State Statistical Office

Department for research and development, inovations and informatical comunications technology

Dame Gruev 4, 1000 Skopje, Republic of North Macedonia

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 units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The results are related to the population of all R&D performing units 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 Revision 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.

Statistics on science, technology and innovation were collected based on Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021. 

Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.

National Classification of Activities - NKD Rev.2; Classification of institutional sectors (2016) Classification of scientific fields and branches of research (2007) Nomenclature for the analysis and comparison of scientific programmes and budgets (2007) The education system

See below.

1) Science & Technology

GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).

Not requested.

Statistical unit is each business entity is defined in the concept 3.3.

See below.

The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units, which can be accessed through the frame and the survey data really refer to this population.

Republic of North Macedonia

Since 1986 year.

Not requested.

Number.

Year.

See below.

Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.

National: Law on State Statistics (“Official Gazette of the Republic of Macedonia” No. 54/97, 21/07, 51/11, 104/13, 42/14, 192/15, 27/16, 83/18 and 220/18) and (“Official Gazette of the Republic of North Macedonia” No. 31/20). Program of Statistical Surveys 2018-2022 (“Official Gazette of the Republic of Macedonia” No. 22/18 and 224/18) and (“Official Gazette of the Republic of Macedonia” No. 18/20 and 300/20). International: Regulation (EU) No 995/2012 of the European Commission.

OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities

Not requested.

Individual data are protected by the Law on State Statistics. Data collected with statistical surveys from the reporting units or indirectly from administrative or other sources are confidential data and are used only for statistical purposes. Results from the statistical processing may also generate information considered as confidential, for example: anonymised individual data, tables with low level of aggregation, as well as unreleased data. The Policy on Statistical Confidentiality contains the basic principles used in the SSO.

All individual or personal data, in each phase of statistical processing, are treated as confidential data and may be used only for statistical purposes. When releasing data from this survey at an aggregated level, there is no need for additional data treatment for the purpose of ensuring confidentiality.

Data are released in accordance with the Release Calendar, which is published on the web site of the State Statistical Office. The Release Calendar is prepared annually before the beginning of each year and is updated quarterly.

http://www.stat.gov.mk/Kalendar_nov_en.aspx

In accordance with the dissemination policy, all users have equal access to statistical data at the same time. Data are released on the web site at the same time for all users, which are informed with the Release Calendar, and no user has privileged access.

Yearly.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

http://makstat.stat.gov.mk/PXWeb/pxweb/mk/MakStat/?rxid=46ee0f64-2992-4b45-a2d9-cb4e5f7ec5ef

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

'Methodological explanations that are part of the publication ''Research & development activity, 2015''. http://www.stat.gov.mk/Publikacii/2.4.16.16.pdf'

Not requested.

See below.

The commitment of the SSO to ensuring quality of products and services is described in the Law on State Statistics, the Strategy of the State Statistical Office (http://www.stat.gov.mk/ZaNas_en.aspx?id=6) and the Quality Policy of the State Statistical Office (http://www.stat.gov.mk/pdf/Politika_za_kvalitet_en.pdf), as well as in the continuous efforts for harmonisation with the European Statistics Code of Practice. The main aspects and procedures for quality management in the phases and sub-phases of the Statistical Business Process Model, as well as the good practices for ensuring quality are documented in the internal document called “Guide for ensuring quality of statistical processes”. Input and output metadata, as well as relevant quality indicators for certain sub-processes are described in the document “Guide for survey managers”.

High quality.

See below.

1)       Users' class codification

1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, Other Ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.

2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.

3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.

4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)

5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)

6- Other (User class defined for national purposes, different from the previous classes.)

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

See below.

In terms of the indicators provided by Regulation. 995/2012 of the European Commission, SSO provides about 50% of them.

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 995/2012.

1)  Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.

2)  Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Stage: P - provisional, F - final. 

2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.

3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

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.

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 GBARD.

1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.  

2) If at least one out of the three criteria above described would not be fully met.

3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.

4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.

5) If all the three criteria above described are not met.

Not requested.

Not requested.

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.

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:

b)      Measures taken to reduce their effect:

Not requested.

Not requested.

Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.

a)       Description/assessment of measurement errors:

b)      Measures taken to reduce their effect:

Non response errors: occur 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.

a) Problems in obtaining data from targeted information providers:

b) Measures taken to reduce their effect:

c) Effect of non-response errors on the produced statistics:

Not requested.

Not requested.

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.

a)       Data processing and editing processes:

b)      Description of errors:

c)       Measures taken to reduce their effect:

Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).

Description/assessment: 

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.

Date of first release of national data:

Date of first release of national data:

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

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

See below.

Not requested.

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 995/2012 or Frascati manual paragraphs with recommendations about these concepts / issues.

GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.

See below.

See below.

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

Coherence between areas is partially provided.

Not requested.

Not requested.

This part compares GBARD statistics from the provisional and final budget for the reference year.

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. 

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

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

In accordance with the Statistical Data Revision Policy. http://www.stat.gov.mk/ZaNas.aspx?id=25

Not requested.

Not requested.

a)       Provisional data:

b)      Final data:

c)       General University Funds (GUF):

See 12.3.3.

See below.

1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.

See below.

Data validation is done in accordance with the defined criteria for control. Initial check of the data is done by the responsible person for the survey in the subject matter department while receiving the completed questionnaires. Data validation is done after data entry too.

See below.

Not requested.

See below.

Not requested.

Not requested.