire, and contact details of our colleagues, who can help to fill the R&D questionnaire.
The statistical processes and activities were supported by HCSO’s main, integrated, metadata-driven IT systems that are in line with the statistical planning and development conventions. Statistical processes of the R&D data collection were monitored based on quality indicators built into these IT systems (Integrated Survey Control System for Business and Social Surveys, Integrated Electronic Data Collection System, Integrated Data Processing System, Data Entry and Validation System).
Main strength of the data collection: Organisations have to provide their R&D data through ELEKTRA, HCSO's online data collection system. Good quality for all variables were achieved by implementing a complex and consistent set of validation rules. Quality checks of interval level data were conducted and data were also confronted with data of the previous years.
The HCSO's electronic registration system is used for incoming questionnaires. Reasons of nonresponse are also coded. The continous monitoring of the response rate was carried out. To increase resonse rate, after the survey deadline non-responding enterprises received several round of reminders by e-mail and by phone. The burden on data providers and data producers was also measured. The questionnaire includes a question on time spent on filling it.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
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 | |
| User satisfaction survey specific for R&D statistics | |
| Short description of the feedback received |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not applicable.
12.3.2. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 0,04%
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | GOV, BES |
| Reasons for not producing separate R&D statistics for the PNP sector | The number of PNP units surveyed is very low, and their contribution to the overall R&D performance is insignificant. To avoid data protection issues PNP sector data is not published separately. |
| Share of PNP expenditure in the total expenditure of the other sector | GOV: 0,25% BES: 0,03% |
| Share of PNP R&D Personnel in the respective figure of the other sector | GOV: 0,24% BES: 0,04% |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | |
| PNP R&D expenditure/ GERD*100) | |
| Share of PNP R&D Personnel in the respective figure of the total national economy |
12.3.2.3. Data availability on more detail level
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables |
Combinations of breakdown variables | Level of detail |
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').
2) Y-start year
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.
13.2. 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)
Coefficient of variation for Total R&D expenditure :
Coefficient of variation for Total R&D personnel (FTE) :
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.
a) Extent of non-sampling errors:
b) Measures taken to reduce the extent of non-sampling errors:
c) Methods used in order to correct / adjust for such errors:
13.3.1. Coverage error
Coverage 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.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Not requested.
13.3.3. Non response error
Not requested.
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
13.3.4. Processing error
Not requested.
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:
b) Date of first release of national data:
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period:
b) Date of first release of national data:
c) Lag (days):
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)
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) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | ||
| Delay (days) | ||
| 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
PNP sector specific R&D data is not published separately.
R&D data of the PNP sector is included in the BES and GOV sectors' data.
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 paragraphs and the EBS Methodological Manual on R&D Statistics 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). | ||
| Researcher | FM2015, § 5.35-5.39. | ||
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | ||
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly paragraph 4.2). | ||
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Reference period for the main data | Reg. 2020/1197: Annex 1, Table 18 | ||
| Reference period for all data | Reg. 2020/1197: Annex 1, Table 18 |
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 method | ||
| Survey questionnaire / data collection form | ||
| Cooperation with respondents | ||
| Data processing methods | ||
| Treatment of non-response | ||
| Data compilation of final and preliminary data |
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) | |||
| Function | |||
| Qualification | |||
| R&D personnel (FTE) | |||
| Function | |||
| Qualification | |||
| R&D expenditure | |||
| 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
There is no difference in data collection for even years.
15.3. Coherence - cross domain
See below.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Coherence with SNA is aimed at in the R&D data collection, with special regards to the sectoralisation of units, and in line with FM2015 recommendations.
The SNA calculation takes into account R&D data (e.g..: data on software development).
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
Restricted from publication
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) | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) |
(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).
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.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs | ||
| Comments on costs | ||
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) | ||
| Average Time required to complete the questionnaire in hours (T)1 | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
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 | |
| Type of survey | |
| Combination of sample survey and census data | |
| Combination of dedicated R&D and other survey(s) | |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | |
| Survey timetable-most recent implementation |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | |||
| Stratification variables (if any - for sample surveys only) | |||
| Stratification variable classes | |||
| Population size | |||
| Planned sample size | |||
| Sample selection mechanism (for sample surveys only) | |||
| Survey frame | |||
| Sample design | |||
| Sample size | |||
| Survey frame quality |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to |
18.2. Frequency of data collection
PNP sector specific R&D data is not collected separately.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | |
| Description of collected information | |
| Data collection method | |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | |
| Incentives used for increasing response | |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
We continously monitored the response rate and we compared it with the statistics of the previous cycle. We also compered the data received with relevant external (eg. Ministry) data source. We investigated inconsistencies in the statistics.
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
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | |
| Data compilation method - Preliminary data |
18.5.3. Measurement issues
| Method of derivation of regional data | |
| Coefficients used for estimation of the R&D share of more general expenditure items | |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics |
18.5.4. Weighting and estimation methods
| Description of weighting method | |
| Description of the estimation method |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the 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 the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
30 October 2023
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested. R&D statistics cover national and regional data.
January 1 - December 31, 2021
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.
R&D personnel: number of persons
R&D expenditure: thousand Euro, thousand HUF
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.
Yearly.
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.
