Farm structure (ef)

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: ISTAT - Italian National Statistical Institute

Time Dimension: 2016-A0

Data Provider: IT1

Data Flow: FSS_ESQRS_A


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: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
1.1. Contact organisation

ISTAT - Italian National Statistical Institute

1.2. Contact organisation unit

ATC - Division for agricultural statistics and surveys

1.5. Contact mail address Restricted from publication


2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 

In 1967, the Italian National Statistical Institute (Istat) carried out the first Italian sample survey on agricultural holdings aiming at providing a statistical tool able to draw a coherent and consistent picture on the primary sector’s structure.
The following surveys were carried out in 1975, 1977, 1982, 1985, 1987, 1990, 1993, 1995, 1996, 1997, 1998, 1999, 2000, 2003, 2005, 2007, 2010, 2013 and 2016.
Four of these surveys (1982, 1990, 2000, 2010) were carried out as full surveys (censuses) and they provided the frame for the following sample surveys.
Starting with the 1993 edition, the survey added to the structural scopes stated by Council Regulation (EEC) n.571/88 the short terms objectives stated by the following European normative:

  • 837/90 (concerning statistical information to be supplied by the member states on cereals production);
  • 959/93, 2197/95, 296/2003 (concerning statistical information to be supplied by member states on crop products other than cereals);
  • 93/16 (on statistical surveys of milk and milk products);
  • 93/23 (on the statistical surveys to be carried out on pig production);
  • 93/24 (on the statistical surveys to be carried out on bovine animal production);
  • 93/25 (on the statistical surveys to be carried out on sheep and goats stocks).

Furthermore, some additional topics were surveyed by a specialised section of the questionnaire as shown in the following list:
- 1997 - fruit trees production;
- 1998 - environments;
- 1999 - rural development.
Since the 2003 survey, an approach closer to local administrations’ purposes has been used; sample design has been determined considering accuracy on variables of local interest and some items were introduced in the questionnaire in order to take into account some local needs. 

 

2. Legal framework of the national survey 
- the national legal framework The FSS is considered of national interest and for this reason it is included in the national statistical programme 2014-2016, updating 2015-2016 (code: PSN-IST 02346), approved by Prime Minister's Decree of 10 September 2015 and it is included in the set of surveys for which answers are mandatory.
The survey activities performed by the Regions and Autonomous Provinces of Trento and Bolzano are established in principle by the Protocol of Understanding signed by Istat, Ministry of agriculture, AGEA and Regions, on 5 September 2012.
- the obligations of the respondents with respect to the survey The respondents are obliged to answer the survey since it is included in the national statistical programme, even if there is no fine in case they do not respond.
- the identification, protection and obligations of survey enumerators

The list of survey enumerators is available from the system of monitoring and management of the survey (SGR). The enumerators are obliged to respect the rules concerning confidentiality and security of the data collected, according to the law on the protection of personal data (Legislative Decree of 9 September 1989, n.322 as amended by Legislative Decree n. 281/99, Legislative Decree of June 30, 2003 – n. 196 and Deontology code and of good practice for the treatment of the personal data for statistical and scientific research within the National Statistical System).



2.2. Classification system

[Not requested]

2.3. Coverage - sector

[Not requested]

2.4. Statistical concepts and definitions
List of abbreviations
ISTAT = National Statistical Institute
MIPAAF = Ministry of Agricultural, Food and Forestry Policies
IACS = Integrated Administration and Control System
AGEA: Agency for the Disbursement in Agriculture
FR = Farm register (register of agricultural holdings mainly based on IACS and BDN data)
BDN = National Register of Livestock (Ministry of Health)
ISPRA =National Institute for Environmental Protection and Research
SISTAN = National Statistical System
SIDI = Informative System on Statistical Processes Quality
SGR = Survey Managment System
GINO = Gathering INformation Online (electronic questionnaire)
CUAA = Unique Code of Agricultural Holdings
2.5. Statistical unit
The national definition of the agricultural holding

The agricultural holding in FSS was defined as a single unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex I to the European Parliament and Council Regulation (EC) No 1166/2008 within the economic territory of the European Union, either as its primary or secondary activity.

These categories of holdings have been also included in the survey:

  • Agricultural holdings managed by non-profit and public entities;
  • Agricultural holdings managed by industrial, good and services enterprises;
  • Holdings with livestock only for reproductive goals, breeding of horses and poultry hatchery;
  • Agricultural holdings without agricultural land (exclusively zoo-technical ones);
  • Zoo-technical holdings which use pasture and meadows belonging to Municipalities and/or other public/private entities;
  • Common lands;
  • Holdings with NACE code rev. 2 number 01.61, which have the task of good status maintenance of land.


2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
The number of holdings before application of any thresholds, according to the EU definition of agricultural holding (art. 2 a and Annex I of EU regulation 1166/2008) is: 1 606 879 (according to Farm register 2014, the most updated available survey frame).
2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage

The geographical coverage is at  NUTS 0 level (national level), including the islands.

The coverage includes all agricultural holdings that exceed at least one of the following thresholds :

A_3_1  UAA - Utilized Agricultural Area  >= 1 hectare

B_4_3  Olive plantations

>= 0.6 hectare

C_2      Bovine animals

>=10 heads

C_4      Pigs

>= 50 heads

C_3_1  Sheep

>=20 heads

C_3_2  Goats

>=20 heads

C_5      Poultry

>= 1000 heads

 

3. The number of holdings in the national survey coverage
The number of holdings in the final weighted population, according to the above definition and thresholds, is 1 145 706.

 

4. The survey coverage of the records sent to Eurostat

The same coverage.

 

5. The number of holdings in the population covered by the records transferred to Eurostat
The records sent to Eurostat cover 1 145 706 agricultural holdings.

 

6. Holdings with standard output equal to zero included in the records sent to Eurostat

We do not calculate SO.

7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat

The survey includes all agricultural holdings with UAA>=1 ha (A_3_1 >=1 ha), thus art. 3.2 is not applicable.

