Orchard (orch)

National Reference Metadata in ESS Standard for Orchard survey Quality Report Structure (esqrsor)

Compiling agency: Statistics Poland (Central Statistical Office)


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)
 



For any question on data and metadata, please contact: Eurostat user support

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1. Contact Top
1.1. Contact organisation

Statistics Poland

(Central Statistical Office)
1.2. Contact organisation unit

Agriculture Department
1.5. Contact mail address

00-925 Warszawa

Al. Niepodległości 208

Poland


2. Statistical presentation Top
2.1. Data description

Main characteristics

2.1.1 Describe shortly the main characteristics of the statistics  

Structural orchard statistics provide data on the area, age and density of apple, pear, peach, nectarine, apricot, citrus fruit and olive orchards and vineyards producing table grapes. The statistics are collected from surveys and administrative data sources. Data are collected on national level and for some variables also at NUTS1 level. The data collection concerns countries with more than 1000 ha of area for any single fruit tree type.
2.1.2 Which of the fruit tree types are covered by the data collection?  Dessert apple trees
Dessert pear trees
Dessert peach and nectarine trees
Apricot trees
2.1.3 If Other, please specify


Reference period

2.1.4 Reference period of the data collection 

2017
2.1.5 When was the data collection done (month(s) and year)

November 2017


National legislation

2.1.6 Is there a national legislation covering these statistics?  Yes
If Yes, please answer all the following questions.   
2.1.7 Name of the national legislation 

Order by the Prime Minister concerning Program of Statistical Surveys
2.1.8 Link to the national legislation 

http://www.stat.gov.pl/bip/4768_PLK_HTML.htm
2.1.9 Responsible organisation for the national legislation 

the Prime Minister
2.1.10 Year of entry into force of the national legislation 

2017
2.1.11 Please indicate which variables required under EU regulation are not covered by national legislation, if any.
2.1.12 Please indicate which national definitions differ from those in the EU regulation, if any. 
2.1.13 Please indicate which additional variables have been collected if compared to Regulation (EU) 1337/2011, if any?
  • area of orchard and its change in comparison to the previous year,
  • age and horticultural education of farmer,
  • possibilities of orchard irrigation and storage of fruit,
2.1.14 Is there a legal obligation for respondents to reply?  Yes


Additional comments on data description
2.2. Classification system

Species and variety group classification, age and density classifications available in RAMON
2.3. Coverage - sector

Growing of perennial crops (NACE A01.2)
2.4. Statistical concepts and definitions

See: Orchard statistics Handbook

2.4.1 Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? No
2.4.2 If Yes, please specify the items and the differences
2.4.3 If the fruits for industrial processing are not separately surveyed, are they included in dessert fruit categories? Yes
2.4.4 If yes, for which fruit tree types? Apple trees
Pear trees
Peach and nectarine trees
In case data are delivered for one of the items below, describe the 10 biggest varieties included in the item:   
2.4.5 Other dessert apples n.e.c.

Ligol,

Gloster,

Antonówka,

Cortland,

Eliza

Jonathan group,

Alwa,

Mutsu,

Rubin,

Malinowa
2.4.6 Other dessert pears n.e.c.

Lukasówka,

Klapsa Faworytka,

Xenia,

Patten,

General Leclerc,

Komisówka,

Concorde,

Alfa,

Amfora,

Erika
2.4.7 Other oranges n.e.c.
2.4.8 Other small citrus fruits, including hybrids
2.5. Statistical unit

Utilised agricultural area used for the cultivation of permanent crops mentioned in point 2.1, cultivated by an agricultural holding producing entirely or mainly for the market.
2.6. Statistical population

All agricultural holdings growing entirely or mainly for the market permanent crops mentioned in point 2.1.
2.7. Reference area
2.7.1 Geographical area covered

The entire territory of the country.
2.7.2 Which special Member State territories are included?
2.8. Coverage - Time

Since 2007 every 5 years (additionally in 1998 and in 2004 - nearly according to EU regulation)
2.9. Base period


3. Statistical processing Top
3.1. Source data

Overall summary

3.1.1 Total number of different data sources used

1
The breakdown is as follows: 
3.1.2 Total number of sources of the type "Census"

0
3.1.3 Total number of sources of the type "Sample survey"

