Crop production (apro_cp)

National Reference Metadata in ESS Standard for CROPS Reports Structure (ESQRSCP)

Compiling agency: Statistical Office of Montenegro - MONSTAT


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



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1. Contact Top
1.1. Contact organisation

Statistical Office of Montenegro - MONSTAT

1.2. Contact organisation unit

SECTOR FOR AGRICULTURAL STATISTICS, FISHERIES, BUSINESS STATISTICS AND ENVIRONMENT AND FORESTRY

Department for Agriculture and Fishery Statistics

1.5. Contact mail address

IV Proleterska 2, 81000 Podgorica, Montenegro


2. Statistical presentation Top
2.1. Data description

Statistical data on areas of agricultural land, area and production of crops collected since the 60s of the last century. Until 2013, the used method was based on the reports of agricultural enterprises and cooperatives and the estimated for the private agricultural holdings. Since 2014, instead of the surveys based on the estimates, were introduced regular annual sample-based surveys. In order to obtain comparable data for the period from 2007 -2013 completed the recalculation of data on agricultural land and crop production.

2.2. Classification system

NACE Rev. 2

2.3. Coverage - sector

Section A – Agriculture, forestry and fishing

2.4. Statistical concepts and definitions

Utilised agricultural land covers arable land, kitchen gardens and/or gardens, orchards, vineyards, nurseries, meadows and pastures, regardless of the type of ownership (land owned or land taken in tenure).

Arable lands are areas of land that are regularly processed and crops are sown/planted according to certain order (crop rotation). Crop rotation represents regular and predetermined replacement of crops (rotation system) for more efficient use of land. On arable land are cultivated grains, industrial crops, potato, roughage, vegetable plants, flowers, seeds and seedlings and other plants on arable land.

Kitchen gardens and/or gardens are areas devoted for growing crops (vegetables, potatoes, fruit and vine) intended for feeding of the holding members and are mainly not intended for sale.

Plantation orchards are areas under the fruit trees, with certain spacing between lines and rows. 

Vineyards - plantations of vine intended for the production of grapes. Plantation vineyards - areas under vine with certain distance between the vines, in which can be performed mechanized processing, and by a rule are conducted other agro-technical measures.

Nurseries are areas of land on which are grown young woody plants intended for transplantation later, and include: fruit seedlings, grapevine seedlings, decorative plants and forest trees.

Meadows - land that is permanently (five or more years) used to grow green animal feed, and is not included in crop rotation.

Pastures - land that is used for grazing of livestock. 

2.5. Statistical unit

Agricultural enterprises, cooperatives and private agricultural holdings engaged in crop production.

2.6. Statistical population

Agricultural holdings engaged in crop production.

2.7. Reference area

Montenegro

2.8. Coverage - Time

Crop year.

2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous Quality Report?  NO      
If yes, which new data sources have been introduced since the previous quality report?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous Quality Report?      
Which data sources have been dropped since the previous quality report?
Type of source?
Why have they been dropped?
Additional comments


Data sources: Please indicate the data sources which were used for the reference year on which is reported

  Type Name(s) of the sources If other type, which kind of data source?
Table 1: crops from arable land      
Early estimates for areas
Final area under cultivation Survey

Survey on the yields of late crops, fruits and grapes 

 

Production Survey

Survey on the yields of late crops, fruits and grapes 

 

Yield Survey

Survey on the yields of late crops, fruits and grapes 

 

Non-existing and non-significant crops Census
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas
Final harvested area Survey

Survey on the yields of late crops, fruits and grapes 

 

Production Survey

Survey on the yields of late crops, fruits and grapes 

 

Non-existing and non-significant crops Census
Table 3: Permanent crops      
Early estimates for production area
Final production area Survey

Survey on the yields of late crops, fruits and grapes 

Production Survey

Survey on the yields of late crops, fruits and grapes 

Non-existing and non-significant crops Census
Table 4: Agricultural land use      
Main area Survey

Survey on the yields of late crops, fruits and grapes 

Non-existing and non-significant crops Census
Total number of different data sources

1

   
Additional comments

Data source for the humidity

Put x, if used

Surveyed: farmers report the humidity

 

Surveyed: farmers convert the production/yield into standard humidity     

 

Surveyed: whole sale purchasers report the humidity

 

Surveyed: whole sale purchasers convert the production/yield into standard humidity     

 

Surveyed by experts (e.g. test areas harvested and measured)

 

Estimated by experts

 x

Other type

 

If other type, please explain

 

Additional information

 

   


Which method is used for calculating the yield for main arable crops? production divided by harvested area
If another method, describe it.
3.2. Frequency of data collection

  Source 1 Source 2 Source 3 Source 4 Source 5 Source 6 Source 7 Source 8 Source 9
Name of data source

Survey on the yields of late crops, fruits and grapes 

Planning (month-month/year)

September - November/2019

Preparation (month-month/year)

September - November/2019

Data collection (month-month/year)

December 2019

Quality control (month-month/year)

April - Jun 2020

Data analysis (month-month/year)

April - Jun 2020

Dissemination (month-month/year)

July 2020

If there were delays, what were the reasons?