8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat

With respect to the thresholds specified in Annex II of the Regulation 1166/2008 it was not possible to take into consideration the following conditions:

  • open fresh vegetables>=0.50 ha;
  • tobacco>=0.50 ha;
  • covered fresh vegetables>=0.10 ha;
  • covered flowers>=0.10 ha;
  • breeding sows>=10 heads.
The reason of that is that the frame (Farm register) does not contain these detailed information for all the units.
Anyway, given the importance of olive plantations in Italy (almost 50% of the total permanent crops), we added one additional threshold on Olive plantations (>=0.6 ha), so we included all the holdings that exceed this threshold.
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding

The criterion used to determine the NUTS 3 region of the holding refers to the location of the main agricultural building.
This building can have different functions: it can be the stable for livestock (for livestock farms) or the main building, which is usually located close to the agricultural activities for the crops and mixed farms (where mechanical equipment used for agricultural activity is stored or buildings used for products storage purpose). Where within the agricultural land perimeter there are no buildings, the holding headquarter is where the criterion of the biggest parcel was used.

 

2.8. Coverage - Time
Reference periods/dates of all main groups of characteristics (both included in the EU Regulation 1166/2008 and surveyed only for national purposes)

The reference periods/dates from the Regulation 1166/2008 are respected.
These are:

  • for livestock characteristics the reference date  is: 1 December 2016.
  • information on land use, labour force and other gainful activities related to the farms refer to the agricultural marketing year: 1st November 2015 to 31st October 2016.
  • data on the professional status of the holder, his/her family and related, on the head of farm, agricultural skills of farm manager refer to the 12 months prior to 31st October 2016.
  • characteristics concerning rural development support refer to the period 2014-2016.

 

2.9. Base period

[Not requested]


3. Statistical processing Top
1.Survey process and timetable

                                                                   Calendar of major FSS 2016 operations

Key Activity

Time (closing time)

Definition of survey objective and requirements December 2015
Set-up organisation of the survey
January 2016
Definition of the survey variables
March 2016
Design of the sampling frame and sampling procedures April 2016
Design of data processing procedures (electronic questionnaire) May 2016
Set up of an Informatical System for data acquision and monitoring September 2016
Sampling frame construction and sample selection August 2016
Delivery of the FSS materials to the network November 2016
Training of interviewers
November 2016
Informative letter to the holders in the list November 2016
Data collection November 2016-May 2017
Data entry November 2016-May 2017
Data correction and imputation
May 2018
Weight calculation and estimation February 2018
Calculation of quality indicators May 2018
Aggregation and tabulation June 2018
Data transmission to Eurostat June 2018
Data dissemination July 2018


In the preparatory phase a lot of meetings with the bodies involved (Regions) have been done, in order to discuss the organisation and the content of the survey. 

Training for FSS 2016
The aim of the FSS training was to transfer methods and organisation for FSS operation to all the network: Contents (FSS organisation, Questionnaire, Definitions, Regulation, etc.), Methods (techniques of statistical data gathering), Roles (FSS operators function).

Training beneficiaries were:
- Territorial Responsible persons and Coordinators (Regions/Provinces),
- Interviewers.

The developed idea was a “fall” training. It consisted in a training for trainers organised in two steps. The first one was a training section between Istat  and Territorial Responsible (Regions/Provinces); the second one was a training  between Territorial Responsibles and the interviewers.

The contents of the training were focussed on:

1. Questionnaire

- contents and definitions,

- how to compile it.

2. Informatical System for data acquision and monitoring

- how to create and monitor the network,

- how to register data on the electronic questionnaire.

Training instruments: classic classroom session supported by slides and handbooks, and simulation to show how to use the informatic tools supporting the survey, surfing the net.

Help desk for the FSS 2016 network
A dedicated mailbox has been set up in order to send specific questions on the survey.

E-mail spa@istat.it

The Survey Management System  (SGR)

In order to support the survey network in conducting the various steps of the FSS survey, an information technology system has been implemented. More specifically, a dedicated application based on the use of web technologies has been set up, enabling data collection and the monitoring of the various data processing phases. The website set up ensures maximum data security during the data transmission and storage phases, in compliance with the National Statistical Institute’s standard rules. The management system can be seen as a distributed workflow system in which each operator can work independently, following a clearly defined procedure. This operating procedure has produced benefits in terms of timeliness, data quality and costs.
The system includes over 50 functions grouped by type and organized into 5 macro-areas:

  • Questionnaire – includes all functions strictly connected to the survey (recording of the interview, data entry);
  • Operators – enables the survey network and user profiles to be defined and the units in the FSS 2016 list to be assigned to a specified enumerator;
  • Summary reports – includes a set of survey progress monitoring reports;
  • Summary forms – includes all functions for primary variables data collection and a summary of the primary variables necessary for publication of provisional data;
  • Utilities – includes a set of network support functions spanning the entire survey process.

 

2. The bodies involved and the share of responsibilities among bodies

 

Key Activity

Actor

Definition of survey objective and requirements Istat- Regions- Mipaaf
Set-up organisation of the survey Istat
Definition of the survey variables Istat- Regions
Design of the sampling frame and sampling procedures Istat
Design of data processing procedures (electronic questionnaire) Istat
Set up of an Informatical System for data acquision and monitoring Istat
Sampling frame construction and sample selection Istat
Delivery of the FSS materials to the network Istat
Training of interviewers Istat- Regions
Informative letter to the holders in the list Istat
Data collection Regions
Data entry Regions
Data correction and imputation Istat
Weight calculation and estimation Istat
Calculation of quality indicators Istat
Aggregation and tabulation Istat
Data transmission to Eurostat Istat
Data dissemination Istat


The Regions have been involved in:
- defining the contents of the questionnaire;
- defining the sample survey size (with respect to their territory);
- recruiting the interviewers;
-
training the interviewers;
- creating the network (via SGR) for their territory;
- doing a first validation of microdata before sending them to Istat;
- monitoring the ongoing of the activities with respect to their territory.

 

3. Serious deviations from the established timetable (if any)

It was necessary to postpone some deadlines relating to the data collection of the questionnaires since a plurality of causes created difficulties, at territorial level, in keeping the schedule.
The most important of them are: a very important earthquake in the centre of Italy (August 2016) and delays in starting creating the network of interviewers in some Regions (Lazio, Sicilia, Toscana).