1
3.1.4 Total number of sources of the type "Administrative source"

0
3.1.5 Total number of sources of the type "Experts"

0
3.1.6 Total number of sources of the type "Other sources"

0


Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.1 of the annexed Excel file 
3.1.7 Name/Title
3.1.8 Name of Organisation responsible
3.1.9 Main scope
3.1.10 Target fruit tree types
3.1.11 List used to build the frame
3.1.12 Any possible threshold values
3.1.13 Population size
3.1.14 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 3.1 of the annexed Excel file 
3.1.15 Name/Title

Orchard survey by species and varieties in 2017
3.1.16 Name of Organisation responsible

Statistics Poland
3.1.17 Main scope

Area of plantations by age and density trees
3.1.18 Target fruit tree types Dessert apple trees
Dessert pear trees
Dessert peach and nectarine trees
Peach and nectarine trees for industrial processing (including group of Pavie)
3.1.19 List used to build the frame

Farms which in Farm Structure Survey, June Agricultural Survey or in Agricultural Census showed orchard area, especially apple, pear, peach and apricot plantations.
3.1.20 Any possible threshold values

farms with area of orchards greater or equal 10ha were all surveyed
3.1.21 Population size

195701 private farms and 207 farms of legal persons and organisational units without legal personality
3.1.22 Sample size

20 thousand private farms and 207 farms of legal persons and organisational units without legal personality
3.1.23 Sampling basis Multiple frame
3.1.24 If Other, please specify
3.1.25 Type of sample design Stratified
3.1.26 If Other, please specify
3.1.27 If Stratified, number of strata

96 for private farms and 1 for farms of legal persons and organisational units without legal personality
3.1.28 If Stratified, stratification criteria Unit size
Unit location
Unit legal status
Unit specialization
Other
3.1.29 If Other, please specify

in each NUTS2 region algorithm of optimal stratification and allocation was applied using as variables: total area of orchards and sum of areas for: apple trees, pear trees, and other trees
3.1.30 Additional comments


Administrative source

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 3.1 of the annexed Excel file 
3.1.31 Name/Title
3.1.32 Name of Organisation responsible
3.1.33 Contact information (email and phone)
3.1.34 Main administrative scope
3.1.35 Target fruit tree types 
3.1.36 Geospatial Coverage
3.1.37 Update frequency
3.1.38 Legal basis
3.1.39 Are you able to access directly to the micro data?
3.1.40 Are you able to check the plausibility of the data, namely by contacting directly the units?
3.1.41 How would you assess the proximity of the definitions and concepts (including statistical units) used in the administrative source with those required in the EU regulation?
3.1.42 Please list the main differences between the administrative source and the statistical definitions and concepts
3.1.43 Is a different threshold used in the administrative source and statistical data?
3.1.44 If Yes, please specify
3.1.45 Additional comments


Experts

If there is more than one Expert source, please describe the main one below and the additional ones in table 3.1 of the annexed Excel file 
3.1.46 Name/Title
3.1.47 Primary purpose
3.1.48 Target fruit tree types
3.1.49 Legal basis
3.1.50 Update frequency
3.1.51 Expert data supplier
3.1.52 If Other, please specify
3.1.53 How would you assess the quality of those data?
3.1.54 Additional comments


Other sources

If there is more than one other statistical activity, please describe the main one below and the additional ones in table 3.1 of the annexed Excel file 
3.1.55 Name/Title
3.1.56 Name of Organisation
3.1.57 Primary purpose
3.1.58 Target fruit tree types 
3.1.59 Data type
3.1.60 If Other, please specify
3.1.61 How would you assess the quality of those data?
3.1.62 Additional comments
3.2. Frequency of data collection

Every 5 years
3.3. Data collection

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 3.3 of the annexed Excel file 
3.3.1 Name/Title
3.3.2 Methods of data collection
3.3.3 If Other, please specify
3.3.4 If face-to-face or telephone interview, which method is used?
3.3.5 Data entry method, if paper questionnaires?
3.3.6 Please annex the questionnaire used (if very long: please provide the hyperlink)
3.3.7 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 3.3 of the annexed Excel file 
3.3.8 Name/Title

