1. Contact |
1.1 Contact organisation |
ISTAT, Italian National Institute of Statistics – Department of statistical production (DIPS) |
1.2 Contact organisation unit |
Directorate for Social Statistics and Welfare (DCSW) - Division for Integrated system for household economic conditions and consumer prices (SWA) - Household Budget Survey Unit |
1.3 Contact name |
1.4 Contact person function |
Head of Household Budget Survey Unit |
1.5 Contact mail address |
Via Cesare Balbo, 16 – 00144 Rome (RM) Italy |
1.6 Contact email address |
1.7 Contact phone number |
1.8 Contact fax number |
2. Statistical presentation |
2.1 Data description |
The HBS aims to measure and analyze expenditure behaviors of households residing in Italy, according to their main social, economic and territorial characteristics. The main focus of the HBS is represented by all expenditures incurred by resident households to purchase goods and services exclusively devoted to household consumption (self-consumptions, imputed rentals and presents are included). Every other expenditure for a different purpose is excluded from the data collection (e. g., payments of fees, business expenditures). The survey is based on the harmonized international classification of expenditure voices, Classification of Individual COnsumption by Purpose - Coicop. Main observed topics are: - sociodemographic characteristics of household members - household housing conditions - household socio-economic conditions - household possession of durable goods - household purchasing behaviours - household expenditure on: food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; miscellaneous other goods and services.
1. Title of the survey |
Household Budget Survey
|
2. Title of the survey at a National level |
Indagine sulle spese delle famiglie |
3. Year of the survey |
2020 |
4. General comments about the survey |
Until 1 January 2021, the IT HBS run under the Gentlemen's agreement reached by the Statistical Programme Committee (SPC, comprising the Director Generals of EU national statistical services and EUROSTAT) during its meeting of 1989.
Since 1 January 2021, the IT HBS is based on Regulation (EU) 2019/1700, also known as IESS - Integrated European Social Statistics, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples. |
5. National questionnaire (Please provide a hyperlink and/ or provide it in an Annex) |
https://www.istat.it/it/archivio/71980
|
|
2.2 Classification system |
Name |
Version Used |
COICOP |
ECOICOP 5-digit (06 December 2013) |
NUTS |
NUTS 2013 |
ISCED |
ISCED 2011 2-digit |
ISCO |
ISCO-08 |
NACE |
NACE Rev. 2 |
Other |
Istat 3-digit classification of foreign countries. A transcoding table to ISO 3166 (the International Standard for country codes) is available. |
|
2.3 Coverage - sector |
Sector coverage (if it is not households)
List the variables which deviate from the standard definition specified in the Transmission document HBS 2020 (including national concepts, method of calculation, and differences between national concepts and standard HBS concepts)
|
2.4 Statistical concepts and definitions |
1. Consumption expenditure
For measuring living conditions, the essential reference for the HBS is the concept of household final consumption expenditure, that is, the expenditure incurred by private households to purchase goods and services exclusively devoted to their consumption.
Consumption Expenditure approaches applied
|
Actual final consumption |
Final consumption |
Monetary final consumption |
Other |
|
|
X |
|
2. Income
Yearly income:
Income in kind from employment |
Income in kind from non-salaried activities |
Imputed rent |
Monetary net income |
Total net income |
|
|
X |
|
|
Monthly income:
Net current monthly household income
|
|
3. Inputed rent
Self-assessment |
Stratification |
Log-linear regression |
Heckman regression |
User cost |
Other (indicate) |
X |
|
|
|
|
|
Variables used
|
Imputed rent for main dwelling; Imputed rent for garage(s) (main dwelling); Imputed rent for basement(s) or attic(s) (main dwelling).
|
4. Other definitions, explanations, comments
Household consumption expenditure includes the monetary value of self-consumptions, in-kind benefits, imputed rentals (for owner-occupied and rent-free dwellings) and presents (purchased). Expenditures for any other purpose than private consumption are excluded (payments of fees, investments, repayment of loans, business expenditures, …). Imputed rents are calculated through self-assessment method and are collected both for main and secondary dwellings.
|
|
2.5 Statistical unit |
1. Definition of Household used:
Household defined as persons sharing |
Accommodation |
Expenditure |
Income |
Family or emotional ties |
X |
X |
X |
X |
Other |
As for the Italian HBS, the basic unit of data collection is the de facto household, which consists of people: - living together in the same dwelling - having legal relationship (by blood, marriage, adoption or guardianship) or affective ties - sharing incomes and expenditures
|
2. Definition of Household member used:
Household membership |
Usually resident, related to other members |
Usually resident, not related to other members |
Resident border, tenant |
Visitor |
Live-in domestic servant, au pair |
Resident, absent from dwelling in the short-term |
Children in household in education away from home |
Long-term absence with household ties: working away from home |
Temporary absence with household ties: in hospital, nursing home or other institution |
X |
X |
|
|
|
X |
X |
|
X |
Other |
As for the Italian HBS:
- friends co-residing "as relatives", having strong affective ties, are considered household members (Usually resident, not related to other members); - people co-residing for economic reasons (Resident boarder, tenant; Live-in domestic servant, au pair) are not considered household members and only some personal information are collected about them.
|
3. Definition of Reference Person
In the context of the EU HBS surveys, a 'Reference person' is the Household member (>= 16) who contributes most to the total income of the household.