Not were delays. Data published according to Annual Plan of Surveys.

3.3. Data collection

Definitions Question In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009? NO
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? NO
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? NO
Are special estimation/calculation methods used for permanent crops for human consumption? NO
Are special estimation/calculation methods used for main land use? NO
Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? NO  
In case yes, how do they differ? ( list all items and explanations)
In case data are delivered for one of the items below, describe the crop species included in the item:


Population

Which measures were taken in order to make sure that the requirement stipulated in Art. 3.2 are met?
(Statistics shall be representative of at least 95 % of the areas of each table in the Regulation).

Survey was carried out in 2019, and it meets the requirements. During the sample design a stratification was done according to the size of agricultural holdings. Also, in exhaustive (take in) strata all agriculture enterprises and private agriculture holdings with more of 20 ha of UAA were included.
Estimation of CV with the data from the Statistical Register of Agricultural Holdings was done - simulation.

Is the data collection based on holdings? YES
If yes, how the holdings were identified? Unique statistical farm identifier
If not, on which unit the data collection is based on?
When was last update of the holding register? (month/year)

2019

Was a threshold applied? YES
If yes, size of the excluded area  Area excluded on the basis of the threshold in % of the total area for that crop
Cereals for the production of grain (in %)
Dried pulses and protein crops (in %)
Root crops (in %)
Oilseeds (in %)
Other industrial crops (included all industrial crops besides oilseeds)  (in %)
Plants harvested green from arable land (in %)
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)


Survey method (only for census and surveys)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

Survey on the yields of late crops, fruits and grapes 

Which survey method was used? Face-to-face interview
If 'other', please specify
Please provide a link to the questionnaire

No.

Data entry method, if paper questionnaires? Manual


Administrative data (This question block is only for administrative data)

  Admin source 1 Admin source 2 Admin source 3 Admin source 4 Admin source 5 Admin source 6
Name of the register
Description
Data owner (organisation)
Update frequency
Reference date (month/year)
Legal basis
Reporting unit
Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
Were the data used for sample frame?
Data used for other purposes, which?
Which variables were taken from administrative sources?
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?
What were the possible limitations, drawbacks of using the data from administrative source(s)?


Expert estimations (This question block is only for expert estimates)

  Expert estimate 1 Expert estimate 2 Expert estimate 3 Expert estimate 4 Expert estimate 5 Expert estimate 6
Name of the estimation
Data owner (organisation)
Update frequency (e.g. 1 year or 6 months)
Reference date (Month/Year  e.g. 1/16 - 8/16)
Legal basis
Use purpose of the estimates?
What kind of expertise the experts have?
What kind of estimation methods were used?
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?
If yes, please describe the differences
What measures were taken to eliminate the differences?
How were the reliability, accuracy and coherence (comparison to other available data) of the data originated from experts' estimates (ante- and/or ex-post)checked?
What were the possible limitations, drawbacks of using the data from expert estimate(s)?
Additional comments
3.4. Data validation

Which kind of data validation measures are in place? Automatic and Manual
What do they target? Completeness
Outliers
Aggregate calculations
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
Other dataset
If other, please describe

Data on subsidies of Ministry of Agriculture and Rural development, data on export and import, data on purchase and sale of agricultural products

Agricultural census

3.5. Data compilation

Not applicable.

3.6. Adjustment

Not applicable.


4. Quality management Top
4.1. Quality assurance

  Completeness Punctuality Accuracy Reliability Overall quality
How would you describe the overall quality development since the previous quality report? Stable Stable Stable Stable Stable
Is there a quality management process in place for crop statistics? NO        
If, yes, what are the components?        
Is there a Quality Report available? YES        
If yes, please provide a link(s)

https://www.monstat.org/eng/page.php?id=62&pageid=62 

       
To which data source(s) is it linked?

