1.1. Contact organisation
ISTAT - Italian National Institute of Statistics
1.2. Contact organisation unit
DEPARTMENT FOR ECONOMIC, ENVIRONMENTAL AND NATIONAL ACCOUNTS STATISTICS (DIAE) - Structural statistics on businesses, governmental and non-profit organizations
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Via Tuscolana, 1788 - 00173 Rome
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
2.1. Metadata last certified
1 November 2025
2.2. Metadata last posted
1 November 2025
2.3. Metadata last update
1 November 2025
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
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.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Business enterprise sector | Total over all NACE Sections with the exception of sections O, T and U (excluded from the survey). |
|---|---|
| Hospitals and clinics | Included if part of the BES. |
| Inclusion of units that primarily do not belong to BES | No. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Yes |
|---|---|
| External R&D personnel | Yes |
| Clinical trials: compliance with the recommendations in FM §2.61. | Yes but they don't have a specific item in the national questionnaire |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | No deviation from FM. |
|---|---|
| Payments to rest of the world by sector - availability | No deviation from FM. |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Yes. No deviation from 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 enterprise) 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 | Two different questions with clear and very detailed instructions |
| Difficulties to distinguish intramural from extramural R&D expenditure | Sometimes the respondents mix up the extramural R&D expenditure and the aquisition of services to support intramural R&D (other current costs). |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
| Coverage of years | Calendar year 2023 and provisional data 2024. |
|---|---|
| Source of funds | No deviation from FM. We collect separately data on internal and external funds. For some fund items, the Italian questionnaire have also sub-items on "research projects" and "non-repayable funding". |
| Type of R&D | No deviation from FM. |
| Type of costs | No deviation from FM. In particular, the Italian questionnaire asks for information on: the expenditure for internal personnel engaged in R&D, broken down into expenditures for researchers, technicians, and other staff; the expenditures for external R&D personnel, broken down by consultants and collaborators under coordinated and continuous contracts, project-based workers or research fellows; other current costs, such as purchase of materials and acquisition of intra-mural R&D services; capital R&D expenditures, broken down into expenditures for land and buildings, machinery and equipment, capitalised computer software, and other intellectual property products. |
| Economic activity of the unit | It is the main economic activity of the unit conducting the R&D activity according to the official national classification of economic activities (ATECO) which is totally consistent with the NACE Rev.2 classification. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Defined with reference to the economic activity that accounts for most of its economic outputs as emerged from the National Business Register. |
| Product field | Industry orientation is identified in terms of the industry served (according to the Nace Rev.2 classification). To date no major problems have been identified. |
| Defence R&D - method for obtaining data on R&D expenditure | No explicit data on defence R&D are collected. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Average number of persons employed during the calendar year. |
|---|---|
| Function | No deviation from FM. |
| Qualification | No deviation from FM. |
| Age | No deviation from FM. |
| Citizenship | No deviation from FM. Disseminated for the first time. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Average number of persons employed during the calendar year. |
|---|---|
| Function | No deviation from FM. |
| Qualification | No deviation from FM. |
| Age | |
| Citizenship |
3.4.2.3. FTE calculation
The ratio of working hours actually spent on R&D during a specific reference period (the calendar year) divided by the total number of hours conventionally worked in the same period by the total personnel.
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
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 Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| 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 | The target population comprises all the Italian active enterprises that could potentially perform R&D. The main statistical source used for defining the target population of R&D performers is the most updated release of the official Italian business Register, Asia 2023. Other information were:
|
|
| Estimation of the target population size | 37.849 business enterprises. | |
| Size cut-off point | No size cut-off point. | |
| Size classes covered (and if different for some industries/services) | No deviation from FM. | |
| NACE/ISIC classes covered | No deviation from FM. |
3.6.2. Frame population – Description
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.
