1.1. Contact organisation
Statistical Office in Szczecin
1.2. Contact organisation unit
Statistics Centre for Science, Technology, Innovation and Information Society
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Jana Matejki Street 22
70-530 Szczecin
Poland
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
23 October 2023
2.2. Metadata last posted
23 October 2023
2.3. Metadata last update
23 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
Not applicable
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | In accordance with FM. There are no difficulties encountered with the definition of R&D. |
| Fields of Research and Development (FORD) | In accordance with FM. There are no difficulties encountered with the FORD classification. |
| Socioeconomic objective (SEO by NABS) | In accordance with FM. There are no difficulties encountered with the SEO classification. |
3.3.2. Sector institutional coverage
| Government sector | In 2016, changes were introduced in the classification of units by institutional sectors. Since 2016 the classification of entities to the Government sector is based on the assumptions of the Frascati Manual 2015. All entities from this sector have to be additional classified by SNA to General Government (S.13) excluded institutions included to Higher Education sector. Until 2015, research institutes were originally classified to the government sector due to their subordination to ministries. Currently, these units are classified in the business enterprise sector, government sector or the higher education sector. Due to this change, data from earlier years are not fully comparable. |
| Hospitals and clinics | Hospitals and clinics can be classified according to SNA classification to the Business enterprise sector (S.11) or to the Government sector (S.13), except university hospitals and clinics classified to the Higher Education sector. |
| Inclusion of units that primarily do not belong to GOV | There are no units that primary do not belong to GOV. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Expenditures on R&D administration and other support activities are included in data only if these costs are integrated part of the R&D activity. |
| External R&D personnel | In accordance with FM. |
| Clinical trials | In accordance with FM. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Data available in breakdowns in accordance with FM. |
| Payments to rest of the world by sector - availability | Data available in breakdowns in accordance with FM. |
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 | Statistics on expenditure on extramural R&D are compiled. The method that is employed to separate it from intramural expenditure is compatible with FM. |
| Difficulties to distinguish intramural from extramural R&D expenditure | All respondents problems with distinguishing intramural fron extramural R&D expenditure are consulted on an ongoing basis with the emloyees responsible for the implementation of the survey. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
| Source of funds | In accordance with FM |
| Type of R&D | In accordance with FM |
| Type of costs | In accordance with FM |
| Defence R&D - method for obtaining data on R&D expenditure | Not all defence R&D data is included in the survey because of the state secret. There are no estimates made for not available data. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year. |
| Function | In accordance with FM |
| Qualification | In accordance with FM. Except ISCED level 5, because in Polish educational system it does not occur. |
| Age | In accordance with FM |
| Citizenship | In accordance with FM |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
| Function | In accordance with FM |
| Qualification | In accordance with FM. Except ISCED level 5, because in Polish educational system it does not occur. |
| Age | No breakdown by age in FTEs. |
| Citizenship | No breakdown by age in FTEs. |
3.4.2.3. FTE calculation
FTE are reported by the reporting unit.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Cross-classification by occupation and qualification of R&D personnel is available for internal and external researchers. | HC | Annual |
| Cross-classification by function and qualification of R&D personnel is available for internal and external personnel. | HC | Annual |
| Cross-classification by occupation and qualification of R&D personnel is available for internal and external researchers. | FTE | Annual |
| Cross-classification by function and qualification of R&D personnel is available for internal and external personnel. | FTE | Annual |
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
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 | All units known as or supposed to be R&D performing units belonging to GOV. | |
| Estimation of the target population size | 330 |
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 | The frame population is the same as the target population. Target population include legal persons, organisational units without legal personality and natural persons conducting economic activity characterised by the following features:
|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | To identify a unit as known or supposed R&D performer there are used: lists of enterprises receiving government grants and contracts for R&D, lists of enterprises reporting R&D activities in previous R&D surveys, in innovation surveys, biotechnology and nanotechnology surveys or other enterprise surveys containing questions about R&D. Other sources of information are: lists of innovative enterprises and the data obtained from Ministry of Finance about entities that have benefited from the R&D relief. The information about R&D performers is taken also from newspapers, journals, reports, websites of Ministry of Economy, other websites. Those resources are regularly checked during the year. The main owner of the sources is Statistics Poland. |
| Inclusion of units that primarily do not belong to the frame population | No |
| Systematic exclusion of units from the process of updating the target population | There are no units that are systematically excluded from the process of updating the target population. |
| Estimation of the frame population | 330 |
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.
The units of measures used in the survey:
- thousand units of national currency,
- HC,
- FTE,
- %.