The edit and imputation procedure, and final dissemination deadline, had to be postponed because of that and also because of an internal reorganization of Istat that caused difficulties in the transfer of the activities between more Directions.

                                                                                                                        Activities postponed

 

Actors

Original Deadline

New date

Data Collection

Regions

October 2016 - March 2017

November 2016 – May 2017

Data Correction and Imputation

Istat

April 2017- November 2017

June 2017 - May 2018

Data transmission to Eurostat

Istat

December 2017

June 2018

3.1. Source data
1. Source of data
Data have been collected by a sample survey, except those concerning Rural developement that have been taken from an administrative source (IACS).
2. (Sampling) frame

The source of the frame was the statistical register (Farm Register).
The type of frame is a list frame.
The time reference of the Farm Register, available as sampling
frame for FSS 2016, was 2013. The Farm Register is updated yearly but because of the availability of data from the different sources that compose it, data included in the frame were delayed in comparison with date of survey.
In addition this frame has been integrated with 2015 Livestock National Register in order to sample with most updated data.

 

3. Sampling design 
3.1 The sampling design

The sample design is a probability design: one-stage stratified random sampling of holdings.  Simple random sampling was used to draw units in each stratum.

3.2 The stratification variables

An independent sample has been drawn at each NUTS2 level since there were different target variables for each sub-domain.
Holdings were stratified by:

  • regions (NUTS2).
  • by means of an iterative genetic algorithm, package R: Sampling strata, where the variables were UAA,LSU (in all regions) and major crops and livestock depending on the region.

Please note that the package "sampling strata" has been independently applied to each NUTS2 region.

3.3 The full coverage strata

The package Sampling Strata automatically computes which are the take-all strata (in a take-all stratum all the units belonging to the stratum are included in the sample).

The criteria used to build the take-all strata is based on the size of the farm (exceeding a certain number of ha UAA or LSU in each Region).

3.4 The method for the determination of the overall sample size
The package iterates the Bethel algorithm to compute the optimal allocation in each strata of the sample size and then compares the overall sample-size according to different stratifications. It then selects the stratifications which lead to the minimum sample sizes until it converges to the minimum.
3.5 The method for the allocation of the overall sample size

Optimal allocation was determined by means of the Bethel algorithm by imposing for each NUTS2 level CVs so to satisfy Annex IV of Regulation 1166/2008. In addition to some extra variables particularly important at a certain NUTS2 level, some extra CVs were imposed. 4% CVs for LSU, 4% CVs for UAA were imposed in all NUTS 2 regions.

3.6 Sampling across time
A new sample is drawn in each occasion.
3.7 The software tool used in the sample selection

Package "Sampling Strata" (Bethel algorithm to compute optimal allocation)

3.8 Other relevant information, if any
Not available.

 

4. Use of administrative data sources
4.1 Name, time reference and updating

Administrative sources have been used in three steps of the FSS activities:

  • Data collection (for data on rural development);
  • Data correction;
  • Data imputation.

Specific sources are: 
1. the Integrated Administration and Control System - IACS(AGEA)  - reference period 2014
The most significant and complete source is the IACS. The database, managed by AGEA, has been set up in accordance with the EC n. 885/2006 that, under the Common Agricultural Policy, acts in the coordination and execution of payments to support farmers. The IACS has been realised in order to record, verify and control data. The core of this system is made of files containing information on data that each agricultural holding is obliged to present for any aid application. In the database there is much available information.
They can be divided into two main groups:
1) identification data of the farmer or the agricultural holding: Unique Code of Agricultural Holdings (CUAA code) that corresponds to the fiscal code of the holder. The CUAA code is mandatory whenever a relation with the Public Administration is undertaken. Holder’s name, permanent address or place of residence, VAT number if present. Dates of inscription and updating.
2) territorial data: agricultural parcels of the holding; information on the use of each parcel (crops, livestock); hectares invested by type of product (cadastral area and agricultural area utilized for farming).
If the holder is not the owner of the parcels, the identification code of landholders and the type of contract that links farmer to landholder are recorded.

2. the System for the Identification and Registration of Bovine Animals and other species (BDN) - Reference period 2015 and 2016
The System for the Identification and Registration of Bovine Animals and other species is an archive (BDN) managed by the Ministry of Health. Recorded units concern animals and their holders with the scope to preserve public welfare. The covered animal species are bovines, pigs, sheep and goats, poultry, equines.

4.2 Organisational setting on the use of administrative sources
The right of access to administrative data for the Istat's offices in charge of all the agricultural surveys, is based on the Protocol of Understanding, signed by Istat, Ministry of Agriculture, AGEA and Regions and other organisations.
Istat do not participate to the conceptual design of the administrative sources.
4.3 The purpose of the use of administrative sources - link to the file
Please access the information in the file at the link: (link available as soon as possible)

 

4.4 Quality assessment of the administrative sources
  Method  Shortcoming detected Measure taken
- coherence of the reporting unit (holding)

AGEA and FSS have the same definition of agricultural holding.

BDN and FSS do not have the same unit of observation. Not available
- coherence of definitions of characteristics AGEA and BDN have the same definitions of characteristics with respect to FSS Not available Not available
- coverage:      
  over-coverage Not available Not available Not available
  under-coverage  

AGEA

Not all units of the survey are present in this source, since it only includes the ones that actually received aid in the reference year.

AGEA

At the moment it was possible to use this source value for validating microdata when present in the AGEA database.

  misclassification Not available Not available Not available
  multiple listings Not available Not available Not available
- missing data Not available Not available  Not available
- errors in data Not available Not available Not available
- processing errors Not available  Not available Not available
- comparability Not available  Not available Not available
- other (if any) Not available  Not available Not available

 

4.5 Management of metadata
The metadata provided by AGEA and BDN are stored in electronic format (dedicated database).
4.6 Reporting units and matching procedures
Both in AGEA and in BDN a big advantage is represented by the presence of a unique identifier of the units (CUAA), collected also in FSS. This code was used in matching FSS microdata with AGEA and BDN microdata.
4.7 Difficulties using additional administrative sources not currently used
Not available.
3.2. Frequency of data collection
Frequency of data collection
The FSS, as a sample survey, is carried out every 3 years in Italy.