| Method used to define the frame population | The frame population comprises all the Italian active enterprises that could potentially perform R&D as identified mainly by the official Italian business Register, Asia 2023 and by other fundamental sources, such as the inventory of the enterprises claiming tax relief for R&D activities and projects in 2022, the list of the enterprises reporting R&D activities in the two previous R&D surveys and the list of the enterprises reporting intramural R&D activities in the previous CIS (CIS2020-2022). |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | See above. |
| Inclusion of units that primarily do not belong to the frame population | No inclusion of units that primarily do not belong to the frame population |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | No statistical activity undertaken to target R&D activities carried out by business enterprises included in the frame population but not included in the target population. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | The 'turnover rate' of known or potential R&D performing business enterprises in the target population was 29.6%. More specifically, 11,194 BES are new in 2023 edition as compared to the 2022 edition. |
| Systematic exclusion of units from the process of updating the target population | No systematic exclusion. |
| Estimation of the frame population | 37.849 business enterprises. |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
The 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 the 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. Regulation No 2020/1197 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. The transmission of R&D data is mandatory for Member States and EEA countries.
The 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.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No R&D specific statistical legislation. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | The obligation to respond to this survey is established by Article 7 of Legislative Decree No. 322/1989 and by the Presidential Decree of 24 September 2024 approving the National Statistical Programme 2023–2025. Failure to comply with this obligation shall be subject to penalties pursuant to Articles 7 and 11 of Legislative Decree No. 322/1989 and the aforementioned Presidential Decree of 24 September 2024. Further information is available at: National Statistical Legislation Anyway, in the case of BERD survey, the penalties are just applied to the enterprise with more than 249 persons employed. |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Istat has to meet national confidentiality standards. We have a strict national legislation (Data Protection Code - Legislative Decree no. 196/2003 and subsequent amendments and additions). According to our national legislative act, data collected as part of statistical surveys included in the National Statistical Program cannot be communicated or disseminated, except in aggregate form and in such a way that no reference to identifiable individuals can be derived. AS a consequence, under no circumstances the data may be used to re-identify the units concerned.
- Confidentiality commitments of survey staff: All staff members of the regional and national offices are subjected to this law and data protection officers supervise the correct application of legal obligations.
7.2. Confidentiality - data treatment
Concerning the question of confidentiality in macrodata (tabular data), the issue arises with variables that do not directly identify individuals but can narrow down the population to which they belong, which the intruder may exploit for his purposes. In this case, we try to balance utility and privacy and to adopt techniques that protect confidentiality striving to minimize data loss. In practice, in line with what done for the other Italian structural statistics, in the release of tables we firstly identify the high-risk cells. The identification of these cells is based on the threshold rule that defines a minimum number of units required in a cell to consider it safe for publication. The threshold value is 3, that is, a cell is susceptible to intrusion if the number of units (enterprises in the case of BERD) it contains is less than 3 units (in the reference population). Then, a primary suppression is applied: cells identified at risk are hidden, left blank or marked as missing. Additional (secondary) suppressions are applied since the primary suppression alone is insufficient to guarantee protection. It is done manually and may change from case to case. Finally, we can think to forms of aggregation, by reducing the level of detail and replacing detailed data with broader categories (for instance, by grouping Nace sectors or using macro-area instead of regional levels – NUTS1 instead of NUTS2).
Abot the microdata, Istat ensures the confidentiality and security in accordance with the principles of the European Statistics Code of Practice and the standards of the European Statistical System (ESS). Confidentiality is safeguarded through a secure research environment, physical and statistical controls, and legally binding agreements. Each researcher must sign a legally binding agreement committing to specific confidentiality rules and to compliance with national and European data protection and statistical confidentiality legislation. Authorized researchers are granted access within a controlled environment. In particular, Istat has a Research Data Centre (Safe Centre), providing researchers with controlled access to detailed microdata. Data analysis is performed within a secure environment, ensuring that no data can be extracted or transmitted outside the protected infrastructure. Access to the research environment is strictly monitored. Physical access is limited to authorized users, and external internet connectivity is disabled. A secure remote access system is also available, enabling accredited users to work from approved external facilities. Finally, all analytical results are subject to ex-ante verification by Istat before being released to prevent any risk of disclosing information that could lead to the identification of enterprises. This process ensures compliance with statistical confidentiality and disclosure control requirements in line with ESS standards.