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 Poland is obliged to submit data on R&D activities to international organisations. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | The production of national R&D statistics is governed by the general national statistical legislation. |
| Legal acts | Law issued on 29 VI 1995 on Official Statistics and Regulation of the Council of Ministers on Statistical Surveys Program of Public Statistics. |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Law issued on 29 VI 1995 on Official Statistics. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Law issued on 29 VI 1995 on Official Statistics. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Law issued on 29 VI 1995 on Official Statistics. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Law issued on 29 VI 1995 on Official Statistics. |
| Planned changes of legislation | Not applicable. |
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:
Law issued on 29 VI 1995 on Official Statistics.
b) Confidentiality commitments of survey staff:
Law issued on 29 VI 1995 on Official Statistics.
7.2. Confidentiality - data treatment
If there are less than 3 units in specific division or if the unit’s share of the market is more than 75% the data cannot be published and must be flagged confidential.
8.1. Release calendar
In Poland there is release calendar.
8.2. Release calendar access
The release calendar is accessible on the Statistics Poland website.
8.3. Release policy - user access
The data is published for the first time in a singature study that is published on the Statistics Poland website.
Frequency of data collection is annually and the published data refer to the 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 | News release - Statistics Poland / Topics / Science and Technology / Science and Technology / Research and experimental development in Poland in 2021 |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
1) Y – Yes, N - No
10.3. Dissemination format - online database
Database:
- Statistics Poland - Local Data Bank
- Bank Danych Makroekonomicznych (stat.gov.pl)
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 | Micro-data are not disseminated. |
| Access cost policy | Micro-data are not disseminated. |
| Micro-data anonymisation rules | Micro-data are not disseminated. |
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 | News release, publications and Database. |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | Specific data not available in official publications can be supplied by order and are usually available as an Excel file. |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Polish R&D survey is based on the metodology included in the FM 2015. The metadata of the R&D survey are described in Methodological report - research and experimental development.
Annexes:
Methodological report research and experimental development
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.) | Metadata (methodological and analytical notes) included in Methodological report - research and experimental development, publications, analysis, tables or graphs. Additional explanations for the users (assistance) are also provided if required, by the Statistical Information Centre as well as by the authors of the survey. |
| Request on further clarification, most problematic issues | Sometimes there are questions about definition or the scope of data. |
| Measure to increase clarity | For example: footnotes. |
| Impression of users on the clarity of the accompanying information to the data | Explanations were comprehensive. |
11.1. Quality assurance
Quality assurance framework is based on quality guidelines, training courses for all persons engaged in the R&D survey, the use of best practices, quality reviews, self-assessments, compliance monitoring.
11.2. Quality management - assessment
National methodology is compatible with the guidelines of the Frascati Manual 2015. Data are of a good quality and are comparable with data from foreign countries.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1- International | Eurostat, OECD | Data used for the European Scoreboard and its further development. |
| 1 - National | National Ministries, governmental agencies and Regional Statistical Offices | Data for analysis, publishing, etc. |
| 3 - Media | National and regional media | Data for analysis, publishing, etc. |
| 4 - Researchers and students | Researchers and students | Data for analysis, publishing, study, etc. |
| 5 - Enterprises or businesses | Enterprises | Data for market analysis, marketing strategy, etc. |
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 | Users’ satisfaction survey is not carried out but the statistical program is announced every year and is given for consultation to ministries, universities and scientists, voivodships’authorities, who can put forward any suggestions which are taken into consideration and statistical plan may be changed. |
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Not available |
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 | 5 | |||||
| Obligatory data on R&D expenditure | 5 | |||||
| Optional data on R&D expenditure | 5 | |||||
| Obligatory data on R&D personnel | 5 | |||||
| Optional data on R&D personnel | 5 | |||||
| Regional data on R&D expenditure and R&D personnel | 5 |
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 - 1994 | annual | ||||
| Type of R&D | Y - 1994 | annual | ||||
| Type of costs | Y - 1994 | annual | ||||
| Socioeconomic objective | Y - 2012 | annual | ||||
| Region | Y - 2000 | annual | ||||
| FORD | Y - 1995 | annual | ||||
| Type of institution | Y - 2021 | annual |
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 - 2000 | annual | ||||
| Function | Y - 1994 | annual | ||||
| Qualification | Y - 1994 | annual | ||||
| Age | Y - 2005 (data only for researchers) | annual | ||||
| Citizenship | Y - 2005 (data only for researchers) | annual | ||||
| Region | Y - 2000 | annual | ||||
| FORD | Y - 2000 | annual | ||||
| Type of institution | Y - 2021 | annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y - 2000 | annual | ||||
| Function | Y - 1994 | annual | ||||
| Qualification | Y - 2018 | annual | ||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y - 2000 | annual | ||||
| FORD | Y - 1995 | annual | ||||
| Type of institution | Y - 2021 | annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
| R&D entities | Y - 1999 | annual | total |
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 | - | - | 5 | - | 5 | 5 | - |
| Total R&D personnel in FTE | - | - | 5 | - | 5 | 5 | - |
| Researchers in FTE | - | - | 5 | - | 5 | 5 | - |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1 |
4 (Good)2 |
3 (Satisfactory)3 |
2 (Poor)4 |
1 (Very poor)5 |
| Total intramural R&D expenditure | 5 | ||||
| Total R&D personnel in FTE | 5 | ||||
| Researchers in FTE | 5 |
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 | N/A |
| Government | N/A |
| Higher education | N/A |
| Private non-profit | N/A |
| Rest of the world | N/A |
| Total | N/A |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | N/A |
| Technicians | N/A | |
| other support staff | N/A | |
| Qualification | ISCED 8 | N/A |
| ISCED 5-7 | N/A | |
| ISCED 4 and below | N/A |
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 applicable.