3.3. Data collection
1. Data collection modes

Data collection has been carried out through a traditional technique based on face-to-face interview of the holder by external enumerators using a paper questionnaire (PAPI).
The traditional technique has required application, precision and knowledge of the technical and organisational rules from the enumerators. Normally the interview has been completed out in more stages:
- identification of the unit in the list through the personal data printed in the questionnaire or in the list;
- first contact with the holder to fix an appointment for the interview;
- updating of the list and of the personal data of the unit as outcome of the first contact;
- interview of the holder;
- check and analysis of the data provided;
- recording of the data on the electronic questionnaire (GINO);
- return to the holder, if necessary.

2. Data entry modes
Electronic questionnaire (GINO via SGR). The software (GINO) has simplified the questionnaire compilation by:
  •        calculating automatically arithmetical operations;
  •        reporting errors in data input, displaying the appropriate message;
  •        displaying additional messages on mouse-over of words or phrases requiring a brief explanation.

In addition, to simplify the compilation of the questionnaire still further, the system has allowed various steps in processing the questionnaire: saving it as a draft, which enables users (the enumerators) to enter data without worrying about their accuracy, or saving a final version which entails the activation of control rules, and final sending.

 

3. Measures taken to increase response rates

To encourage the collaboration of farm holders, just before the starting of the fieldwork, a letter from Istat's President was sent to every respondent participating in the survey informing them about the survey and stressing the importance of their participation. The letter informed also on the compliance with data protection and statistical confidentiality.
The proper training of interviewers also contributed to the reduction of the number of refusals since they were able handle difficult respondents.
In order to simplify the data collection, the interviewers had personal holders details, name, surname, address, telephone and mobile numbers. However, when this information was not available, the interviewer had to proceed to the holding’s address without notice. When the holder was not found, a note stating that an interviewer had passed was left in the letter box. The interviewer left details for an appointment and also a contact number.
When personal holders information was present in the FSS 2016 list, the interview contacted holders for an appointment. If the holder failed to attend the interview, he/she fixed a second appointment. If a holder was absent, the interviewers were able to conduct interviews with adult members of the holder's household. In case of refusal, the agricultural holders were not legally pressed.
To answer the respondents on questions related to the FSS 2016 some Istat dedicated telephone numbers have been indicated in the letter sent before the starting of the interviews.
The work of interviewers was monitored by regional Organizational Coordinator using the management application (SGR)

 

4. Monitoring of response and non-response
1

Number of holdings in the survey frame plus possible (new) holdings added afterwards

In case of a census 1=3+4+5

1 168 085
2

Number of holdings in the gross sample plus possible (new) holdings added to the sample

Only for sample survey, in which case 2=3+4+5

34 765
3 Number of ineligible holdings 3 588

 

3.1

Number of ineligible holdings with ceased activities

This item is a subset of 3.

  3 069
4

Number of holdings with unknown eligibility status

4>4.1+4.2

2 050
4.1 Number of holdings with unknown eligibility status – re-weighted 2 050
4.2 Number of holdings with unknown eligibility status – imputed 0
5

Number of eligible holdings

5=5.1+5.2

29 127
5.1

Number of eligible non-responding holdings

5.1>=5.1.1+5.1.2

0
5.1.1 Number of eligible non-responding holdings – re-weighted 0
5.1.2 Number of eligible non-responding holdings – imputed  0
5.2 Number of eligible responding holdings  

29 127

 

6

Number of the records in the dataset 

6=5.2+5.1.2+4.2

29 127

 

5. Questionnaire(s) - in annex
 See Annex for the Italian version of the questionnaire.


Annexes:
3.3-5. mod_Istat_spa_2016
3.4. Data validation
Data validation

Data validation took place at the data collection level (interviewers), regional level (Regional Coordinators) and central level (Istat experts).
The electronic questionnaire (GINO) contained the algorithms of accounting and logical control. At the same time, the applications did not allow the omission of the questions which were required to be completed on certain “paths” of the interview. The data were subject to check and approval by the regional coordinators before they were sent to Istat. If any data was found to be potentially incorrect, a regional coordinator could make the necessary adjustments (if on the basis of the available data such an adjustment was possible) or request that the interview be repeated.

In order to improve data quality, different types of checks have been performed during data collection phase, trying to limit the respondent burden. Two types of edit rules have been used:

  1. hard edits rules, to underline errors and force the respondent or the interviewer to restore data correctness (e.g. balance edits and range restrictions);
  2.  soft edits rules, to highlight the need to do further investigation on the information gathered (e.g. ratio edits). 


Strategy of data editing and imputation
 An editing and imputation strategy was developed in order to ensure that the final survey data were complete, consistent and valid. 
The editing strategy consists of the following steps:

(1) identify and eliminate systematic errors that are evident and easy to treat;
(2) identify critical units with potentially influential errors that need to be treated with interactive treatment or with a manual review;
(3) identify those variables for which different methods can be used depending on the type of error and on the availability of one or more auxiliary variables. Such methods include mean imputation, donor imputation and k-nearest-neighbors imputation (KNN);
(4) automatic editing for the remaining random errors.     

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
Usual weights
2. Adjustment of weights for non-response

Design weights were adjusted for non-response by multiplying them by the inverse of the response rate in each stratum (in a few cases the factor has been computed collapsing two similar strata).

3. Adjustment of weights to external data sources
Weights were further adjusted through calibration to include auxiliary information in order to achieve the consistency of sample estimates with respect to some known totals of the population.
4. Any other applied adjustment of weights
Not applied.
3.6. Adjustment

[Not requested]


4. Quality management Top
4.1. Quality assurance

[Not requested]

4.2. Quality management - assessment

[Not requested]


5. Relevance Top
5.1. Relevance - User Needs
Main groups of characteristics surveyed only for national purposes 
For national purposes and for keeping comparisons with the past, the characteristics stated in the Annex III of the Regulation (EC) n.1166/2008 have been integrated with new items or some new characteristics have been added at the list.