8.1. Release calendar
The release calendar for the R&S data set for business sector in question exists and it is publicly accessible.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu).
At national level this is: Calendario-CS-2025.pdf
8.3. Release policy - user access
Through Istat databases and information systems users can choose the information according to their needs. In some cases it is possible to build customised tables, in other cases, data are structured in prepackaged downloadable tables. Each database is accompanied by methodologies, classifications, definitions related to the topic.
The datasets are collections of data for prompt publication of the results of a statistical survey or analysis disseminated without a regular basis.
Microdata files are collections of elementary data. Referring to Istat’s surveys, these files are released free of charge and in compliance with the principle of statistical secrecy and protection of personal data.
Survey results are made available through press releases; they include forecasting and short-, medium- and long-term economic analysis and the development of microsimulation models of the effects of fiscal policies on households, businesses and institutions.
Through Paper, electronic and interactive publications and specialist publications (Rivista di statistica ufficiale, Istat Working Papers) Istat promotes and enhances the research activity, sharing the results of studies in the field of official statistics.
The A-Z Statistics section collects all documents published on this website, in alphabetical order according to the tags that have been attributed to each one.
For journalists there is a press room. The Istat Media Relations Office is responsible for all dealings with the media – from national newspapers, magazines, broadcasters and online publications – and provides ongoing information, monitoring and support for publications, articles and television programmes. The press releases are issued by Istat press office from 10 a.m. on the date indicated, after a briefing to illustrate the main data, reserved for certified news agencies. During the briefing, journalists prepare the launch texts with no outside contact until a member of the press office staff approves the data release (lock-up system). Press office staff monitor agencies constantly throughout the briefing to ensure the embargo is respected. Accredited news agencies agree to respect this dissemination procedure. Failure to comply with the lock-up system may lead to temporary – or complete, in the case of repeated infractions – suspension of access to the press room.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data. Here is the link to the Italian R&D data warehouse (category: Enterprises/Research and Development).
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | On-line press release: La ricerca e sviluppo in Italia – Anni 2023-2025 – Istat |
| Ad-hoc releases | Y | See above. |
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) | Links |
|---|---|---|
| General publication/article | Y | ISTAT annual Yearbook: Annuario statistico italiano 2024 – Istat Noi Italia: Noi Italia 2025 - home The Bes Report, the report on equitable and sustainable well-being: BES Report – Istat SDGs Report. Statistical information for 2030 Agenda in Italy: SDGs Report – Istat |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
IstatData is the platform to disseminate Istat aggregate data.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Information on the Istat Laboratory for Elementary Data Analysis (ADELE) page: ADELE Laboratory – Istat |
|---|---|
| Access cost policy | Information on the Istat Laboratory for Elementary Data Analysis (ADELE) page: ADELE Laboratory – Istat |
| Micro-data anonymisation rules | Information on the Istat Laboratory for Elementary Data Analysis (ADELE) page: ADELE Laboratory – Istat |
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 | Users can ask for ad-hoc statistical tables to fill specific needs. As well as data confidentiality is protected. |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodological note in specific Report.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Graphs, tables, metadata, methodological note, glossary. |
|---|---|
| Requests on further clarification, most problematic issues | The survey staff can be contacted by users – as well as by data providers – for clarifying any methodological and conceptual issue. Phone numbers and e-mail addresses are available on the ISTAT website. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
11.2. Quality management - assessment
The survey provides a highly consistent information on the trend of R&D expenditure and personnel of the Italian enterprises since the data concerning the largest enterprises (with at least 100 persons employed) that are also the the greatest R&D performers are carefully monitored.
Since 2016 ISTAT has implemented an imputation method to take into account the non response units or the cases of response instability over time. This process is based on a predictive regression imputation, applied to the two key variables (R&D expenditure and R&D personnel in fte) and it is a partial imputation that regards those units that in the previous two surveys (undertaken with reference to 2021 and 2022) gave 2023 preliminary R&D data.