b) Measures taken to reduce their effect:
Not applicable.
c) Share of PNP (if PNP is included in GOV):
Not applicable.
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:
Measurement errors included errors with data collection and respondent mistakes.
b) Measures taken to reduce their effect:
To reduce measurement errors we train persons responsible for R&D survey, before the survey starts we do questionnaire testing and prepare guidelines for responsents.
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) |
| 325 | 330 | 0.02 |
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 |
| N/A | N/A | N/A |
13.3.3.3. Measures to increase response rate
When the online questionnaire is enabled, there are sent four reminders about the upcoming deadline for submission of the report. After that there are also phone calls made and urging e-mails and letters sent.
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 | Electronic online questionnaires. Data keying from paper questionnaires into electronic format. (CENSUS) |
| Estimates of data entry errors | not available |
| Variables for which coding was performed | not available |
| Estimates of coding errors | not available |
| Editing process and method | Electronic questionnaire includes build-in rules was used. Different methods of editing the data (both manual and computer editing): comparisons with data from previous collections of the same statistics, comparisons with data from other surveys including the same variables, extreme values checking, logical and numerical editing (computers programs detecting errors). (CENSUS) |
| Procedure used to correct errors | Re-contact with information provider. (CENSUS) |
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: 24.10.2022
c) Lag (days): 297
14.1.2. Time lag - final result
a) End of reference period: 31.12.2021
b) Date of first release of national data: 24.10.2022
c) Lag (days): 297
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)
Punctuality of time schedule of data release = 0
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| 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
Survey accordande with FM. There is no problems with international comparability.
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 | No | |
| 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 | |
| Socioeconomic objectives 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 | |
| Data processing methods | No | |
| Treatment of non-response | No | |
| Variance estimation | 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) | 5 years | Included in data internal and external personnel, in previous years data included only internal personnel | |
| Function | 4 years | Included in data internal and external personnel, in previous years data included only internal personnel | |
| Qualification | 5 years | Included in data internal and external personnel, in previous years data included only internal personnel | |
| R&D personnel (FTE) | 5 years | Included in data internal and external personnel, in previous years data included only internal personnel | |
| Function | 4 years | Included in data internal and external personnel, in previous years data included only internal personnel | |
| Qualification | 4 years | Included in data internal and external personnel, in previous years data were estimated | |
| R&D expenditure | 6 years | Changes in the method of classifying units to this sector | |
| Source of funds | 6 years | Changes in the method of classifying units to this sector | |
| Type of costs | 6 years | Changes in the method of classifying units to this sector | |
| Type of R&D | 6 years | Changes in the method of classifying units to this sector | |
| 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
The data produced in the same way in the odd and even years.
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
R&D survey use SNA classification of units to classifie R&D units to institutional sectors.
