Agency for Statistics of Bosnia and Herzegovina - BHAS

Business Statistics Sector – Department for Industry and Construction Statistics

Zelenih beretki 26, 71000 Sarajevo, Bosnia and Herzegovina

Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.

The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology. 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.

• The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
• The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).

See below.

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).

See below.

See below.

Total number of persons employed during the calendar year. Full-time equivalent (FTE) is calculated as a full-time employee spending 100% of the time on R&D during a year = 1 FTE. Shorter than the full-time equivalent are employees spending less than 90% and more than 10% of time on R&D.

Example for the calculation of FTE:

• a full-time employee spending 100% of time on R&D during a year = 1 FTE

• a full-time employee spending 30% of time on R&D during a year = 0.3 FTE

• a full-time R&D person spending 100% of time on R&D employed at an R&D institution only for six months = 0.5 FTE

• a full-time employee spending 40% of time on R&D during half of the year (the person is only active for 6 months per year) = 0.2 FTE

• a part-time employee (working 40% of a full-time year) engaged only in R&D (spending 100% of time on R&D) during a year = 0.4 FTE

• a part-time employee (working 40% of a full-time year) spending 60% of time on R&D during half of the year (person is only active for 6 months per year) = 0.12 FTE

The statistical unit is the university or higher education center or research organization the R&D of which is controlled by higher education institutions.

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 of institutional units.

The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level. 

Not requested. R&D statistics cover the data for the whole territory of Bosnia and Herzegovina 

Not requested. See point 3.4. and 5.

Not requested. 

Economic data are provided in thousand BAM. R&D personnel data are provided in headcount and in full-time Equivalent.

For the expenditure feature, the reference period will be the calendar year. Concerning personnel, to determine the number of persons who work in R&D, the statistics use both the annual average and the full-time equivalence of the personnel who carry out R&D activities (persons/year).

Data referred to the period: Annual A: 2021

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- European Business Statistics Methodological Manual on R&D Statistics

Not requested.

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:

The Law on Statistics of Bosnia and Herzegovina (Official Gazette BiH, 26/04 and 42/04) in Chapter XI – Confidentiality of statistical data (Articles 23-29) and Law on Personal Data Protection of Bosnia and Herzegovina ("Official Gazette of BiH", No. 32/01),  establishes the principle of confidentiality as one of the main principles. In performing its tasks determined by this Law, the Agency for Statistics of Bosnia and Herzegovina acts in accordance with the Law on the Protection of Personal Data of Bosnia and Herzegovina (Official Gazette of BiH, 32/01).

The Agency for Statistics of BIH distributes statistics in accordance with the statistical principles of the European Statistics Code of Practice, and in particular in accordance with the principle of statistical confidentiality

b)       Confidentiality commitments of survey staff:

The Law on Statistics of Bosnia and Herzegovina (Official Gazette BiH, 26/04 and 42/04), Article 28: "Persons who have access to confidential information in the course of their duties must comply with the provisions of this law and after they cease to perform their functions".

The Law on Statistics of Bosnia and Herzegovina (Official Gazette BiH, 26/04 and 42/04), Article 27: When collecting, processing, submitting and distributing statistical data of Bosnia and Herzegovina, the competent authorities, including the authorities at the entity level, take all measures of an organizational, regulatory, administrative and technical nature necessary to protect the confidentiality of data from unauthorized access, disclosure and use.

In the process of statistical data collection, processing, analyses, and dissemination of statistical information, BHAS fully guarantees the confidentiality of the data submitted by respondents (enterprises, institutions, organizations and other statistical units).

The document " Rulebook on the Protection of Statistical Data in the Agency for Statistics" lists procedures for ensuring confidentiality during collection, processing and dissemination - including protocols for ensuring access to the individual data, rules for the definition of confidential cells in the output tables and procedures for detection and prevention of subsequent disclosures, as well as and access to microdata for research purposes. Confidential data are not published.

The Release Calendar of statistical releases is published in advance for the whole year and contains detailed information on all releases planned for publication. In December each year, the Agency for Statistics of BiH publishes the Release Calendar with the exact date and time of publication of statistics for the next year on the website, in 4 language versions (Bosnian, Croatian, Serbian and English) The Release Calendar is also available in .pdf format. R&D press release is published T + 12 months after the final reference period. The release calendar for Statistical Press Releases in BiH is available on the following link: http://bhas.gov.ba/Calendar/?lang=en

The release calendar for Statistical Press Releases in BiH is available on the following link: http://bhas.gov.ba/Calendar/?lang=en

All releases are published following the defined Release Calendar on a specific day, at 11 am on the BHAS’s website. In case if there are delays with publishing a release, a notice is posted on the website and a new publication date/time is set. All releases are available in 4 language versions, namely 3 local languages and the English version. Users do not have access to the new data before their official publication on the BHAS's website, nor can they gain access to the data earlier. The principle that all users have equal access to statistical data on an impartial basis is established in Article 8 (2) h), and Article 19 (2) of the Law on Statistics of Bosnia and Herzegovina, and it is consistently implemented in practice.