Satistical Office of Montenegro - MONSTAT

       
Has a peer-review been carried out for crop statistics? NO        
If, yes, which were the main conclusions?        
What quality improvement measures are planned for the next 3 years? Systematic validation improvements
Quality report
       
If, other, please specify        
Additional comments

If  human resources  and financial resources will be available, two surveys related to early estimates will be carried out; June survey on area sown and September survey on harvested area and production of cereals. For both surveys all survey instruments are prepared (questionnaires, guidelines, data entry program).

       
4.2. Quality management - assessment

Quality is monitored at all stages of data production, by accountable statisticians. Collecting, entering, and processing data are commonly the subject of this monitoring. One of the criteria for checking the data is administrative data sources. Data obtained through this research are checked with data obtained from the Agricultural Census, regular annual statistical surveys.


5. Relevance Top
5.1. Relevance - User Needs

Are there known unmet user needs? NO
Describe the unmet needs
Does the Regulation 543/2009 meet the national data needs? YES
Does the ESS agreement meet the national needs? YES
If not, which additional data are collected?
Additional comments
5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done? YES
If yes, how satisfied the users were? Satisfied
Additional comments

In order to measure the degree to which fulfills obligations towards users and within the new quality policy, the Statistical Office conducted User satisfaction survey. Data collection was carried out through a web survey, for the first time in the period from 1 September to 20 October, 2017 and for the second time in the period from 6 March to 27 April, 2020. The results of the survey are available on the Statistical Office website, link: https://www.monstat.org/eng/page.php?id=1502&pageid=1 

5.3. Completeness

Not available.


6. Accuracy and reliability Top
6.1. Accuracy - overall

The Survey on the yields of late crops, fruits and grapes is carried out on a sample basis and the data is obtained by a reporting method and are subject to common types of errors related to sampling technique, non-sampling errors, processing errors, and non-response.

6.2. Sampling error

Sampling method and sampling error

  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Survey on the yields of late crops, fruits and grapes 

Sampling basis? List
If 'other', please specify
Sampling method? Random
Stratified
If stratified, number of strata?

5

If stratified, stratification basis? Size
Other
If 'other', please specify

Family agricultural holdings and agricultural enterprises

Size of total population

46421

Size of sample

5000

Which methods were used to assess the sampling error?  Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
Non-response
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

Taylor linearization

If the results were compared with other sources, please describe the results

Some of the data were compared with the data on subsidies from Ministry of Agriculture and Rural development, and statistical data were mostly higher than the data of Ministry of Agriculture and Rural development.

Which were the main sources of errors?

Considering the time lag between the AC 2010 and the survey it can be expected that the sampling frame does not represent the entire population and problems of under coverage and over coverage may occur. Statistical Farm Register were not updated with administrative data sources.


Sampling error - indicators

Not applicable


Coefficient of variation (CV) for the area (on the MS level)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

Survey on the yields of late crops, fruits and grapes 

Cereals for the production of grain (in %)

5,1 %

Dried pulses and protein crops (in %)

 45,2 %

Root crops (in %)

5,7 %

Oilseeds (in %)

Not existing

Other industrial crops (included all industrial crops besides oilseeds)  (in %)

37,5 %

Plants harvested green from arable land (in %)

3,8 %

Total vegetables, melons and strawberries (in %)

4,2 %

Cultivated mushrooms (in %)

Not collected

Total permanent crops (in %)

 2,0 %

Fruit trees (in %)

2,8 %

Berries (in %)

 5,5 %

Nut trees (in %)

 10,4 %

Citrus fruit trees (in %)

5,7 %

Vineyards (in %)

2,3 %

Olive trees (in %)

3,6 %

Additional comments            
6.3. Non-sampling error

Undercoverage and overcoverage

The probability of undercoverage and overcoverage in the crop production statistics exist. 

Considering the time lag between the AC2010 and the survey it can be expected that the sampling frame does not represent the entire population and problems of under coverage and over coverage may occur.

Statistical Register of agricultural holdings were not updated with administrative data sources. 

Misclassification 

Misclassification was assessed because of three years old AC2010 and frame. Post-stratification is done. However, post-stratification solve the problems with misclassification. 

Contact errors 

Each interviewer had to visit a private agriculture holding from the list at least 3 times and leave the notification of its re-arrival. These private agriculture holdings were treated as “they would not respond”.

For agriculture enterprises questionnaire had been sent by mail, and if agriculture enterprise hadn't responded we contact them by e mail and telephone.

Multiple listing errors 

Agriculture holdings were treated as ineligible if has been listed twice.

Measurement errors 

Statistics corrects possible errors of measurement using the logic-numeric control.

We are trying to avoid the measurement error by training of interviewers and supervisor, control data and process validation.

After data entry, outlier values of variables are checked and corrected if necessary. 