The request of new characteristics or items comes mainly from:
- National Account Service of Istat (more details on Labour force)
- Environment Service of Istat (more details on Irrigation)
- ISPRA (renewable energy)
5.2. Relevance - User Satisfaction

[Not requested]

5.3. Completeness
Non-existent (NE) and non-significant (NS) characteristics - link to the file. Characteristics possibly not collected for other reasons
The following NS1 characteristics have been collected within the unique item Other fibre crops (B_1_6_11):
- B_1_6_2 (hops)
- B_1_6_3 (cotton)
- B_1_6_9 (Flax)
- B_1_6_10 (Hemp)

The following NS1 characteristics have been collected within the unique item Other oil seed crops (B_1_6_8):
- B_1_6_7 Linseed (oil flax)

Please access the information on NE and NS characteristics in the file at the link: (link available as soon as possible)
5.3.1. Data completeness - rate

[Not requested]


6. Accuracy and reliability Top
6.1. Accuracy - overall
Main sources of error

In a sample survey, such FSS 2016, two main kinds of errors could be encountered: sampling errors, and measurement (or response) error.
The sampling error is especially high for minor variables for which the precision were not established in defining the sample size (since not included in Annex IV of Reg. 1166/2008).

6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)

The survey estimates of totals for regional domains have been produced using a direct estimator where the final weight of each unit has been obtained adjusting sampling weight for non response by multiplying by the inverse of the response rate on each stratum (in few cases the factor has been computed collapsing two similar strata) and through calibration to include auxiliary information in order to achieve the consistency of sample estimates with respect to some known totals of the population.

The auxiliary variables used by Regions are: Number of holdings, Total Area, UAA, Arable land, Permanent crops, Bovine animals (heads).The variance estimator for regional and national estimates is defined by formula 5.14 of Estevao, Hidiroglou and Särndal (1995) “Methodological Principles for a Generalized Estimation System at Statistics Canada”, Journal of Official Statistics, vol. 11, n.2, pp181-204 that is implemented in the software ReGenesees (available on Istat’s web site:
https://www.istat.it/it/metodi-e-strumenti/metodi-e-strumenti-it/elaborazione/strumenti-di-elaborazione/regenesees)

6.2.1. Sampling error - indicators

1. Relative standard errors (RSEs) - in annex

  

2. Reasons for possible cases where precision requirements are applicable and estimated RSEs are above the thresholds

Registers on agricultural holdings are going better year by year. Nevertheless we can not exclude we are still experiencing quality problems on some variables. Moreover the reference year of the sampling frame is not the same of the survey. Even if this represents the best available sampling frame a possible consequence of using a non-updated list is to observe discrepancies between frame and survey data.
This is expected, in particular, for some livestock variables (poultry). In some cases the sample was built to achieve precision on variables proxy of those of interest but available at unit level in the register. For example for pasture we considered the aggregation of all pasture, including rough grazings; CVs  for this variable appear better than those required by Eurostat. The same can be stated for variables related to pigs categories for which we considered only the total of pigs.
Moreover, some CVs higher than expected can be due to other factors that we are not able to clearly assess, such as locally (both geographically and in terms of variables) low response rate or the earthquake that affected some regions.



Annexes:
6.2.1-1. Relative standard errors
6.3. Non-sampling error

Not available.

6.3.1. Coverage error
1. Under-coverage errors
The under coverage can be roughly estimated on the basis of the new units arising from demerging observed in the sample survey. From the holdings belonging to the sample 2016 it turned out there were 129 holdings that led to new activities thus giving rise to 320 new holdings. The new holdings maintained the weight of the original holdings in order to preserve the total land surface.

 

2. Over-coverage errors
Data were corrected for over-coverage, units not belonging to the target population were disregarded and the weights corrected through calibration.
2.1 Multiple listings 
Not available.

 

3. Misclassification errors
We considered the units that changed the geographical area (Nuts 2 Regions) and weights were corrected through calibration (52 units).

 

4. Contact errors
Due to wrong contact data from the list it was not possible to reach about 2.7% of the holdings of the sample.
For the rest of the holdings in the sample the address data and telephone number, if necessary, have been updated during the interview.

 

5. Other relevant information, if any
Not available.
6.3.1.1. Over-coverage - rate
Over-coverage - rate

The over-coverage rate, computed as the proportion of units from the sample which do not belong to the target population to the overall sample size, depends on how the units not belonging to the population are defined.

a) considering only the units out of scope (exclusively forestry holdings, only kitchen gardens, abandoned land and holdings with no agricultural scope) we obtain 1 658 units and an over-coverage rate of 4.7%

b)adding to the previous ones the ceased units for splitting/incorporations we obtain 3 069 units and an over-coverage rate of 8.8%.

c) considerng also the temporary inactive holdings we obtain 3 588 units and an over-coverage rate of 10.3%.

6.3.1.2. Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors

The non-sampling errors could seriously affect the reliability of final results, particularly in complex surveys such as those on agricultural topics that require a considerable effort of memory by the respondent and knowledge of the productive and socio-economic phenomena by the interviewer.
To minimise such kinds of errors some metodology have been implemented during the data collection phase.

  •  Interview techniques: interviewers were strongly requested to pose the questions to the interviewee in a way to avoid personal interpretations;
  • "Annotations" field of the questionnaire: it should include all information deemed relevant by the interviewer, which would help to validate and analyse collected data.This prevented questionnaires from being returned and/or avoided subsequent contacts with the interviewee to confirm/justify the information.
6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment

The reasons for unit non response were lack of contact, due to different causes:
1- absence of the holder (and of anyone else be able to answer to the interview);
2- wrong address;
3- refusal;
4- other (illness, judicial measures, etc.).

Since the unit non-response rate was quite low, no non-response analysis has been carried out in order to verify the presence of any bias. The unit non-response has been corrected by re-weighting (according to the strata the non-respondents belong to).