Orchard survey by species and varieties in 2017
3.3.9 Methods of data collection Electronic questionnaire
Telephone interview
Face-to-face interview
3.3.10 If Other, please specify

No paper questionnaires - they were used only in exceptional situations
3.3.11 If face-to-face or telephone interview, which method is used? Electronic questionnaire
3.3.12 Data entry method, if paper questionnaires? Manual
3.3.13 Please annex the questionnaire used (if very long: please provide the hyperlink)

http://form.stat.gov.pl/formularze/2017/passive/R-r-s.pdf
3.3.14 Additional comments

The questionnaire available on the website should be treated only as a model (pattern) indicating the scope of the survey. Only the electronic version of questionnaire was used (electronic application).

 

 


Administrative source

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 3.3 of the annexed Excel file 
3.3.15 Name/Title
3.3.16 Extraction date
3.3.17 How easy is it to get access to the data?
3.3.18 Data transfer method
3.3.19 Additional comments


Experts

If there is more than one other statistical activity, please describe the main one below and the additional ones in table 3.3 of the annexed Excel file 
3.3.20 Name/Title
3.3.21 Methods of data collection
3.3.22 Additional comments
3.4. Data validation
3.4.1 Which kind of data validation measures are in place? Automatic
Manual
3.4.2 What do they target? Completeness
Outliers
Consistency
Aggregates
3.4.3 If Other, please specify
3.5. Data compilation
3.5.1 Describe the data compilation process

Each step of weighting was described separately in attached file
3.5.2 Additional comments


Annexes:
Weighting
3.6. Adjustment

not applicable


4. Quality management Top
4.1. Quality assurance
4.1.1 Is there a quality management system used in the organisation? Yes
4.1.2 If yes, how is it implemented?

quality report
4.1.3 Has a peer review been carried out? No
4.1.4 If Yes, which were the main conclusions?
4.1.5 What quality improvements are foreseen? Improve data validation
Further automation
Further training
4.1.6 If Other, please specify
4.1.7 Additional comments
4.2. Quality management - assessment

Development since the last quality report

4.2.1 Overall quality Stable
4.2.2 Relevance Stable
4.2.3 Accuracy and reliability Deterioration
4.2.4 Timeliness and punctuality Improvement
4.2.5 Comparability Stable
4.2.6 Coherence Stable
4.2.7 Additional comments


5. Relevance Top
5.1. Relevance - User Needs
5.1.1 If certain user needs are not met, please specify which and why
5.1.2 Please specify any plans to satisfy needs more completely in the future

In the next survey is planned improvement of the way of data collection by Internet (in CAII method) i.e. on-line questionnaires directly by portal - without necessity of downloading special application).
5.1.3 Additional comments
5.2. Relevance - User Satisfaction
5.2.1 Has a user satisfaction survey been conducted? No
If Yes, please answer all the following questions 
5.2.2 Year of the user satisfaction survey
5.2.3 How satisfied were the users?
5.2.4 Additional comments
5.3. Completeness
5.3.1 Data completeness - rate

nearly 100%
5.3.2 If not complete, which characteristics are missing?
5.3.3 Additional comments


6. Accuracy and reliability Top
6.1. Accuracy - overall
6.1.1 How good is the accuracy? Good
6.1.2 What are the main factors lowering the accuracy? Non-response error
6.1.3 If Other, please specify
6.1.4 Additional comments
6.2. Sampling error

Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.2 of the annexed Excel file 
6.2.1 Name/Title

Orchard survey by species and varieties in 2017
6.2.2 Methods used to assess the sampling error Relative standard error
6.2.3 If Other, please specify
6.2.4 Methods used to derive the extrapolation factor Basic weight
Non-response
Wrong classifaction
6.2.5 If Other, please specify
6.2.6 If coefficients of variation are calculated, please describe the calculation methods and formulas

We applied formulas for variance estimation of total known from classical theory for stratified random sampling.