6.3.1. Coverage error

Over-coverage - rate

5,1%


Common units - proportion

Not applicable


  Data source 1 Data source 2 Data source 3 Data source 4 Data source 5 Data source 6 Data source 7 Data source 8 Data source 9
Name of the data source

Survey on the yields of late crops, fruits and grapes 

Error type Under-coverage
Over-coverage
Contact errors
Multiple listing errors
Degree of bias caused by coverage errors Unknown
What were the reasons for coverage errors?

Time lag exists between the AC 2010 and the survey. Statistical Farm Register was not updated in the mentioned period with data from administrative sources. During the year, happens that agricultural holdings is holding is no longer engaged in agriculture production. This information we have after field work, but only for holdings which is include in sample.

 

Which actions were taken for reducing the error or to correct the statistics?

SFR is updated from internal statistical sources, for example Livestock survey.

MONSTAT conducted survey which main goal was update of SFR, according to the information from Ministry of Agriculture and Rural development.

Additional comments
6.3.2. Measurement error

  Data source 1 Data source 2 Data source 3 Data source 4 Data source 5 Data source 6 Data source 7 Data source 8 Data source 9
Name of the data source

Survey on the yields of late crops, fruits and grapes 

Was the questionnaire based on usual concepts for respondents? YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

2

Preparatory (field) testing of the questionnaire? YES
Number of units participating in the tests? 

500

Explanatory notes/handbook for surveyors/respondents?  YES
On-line FAQ or Hot-line support for surveyors/respondents? YES
Were pre-filled questionnaires used? NO
Percentage of pre-filled questions out of total number of questions
Were some actions taken for reducing the measurement error or to correct the statistics? YES
If yes, describe the actions and their impact

Training of interviewers

Logic and numeric controls and process validation

6.3.3. Non response error

Unit non-response - rate

4,3%


Item non-response - rate

Not applicable


  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Survey on the yields of late crops, fruits and grapes 

Unit level non-response rate (in %)

4,3%

Item level non-response rate (in %)              
               - Min% / item
               - Max% / item
               - Overall%
Was the non-response been treated ? YES
Which actions were taken to reduce the impact of non-response?

Weight correction

Which items had a high item-level non-response rate? 
Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Reminders
Imputations
In case of imputation which was the basis? Imputation based on the same unit in previous data
Imputation based on similar units
Imputation based on other sources
In case of imputation, which was the imputation rate (%)?

Not applicable.

Estimated degree of bias caused by non-response? Unknown
Which tools were used for correcting the data?

Tailor made application in Excel and SAS.

Which organisation did the corrections?

Department of agriculture and fishery, MONSTAT

Additional comments
6.3.4. Processing error

Not available.

6.3.4.1. Imputation - rate

Not available.

6.3.5. Model assumption error

Not available.

6.4. Seasonal adjustment

Not relevant.

6.5. Data revision - policy

Statistical Office has adopted the revision policy and it is available on the website: https://www.monstat.org/eng/page.php?id=1411&pageid=3 Also, we have Statistical Release Calendar where all statistical data for the next year are planned. It is available on: https://www.monstat.org/eng/page.php?id=12&pageid=12 

6.6. Data revision - practice

Preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.


7. Timeliness and punctuality Top

Data for Survey on the yields of late crops, fruits and grapes for 2019 treated as preliminary, as well as all data from 2016. The main reason is that Survey on the yields of late crops, fruits and grapes in 2016 implemented together with Farm structure survey. The results of the Farm structure survey are considered preliminary until the final validation by Eurostat. So, the results for the Survey on the yields of late crops, fruits and grapes considered preliminary since 2016.

7.1. Timeliness

Time lag - first result

Preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020 and data sent to Eurostat 18 September, 2020.


Time lag - final result

Preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020 and data sent to Eurostat 18 September, 2020.


  Cereals Dried pulses and protein crops Root crops Oilseeds Other industrial crops Plants harvested green Vegetables and melons Strawberries Cultivated mushrooms Fruit trees Berries Nut trees Citrus fruit trees Vineyards Olive trees
How many main data releases there are yearly in the national crop statistics for the following types of crops?

 1

1

 1

 1

 1

 1

 1

 1

 1

 1

 1

 1

 1

 1

 1

How many of them are forecasts (releases before the harvest)?

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

When was the first  forecasting published for the crop year on which is reported? (day/month/year)
When were the final results published for the crop year on which is reported? (day/month/year)
Additional comments

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

There are only available the preliminary data of Survey on the yields of late crops, fruits and grapes was published on July 1, 2020.

