1.1. Contact organisation
Hungarian Central Statistical Office
1.2. Contact organisation unit
Business Statistics Department, Internal Trade 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
27 October 2023
2.2. Metadata last posted
27 October 2023
2.3. Metadata last update
27 October 2023
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
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 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).
3.2.1. Additional classifications
No further classification is used.
| 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) | No deviation |
| Socioeconomic objective (SEO by NABS) | No deviation |
3.3.2. Sector institutional coverage
| Higher education sector | |
| Tertiary education institution | Institutions of tertiary education acknowledged by the Hungarian state as such are considered as part of higher education sector in Hungary. |
| University and colleges: core of the sector | Included (total). |
| University hospitals and clinics | University hospitals and clinics associated and controlled by HE institutions are identified (based on an annually updated, internal Register) and are included in the HES sector. |
| HES Borderline institutions | R&D performed by non-educational units managed by universities are included in the higher education sector (total). |
| Inclusion of units that primarily do not belong to HES | Not included. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations from FM2015. |
| External R&D personnel | Categories included: R&D consultants, leased R&D employees, Doctoral or master students and R&D grant holders |
| Clinical trials | The R&D part of the clinical trials performed by units belonging to HES are included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available |
| Payments to rest of the world by sector - availability | No statistics 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) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Survey is designed to collect data on extramural R&D expenditure separately. Guidelines are provided in the filling instructions to help data providers distinguish between extramural and intramural R&D activities (e.g. the organisation in charge of the management of the R&D project). Expenditures related to extramural R&D activities are to be reported under "contracted-out R&D costs". |
| Difficulties to distinguish intramural from extramural R&D expenditure | Yes, in some cases (i.e.: to distinguish between purchased R&D services from fully contacted-out projects). |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
| Source of funds | There is a complete report of HERD by sources of funds. Data is collected by internal/external breakdown and by transfer/exchange breakdown. (Transfer/exchange breakdown data is not collected for funds from "Rest of world"). |
| Type of R&D | No deviations. Breakdown of R&D expenditure by type of R&D is estimated based on the breakdown of R&D costs by type of R&D. |
| Type of costs | No deviations. (Some optional breakdown is not available, e.g.: 'Land', 'Building'). |
| Defence R&D - method for obtaining data on R&D expenditure | Data on defence R&D is collected by SEO breakdown. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons employed during the calendar year. |
| Function | Data are available by functions: 'Researchers', 'technicians' and 'other supporting staff'. Data on R&D personnel is collected separately and both for internal and external personnel, in line with the Frascati Manual. |
| Qualification | Since 1999 qualification of R&D personnel is surveyed according to ISCED. Available for internal personnel only. Data on qualification of the external R&D personnel is not collected, as data is not available in good quality. |
| Age | Available for internal personnel only. Data on the age of the external R&D personnel is not collected, as data is not available in good quality. |
| Citizenship | Available for internal personnel only. Data on citizenship of the external R&D personnel is not collected, as data is not available in good quality. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
| Function | Data are available by functions: 'Researchers', 'technicians' and 'other supporting staff'. Data on R&D personnel is collected separately and both for internal and external personnel, in line with the Frascati Manual. |
| Qualification | Not available |
| Age | Not available |
| Citizenship | Not available |
3.4.2.3. FTE calculation
R&D reporting units report data on researchers, technicians and other staff in headcount and also provide the ratio of time spent on R&D, based on which HCSO calculates the FTE data for internal R&D personnel.
Post-gradute students can be reported as both internal or external R&D personnel. In their case, the time devoted to research is accounted for similarly to the other R&D personnel.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Available for internal R&D personnel only. | Headcount | Annual |
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
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 HES Sector should consist of all R&D performing institutional 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 | All known or potential R&D performing higher education institutions and their organisational units (both occasional and regular R&D). | |
| Estimation of the target population size | 1403 units |
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) | Yes |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Yes |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Yes |
| Planned changes of legislation | No |
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 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:
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 data providers 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.