R&D data in BiH is disseminated on an annual basis.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Not available

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

The definitions of the methodology are given in the Frascati Manual 2015. Also, methodological explanations are available at the end of each R&D release available on the BHAS's website: https://bhas.gov.ba/data/Publikacije/Saopstenja/2023/RDE_01_2021_Y1_1_BS.pdf

Not requested.

See below.

BHAS follows the recommendations on organization and quality management given in the European Statistics Code of Practice (CoP) and implements the guidelines given in the European Statistical System Quality Assurance Framework (QAF). More, regarding quality assurance, all the procedures that describe the quality policy in BHAS can be found in the document: Quality management policy and mechanisms in BHAS: https://bhas.gov.ba/data/Dokumenti/Kvalitet/QM_Policy_and_Programme_BHAS.pdf

The user satisfaction survey is undertaken to monitor the quality of our statistical data and services. Please note, that this survey contains only data on user’s satisfaction with STI data, and not specifically R&D. User satisfaction survey results are available on: 

https://bhas.gov.ba/data/Dokumenti/Kvalitet/AZK2020_BS.pdf

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.

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 the periodicity of variables that should be provided, the breakdowns, and whether they should be provided mandatory or on a voluntary basis.

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.

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%.

See below.

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').

2) Y-start year

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 R&D.

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met.

3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.

5) 'Very Poor' = If all the three criteria are not met.

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.

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)

Not applicable

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:

Coverage errors are kept to a minimum.

Data are the result of statistical processing of annual reports collected from legal units dealing with the R&D in Bosnia and Herzegovina in the 2021 calendar year, from Higher Education Institutions on the Annual Research and Development Report for Higher Education Institutions ( IR-2). An address book was compiled containing all units to which the statistical survey was sent

Due to the importance of R&D, statistics analyzed different data sources to improve coverage research and identify legal units dealing with R&D, which is unknown. These are the following sources: Register of the Ministry, previous statistical research "Research and Development", Horizon 2020 project database for Bosnia and Herzegovina on the funds allocated by the applicant, etc.

b)      Measures taken to reduce their effect:

 Not applicable

Not applicable 

Not requested.

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 main reason that a measurement error can occur is  measurement error by the respondent concern:

- the person who fills in the questionnaire reads the instructions superficially or does not read them at all,

- the person filling in the questionnaire does not understand the instructions,

- the person who fills in the questionnaire does so superficially and does not fill in all the necessary information,

- the questionnaire is not always filled in by the same person (in the previous year it was a different person),

- reporting units do not have adequate records on allocations for research and development.

b)      Measures taken to reduce their effect:

If this type of error is noticed, the reporting unit is contacted. Also, the reporting unit is contacted if the data differ significantly from the data submitted in the previous year. 

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. 

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) 

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

A statistical survey is not based on a sample. Sampling errors do not exist. The statistical survey is based on an analysis of the source, an Address book was compiled containing units to which the statistical form (IR-2 ) was sent, and coverage errors are minimized. In case of non-response of reporting units, written reminders are sent to the reporting units after the scheduled deadline, and the reporting unit is contacted by phone to obtain the necessary data.

Non-response results from a failure to collect complete information on all units in the selected sample except non-eligible units (i.e. out-of-scope units ). There are two types of non-response. Firstly, a  unit that is contacted may fail to respond; this is called "unit non-response". Secondly, the sampled unit may respond incompletely to the questionnaire; this is called "item non-response". The unit non-response is sometimes adjusted by imputation methods and auxiliary information where it is available. The item non-response is adjusted either by imputation methods or estimation methods (if there is valid information from the previous year). Imputation or estimation methods were not  applied to new observation units ( units included in the survey for the first time and previous data are not available)

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.

Not requested.

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.

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: 2021

b) Date of first release of national data: T+12

c) Lag (days): -

a) End of reference period: 2021

b) Date of first release of national data: T+18

c) Lag (days): - 

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.

Not known

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197  or Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.

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.

See below.

See below.

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

Are the data produced in the same way in the odd and even years? If no, please explain the main differences.

This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.

Not requested.

Not applicable

Not applicable

See below.

This part compares key R&D variables as preliminary and final data.

(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. 

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’)

Not requested.

Not requested.

Not requested.

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.

See 12.3.3.

See below.

All the obtained data were entered into an application in which logical controls were set for the entered data sums. No discrepancies were observed when entering data.

See below.

Not applicable

Not requested.

Not requested.