Method of calculation relative standard errors (CV) has been described in attached Word file.
6.2.7 Sampling error - indicators

Please provide the coefficients of variation in %

  CV (%)
Dessert apple trees 1,3
Apple trees for industrial processing  
Dessert pear trees 3,5
Pear trees for industrial processing  
Apricot trees 10,5
Dessert peach and nectarine trees 11,8
Peach and nectarine trees for industrial processing (including group of Pavie)  
Orange trees  
Small citrus fruit trees  
Lemon trees  
Olive trees  
Table grape vines  
6.2.8 Additional comments


Annexes:
relative standard errors
6.3. Non-sampling error

See sections below.
6.3.1. Coverage error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.1 Name/Title
Over-coverage
6.3.1.2 Does the sample frame include wrongly classified units that are out of scope?
6.3.1.3 What methods are used to detect the out-of scope units?
6.3.1.4 Does the sample frame include units that do not exist in practice?
6.3.1.5 Over-coverage - rate
6.3.1.6 Impact on the data quality
Under-coverage
6.3.1.7 Does the sample frame include all units falling within the scope of this survey?
6.3.1.8 If Not, which units are not included?
6.3.1.9 How large do you estimate the proportion of those units? (%)
6.3.1.10 Impact on the data quality
Misclassification
6.3.1.11 Impact on the data quality
Common units
6.3.1.12 Common units - proportion

not applicable
6.3.1.13 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.14 Name/Title

Orchard survey by species and varieties in 2017
Over-coverage
6.3.1.15 Does the sample frame include wrongly classified units that are out of scope? Yes
6.3.1.16 What methods are used to detect the out-of scope units?

this is the result from the answer to the initial question from the questionnaire during the survey
6.3.1.17 Does the sample frame include units that do not exist in practice? Yes
6.3.1.18 Over-coverage - rate

13,1%
6.3.1.19 Impact on the data quality Moderate
Under-coverage 
6.3.1.20 Does the sample frame include all units falling within the scope of this survey? Yes
6.3.1.21 If Not, which units are not included?

not applicable
6.3.1.22 How large do you estimate the proportion of those units? (%)

not applicable
6.3.1.23 Impact on the data quality None
Misclassification
6.3.1.24 Impact on the data quality Low
Common units 
6.3.1.25 Common units - proportion
6.3.1.26 Additional comments

There were difficulties with contact with some units and some units changed character of their production.


Administrative data

These questions only apply to administrative sources. If there is more than one administrative source, please describe the main source below and the additional ones in table 6.3 of the annexed Excel file 
6.3.1.27 Name/Title of the administrative source
Over-coverage
6.3.1.28 Does the administrative source include wrongly classified units that are out of scope?
6.3.1.29 What methods are used to detect the out-of scope units?
6.3.1.30 Does the administrative source include units that do not exist in practice?
6.3.1.31 Over-coverage - rate
6.3.1.32 Impact on the data quality
Under-coverage
6.3.1.33 Does the administrative source include all units falling within the scope of this survey?
6.3.1.34 If Not, which units are not included?
6.3.1.35 How large do you estimate the proportion of those units? (%)
6.3.1.36 Impact on the data quality
Misclassification 
6.3.1.37 Impact on the data quality
6.3.1.38 Additional comments
6.3.2. Measurement error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.2.1 Name/Title
6.3.2.2 Is the questionnaire based on usual concepts for respondents?
6.3.2.3 Number of censuses already performed with the current questionnaire?
6.3.2.4 Preparatory testing of the questionnaire?
6.3.2.5 Number of units participating in the tests? 
6.3.2.6 Explanatory notes/handbook for surveyors/respondents? 
6.3.2.7 On-line FAQ or Hot-line support for surveyors/respondents?
6.3.2.8 Are there pre-filled questions?
6.3.2.9 Percentage of pre-filled questions out of total number of questions
6.3.2.10 Other actions taken for reducing the measurement error?
6.3.2.11 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.2.12 Name/Title
6.3.2.13 Is the questionnaire based on usual concepts for respondents? Yes
6.3.2.14 Number of surveys already performed with the current questionnaire?