 

2. Item non-response: characteristics, reasons and treatment
The electronic questionnaire did not allow skipping the most important questions. For the remaining questions (admitting no response) imputation has been performed, with the exception of irrigation method and water source missing in presence of irrigable area (2124 holdings). This choice depends on the fact that we considered we had little information to implement a donor method with good results.
6.3.3.1. Unit non-response - rate
Unit non-response - rate
The percentage of non-responding holdings is 6.6%, as the rate of non-respondent units out of eligible units (according to Eurostat definition).
With respect to the non-responding units with unknown eligibility status they have been treated the same way as the eligible units.
6.3.3.2. Item non-response - rate
Item non-response - rate
Not computed
6.3.4. Processing error
1. Imputation methods

As far as item non-response is concerned the imputation process consisted of different modules:

  1. selective editing via mixture model was applied in order to detect critical units with potentially influential errors which needed to be corrected interactively. To this aim an R package developed by Istat called SeleMix was used;
  2. a set of deterministic rules (if-then rules) and ad hoc procedures were developed in order to correct systematic errors. To this aim ad hoc SAS programs were developed;
  3. different methods, which include mean imputation, hot-deck donor imputation and k-nearest-neighbors imputation (KNN), were developed in order to solve some specific inconsistencies for a subset of qualitative and quantitative variables. For this purpose SAS and R programs were developed;
  4. automatic imputation according to Fellegi and Holt methodology was performed in order to solve problems of missing, invalid or inconsistent data for random errors. It was performed for categorical and quantitative variables separately.

 

2. Other sources of processing errors

Non-sampling errors, including missing, invalid or inconsistencies data, which may occur during each stage of the survey process were corrected following the strategy of editing and imputation showed in item 6.3.4-1.

Different methods were used: selective editing for those units which had in one or more variables potentially influential errors for estimates; deterministic rules to correct systematic errors; ad hoc methods for a subset of qualitative and quantitative variables; automatic editing for random errors. 

With regard to ad hoc methods of imputation the choice based on different factors: type of variable, type of error and availability of auxiliary information.  

 

3. Tools used and people/organisations authorised to make corrections
Editing and imputation process was carried out by the division of methodologists responsible for the development of editing and imputation strategies in survey data of Istat
Selective editing was applied by means of SeleMix, an R package developed by Istat.
SAS and R programs were developed for implementing ad hoc imputation methods and deterministic rules for several variables both qualitative and quantitative.
Categorical variables related to farm labour force and affected by random errors were imputed using SCIA, a module implementing Fellegi and Holt methodology, which is part of an open source software named ConcordJava developed by Istat.
The agricultural land variables affected by random errors were imputed according to Fellegi and Holt methodology by using Banff Processor, which is an application developed by Statistics Canada.
6.3.4.1. Imputation - rate
Imputation - rate
Not available.
6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
Preliminary data are not published. Only final estimates are disseminated.
6.6. Data revision - practice
Data revision - practice
Preliminary data are not published. Only final estimates are disseminated.
6.6.1. Data revision - average size

[Not requested]


7. Timeliness and punctuality Top
7.1. Timeliness

Not available.

7.1.1. Time lag - first result
Time lag - first result
Not available (only final and complete data are published)
7.1.2. Time lag - final result
Time lag - final result

18 months, considering the delivery to Eurostat in June 2018.

22 months, considering the publication of complete and final results on Istat web site .

7.2. Punctuality

Not available.

7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication
The data transmission to Eurostat has taken place 6 months after the deadline forecasted by Regulation (EC) 1166/2008. Concerning the national release, the delay respect to the scheduled data  was of 5 months.


8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
There is no difference.

 

2.National survey coverage vs. coverage of the records sent to Eurostat

No differences.

 

3. National vs. EU characteristics

Handbook on implementing the FSS definitions – Revision 1 has been used. (WG 2015/1/11).
The number of hours per year for a full-time employee, used to calculate the Annual Work Unit, was 1 800 hours.
The definitions of characteristics for the FSS 2016 were in accordance with the EU requirements and in compliance with the Handbook FSS 2016 except for the following:

Agricultural Training of the Manager

A different definition than Eurostat is applied for the categories of agricultural training of  manager “practical experience only” and “basic training”. In Italy, “practical experience only” refers to cases where the manager has completed no type of education and “basic training” refers to cases where the manager has completed a level of education (primary school, secondary education, higher education) but not directly related to agriculture.

Buffaloes
Since in Italy Buffaloes are very important we considered them toghether with Bovines.
In particular:
C_2=Bovine and Buffaloes animals
C_2_1= bovine and buffaloes animals under 1 year old (male + female)
C_2_4= male bovine animals and other buffaloes animals (different from the animals included in C_2_1 and C_2_6) 2 years old and over
C_2_6= dairy cows and dairy buffaloes

4. Common land
4.1 Current methodology for collecting information on the common land

The common land is a public or private good on which individuals belonging to a determinate community have some rights of use. The rights concern area of different kind and destination (pasture, wood, water bodies, etc.).

For the purpose of the 2016 FSS, the common land taken into consideration has been the area where the agricultural activity is made, generally permanent grassland (B_3) (in rare cases also arable land/permanent crops are included in common land). Therefore, the common land concerning wood and non-agricultural area has been excluded from the survey.
Data on common land were collected via statistical survey (FSS). To avoid duplication of land a specific preliminary item has been introduced in the questionnaire. The preliminary question, to be compiled only by the public Institution/ Municipality, concerned the total land they owned and how it was managed.
Three different situations could occur:
1) the public Institution/ Municipality managed its land as an agricultural holding: in this case it was treated as a "normal" agricultural holding;
2) the land was allotted to agricultural holdings, in specific and formal way: the part of land allotted was recorded by each beneficiary farm. In the land ownership, this area has been indicated as rented or in free use in accordance with the kind of formal agreement between the Institution/Municipality and the farm;
3) the land was not allotted to one or more agricultural holdings but it was at disposal of the individuals having rights of use: the Institution or the Municipality managing the common land was considered as an agricultural holding and it had to compile the questionnaire with the information.

For the purpose of the 2016 FSS, the common land taken into consideration was those referring to point 3).

4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys

It is quite difficult for Institutions/Municipality to know exactly the entity of the land that fits with statistical definitions. It is also difficult to avoid, in some cases, duplications. For the future, a possible solution could be to acquire information on common land from administrative sources. 