7.2. Punctuality

Preliminary data was published 1 July 2020 according to the Statistical Release Calendar of Statistical Office. 

7.2.1. Punctuality - delivery and publication

Preliminary data was published 1 July 2020 according to the Statistical Release Calendar of Statistical Office. 


8. Coherence and comparability Top
8.1. Comparability - geographical

The methodology for Survey on the yields of late crops, fruits and grapes is in line with EU Regulation 543/2009, which allows comparability between countries.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not available.

8.2. Comparability - over time

Length of comparable time series

The data are fully comparable. Since the 2014, instead surveys based on estimates were introduced regular annual sample-based surveys. In aim of obtaining comparable data for period 2007 - 2013, was made the recalculation of data about agricultural land and crop production.


  Crops from arable land
(Table 1)
Vegetables, melons and strawberries (Table 2) Permanent crops
(Table 3)
Agricultural land use
(Table 4)
Have there been major breaks in the time series in the previous 5 years? NO NO NO NO
If yes, to which were they related?
If other, which?
Which items were affected?
Year of break (number)

 

 

 

 

Impact on comparability
Additional comments

 

 

 

 

8.3. Coherence - cross domain

With which other data sources the crop statistics data have been compared?  Farm structure survey 2016
Other
If others, which?

Ministry of Agriculture and Rural Development

If no comparisons have been made, why not?


Differences between ACS and other data sources (%)

Results of comparisons FSS 2016 Orchard survey 2017 IACS Other source(s)  In case of other sources, which?
Cereals    

The data are very similar to the data from Agricultural Census, and follow the trend of data on subsidies and investments from Ministry of Agriculture and Rural Development.  

Dried pulses and protein crops    
Root crops    
Oilseeds    
Other industrial crops (than oilseeds)    
Plants harvested green    
Total vegetables, melons and strawberries    
Vegetables and melons    
Strawberries    
Cultivated mushrooms  
Total permanent crops

The data are very similar to the data from Farm structure survey 2016. Differences for total permanent crops between the survey on the yields of late crops, fruits and grapes with FSS 2016 is -0,10%.

 
Fruit trees
Berries  
Nut trees  
Citrus fruit trees
Vineyards

Differences for vineyards between the survey on the yields of late crops, fruits and grapes with FSS 2016 is 0,69%.

Olive trees
If there were considerable differences, which factors explain them?  
8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

Not applicable.

8.6. Coherence - internal

Not applicable.


9. Accessibility and clarity Top
9.1. Dissemination format - News release

Availability Links
YES

https://www.monstat.org/eng/page.php?id=1357&pageid=62

9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

https://monstat.org/cg/novosti.php?id=3181

Publications in English Electronic

https://monstat.org/eng/novosti.php?id=2961

9.3. Dissemination format - online database

Data tables - consultations

Not applicable


  Availability Links
On-line database accessible to users NO
Website None
National language

http://www.monstat.org/cg/page.php?id=1354&pageid=62

9.4. Dissemination format - microdata access

Availability Links
NO
9.5. Dissemination format - other

Not applicable.

9.6. Documentation on methodology

  Availability Links
Methodological report National language

The methodology is not published on the website.

Quality Report English

http://monstat.org/eng/page.php?id=62&pageid=62

Metadata English

http://monstat.org/eng/page.php?id=62&pageid=62 

Additional comments  
9.7. Quality management - documentation

The Law on Official Statistics and Official Statistical System (Official Gazette of Montenegro No 18/12 and 047/19) defines the commitment to quality which ensures that official statistical producers in Montenegro work and cooperate in accordance with the international principles of statistical system quality. Development Strategy and Programme of Official Statistics for 2019-2023 define the objectives of development. One of objectives is quality management via monitoring the implementation of European Statistical Code of Practice. Pursuant to the ESS Quality Declaration, Article 338 of the Contract on EU Functioning, Regulation 759/2015 and Regulation 223/2009 as well as European Statistical Code of Practice, the following documents are in adoption procedure: 1. Quality Strategy of Statistical Office, 2. Guide for Implementation of Quality Strategy in Statistical Office. 3. Implementation Plan.

9.7.1. Metadata completeness - rate

Not applicable.

9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top

Efficiency gains if compared to the previous quality report? Staff further training
If other, which?

 

Burden reduction measures since the previous reference year 
If other, which?


11. Confidentiality Top
Restricted from publication
11.1. Confidentiality - policy
Restricted from publication
11.2. Confidentiality - data treatment
Restricted from publication


12. Comment Top


Related metadata Top


Annexes Top