0
6.3.2.15 Preparatory testing of the questionnaire? Yes
6.3.2.16 Number of units participating in the tests? 

examples during the training only
6.3.2.17 Explanatory notes/handbook for surveyors/respondents?  Yes
6.3.2.18 On-line FAQ or Hot-line support for surveyors/respondents? No
6.3.2.19 Are there pre-filled questions? No
6.3.2.20 Percentage of pre-filled questions out of total number of questions

0
6.3.2.21 Other actions taken for reducing the measurement error?

No
6.3.2.22 Additional comments
6.3.3. Non response error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.3.1 Name/Title of the survey
6.3.3.2 Unit non-response - rate
6.3.3.3 How do you evaluate the recorded unit non-response rate in the overall context?
6.3.3.4 Measures taken for minimising the unit non-response
6.3.3.5 If Other, please specify
6.3.3.6 Item non-response rate
6.3.3.7 Item non-response rate - Minimum
6.3.3.8 Item non-response rate - Maximum
6.3.3.9 Which items had a high item non-response rate? 
6.3.3.10 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.3.11 Name/Title of the survey

Orchard survey by species and varieties in 2017
6.3.3.12 Unit non-response - rate

24.8%
6.3.3.13 How do you evaluate the recorded unit non-response rate in the overall context? High
6.3.3.14 Measures taken for minimising the unit non-response Weighting
6.3.3.15 If Other, please specify
6.3.3.16 Item non-response rate

In general, the completeness of each item was rather good.
The electronic application enforced the completion of all required items.
6.3.3.17 Item non-response rate - Minimum

Only single cases
6.3.3.18 Item non-response rate - Maximum
6.3.3.19 Which items had a high item non-response rate? 

It concerns every item i.e. whole questionnaire
6.3.3.20 Additional comments
6.3.4. Processing error

Census

These questions only apply to censuses. If there is more than one census, please describe the main census below and the additional ones in table 6.3 of the annexed Excel file 
6.3.4.1 Name/Title
6.3.4.2 Imputation - rate
6.3.4.3 Imputation - basis
6.3.4.4 If Other, please specify
6.3.4.5 Additional comments


Sample survey

These questions only apply to surveys. If there is more than one survey, please describe the main survey below and the additional ones in table 6.3 of the annexed Excel file 
6.3.4.6 Name/Title

Orchard survey by species and varieties in 2017
6.3.4.7 Imputation - rate

not applicable
6.3.4.8 Imputation - basis
6.3.4.9 If Other, please specify
6.3.4.10 How do you evaluate the impact of imputation on Coefficients of Variation?
6.3.4.11 Additionnal comments

Imputation was not used
6.3.5. Model assumption error

not applicable
6.4. Seasonal adjustment

not applicable
6.5. Data revision - policy

Data from the survey are made available as final after all validations and corrections, however, in case of any errors, we check and correct the dataset again. After that the corrected data are also disseminated.
6.6. Data revision - practice
6.6.1 Data revision - average size

not applicable
6.6.2 Were data revisions due to conceptual changes (e.g. new definitions)  carried out since the last quality report? No
6.6.3 What was the main reason for the revisions?

not applicable
6.6.4 How do you evaluate the impact of the revisions?
6.6.5 Additional comments


7. Timeliness and punctuality Top
7.1. Timeliness
7.1.1 When were  the first  results for the reference period published?

November 2018
7.1.2 When were  the final results for the reference period published?

November 2018
7.1.3 Reasons for possible long production times?
7.2. Punctuality
7.2.1 Were data released nationally according to a pre-announced schedule (Release Calendar)? Yes
7.2.2 If Yes, were data released on the target date?
7.2.3 If No, reasons for delays?
7.2.4 Number of days between the national release date of data and the target date

0


8. Coherence and comparability Top
8.1. Comparability - geographical

To be assessed by Eurostat
8.1.1. Asymmetry for mirror flow statistics - coefficient
8.2. Comparability - over time
8.2.1 Length of comparable time series

since 2007 - 3
8.2.2 Have there been major breaks in the time series? No
8.2.3 If Yes, please specify the year of break and the reason
8.2.4 Additional comments
8.3. Coherence - cross domain
8.3.1 With which other national data sources have the data been compared? None
8.3.2 If Other, please specify

 
8.3.3 Describe briefly the results of comparisons

Results should be expressed as percentage deviation from the corresponding areas in the orchard survey.