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
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 | Y | On HCSO's website a short news release with key figures is published to announce the release of the annual R&D publication. |
| Ad-hoc releases | N |
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 | Annual R&D Publication (includes analysis of R&D data, in Hungarian only): Kiadványtár - Központi Statisztikai Hivatal (ksh.hu) Statistical Yearbook, 2021 - (with some selected R&D data): Publication Repertory - Hungarian Central Statistical Office (ksh.hu) |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
No |
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. In order to support scientific research, HCSO opens up data files for accredited researchers. R&D microdata is disseminated at HCSO' Safe Center.
10.4.1. Provisions affecting the access
| Access rights to the information | Available only in safe centre. |
| Access cost policy | Basic access to the safe center is free of charge. HCSO charges for individual data requests in the safe center. |
| Micro-data anonymisation rules | Direct identification details of respondents is dropped from dataset to avoid identification. |
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 | Aggregate figures. | Database access via website is free of charge for the public. No registration is needed to access the public database. |
| Data prepared for individual ad hoc requests | Y | Aggregate figures. | Ad hoc, individual data requests are usually received from government bodies, educational insitutionals, or other third parties. Pricing of such data provision is set in line with HCSO's general data provision pricing policy, and is assessed on an individual basis (depending on the related workload). |
| Other | N | Aggregate figures. | Regular data requests from government bodies are based on official cooperational agreements. R&D data is regulary transmitted to and published by the OECD. |
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.) | Each publication with R&D data contains R&D metadata (summary of the methodology, concepts, definitions). Detailed R&D metadata is available on HCSO's website both in English and Hungarian: http://www.ksh.hu/apps/meta.menu?p_lang=EN&p_menu_id=210&p_session_id=38254329 |
| Request on further clarification, most problematic issues | Requests were sent for more detailed data as published. |
| Measure to increase clarity | When publishing R&D data, HCSO provides explanatory notes and description of data. |
| Impression of users on the clarity of the accompanying information to the data | Data users are mostly satisfied. |
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 - European level | Eurostat The European Commission (DG RTD), OECD, UN | According to Commission regulations No. 2019/2152 and 2020/1197 Various time series data on R&D indicators. |
| 1- National level | Ministry of Finance, Ministry of Human Capacities, Ministry for Innovation and Technology, Prime Minister’s Office, other Ministries. | The main indicators of R&D activities concerning the expenditure and personnel data. |
| 1- National level | National Research, Development and Innovation Office, Regional innovation and knowledge centres. |
The main indicators of R&D activities concerning the expenditure and personnel data detailed by NACE categories, size classes, regions and other breakdowns. |
| 1- National level | Hungarian Central Statistical Office | R&D data needs of other sections and departments within the Office for analysis and publication purposes. |
| 3- Media | Various newspapers, periodicals. | R&D data needs of journalists for keeping the general public informed. |
| 4- Researchers | Hungarian Academy of Science, Research institutes, Higher educational institutes, Researchers and students. | Data by different breakdowns according to needs of analyses. |
| 5- Enterprises | Enterprises | Data by different breakdowns according to needs of analyses. |
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 | A separate user satisfaction survey is not carried out, HCSO register data user’s opinion/feedback regularly. |
| User satisfaction survey specific for R&D statistics | A dedicated user satisfaction survey for R&D data is not carried out. Feedback from key users of R&D data (e.g. government bodies) is collected at regular consultations. |
| Short description of the feedback received | The data users are mainly satisfied. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not available.