4.3 Total area of common land in the reference year

Considering only case (3) in item 4.1. above as Common land units, the UAA belonging to Common land has been estimated at 436 108 hectares (including temporary inactive units). Please note that the data transmitted only refers to active units (622 common land units), so the estimated figures are different, specifically:

- UAA=102 739 ha, split as follows:

- permanent grassland: 99 520 ha

- arable land: 2 151 ha

- permanent crops: 1 067 ha

Note that not all the permanent grassland and permanent crops recorded under the 622 active common land units is common land, since a small part of them is managed by the Institution/ Municipality as an agricultural holding. Common land is only 98 834 ha UAA.

4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
Considering only case (3) in item 4.1. above as Common land we obtained 719 common land units (including temporary inactive units). Please note that the data transmitted only refers to active units, so the estimated figures are different (namely 622 units).

 

5. Differences across regions within the country
In August 2016 a very strong earthquake damaged the Regions of the Central Italy. This implied that we had to change a part of the sample even if it was not possible to substitute all the holdings belonging to the sample of the Regions affected by the earthquake.

 

6. Organic farming. Possible differences between national standards and rules for certification of organic products and the ones set out in Council Regulation No.834/2007
No differences.
8.1.1. Asymmetry for mirror flow statistics - coefficient

[Not requested]

8.2. Comparability - over time
1. Possible changes of the definition of the agricultural holding
There have been no changes in the definition of agricultural holding in comparison to the FSS 2013.

 

2. Possible changes in the coverage of holdings for which records are sent to Eurostat

With respect to 2013 some thresholds changed (see item 2.6-2). This should be taken into account when comparing the main indicators.

For example, between 2013 and 2016, the number of holdings apparently increased by 13.4%, but when comparing over the same coverage, it increased only by 1.8%.

The total labour force is also influenced: considering the same coverage it increased by 2.6% instead of  5.2%. For UAA the difference is minor (3.5% instead of 4.1%) while there is no difference for LSU (1.0% both considering the 2013 thresholds and the 2016 ones).

3. Changes of definitions and/or reference time and/or measurements of characteristics

Definitions of characteristics and/or reference time and/or measurement are not changed compared to the previous survey (FSS 2013).
The number of animals in the lease agreement (Soccida) have been indicated only by the lessee instead of the lessor  as in FSS 2013.  The “soccida” is a livestock lease agreement between the owner of livestock (lessor, “soccidante” in italian) and a farmer (lessee, “soccidario”in italian) who breeds the animals. The lessee has the benefit of the income and profits from the livestock during the term of the lease. At the end of the lease, the lessee has to return livestock of a similar type and age as the stock were at the outset, unless other provisions are required. The agreement contemplates a lease of one or more years.
Traditionally, the agreement enters between two agricultural holders in Italy. A new kind of “soccida” is developing more and more often in the recent years where the lessor is a manufacturing company.
In order to avoid duplications for compiling the questionnaire the number of animals in the lease agreement has been indicated only by the lessee.
It was the first time we used this rule. Until FSS 2013 the number of animals was indicated only by the lessor.
We changed because we think this way better describes the real number of animals.

 

4. Changes over time in the results as compared to previous FSS, which may be attributed to sampling variability

Since FSS 2013 and 2016 are sample survey, both data are affected by the sampling errors. This circumstance should be taken into consideration when comparing the results of FSS 2016 with those of the previous survey.
See item 6. below (Major trands) for more information.

 

5.Common land
5.1 Possible changes in the decision or in the methodology to collect common land
In FSS 2016 we changed methodology of collecting data on common land, in order to avoid duplication of land. For more details see item 4.1 above (Current methodology for collecting information on the common land).
5.2 Change of the total area of common land and of the number of agricultural holdings making use of the common land / number of common land holdings

Estimates on common land from FSS 2016 are very different from those of FSS 2013:
Common land units 2013 = 1 447
Common land units 2016 = 622
UAA Common land 2013= 285 266 ha
UAA Common land 2016= 98 834 ha
Besides the sampling errors, the effect of changing the methodology for collecting data on common land is evident.

6. Major trends on the main characteristics compared with the previous FSS survey
Main characteristic Current FSS survey Previous FSS survey - Eurostat estimates
Difference in % Comments
Number of holdings 1 145 706 1 010 328 13.4 While in FSS 2013 the frame was the Census list, for FSS 2016 it was  based on an administrative source (the farm register). Moreover the survey coverage changed (the survey thresholds). We consider these two evidences represent the most important reasons for the differences observed.
Utilised agricultural area (ha) 12 598 163 12 098 891 4.1  
Arable land (ha) 7 145 041 6 728 362 6.2  
Cereals (ha) 3 533 860 3 503 130 0.9  
Industrial plants (ha) 429 144 385 810 11.2 The increase observed is mainly due to the increase in soy.
Plants harvested green (ha) 2 153 889 1 939 410 11.1 The increase observed is mainly due to the increase in temporary grassland.
Fallow land (ha) 377 831 365 307 3.4  
Permanent grassland (ha) 3 233 232 3 316 429 -2.5  
Permanent crops (ha) 2 200 832 2 032 308 8.3
 
Livestock units (LSU) 9 467 716 9 374 265 1.0
 
Cattle (heads) 6 114 514 5 704 927 7.2
 
Sheep (heads) 7 026 540 6 597 690 6.5  
Goats (heads) 981 995 921 730 6.7  
Pigs (heads) 8 375 525 8 598 460 -2.6  
Poultry (heads) 158 032 627 164 901 177 -4.2  
Family labour force (persons) 1 813 678 1 992 690 -9.0
 
Family labour force (AWU) 602 203 617 153 -2.4
 
Non family labour force regularly employed (persons) 231 732 146 370 58.3
The increase observed in non family labour force regularly employed partially compensates the decrease of family workers. 
 observed Non family labour force regularly employed (AWU) 109 487 79 092 38.4
8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
During the edit and imputation process some comparisons at microdata level have been done to impute/correct data with administrative sources (IACS and BDN register). In the majority of the cases survey and aministrative data were the same or very similar. For the rest, the differences between survey and aministrative data depend on methodologies, definition and time of reference.