  Annual Crop Statistics (2017) FSS (2016) IACS Other source
Dessert apple trees        
Apple trees for industrial processing        
Dessert pear trees        
Pear trees for industrial processing        
Apricot trees        
Dessert peach and nectarine trees        
Peach and nectarine trees for industrial processing (including group of Pavie)        
Orange trees        
Small citrus fruit trees        
Lemon trees        
Olive trees        
Table grape vines        
8.3.4 If no comparisons have been made, explain why

Data obtained from the orchard survey are not comparable with data received from other sources. The orchard survey was not dedicated to all orchard holdings but mainly to holdings with some special production (apple, pear, peach and apricot production). Moreover the orchard survey was carried out in the late autumn 2017 whereas Farm Structure Survey was carried out in June/July 2016. In Annual Crop Statistics we include many holdings with very small fruit trees area (especially peach and appricot trees) which produce also fruit for the market, but in the whole population of Polish orchards farms, these are marginal crops and it is difficult "to find" them in the sample survey.
8.3.5 Additional comments
8.4. Coherence - sub annual and annual statistics

Coherence between outputs of the survey and annual estimates and annual statistical outputs was rather good, but only for main variables.
8.5. Coherence - National Accounts

It has not been examined
8.6. Coherence - internal

The survey internally consistent


9. Accessibility and clarity Top
9.1. Dissemination format - News release
9.1.1 Do you publish a news release? No
9.1.2 If Yes, please provide a link
9.2. Dissemination format - Publications
9.2.1 Do you produce a paper publication? No
9.2.2 If Yes, is there an English version?
9.2.3 Do you produce an electronic publication? Yes
9.2.4 If Yes, is there an English version? No
9.2.5 Please provide a link

http://stat.gov.pl/obszary-tematyczne/rolnictwo-lesnictwo/uprawy-rolne-i-ogrodnicze/produkcja-ogrodnicza-badanie-sadow-w-2017-roku,8,4.html
9.3. Dissemination format - online database
9.3.1 Data tables - consultations

not applicable
9.3.2 Is an on-line database accessible to users? No
9.3.3 Please provide a link
9.4. Dissemination format - microdata access
9.4.1 Are micro-data accessible to users? No
9.4.2 Please provide a link
9.5. Dissemination format - other
9.6. Documentation on methodology
9.6.1 Are national reference metadata files available? No
9.6.2 Please provide a link
9.6.3 Are methodological papers available? No
9.6.4 Please provide a link
9.6.5 Is a handbook available? No
9.6.6 Please provide a link
9.7. Quality management - documentation
9.7.1 Metadata completeness - rate

1:1
9.7.2 Metadata - consultations

several
9.7.3 Is a quality report available? No
9.7.4 Please provide a link


10. Cost and Burden Top
10.1 Efficiency gains if compared to the previous quality report Further automation
Further training
Other
10.2 If Other, please specify

simplification
10.3 Burden reduction measures since the previous quality report Less variables surveyed
More user-friendly questionnaires
Easier data transmission
10.4 If Other, please specify


11. Confidentiality Top
11.1. Confidentiality - policy
11.1.1 Are confidential data transmitted to Eurostat? No
11.1.2 If yes, are they confidential in the sense of Reg. (EC) 223/2009?
11.1.3 Describe the data confidentiality policy in place

Statistical confidentiality - Law on official statistics, 29th June 1995, art. 10 and art. 38.

 
11.2. Confidentiality - data treatment
11.2.1 Describe the procedures for ensuring confidentiality during dissemination

Regulation

The collected and gathered in the statistical surveys of official statistics individual and personal data (personal data which can be linked or can identify natural persons or individual data characterising business entities) shall be confidential and subject to particular protection. The data shall be used exclusively for statistical calculations, compilations and analyses (statistical confidentiality - Law on official statistics, 29th June 1995, art. 10 and art. 38).

Providing or use of individual and personal data for other than specified above purposes is prohibited.
11.2.2 Additional comments


12. Comment Top

Since 2018 the name of our institution has been changed from Central Statistical Office into Statistics Poland

 

 

 


Related metadata Top


Annexes Top
ESQRS_ANNEX_PL