1.1. Contact organisation
Statistics Finland
1.2. Contact organisation unit
Economic statistics / Enterprises, globalisation and innovation
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
FIN-00022 Statistics Finland
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
2.1. Metadata last certified
31 October 2023
2.2. Metadata last posted
31 October 2023
2.3. Metadata last update
31 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
| Additional classification used | Description |
| Only standard classifications like FORD used |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Frascati manual's definition |
| Fields of Research and Development (FORD) | OECD FORD-classification applied up to 2 digit level. Classification by six major fields: natural sciences, engineering and technology, medical sciences, agricultural sciences, social sciences, humanities. Also by subgroups. |
| Socioeconomic objective (SEO by NABS) | - |
3.3.2. Sector institutional coverage
| Higher education sector | Local government (largest municipalities) included. State government not relevant. All other sectors and fields of R&D covered. |
| Tertiary education institution | |
| University and colleges: core of the sector | Universities, universities of applied sciences, Academy of Defence |
| University hospitals and clinics | All university hospitals (clinics) are included in the higher education sector. |
| HES Borderline institutions | Included, however, there are only very few of them. |
| Inclusion of units that primarily do not belong to HES |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Administrative and other R&D support staff is reported according to the FM as an element of overheads. |
| External R&D personnel | Post-graduate students with post in university, including PhD students, are included. Post-graduate students not receiving any form of salary or grant are excluded. Post-graduate students conducting research with external funds are included. |
| Clinical trials | Corresponds to Frascati Manual |
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) | Yes |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | There is a question on the extramural R&D in all sectors. The main purpose of the question is to make sure that respondents do separate extramural and intramural expenditure. Detailed instructions provided. Published results cover R&D performers only. |
| 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 | Calendar year |
| Source of funds | No deviations from FM, also GUF separately available. |
| Type of R&D | FM breakdown available for university hospitals and universities of applied sciences. Not available for universities. |
| Type of costs | Wages and salaries; other current costs; capital expenditure |
| 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 | End of calendar year for university hospitals and Universities of applied sciences. Universities: Average situation for the year |
| Function | Data available by breakdown: researchers; other. |
| Qualification | Data available by breakdown: researchers; other |
| Age | Data available only for Universities |
| Citizenship | - |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
| Function | Data available by breakdown: researchers; other |
| Qualification | Data available by breakdown: PhD’s or equivalent (ISCED 8); university degree, polytechnics or equivalent, master or bachelor level (ISCED 6-7); other education. |
| Age | - |
| Citizenship | - |
3.4.2.3. FTE calculation
The national questionnaire is used for all other performers than universities and the number of R&D employed persons attached to the unit at the end of the year of survey is requested. R&D person-years performed during calendar year of survey by all staff attached to the unit. A R&D person-year is defined as full-time R&D work for one person (including 4-6 weeks annual leave). For universities the time-use-coefficients are used. The time-use coefficients are calculated by using time use monitoring records or work plans of the university personnel. Post-graduate students employed by university treated accordingly.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| - | ||
| - | ||
| - |
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.
University survey: Primarly institutes performing research, but institutes serving the whole university (administration, libraries etc.) are also included in the calculations.
University hospital survey: Statistical unit is university hospital
Universities of applied sciencies survey: Statistical unit is university of applied sciencies
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 | Census | Census |
| Estimation of the target population size | Census | Census |
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.
Monetary variables requested in euros.
The surveys are performed annually and the reference period is a calendar year
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 | Statistics stipulated by the EU regulations are considered as mandatory to produce by Statitics Finland. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | The production of national R&D statistics governed by the general national statistical legislation |
| Legal acts | Statistics Act (280/2004) |
| 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) | As for the confidential data access by protocols of Safe Centre of Statistics Finland only |
| 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 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:
Confidentiality protection is required by statistics act law
b) Confidentiality commitments of survey staff:
Staff have confidentiality commitments
7.2. Confidentiality - data treatment
-
8.1. Release calendar
Data is published annually at the end of October.
