1.1. Contact organisation
Statistics Finland
1.2. Contact organisation unit
Economic Statistics
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
Confidential because of GDPR
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 Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government 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.
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 the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The 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 are based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| - | |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | According to the Frascati manual guidelines. |
| Fields of Research and Development (FORD) | Breakdown by field of R&D by six main 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
| Government sector | Government research institutes, other central government agencies, local government, social security funds. Census of known or supposed R&D performing units. |
| Hospitals and clinics | University hospitals (clinics) are included in the higher education sector. Other local or regional hospitals are excluded (non-existent or insignificant R&D). |
| Inclusion of units that primarily do not belong to GOV | - |
3.3.3. R&D variable coverage
| R&D administration and other support activities | According to the Frascati manual guidelines. |
| External R&D personnel | According to the Frascati manual guidelines in the other current costs. |
| Clinical trials | According to the Frascati manual guidelines. |
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 are provided for the respondents. Published results cover R&D performers only. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Quality is assured by software checks and manual editing. In order to clarify the distinction there is in the intramural R&D expenditure a cost item: purchased services fully integrated in the unit's own R&D. |
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. Data on the funding sources requested by the Eurostat are produced. Transfer/exchange funds can be estimated. |
| Type of R&D | FM breakdown available. |
| Type of costs | Wages and salaries; other current costs with subgroups: other current costs, purchased services (services directly linked to own R&D); capital expenditure. |
| Defence R&D - method for obtaining data on R&D expenditure | Defence units are included in the survey. |
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 |
| Function | Data available by breakdown: researchers; other. |
| Qualification | Data available by breakdown: PhD’s or equivalent (ISCED 8); university degree (ISCED 6-7); polytechnics or equivalent (ISCED 6-7); all other education. |
| Age | - |
| 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 (ISCED 6-7); polytechnics or equivalent (ISCED 6-7); all other education. |
| Age | - |
| Citizenship | - |
3.4.2.3. FTE calculation
R&D person-years performed during a calendar year of survey by persons employed of the unit. A R&D person-year is defined as full-time R&D work for one person (including 4-6 weeks annual leave). Frascati recommendation at least 0,1 FTE applied.
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.
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 Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | Known or assumed R&D performers in: S.1311 Central government |
|
| Estimation of the target population size | Number of units in the target population is not relevant as the survey in practice covers all the potential R&D performers (census) in the GOV sector. |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | Official register of government sector organisations maintained by Statistics Finland (part of the business register). |
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Potential R&D units are identified: 1) units reporting R&D activities t-1 or t-2; 2) based on the updates of the register (e.g. new units in the NACE 72); 3) other sources: internet, media, discussions with partner organisations. Units are dropped if they do not report R&D in two consecutive years. |
| Inclusion of units that primarily do not belong to the frame population | No. PNP data is actually collected in the same survey vehicle but kept separate. |
| Systematic exclusion of units from the process of updating the target population | - |
| Estimation of the frame population | Number of units in the frame population is not relevant as the survey in practice covers all the potential R&D performers (census) in the GOV sector. |
3.7. Reference area
Not requested.
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.
HC: number of persons; FTE: person-years; expenditure, source of funds: 1 000 euros.
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 | The statitics Act of 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 researcher's service unit of Statistics Finland. |
| 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:
Unit level data on persons, households and enterprises are confidential. However, data on the government sector organisations are in principle not confidential.
b) Confidentiality commitments of survey staff:
As specified in the Statistics Act and it's implementation in Statistics Finland.
7.2. Confidentiality - data treatment
In principle data on the government units are public. However, if a response is edited by Statistics Finland then it is confidential and can be published by the permission of the reporting unit only.
8.1. Release calendar
Publicly available release calendar.
8.2. Release calendar access
Open access in the website of Statistics Finland: https://www.tilastokeskus.fi/en/future-releases
8.3. Release policy - user access
Open access to publication and databases in the website of Statistics Finland. Release information for users by standard protocols of Statistics Finland.
Annual.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | https://stat.fi/en/statistics/tkke |
| Ad-hoc releases | Y | https://stat.fi/en/statistics/tkke |
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) |
General publication online. | https://stat.fi/en/statistics/tkke |
| 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 | Research services unit of Statistics Finland. |
| Access cost policy | According to protocols of the Research services unit. |
| Micro-data anonymisation rules | Not applicable in the government sector. |
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 | |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other | - |
1) Y – Yes, N - No
10.6. Documentation on methodology
https://stat.fi/en/statistics/documentation/tkke
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, statistical processing: https://stat.fi/en/statistics/tkke#documentation |
| Request on further clarification, most problematic issues | Occasional discussions with users on different topics. |
| Measure to increase clarity | According to the overall policy of Statistics Finland. |
| Impression of users on the clarity of the accompanying information to the data | Feedback not collected, but clarifications are provided when requested. |
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 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.