Starting from the 2020 edition, even if data collection has been conducted among Legal Units (LUs), the unit of analysis has been the “enterprise” as defined by the official national Business Register Asia-Ent (Ent=enterprise), that is a simple or complex unit with a single or multiple relationships with LUs. Previously LU was the unit of analysis of BERD survey. Changes in the definition process for the statistical unit of the enterprise were introduced to fully apply the EU Regulation 696/1993 definition.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
|---|---|---|
| 1- Institutions | European level: Eurostat International level : OECD National level: Ministry of Economic Develpment Regional authorities |
Regional authorities have the right to receive information on the R&D performing enterprises based in their territories (even though only for statistical purposes) and often they request micro-data to ISTAT. In Trento and Bolzano Provinces, as well as in other selected regions, the local authorities collects the R&D questionnaires on behalf of ISTAT. |
| 2 - Social actors | Industrial associations | Some industrial associations are using the ISTAT R&D data to monitor their associates’ R&D activities (using aggregated data). |
| 3 - Media | News, media | Press Release. |
| 4 - Researchers | Researchers in universities and public research centres | Detailed tables are often requested by researchers and analysts. |
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 | No user satisfaction surveys on R&D statistics have been carried out in Italy in the last years. |
|---|---|
| User satisfaction survey specific for R&D statistics | Not applicable |
| Short description of the feedback received | Not applicable |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All mandatory datasets were transmitted. 100% completeness
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.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | We provide with 2023 BERD survey preliminary 2024 data |
| Obligatory data on R&D expenditure | No missing cells |
| Optional data on R&D expenditure | Not collected information |
| Obligatory data on R&D personnel | No missing cells |
| Optional data on R&D personnel | Not collected information or problems with data quality |
| Regional data on R&D expenditure and R&D personnel | No missing cells |
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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y | Annual | ||||
| Type of R&D | Y | Annual | ||||
| Type of costs | Y | Annual | ||||
| Socioeconomic objective | N | |||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | 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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y | Annual | ||||
| Citizenship | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual | ||||
| Economic activity | Y | Annual | ||||
| Product field | N | |||||
| Employment size class | Y | 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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y | Annual | ||||
| Citizenship | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual | ||||
| Economic activity | Y | Annual | ||||
| Product field | N | |||||
| Employment size class | Y | 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 |
|---|---|---|---|---|---|
| No additional dimension/variable | |||||
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'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | ||
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 errors1) | 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 | : | 3 | 2 | 1 | 4 | : | - |
| Total R&D personnel in FTE | : | 1 | 3 | 2 | 1 | : | + |
| Researchers in FTE | : | 1 | 3 | 2 | 1 | : | + |
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 (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
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 (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not 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
See below.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for key variables by NACE
Not applicable
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not applicable | ||
| R&D personnel (FTE) | Not applicable |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
Not appplicable
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | Not applicable | ||||
| R&D personnel (FTE) | Not applicable |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame 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:
The set of units identified as potential R&D performers could be a not complete set of he target population of R&D performers.
b) Measures taken to reduce their effect:
Improve the identification of potential R&D performers
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | 660 | 3028 | 2726 | 1210 | 7624 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0 | 0 | 0 | 0 | 0 |
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | 1756 | 1766 | 790 | 442 | 4754 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0 | 0 | 0 | 0 | 0 |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors:
The most concerning issue in recent years has been the misuse of the unit of measurement in the R&D expenditure report, where euros were used instead of thousands of euro.