8.2. Release calendar access
Release calendar for R&D-statistics can be found at https://stat.fi/en/statistics/tkke#calendar
8.3. Release policy - user access
-
Database updated once a year
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 | https://stat.fi/en/statistics/tkke#pastPublications |
| 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 | online |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__tkke/
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 | Granted by the Safe Centre of Statistics Finland |
| Access cost policy | Depends on the contract |
| Micro-data anonymisation rules |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1 | Micro-data / Aggregate figures | Comments |
| Internet: main results available on the national statistical authority’s website | Y | ||
| Data prepared for individual ad hoc requests | Yes, on demand | ||
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Quality descriptions, classifications, concepts and definitions
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.) | Quality descriptions, classifications, concepts and definitions |
| Request on further clarification, most problematic issues | No |
| Measure to increase clarity | No |
| Impression of users on the clarity of the accompanying information to the data | Not known |
11.1. Quality assurance
Quality management requires comprehensive guidance of activities. The principles of the European Foundation for Quality Management (EFQM principles) are employed by Statistics Finland as its the overall framework for quality management. The quality management framework of the field of statistics is the European Statistics Code of Practice (CoP). The frameworks complement each other. The quality criteria of Official Statistics of Finland are also compatible with the European Statistics Code of Practice. The general quality assurance framework of Statistics Finland is implemented in the production of the statistics.
11.2. Quality management - assessment
Universities:
- The university reform in 2010 lead to an update of the R&D statistics methodology. Methodology of university R&D statistics is build according FM recommendations like earlier. Detailed data about the personnel, university eshtablishments and economy became available from the Ministry of education and culture. This new data made it possible to reduce the number of data in direct questionnaire to the universities.
- Time use coeffients were based on the universities time use monitoring records.
- The FORD classification is based on the individual person's FORD obtained from the administative data.
University hospitals and Universities of applied sciences:
the definitions are quite clear and the results are consistent with previous ones.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 | Finland, the national level Ministries (Employment and Economy as well as, Education and Culture), Research and Innovation Council; Business Finland, the Finnish Funding Agency for Technology and Innovation, Academy of Finland, etc | Statistics used for the follow up of the development and for policy purposes |
| 1 | International organisations OECD, UN etc, Research institutes and statistical agencies in other countries | Data and statistics used for international comparisons |
| 2 | Associations etc., the national level Employers’ associations, trade unions etc. | Statistics used for the follow up of the development and for their specific issues |
| 3 | Media magazines and newspapers | Statistics used for the information on the development and as a basic material for specific articles |
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 | Statistics Finland has a close co-operation with the main users, the Ministry of Education and Culture and the Academy of Finland. Annual seminar with universities and universities of applied sciences are carried out. |
| User satisfaction survey specific for R&D statistics | Above mentioned meetings and seminar are partly dedicated to R&D statistics. |
| Short description of the feedback received | The data is produced in a more detailed FORD level because of user needs. The burden of data delivery has been reduced by using more administrative sources of data. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
See below.
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-1971 | until 1997 biennial, from 1997 annual data | 1985 | |||
| Type of R&D | Y-1971 | |||||
| Type of costs | Y-1971 | until 1997 biennial, from 1997 annual data | 1985 | |||
| Socioeconomic objective | - | |||||
| Region | Y-1973 | until 1997 biennial, from 1997 annual data | 1985 | |||
| FORD | Y-1971 | until 1997 biennial, from 1997 annual data | 1985 | |||
| Type of institution | - |
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-1987 | until 1997 biennial, from 1997 annual data | ||||
| Function | Y-2004 | annual | ||||
| Qualification | Y-1971 | until 1997 biennial, from 1997 annual data | 1985 | |||
| Age | Y-2007 (only Universities) | from 2007 annual data | ||||
| Citizenship | - | |||||
| Region | Y-1995 | until 1997 biennial, from 1997 annual data | ||||
| FORD | Y-1971 | until 1997 biennial, from 1997 annual data | 1985, 1987, 1989, 1991 | |||
| Type of institution | - |
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 | - | |||||
| Function | Y-2004 | annual data | ||||
| Qualification | Y-1971 | until 1997 biennial, from 1997 annual data | 1985 | |||
| Age | - | |||||
| Citizenship | - | |||||
| Region | Y-1995 | until 1997 biennial, from 1997 annual data | ||||
| FORD | Y-1971 | until 1997 biennial, from 1997 annual data | 1985 | |||
| Type of institution | - |
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 |
| no additional variables available |
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 known, assumed small | - | - | Not known | |
| Total R&D personnel in FTE | - | - | Not known, assumed small | - | - | Not known | |
| Researchers in FTE | - | - | Not known, assumed small | - | - | Not known | |
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)
13.2.1.1. Variance Estimation Method
Census, thus calculation of CV is not applicable
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Not relevant |
| Government | Not relevant |
| Higher education | Not relevant |
| Private non-profit | Not relevant |
| Rest of the world | Not relevant |
| Total | Not relevant |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Not relevant |
| Technicians | Not relevant | |
| Other support staff | Not relevant | |
| Qualification | ISCED 8 | Not relevant |
| ISCED 5-7 | Not relevant | |
| ISCED 4 and below | Not relevant |
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:
R&D personnel for universities is an average of the situation for the year. No coverage errors.