11.2. Quality management - assessment
Some of the activities undertaken in order to assure a high quality of business R&D statistics:
- use of official registers of high quality
- two reminding letters to the non-respondents, personal contacts to the large R&D performers
- training of the personnel responsible for data editing
- external audits of the R&D statistics according to the practices of Statistics Finland's quality management
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1- Institutions | The European Commission (Eurostat, other DGs etc.). |
Data used for the compilation of the European statistics and policy analysis. |
| 1- Institutions | Finland, the national level Ministries (Employment and the Economy; 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- Institutions | OECD, UN etc, Research institutes and statistical agencies in other countries. | Data and statistics used for international comparisons. |
| 2- Social actors | Employers’ associations, Trade unions etc. | Statistics used for the follow up of the development and for their specific issues. |
| 3- Media | Magazines and newspapers, social media. | Statistics used for the information on the development and as a basic material for specific articles. |
| 4- Researchers and students | Researchers and research institutes, students. | Need for the statistics and analyses, need for the access to data. |
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 monitors user satisfaction. |
| User satisfaction survey specific for R&D statistics | Satisfaction surveys in the field of R&D statistics are not conducted but instead there are meetings and other frequent contacts with the national key STI policy experts and researchers to gather feedback. |
| Short description of the feedback received | - |
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 of 30 July 2020. 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-2011 | Annual. | ||||
| Type of costs | Y-1971 | Until 1997 biennial, from 1997 annual data. | 1985 | |||
| Socioeconomic objective | - | 1985 | ||||
| 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 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, end 1983, Y-2012 | Until 1997 biennial, from 2012 annual data. | ||||
| 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 |
| - | |||||
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 | - | 2 | 1 | 4 | 3 | - | +/- |
| Total R&D personnel in FTE | - | 2 | 1 | 4 | 3 | - | +/- |
| Researchers in FTE | - | 2 | 1 | 4 | 3 | - | +/- |
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 described above 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
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 applicable, census. |
| Government | Not applicable, census. |
| Higher education | Not applicable, census. |
| Private non-profit | Not applicable, census. |
| Rest of the world | Not applicable, census. |
| Total | Not applicable, census. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Not applicable, census. |
| Technicians | - |
|
| other support staff | Not applicable, census. | |
| Qualification | ISCED 8 | Not applicable, census. |
| ISCED 5-7 | Not applicable, census. | |
| ISCED 4 and below | Not applicable, census. |
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 :
Not known, assumed to be small.
b) Measures taken to reduce their effect:
Use of official business register of high quality.
c) Share of PNP (if PNP is included in GOV):
PNP units are not included in the GOV sector.
13.3.1.1. Over-coverage - rate
Not requested.
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:
Not known, assumed to be small.
b) Measures taken to reduce their effect:
Detailed instructions accompany the survey questionnaire, respondent support by phone and email. The online questionnaire assists the respondent by alerting logical inconsistencies, missing items etc.
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) |
| 65 | 73 | 11% |
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 |
| R&D expenditure | In practice non-existent. | Item non-response is not monitored, but it is in practice non-existent. All the units reporting yes to the R&D question do provide basic information on the R&D expenditure, FTE and personnel. |
| R&D Personnel in FTE | In practice non-existent. | Item non-response is not monitored, but it is in practice non-existent. All the units reporting yes to the R&D question do provide basic information on the R&D expenditure, FTE and personnel. |
| Researchers in FTE | In practice non-existent. | Item non-response is not monitored, but it is in practice non-existent. All the units reporting yes to the R&D question do provide basic information on the R&D expenditure, FTE and personnel. |
13.3.3.3. Measures to increase response rate
Two reminders (letters). Missing units contacted also by email or phone.
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 | Online questionnaire. |
| Estimates of data entry errors | Not applicable. |
| Variables for which coding was performed | Online questionnaire, no coding: for example, respondent selects regions in which R&D is performed from the list provided. |
| Estimates of coding errors | Not applicable. |
| Editing process and method | 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 | Response t-1, logical relations, imputation based on the ratios and distributions, re-contacts. |
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: 31.12.2021
b) Date of first release of national data: 27.10.2022
c) Lag (days): 301
14.1.2. Time lag - final result
a) End of reference period: 31.12.2021
b) Date of first release of national data: 27.10.2022
c) Lag (days): 301
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. | T+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
No deviations from the Frascati manual or other international 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, 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's EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining 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. |
Pilot data on the total FTEs of external personnel available. |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | FORD on the 2-digit level. |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | Not applicable. | |
| 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 | |
| Data processing methods | No | |
| Treatment of non-response | No | |
| Variance estimation | Not applicable, | |
| 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) | National R&D statistics published as from 1971 | 2007, 1983, 1981 |
2007: inclusion of local government (minor impact on the figures only). 1983: revised guidelines to respondents in the 1983 questionnaire referring to borderline problems between R&D and non-R&D. Impact on the total figures not very significant. 1981: some partial retrospective revisions submitted by national authorities hamper the comparability of the 1981 data at the sector level to the previous years. |
| Function | As from 2004 | - | Data available 2004 onwards by breakdown: researches; other. |
| Qualification | National R&D statistics published as from 1971 | 2015, 2004, 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) | National R&D statistics published as from 1971 | 2007, 1983, 1981 |
2007: inclusion of local government (minor impact on the figures only). 1983: revised guidelines to respondents in the 1983 questionnaire referring to borderline problems between R&D and non-R&D. Impact on the total figures not very significant. 1981: some partial retrospective revisions submitted by national authorities hamper the comparability of the 1981 data at the sector level to the previous years |
| Function | As from 2004 | - | Data available 2004 onwards by breakdown: researches; other. |
| Qualification | National R&D statistics published as from 1971 | 2015, 2004, 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 | National R&D statistics published as from 1971 | 2007, 1983, 1981 |
2007: inclusion of local government (minor impact on the figures only). 1983: revised guidelines to respondents in the 1983 questionnaire referring to borderline problems between R&D and non-R&D. Impact on the total figures not very significant. 1981: some partial retrospective revisions submitted by national authorities hamper the comparability of the 1981 data at the sector level to the previous years. |
| Source of funds | National R&D statistics published as from 1971 | 2017 | 2017: own funds broken down into internal funds and basic funding (budget funding). No impact on the international time series as both sources are GOV funding. |
| Type of costs | National R&D statistics published as from 1971 | - | |
| Type of R&D | As from 2011 | - | |
| 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
Annual data collection by the same methodology.