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 |
| not applicable | not applicable | not applicable | not applicable | not applicable | not applicable |
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) | 770294 thousand of national currency |
4887.1 | 3546.8 |
| Final data (delivered T+18) | 770294 thousand of national currency | 4887.1 | 3546.8 |
| Difference (of final data) | 0 | 0 | 0 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 91.8 thousand of national currency |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 25.3 thousand of national currency |
(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 | not available |
| Data collection costs | not available | not available |
| Other costs | not available | not available |
| Total costs | not available | not available |
| Comments on costs | ||
| Details of costs by requested structure are not available | ||
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) | 235 | data from questionarie |
| Average Time required to complete the questionnaire in hours (T)1 | 4.3 | data from questionarie about the time needed to prepare the data and fill in the questionarie |
| Average hourly cost (in national currency) of a respondent (C) | not available | |
| Total cost | not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Report on research and experimental development (R&D) - PNT-01 |
| Type of survey | Census |
| Combination of sample survey and census data | N/A |
| Combination of dedicated R&D and other survey(s) | N/A |
| Sub-population A (covered by sampling) | N/A |
| Sub-population B (covered by census) | N/A |
| Variables the survey contributes to | Intramural R&D expenditure by:source of funds, type of costs, type of R&D, field of R&D, NACE rev. 2 (type of economic activity), product field, socio-economic objectives, size class, NUTS 2. R&D personnel by: occupation, qualification, NACE rev. 2 (type of economic activity), size class, sex, NUTS 2. |
| Survey timetable-most recent implementation | By the end of August date are collected by electronic questionnaire portal. In the third quarter, data is verified, explained with reporting units and corrected if necessary. At the beginning of the fourth quarter, the data set is accepted and the results published. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | not applicable | ||
| Stratification variables (if any - for sample surveys only) | not applicable | ||
| Stratification variable classes | not applicable | ||
| Population size | 330 | ||
| Planned sample size | not applicable | ||
| Sample selection mechanism (for sample surveys only) | not applicable | ||
| Survey frame | The general frame built in cooperation with the Ministry of Science and Higher Education and other Ministries andon the basis of an official register of national economic entities named REGON (Rejestr Gospodarki Narodowej - Register of National Economy) | ||
| Sample design | not applicable | ||
| Sample size | not applicable | ||
| Survey frame quality | Only the Rector's Office is taken from the Register of National Economy. Their faculties are informed about the obligation of data transfer by the head office. There are no double entries of units in the frame. There is a possibility that some of the units existing in the frame have suspended their activity or that they have not started their activity yet, but this is revealed after the survey is carried out. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | not applicable |
| Description of collected data / statistics | not applicable |
| Reference period, in relation to the variables the survey contributes to | not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Government and local government units (mainly scientific units of the Polish Academy of Sciences or research institutes) and cooperating foundations and societies known or supposed to perform R&D |
| Description of collected information | The scope of data obtained is consistent with the questionnaire |
| Data collection method | Most data were collected via reporting portal made available to units on the Statistics Poland website, some questionnaires were sent by post and e-mail or rarely was collected by telephone interview |
| Time-use surveys for the calculation of R&D coefficients | not applicable |
| Realised sample size (per stratum) | not applicable |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | The questionnaire is made available through the reporting portal, in addition it is possible to send the completed report by post or e-mail |
| Incentives used for increasing response | No incentives are used, except for reminding of the obligation of statistical data transfer in accordance with the Law issued on 29 VI 1995 on Official Statistics and Regulation of the Council of Ministers on Statistical Surveys Program of Public Statistics |
| Follow-up of non-respondents | not available |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | We do not replace units which do not respond after follow-up interview |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 98% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not used |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | PNT-01_2021_ENG |
| R&D national questionnaire and explanatory notes in the national language: | PNT-01_2021 |
| Other relevant documentation of national methodology in English: | Methodological_report_research_and_experimental_development |
| Other relevant documentation of national methodology in the national language: | Zeszyt_metodologiczny_dzialalnosc_badawcza_i_rozwojowa |
Annexes:
Report on R&D for 2021 - in English
Report on R&D for 2021 - in the national language
Methodological report research and experimental development - in English
Methodological report research and experimental development in national language
18.4. Data validation
Procedures for checking and validating data include:
- checking response rates are as required;
- comparing the statistics with previous cycles (if applicable);
- confronting the statistics data from survey against administrative data;
- investigating inconsistencies in the statistics.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Not applicable
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The final data comes from the R&D survey. |
| Data compilation method - Preliminary data | The preliminary data comes from the R&D survey before its completion and approval of the result data set. |
18.5.3. Measurement issues
| Method of derivation of regional data | Data for regions are prepared on the basis of reports of units from the given region (adequatelly to the seat of unit). |
| Coefficients used for estimation of the R&D share of more general expenditure items | Assumptions made by those who compile the statistics: - employee who is working full-time, spending during the reference year on R&D: |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Exclusion of VAT and provisions for depreciation in the measurement of expenditures. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | - |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not used. |
| Description of the estimation method | Not used. |
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.
23 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.
The units of measures used in the survey:
- thousand units of national currency,
- HC,
- FTE,
- %.
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.
Frequency of data collection is annually and the published data refer to the 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.