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
-
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:
Assumed to be very small.
b) Measures taken to reduce their effect:
Detailed instructions accompany the survey questionnaire, respondent support by phone and email.
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) |
| 44 | 44 | 1.0 |
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 |
| - | ||
| - | ||
| - |
13.3.3.3. Measures to increase response rate
Unit response rate 100%
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 | Universities: administrative data on university personnel, establishments and economy. direct Excel questionnaire to universities. University hospitals and universities of applied sciences: online questionnaire |
| Estimates of data entry errors | Negligible |
| Variables for which coding was performed | Use of codes in universities:
Unicersity hospitals and universities of applied sciences: No coding done, all information comes from the questionnaire. |
| Estimates of coding errors | Not known but assumed small. |
| Editing process and method | Universities: in some cases the FORD code for the unit is not obtained from personnel's FORD. If the unit reports R&D activity FORD is edited. Editing is baced on previous years data or often on information obtained from the universities's wed pages. Very little editing is done. Universities of applied sciences and university hospitals: Automatic and manual editing based on the lists of errors due to logical checks and missing values. Largest R&D performers handled like case-studies. |
| Procedure used to correct errors | Editing based on previos year's data, internet research e.g. data from annual reports of the information provider, re-contact with information provider. |
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: t+10
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period:
b) Date of first release of national data: t+10
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) | Final data transmitted T+10 | 10 |
| 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
Not applicable.
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 | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat'EBS Methodological Manual on R&D Statistics). | No | |
| 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 | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | Internal R&D personnel only. | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| 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 | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
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 | |
| Survey questionnaire / data collection form | No | |
| Cooperation with respondents | No | |
| Coverage of external funds | No | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | No | |
| Data processing methods | No | |
| Treatment of non-response | - | |
| Variance estimation | - | |
| Method of deriving R&D coefficients | No | |
| Quality of R&D coefficients | No | |
| Data compilation of final and preliminary data | No |
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) | 1999, 1997 | 1999: universities of applied science included (founded in the Finnish higher education reform) 1997: university clinics as a separate survey (formely included in the university calculations) |
|
| Function | 2004 | Data available 2004 onwards by breakdown: researches; other | |
| Qualification | 2015, 2004, 1997, 1987 | Minor updates of the breakdown of R&D personnel by formal qualification. No important impact on the indicators by ISCED-classification. | |
| R&D personnel (FTE) | 2005, 1999, 1997, 1991, 1981, 2011 | 2011 calculation method of universities' FTEs updated, proportion of research from time-use monitoring records or work plans of the university personnel. 2005, 1991, 1981: time use surveys to determine the proportion of research; before 1981 reported by central administration. 1999: universities of applied science included 1997: university clinics as a separate survey (formely included in the university calculations). |
|
| Function | 2004 | Data available 2004 onwards by breakdown: researches; other | |
| Qualification | 2015, 2004, 1997,1987 | Minor updates of the breakdown of R&D personnel by formal qualification. No important impact on the indicators by ISCED-classification. | |
| R&D expenditure | 2005, 1999, 1997, 1991, 1981, 2011 | 2011 calculation method of universities' FTEs updated, proportion of research from time-use monitoring records or work plans of the university personnel. 2005, 1991, 1981: time use surveys to determine the proportion of research; before 1981 reported by central administration. 1999: universities of applied science included 1997: university clinics as a separate survey (formely included in the university calculations). |
|
| Source of funds | N/A | ||
| Type of costs | 1997 | 1997: Investments in land and buildings are no longer separately available; they are included in the currents costs as rents. | |
| Type of R&D | 2011 | 2011 available for university hospitals and polytechnics | |
| Other | N/A |
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
-
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
| - |
15.3.4. Coherence – Education statistics
-
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) | 1 731 338,1 | 16 969,5 | 12 671,6 |
| Final data (delivered T+18) | 1 731 338,1 | 16 969,5 | 12 671,6 |
| Difference (of final data) |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not available. |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available. |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
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 | Not available. | no subcontracting |
| Data collection costs | Not available. | no subcontracting |
| Other costs | Not available. | no subcontracting |
| Total costs | Not available. | no subcontracting |
| 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) | Not available. | |
| Average Time required to complete the questionnaire in hours (T)1 | Not available. | |
| Average hourly cost (in national currency) of a respondent (C) | Not available. | |
| Total cost | N/A |
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 | Research and development (R&D) in universities 2. Public sector research and development (R&D) (used for university hospitals and universities of applied sciences) |
| Type of survey | 1. Combination of census survey, administrative sources and time-use survey of university personnel (universities). 2. Census survey (university hospitals and universities of applied sciences) |
| 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 | N/A |