 

2. Coherence at macro level with other data collections

FSS statistics are comparable with other statistics (namely crop and animal) and with administrative sources.
Concerning the other statistical data collections, while for Animal production statistics (a sample survey) the definitions and the reference period are the same as for FSS, in Annual crops statistics - by definition non-structural information- there are many differences as, notably, some definitions and the reference period of observation. Moreover, in Italy Annual crops statistics are based on expert estimates, whose quality is difficult to assess. As a consequence, we consider that differences from FSS and Annual crops statistics data, even significant ones, arise because of the combination of all these causes.
We consider that consistency at macro data level with other structural sources is more conclusive.

The comparison with the following sources shows a general coherence of the main results:
- FR 2014 (the most updated version)
- BDN 2016
- Animal surveys 2016

The annex presents the main results of such comparisons, respectively for land and animals data.



Annexes:
8.3-2. Comparisons with other sources
8.4. Coherence - sub annual and annual statistics

[Not requested]

8.5. Coherence - National Accounts

[Not requested]

8.6. Coherence - internal

[Not requested]


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

[Not requested]

9.2. Dissemination format - Publications
1. The nature of publications

Data of FSS 2016 survey are disseminated through tables on the Istat's website, concerning the main structural characteristics of the Italian agriculture at NUTS 2 level (25 tables on: general characteristics, land use, irrigation, livestock, employment, time series).

 

2. Date of issuing (actual or planned)

The tables have been disseminated on the website in July 2018.

 

3. References for on-line publications

Farm structure survey: http://www.istat.it/en/archive/167694 (FSS 2013 at the moment)

Tables: agri.istat.it

9.3. Dissemination format - online database
Dissemination format - online database

Tables concerning the main structural characteristics of the Italian agriculture at NUTS 2 will be disseminated on the dedicated website at the following link: http://agri.istat.it/.  A serious update of the website is foreseen in next future and the address will be changed.

9.3.1. Data tables - consultations
Data tables - consultations
Not available.
9.4. Dissemination format - microdata access
Dissemination format - microdata access
FSS 2016 micro-data will be released to specific users (members of SISTAN) and the confidentiality provisions applied are those foreseen by the Legislative Decree of 9 September 1989, n.322 (concerning the statistical confidentiality) as amended by Legislative Decree n. 281/99, Legislative Decree of June 30, 2003.
9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
All the publications (both on web and on paper) contain methodological information.

 

2. Main scientific references
- EDIMBUS (2007). Recommended Practices for Editing and Imputation in Cross-sectional Business Surveys; available at
- CONCORDJAVA (2014). Data Editing and Imputation, Version JAVA, available at https://www.istat.it/en/methods-and-tools/methods-and-it-tools/process/processing-tools/concordjava
- Fellegi I.P., Holt D. (1976). A Systematic Approach to Automatic Edit and Imputation, Journal of the American Statistical Association, 71, 17-35.
- Guarnera U., Buglielli M.T.(2013). SeleMix: an R Package for Selective Editing; available at https://cran.r-project.org/web/packages/SeleMix/vignettes/SeleMix-vignette.pdf 
- Kowarik A., Templ M.(2016). Imputation with the R Package VIM. Journal of Statistical Software, Volume 74, Issue 7.
- MEMOBUST (2014). Handbook on Methodology of Modern Business Statistics, Statistical Data Editing Imputation, available at https://ec.europa.eu/eurostat/cros/content/statistical-data-editing_en
- Statistics Canada (2014). Functional Description of the Banff System for Edit and Imputation Banff, Version 2.06.
- Templ M., A. Kowarik, P. Filzmoser (2011). Iterative stepwise regression imputation using standard and robust methods. Journal of Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.
- Templ M., A. Alfons, P. Filzmoser (2012). Exploring incomplete data using visualization tools. Journal of Advances in Data Analysis and Classification.
- R. Torelli (2012). GINO++, UN SISTEMA GENERALIZZATO PER INDAGINI STATISTICHE VIA WEB. Istat Newstat4
-
Statistics Canada (2011). Strategies for Standardization of Methods and Tools. How to get there . Proceedings Statistics Canada's International Methodological Symposium
9.7. Quality management - documentation
Quality management - documentation
Metadata on the FSS 2016 are stored in SIDI which is an informative system for documenting the process and the quality of all surveys carried out by Istat, in a standard way.
9.7.1. Metadata completeness - rate

[Not requested]

9.7.2. Metadata - consultations

[Not requested]


10. Cost and Burden Top
Co-ordination with other surveys: burden on respondents
To the extent it was possible, we tried to avoid that the same holding was interviewed, at the same period and on similar kind of questions, for FSS 2016 and other short term surveys, by avoiding to select the same unit for two differents surveys (the units already selected for a survey having been flagged in the list). Anyway, in these cases, it was not always possible to replace a unit with another from the same strata. 


11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
Several national legal acts guarantee the confidentiality of data requested for statistical purposes. According to art. 9, paragraph 1 of the Legislative Decree n. 322 of 1989, personal data cannot be disseminated but in aggregated form, in order to make it impossible to make any reference to identifiable individuals. They can only be used for statistical purposes. Legislative Decree n. 322 of 1989, art. 6 bis and Legislative Decree n. 196 of 2003 Annex A3 (Code of conduct and professional practice applying to the processing of personal data for statistical and scientific research purposes within the framework of the national statistical system), art. 8, provide that the exchange of personal data within the National Statistical System (Sistan) is possible if it is necessary to fulfil requirements provided by the National Statistical Programme or to allow the pursuit of institutional purposes. The supply of the identification data of statistical units is allowed within the framework of entities included in the National Statistical System if the requesting party declares that no identical statistical result can be obtained otherwise . Regarding subjects who do not belong to Sistan, Article. 7 of the Code of conduct (Decree n. 196/2003, Annex A3) states that it is possible to transmit individual data files without direct identifiers within the framework of specific laboratories set up by entities included in the National Statistical System, under certain conditions and only if that the data are protected by the application of different statistical methods that make it highly unlikely the identification of statistical units.

 

11.2. Confidentiality - data treatment
Confidentiality - data treatment
In case of confidentiality we have to suppress cells.


12. Comment Top
1. Possible improvements in the future
Not available.

 

2. Other annexes
Not available.


Related metadata Top


Annexes Top