b) Measures taken to reduce their effect:
To handle this question, substantial changes in the questionnaire were implemented to enhance the quality of the data collected (Italian Journal of Economics)
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 satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | 7357 | 17928 | 8151 | 4329 | 37765 |
| Total number of units in the sample | 3498 | 11209 | 5919 | 3921 | 24547 |
| Unit Non-response rate (un-weighted) | 52.5 | 37.5 | 27.4 | 9.4 | 35.0 |
| Unit Non-response rate (weighted) | 52.5 | 37.5 | 27.4 | 9.4 | 35.0 |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 20578 | 17187 | 37765 |
| Total number of units in the sample | 13406 | 11141 | 24547 |
| Unit Non-response rate (un-weighted) | 34.9 | 35.2 | 35.0 |
| Unit Non-response rate (weighted) | 34.9 | 35.2 | 35.0 |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
Two e-mail reminders are usually undertaken before coding an enterprise as “non respondent”.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No such survey conducted. |
|---|---|
| Selection of the sample of non-respondents | |
| Data collection method employed | |
| Response rate of this type of survey | |
| The main reasons of non-response identified |
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 Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 | 0 | 0 |
| Imputation (Y/N) | |||
| If imputed, describe method used, mentioning which auxiliary information or stratification is used |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | 0.05 |
| Total R&D personnel in FTE | 0.05 |
| Researchers in FTE | 0.05 |
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 | The Italian BES R&D survey is a web survey. The data capturing is a generalized system for aided development and monitoring of web surveys called GINO++ [much more than Gathering INformation Online] that allows the survey manager himself (that is without software developers) to perform three key phases of a survey: designing, capturing and monitoring. In particular, GX doesn’t allow just the data entry, but it allows the automatic management of skipping rules (helping respondents in filling in the questionnaire) and the management of the consistency rules (through a set of on-line edits according to the compatibility plan), taking into consideration different aspects, like: rules regarding variables of the same web-page or different ones, number of checking rules on a single web-page, relation between skipping and checking rules, etc. |
|---|---|
| Estimates of data entry errors | No errors reported. |
| Variables for which coding was performed | The country of the owner company for foreign affiliates. |
| Estimates of coding errors | No errors reported. |
| Editing process and method | An automatic edit procedure runs at the stage of the data capturing. In particular, we define a wide set of on-line edits according to a compatibility plan through which we check a lot of inconsistencies. We cannot acquire the data if the inconsistencies found are not solved. At the end of data capturing, we checked the very few missing data and the outliers were treated: 1) by calling back the respondents and collecting additional (missing) information; 2) by applying some automatic imputation procedures based mainly on the use of the rate of change (of the variable between 2023 and 2022, in cases where the data are provided in 2022) and the ratio mean (of two variables closely linked each other within the same record). |
| Procedure used to correct errors | Re-contact and imputation. |
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)
No preliminary data
- End of reference period: 2023
- Date of first release of national data: September 2025
- Lag (days): 630
14.1.2. Time lag - final result
- End of reference period: 2023
- Date of first release of national data: June 2025
- Lag (days): 540
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) | No preliminary data | 18 |
| Delay (days) | 0 | |
| Reasoning for delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
High convergences with FM and concordance to international classifications.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) 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 sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No data weighting | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No sample deisgn | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
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) | Fully comparable to the previous period | ||
| Function | Fully comparable to the previous period | ||
| Qualification | Fully comparable to the previous period | ||
| R&D personnel (FTE) | Fully comparable to the previous period | ||
| Function | Fully comparable to the previous period | ||
| Qualification | Fully comparable to the previous period | ||
| R&D expenditure | Fully comparable to the previous period | ||
| Source of funds | Fully comparable to the previous period | ||
| Type of costs | Fully comparable to the previous period | ||
| Type of R&D | Fully comparable to the previous period | ||
| Other | Fully comparable to the previous period |
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
Data were 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. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
BERD survey's data are sistematically provided to the office responsible for the System of National Accounts (SNA) and for the use of BERD data in the SNA calculations.
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 |
|---|---|---|---|---|---|
| No variable can be compared | |||||
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
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 (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | No preliminary data | No preliminary data | No preliminary data |
| Final data (delivered T+18) | |||
| Difference (of final data) |
Comments :
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not available | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | 90,000 | |
| Data collection costs | 0 | |
| Other costs | 0 | |
| Total costs | 0 |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs :
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Around 25,000 enterprises | |
| Average Time required to complete the questionnaire in hours (T)1 | Information not available | |
| Average hourly cost (in national currency) of a respondent (C) | Information not available | |
| Total cost | Information 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. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
Survey name: Statistical Survey on the Research and Develpment in Business Enterprises Final data 2023 - Preliminary data 2024 and 2025
Type of survey: The survey is census-based, considering that our target population is composed of all the active enterprises that potentially perform R&D, according to the information we received from other statistical or administrative sources.