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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
According to the FM. R&D data are used in the SNA.
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 |
| - | |||||
15.4. 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 – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | Final data only. | Final data only. | Final data only. |
| Final data (delivered T+18) | Final data only. | Final data only. | Final data only. |
| Difference (of final data) | Not applicable. | Not applicable. | Not applicable. |
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) | EUR 71 133 |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | - |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | - | |
| Data collection costs | - | |
| Other costs | - | |
| Total costs | - | |
| Comments on costs | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | - | |
| Average Time required to complete the questionnaire in hours (T)1 | - | |
| Average hourly cost (in national currency) of a respondent (C) | - | |
| Total cost | - |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Public sector research and development (R&D). The survey collects both GOV and PNP data, however results are reported separately for both sectors. |
| Type of survey | Census survey, panel of known or supposed R&D performers. |
| Combination of sample survey and census data | - |
| Combination of dedicated R&D and other survey(s) | Census survey with full coverage of R&D in the target population. |
| Sub-population A (covered by sampling) | - |
| Sub-population B (covered by census) | - |
| Variables the survey contributes to | Variables requested in the Eurostat data collection. |
| Survey timetable-most recent implementation | The survey is launched in April; 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 | Organisation (research institute, administrative unit or other organisation). | ||
| Stratification variables (if any - for sample surveys only) | Not applicable. | ||
| Stratification variable classes | Not applicable. | ||
| Population size | 73 | ||
| Planned sample size | Census | ||
| Sample selection mechanism (for sample surveys only) | - | ||
| Survey frame | Panel of R&D performers maintained by Statistics Finland | ||
| Sample design | - | ||
| Sample size | - | ||
| Survey frame quality | Official business register of Statistics Finland |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | - |
| Description of collected data / statistics | - |
| Reference period, in relation to the variables the survey contributes to | - |
18.2. Frequency of data collection
Annual survey.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Ministries, government research institutes, other government agencies (including defence units), biggest municipalities |
| Description of collected information | - Engagement in R&D activities in the statistical year (yes/no) - R&D personnel by formal qualification (total, women and FTE) - R&D personnel by function (total, women and FTE) - Intramural R&D expenditure divided by type of costs - R&D personnel, person-years (FTE), and expenditure by location - R&D expenditure by type - Distribution (%) of R&D expenditure by field of R&D (FORD) - R&D by source of funding - Estimate of the R&D expenditure for current year |
| Data collection method | The data are collected directly from each unit by online electronic questionnaire. |
| Time-use surveys for the calculation of R&D coefficients | No |
| Realised sample size (per stratum) | 73 |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | The R&D surveys is conducted annually by electronic online questionnaire. |
| Incentives used for increasing response | Reminding letters, other contacts to the respondents. |
| Follow-up of non-respondents | The non-respondents are contacted by email or phone. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 89% |
| Non-response analysis (if applicable -- also see section 18.5.4 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: | See annex files. |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
GOV R&D questionnaire in national language
GOV R&D survey explanatory notes in national language
18.4. Data validation
1) comparison of the responses against the previous year, checking any inconsistencies with particular attention to the large R&D performers
2) checking the outliers in respect to overall distributions
3) micro editing based on the logical rules
4) macro level checks for any inconsistencies in the tabulations
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) | Final data available at T+10, annual survey. |
| Data compilation method - Preliminary data | Final data available at T+10, annual survey. |
18.5.3. Measurement issues
| Method of derivation of regional data | Direct question requesting breakdown of the main variables by municipality. |
| Coefficients used for estimation of the R&D share of more general expenditure items | - |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | According to the FM. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | FM classifications respected. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable. |
| Description of the estimation method | Not applicable. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government 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.
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 the 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.
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.
See below.
Not requested.
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.
HC: number of persons; FTE: person-years; expenditure, source of funds: 1 000 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.
Annual.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