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. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | x | |||||
| 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 | Annual | 2018: Data on HES sector as source of funds is available. | 2018 | FM2015 | |
| Type of R&D | Y-1990 | Annual | ||||
| Type of costs | Y-1990 | Annual | 2018: In line with FM2015, employment costs of the external personnel are included in the 'other current costs' category only. | 2018 | FM2015 | |
| Socioeconomic objective | Y-1990 | Annual | ||||
| Region | Y-1990 | Annual | ||||
| FORD | Y-1994 | Annual | ||||
| 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(Researchers) | Annual | 2000: Data on technicians and other supporting staff category is collected. 2018: HC(total) =HC (internal)+HC(external) In line with FM2015, in addition to internal R&D personnal, gender data is collected for external R&D personnel, too. |
Y-2000 (researchers, technicians and other supporting staff categories) Y-2018 (internal and external R&D personnel) |
FM2015 | |
| Function | Y-1990 | Annual | In line with FM2015, in addition to internal R&D personnel, occupation data is collected for external R&D personnel, too. | Y-2018 (internal and external R&D personnel) | FM2015 | |
| Qualification | Y-2000 (for internal R&D personnel) | Annual | ||||
| Age | Y-2003 (for internal R&D personnel) | Annual | Before 2003, data on age dimension was available every 5 year only. | |||
| Citizenship | Y-2004 (for internal R&D personnel) | Annual | ||||
| Region | Y-1995 | Annual | Y-2018 (for internal and external R&D personnel) | FM2015 | ||
| FORD | Y-1981 | Annual | Y-2018 (for internal and external R&D personnel) | FM2015 | ||
| Type of institution | N |
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-2006 | Annual | 2018: HC(total) =HC (internal)+HC(external) In line with FM2015, in addition to internal R&D personnal, gender data is collected for external R&D personnel, too. | Y-2018 (internal and external R&D personnel) | FM2015 | |
| Function | Y-1990 | Annual | In line with FM2015, in addition to internal R&D personnal, data on occupation is collected for external R&D personnel, too. | Y-2018 (internal and external R&D personnel) | FM2015 | |
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1990 | Annual | Y-2018 (internal and external R&D personnel) | FM2015 | ||
| FORD | Y-1990 | Annual | Y-2018 (internal and external R&D personnel) | FM2015 | ||
| Type of institution | N |
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 |
| R&D personnel | Y-2007 | annual | FoS | 2-digit level | |
| Number of researchers | Y-2007 | annual | FoS | 2-digit level | |
| Intramural R&D expenditure | Y-2007 | annual | FoS | 2-digit 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').
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.
13.1.1. 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 | Not applicable. | - | 5 | - | - | - | +/- |
| Total R&D personnel in FTE | Not applicable. | - | 5 | - | - | - | +/- |
| Researchers in FTE | Not applicable. | - | 5 | - | - | - | +/- |
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. 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)
Not applicable.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | |
| Government | |
| Higher education | |
| Private non-profit | |
| Rest of the world | |
| Total |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | |
| Technicians | ||
| Other support staff | ||
| Qualification | ISCED 8 | |
| ISCED 5-7 | ||
| ISCED 4 and below |
Not applicable.
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 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:
An annually updated internal Register of Higher Education Institutions is used as a frame for the data collection (the data on the units is collected directly from the higher education institutions.) Therefore the frame and the target population do not diverge, there is no coverage error.
b) Measures taken to reduce their effect:
Not applicable.
13.3.1.1. Over-coverage - rate
Not applicable.
13.3.1.2. Common units - proportion
Not requested.
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:
The difficulty for data providers to distinguish between purchased R&D services from fully contacted-out projects, and to calculate the FTE for external personnel can lead to measurement errors.
b) Measures taken to reduce their effect:
Guidelines are provided in the filling instructions to help data providers distinguish between extramural and intramural R&D activities, and to deliniate fully contracted-out activities from purchased R&D services (which belong to intramural R&D activities).
During data collection, R&D personnel data in FTE and the R&D expenditure data are continuously monitored. All interrelated data being provided is checked for logical consistency, and when problematic data is found, data providers are requested for correction.
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 satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| 1403 | 1403 | 0.0 |
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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| Every R&D variable | 0 | |
13.3.3.3. Measures to increase response rate
The first reminding e-mail was sent out 7 days before the deadline and the second was 7 days after the deadline. There is a third, a warning letter 15 days after the deadline. Those who did not send back the questionnaire after the third letter were contacted by phone as many times as neccessary. In case of no response after 60 days, the head of the higher-education institute unit was contacted.