18.1.2. Sample/census survey information
| Sampling unit | Enterprise |
|---|---|
| Population size | 38,000 |
| Survey frame | The most updated release of the official Italian business Register. |
| Survey frame quality | Good. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No administrative data or pre-compiled statistics are collected |
|---|---|
| Description of collected data / statistics | No administrative data or pre-compiled statistics are collected |
| Reference period, in relation to the variables the administrative source contributes to | |
| Variables the administrative source contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Mode of data collection | The Italian BES R&D survey is a web survey. The data collection made use of the Istat Business Statistical Portal, a single entry point for Istat Web-based data collection from enterprises. In particular, a single software tool was used for electronic questionnaires, a generalised in-house product based on XML that allows to create the main survey’s contents: survey metadata, survey variables, questionnaire structure, management of skipping rules and checking rules plan. |
|---|---|
| Follow-up of non-respondents | Yes |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Imputation of the non-respondents who declared R&D expenditure for 2023 in the two previous years. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 65,1% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | No |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available. |
| R&D national questionnaire and explanatory notes in the national language: | Questionario sulla Ricerca e sviluppo |
| Other relevant documentation of national methodology in English: | Not available. |
| Other relevant documentation of national methodology in the national language: | Lettera informativa and Guida alla compilazione available in the Portal (website for the respondents) |
18.4. Data validation
The validation activities consist of:
- checking the quality of population coverage;
- monitoring that the response rate is good;
- detecting and correcting individual errors in data records through a set of edits (checks for identifying routing errors, coding errors, inconsistencies, outliers and missing answers) that are performed for Bes R&D survey;
- contacting the respondent concerning inconsistencies, errors or missing data;
- comparing the 2023 data with the 2022 data at micro, whereas the respondents had already filled in questionnaire in the previous edition;
- comparing the 2023 data with the 2022 data at macro level.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | not applicable | not applicable | not applicable | not applicable |
| 10-49 employees and self-employed persons | not applicable | not applicable | not applicable | not applicable |
| 50-249 employees and self-employed persons | not applicable | not applicable | not applicable | not applicable |
| 250-and more employees and self-employed persons | not applicable | not applicable | not applicable | not applicable |
| TOTAL | not applicable | not applicable | not applicable | not applicable |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | not applicable | not applicable | not applicable | not applicable |
| Services2) | not applicable | not applicable | not applicable | not applicable |
| TOTAL | not applicable | not applicable | not applicable | not applicable |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | The survey is carried out every year. |
|---|---|
| Data compilation method - Preliminary data | No preliminary data |
18.5.3. Measurement issues
| Method of derivation of regional data | The regional data are produced on the basis of the place where R&D is really undertaken. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excluded. |
18.5.4. Weighting and estimation methods
| Weight calculation method | Not applicable |
|---|---|
| Data source used for deriving population totals (universe description) | Not applicable |
| Variables used for weighting | Not applicable |
| Calibration method and the software used | Not applicable |
| Estimation | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
The survey provides a consistent information on the trend of R&D expenditure and personnel of the Italian enterprises since the data concerning the largest enterprises are carefully monitored. Since 2016, ISTAT has implemented an imputation method to take into account the non response units. This action has allowed to overcome the previous problem of “under-estimations” of the Italian business R&D expenditures and personnel and to to improve the quality of final results. From 2020 we implemented the statistical unit 'Enterprise' in the survey in the following way: we collected the data using as reporting unit the 'legal unit', but we produce the final estimates using as unit of analysis the 'Enterprise'.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
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.
1 November 2025
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested. R&D statistics cover national and regional data.
The 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.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data. Here is the link to the Italian R&D data warehouse (category: Enterprises/Research and Development).
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.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
