Hungarian Central Statistical Office

Business Statistics Department, Internal Trade Information and Research and Development Statistics Section

1024 Budapest, Keleti Károly utca 5-7, Hungary

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.  

• 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).

See below.

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).

See below.

See below.

Reporting units make the calculations of FTE for RSE and technicians and other personnel.

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.

See below.

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. 

Not requested. R&D statistics cover national and regional data.

Not requested. See point 3.4.

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

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- European Business Statistics Methodological Manual on R&D

Not requested.

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.

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.

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.

HCSO's publication and revision calendar is publicly available on the website: 

https://www.ksh.hu/katalogus/#/en

https://www.ksh.hu/katalogus/#/

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.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

http://statinfo.ksh.hu/Statinfo/themeSelector.jsp?page=2&szst=OHK&lang=en

https://www.ksh.hu/stadat_eng?lang=en&theme=tte

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

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

Not requested.

See below.

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.

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.

See below.

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.)

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

See below.

Not applicable.

Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 0,04%

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

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.

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.

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) : 

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:

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.

Not requested.

Not requested.

Not requested.

Not requested.

Not requested.

Not requested.

Not requested.

Not requested.

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.

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):

a) End of reference period:

b) Date of first release of national data:

c) Lag (days):

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.

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)

See below.

Not requested.

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.

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.

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.

See below.

See below.

1)       Breaks years are years for which data are not fully comparable to the previous period.

There is no difference in data collection for even years.

See below.

Not requested.

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).

See below.

(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. 

1)       The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

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’)

Not requested.

Not requested.

Not requested.

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.

PNP sector specific R&D data is not collected separately.

See below.

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.  

See below.

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

Not requested.

Not requested.