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 | Respondents have to provide their R&D data through HCSO’s online data collection system called ELEKTRA. This system contains checking rules to ensure that no contradictions or severe errors are left in the data sent by the respondents. After arrival of data, data undergoes a more thorough data checking procedure which can identify possible errors. In these cases, colleagues responsible for data checking have to decide if those data are acceptable or they have to contact respondents to clarify data. The only way for respondents to provide data is via ELEKTRA, questionnaires are not accepted by email or by post. |
| Estimates of data entry errors | Not applicable |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | Not applicable |
| Editing process and method | 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 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). |
| Procedure used to correct errors | Re-contact with information provider, the survey needs to be resent in the online system. |
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: December 31, 2021
b) Date of first release of national data: October 12, 2022
c) Lag (days): 285
14.1.2. Time lag - final result
a) End of reference period: December 31, 2021
b) Date of first release of national data: June 28, 2023
c) Lag (days): 544
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) | 10 | 18 |
| Delay (days) | -19 | -2 |
| Reasoning for delay | n.a. | n.a. |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No deviation from FM2015 and Eurostat Methodological Guidelines.
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'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. | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation. | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation. | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation. | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation. | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation. | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EEBS Methodological Manual on R&D Statistics). | No deviation. | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation. | |
| Reference period | 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 method | No deviation. | |
| Survey questionnaire / data collection form | No deviation. | |
| Cooperation with respondents | No deviation. | |
| Coverage of external funds | No deviation. | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | GUF is not calculated as a separate source. | |
| Data processing methods | No deviation. | |
| Treatment of non-response | No deviation. | |
| Variance estimation | Not applicable. | |
| Method of deriving R&D coefficients | Not applicable. | |
| Quality of R&D coefficients | Not applicable. | |
| Data compilation of final and preliminary data | 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) | 4 years | 1998 2018 |
Since 1998 personnel dealing with activities related to safety and warehouse operations in R&D units are excluded as recommended in FM. In 2018, the 'external R&D personnel' with several sub-categories is introduced, therefore there is a break in the total R&D personnel data. The impact is significant. TOTAL R&D personnel (HC) = Internal (HC) + External personnel (HC) |
| Function | 4 years | 2018 | There is a break due to the break in the total R&D personnel data. |
| Qualification | 20 years | ||
| R&D personnel (FTE) | 4 years | 1998 2018 |
Since 1998 personnel dealing with activities related to safety and warehouse operations in R&D units are excluded as recommended in FM. In 2018, the 'external R&D personnel' with several sub-categories is introduced, therefore there is a break in the total R&D personnel data. The impact is significant. TOTAL R&D personnel (HC) = Internal (HC) + External personnel (HC) |
| Function | 4 years | 2018 | There is a break due to the break in the total R&D personnel data. |
| Qualification | Not available. | ||
| R&D expenditure | 4 years | 1994
2018 |
Purchase of licences and know-how, previously included in business enterprise R&D expenditure, has been excluded since 1994. The Central Technological Fund (KMÜFA) as source of fund has changed over time: KMÜFA was classified as business enterprise sector fund until 1993 and as government sector fund since 1994. In 2018, the R&D expenditure data has a break due to the break in the Type of costs data (i.e. capital costs on other intellectual property products is included). |
| Source of funds | 4 years | 2018 | In 2018, the source of funds data has a break due to the break in the Type of costs data and HES fund. |
| Type of costs | 4 years | 2018 | In 2018, there is a break in the capital costs because expenditure on 'other intellectual property products' is included. |
| Type of R&D | 4 years | 2018 | In 2018, the Type of R&D data has a break due to the break in the Type of costs data. |
| 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
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
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. Regarding the sectoralisation of units in HES, the FM2015 recommendations are followed.
The SNA calculation takes into account R&D data (e.g..: data on software development).