| Survey timetable-most recent implementation | Universities: The survey is launched in February; the collection phase is considered to be completed by May; results are available in October. Other: The survey is launched in March; the collection phase is considered to be completed by August; results are available in October. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Universities: in the calculations: university, university department or other unit; Other: in the survey: university hospital; universitiy of applied sciences |
||
| Stratification variables (if any - for sample surveys only) | N/A | ||
| Stratification variable classes | N/A | ||
| Population size | 14 universities, 6 university clinics, 24 universities of applied sciences | ||
| Planned sample size | N/A | ||
| Sample selection mechanism (for sample surveys only) | N/A | ||
| Survey frame | N/A | ||
| Sample design | |||
| Sample size | |||
| Survey frame quality |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Administrative data on the universities: Statistics Finland: Register of completed degrees Statistics Finland: Wages statistics (original data: Confederation of Finnish Employers) Statistics Finland: R&D time-use coefficients. Ministry of education and culture: Register of university personnel includes data on FTEs, FORD. Economic data icludes data on financial statements. Register of university establishments includes data on location of the university staff and buildings. University hospitals and universities of applied sciences: Direct survey Universities, direct survey on: external R&D funding, use of own funds for R&D, R&D grants paid, investments and university personnel. |
| Description of collected data / statistics | R&D time-use coefficients calculated by Statistics Finland from administrative data (time-use monitoring, work plans of the university personnel) to determine the proportion of work-time devoted to research. |
| Reference period, in relation to the variables the survey contributes to | N/A |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Universities: Central university administration, Statistics Finland (register data), Ministry of Education and Culture (administrative data) University hospitals: Central offices of the hospitals Universities of applied sciencies: Central offices of the universities of applied sciencies |
| Description of collected information | Universities: 1. Central administration - Capital investments for R&D - Commissioned R&D 2. Statistics Finland 3. Ministry of Education and Culture University hospitals and Universities of applied sciencies: - Research personnel by gender, educational degree and occupation (researchers, other supporting personnel) |
| Data collection method | Universities: Structured Excel-files emailed to the contact persons in universities University hospitals and Universities of applied sciencies: As a part of the web survey for the government sector R&D, with additional adjustments. |
| Time-use surveys for the calculation of R&D coefficients | R&D time-use coefficients for universities: Excel-files emailed to the contact persons in universities (every 3-4 years) |
| Realised sample size (per stratum) | Census 14 universities 6 university hospitals 24 universities of applied sciences and |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Register data (universities) Additional data collection by excel-sheets (universities) Electronic questionnaire (universities of applied sciences, university hospitals) |
| Incentives used for increasing response | Not relevant |
| Follow-up of non-respondents | Email remainders if needed. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not relevant |
| 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: | kotk_lomake_2021_fi.xlsx and tkke_2021_js_lomake_fi_PostiM.docx |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
R&D national questionnaire for universities
R&D national questionnaire for universities of applied sciences and university hospitals
18.4. Data validation
Comparison of the responses against the previous year.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Not relevant.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The final data of HES is available on t+10 |
| Data compilation method - Preliminary data | The final data of HES is available already on t+10 |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | The coefficients are used for the compilation of the universities’ R&D. The coefficients (R&D shares) were computed from universities time use monitoring records for the year 2021 |
| Revision policy for the coefficients | Update every 3-4 years. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | The coefficients were computed by the main field of science and group of post. The result was in line with the previous time use surveys |
18.5.4. Measurement issues
| Method of derivation of regional data | University hospitals and universities of applied science: respondents allocate R&D to the municipalities. Universities: register of locations of the units. |
| Coefficients used for estimation of the R&D share of more general expenditure items | Universities: R&D time-use coefficients calculated by Statistics Finland to determine the proportion of work-time devoted to research. Based on the time-use monitoring data of the universities or work plans of the university personnel. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | According to the FM. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | The total R&D expenditure is calculated first. External funding and own funds for R&D are requested in R&D survey. GUF for R&D = Total R&D expenditure - External funding - Own funds used for R&D |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | FM classifications respected. |
18.5.5. 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 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.
31 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.
University survey: Primarly institutes performing research, but institutes serving the whole university (administration, libraries etc.) are also included in the calculations.
University hospital survey: Statistical unit is university hospital
Universities of applied sciencies survey: Statistical unit is university of applied sciencies
See below.
Not requested. R&D statistics cover national and regional data.
The surveys are performed annually and the reference period is a calendar 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.
Monetary variables requested in euros.
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.
Database updated once a year
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.