15.3.3. National Coherence Assessments
Not National Coherence Assessments are used.
| 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 |
15.3.4. Coherence – Education statistics
Education data is collected by a seperate government entity (Ministry), and not by the Hungarian Statistical Office (HCSO). HCSO uses the education data to calculate/estimate some optional data (e.g. seniority level of R&D personnnel) for the R&D statistics in the HES sector.
HCSO collects R&D expenditure data directly from the units in the HES sector, within the R&D data collection framework. Conceptually there is no difference between the two statistical domains on R&D expenditure data.
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 – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 125 009 817 thousand HUF | 14 303 | 10 346 |
| Final data (delivered T+18) | 125 009 817 thousand HUF | 14 303 | 10 346 |
| Difference (of final data) | 0 | 0 | 0 |
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) | 5 601 317 HUF |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 5 979 238 HUF |
(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 | ||
| Not available separately. No work sub-contracted. | ||
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 | Report on research and development activities at research units in higher education in 2021 |
| Type of survey | Census |
| Combination of sample survey and census data | No |
| Combination of dedicated R&D and other survey(s) | No |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | Target population is covered by census only. |
| Variables the survey contributes to | The survey contributes to the production of all mandatory variables. |
| Survey timetable-most recent implementation | The survey was sent to data providers at the beginning of March, 2022. Data was processed by the end of June, 2022. |
18.1.2. Sample/census survey information
Not applicable.
| 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 | No administrative data is collected and used for this statistic. |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Deparments and other units of the higher educational institutions. |
| Description of collected information | The respondent’s details: Name; Address; Register number; Primary activity; County Number of internal and external R&D personnel by function (researchers, technicians and other supporting staff), and by sex. Number of PhD, DLA and other degree holders within internal R&D personnel, and the number of women among them. Number of internal researchers according to age categories, and also the number of women within these age categories. Data on R&D expenditure Current costs (labour cost for internal personnel and other current costs) and capital expenditure (land and buildings, instruments and equipments, vehicle, computer software, purchase of intangible assets) on research and experimental development Costs and capital expenditure of research and experimental development according to socio-economic objectives (excluding intangible assets) |
| Data collection method | Online survey |
| Time-use surveys for the calculation of R&D coefficients | Not applicable. |
| Realised sample size (per stratum) | Not applicable. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | The data collection method for R&D statistics in HCSO is the on-line data collection through the so-called ELEKTRA system. Respondents get questionnaires on-line and their data are loaded automatically in a database when they submit the questionnaire in the on-line system. Data entry program contains checking as well. |
| Incentives used for increasing response | None |
| Follow-up of non-respondents | The first reminding e-mail was sent out 7 days before the deadline, the second 7 days after the deadline. A third, warning letter was sent 28 days after the deadline. Selected non-responding enterprises were followed-up by phone as well. 60 days after the deadline, in case of non-response the heads of higher education institutes were contacted as well. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100 |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not applicable. |
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: | R&D_HES_2021_questionnaire R&D_HES_2021_explanatory notes |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
R&D_HES_2021_questionnaire
R&D_HES_2021_explanatory notes
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
Not applicable (there is no imputation used).
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | We have an annual R&D survey. |
| Data compilation method - Preliminary data | We have an annual R&D survey. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Not applicable. |
| Revision policy for the coefficients | Not applicable. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable. |
18.5.4. Measurement issues
| Method of derivation of regional data | In alll sectors R&D variables are classified on the basis of residence of the company/ institution, except in HES, where the basis of the regional data is the local unit. |
| Coefficients used for estimation of the R&D share of more general expenditure items | No estimation used. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Exclusion of reclaimable VAT. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | GUF as source of fund is not calculated (respondents' estimation on GUF is not in good quality). |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | We have no other differences in classifications. |
18.5.5. Weighting and estimation methods
| Description of weighting method | No weighting. |
| Description of the estimation method | No weighting. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
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 October 2023
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
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.