Definition of Reference Person used, if different from the above |
As for the Italian HBS, the Reference Person is the person referred to, in the Municipality Population Register, as the head of the household. |
Statistical units: Households: A de facto household consists of people: - living together in the same dwelling; - having legal relationship (by blood, marriage, adoption or guardianship) or affective ties; - sharing incomes and expenditures. |
2.6 Statistical population |
All private households and their members are considered in the statistical population. |
In this respect, collective households are normally excluded from the survey: elderly homes, hospitals, establishments for the disabled, boarding schools, military barracks, jails, and welfare institutions including those for the homeless, asylum seekers or refugees. |
|
2.7 Reference area |
National and regional level (NUTS level 2) |
|
2.8 Coverage - Time |
The survey provides yearly estimates. |
|
2.9 Base period |
Not applicable |
3. Statistical processing |
3.1 Source data |
1.Sampling frame name
The sampling frame is partly single-stage and partly two-stage, with stratification of primary sampling units (municipalities). In each sampled municipality, private households participating in the survey are randomly selected from the Municipality Population Register, that contains demographic information on all individuals residing within the municipal borders and distinguishes between those living in private households and those living in collective households.
Substitutive households are also selected to replace non-responding households. |
2.Data source used for building the sampling frame
In each region (NUTS level 2), selected municipalities are stratified by typology and demographic size (in terms of resident population), so that they are divided into 2 groups: - auto-representative municipalities - not auto-representative municipalities
Each auto-representative municipality represents a stratum and participates in the survey all months. Not auto-representative municipalities are first grouped in strata of the same demographic size and then 3 municipalities are randomly selected from each stratum to participate in the survey once a quarter (i.e., 4 months per year). |
3. Frequency and the year of the last update of the data source
Data source is updated yearly. |
4.Sample design of the survey
Ultimate sampling unit(s) |
Household |
X |
Other |
|
Probability sampling |
X |
Sampling design used |
X |
Oversampled populatons |
|
|
3.2 Frequency of data collection |
Frequency of data collection is monthly. |
|
3.3 Data collection |
1. Reference year: 2020 2. Survey instruments: interview and diary
2.1. Interview |
|
Traditional face-to-face interview, pen and pencil (PAPI) |
|
Telephone interview (CATI) |
X |
Computer-assisted personal interview (CAPI) |
X |
Self-completed computer-based interview (CASI) |
|
Self-completed web or mobile app based interview (CAWI) |
|
Other sources (e.g. administrative data). Please list variables |
|
Items covered in the interview |
Socio-demographic characteristics at household member level; expenditure on clothing and footwear, housing, water, electricity, gas and other fuels, furnishings, household equipment and routine household maintenance, health, transport, communication, recreation and culture, education, restaurants and hotels and miscellaneous other goods and services (not included in the diary), at household level |
Recording period |
Moving reference periods (last year, last 3 months, last month, last bill paid for domestic utilities) |
Recording unit (household; household member) |
Household |
Diaries |
|
Traditional Pen and Paper diary |
X |
Computer-based Diary |
|
Web-Diary |
|
Cash Register Receipts |
|
Receipt Scanner |
|
Loyalty-Scheme cards/ metadata |
|
Other (e.g. Administrative Data, please list variables) |
|
Items covered in the diary |
Food and beverages, tobaccos, pharmaceutical products , newspapers and other almost daily expenditures |
Recording period |
14 days |
Recording unit (household; household member) |
Household
|
3. Additional remarks about the Data collection
In 2020, due to the Covid-19 pandemic, Italy experimented a national lockdown during the months of March, April and May. For this reason: - in April, the HBS was temporarily stopped and data for this month estimated on the basis of the interviews conducted in March and May; - in March, April, May and June, CAPI methodology was replaced by CATI methodology, to protect interviewers and households from Covid-19. From July to December 2020, both CAPI and CAPI methodology were used, having households the possibility to choose between the two.
|
|
3.4 Data validation |
Basic Data validation workflow |
- Coherence control with previous HBS data
- Coherence control with data from other sources (National Accounts, Prices)
|
|
3.5 Data compilation |
1. Calculation of the household design weights |
Each sampled household is initially assigned a base weight given by the inverse of its probability of selection as part of the sample. |
2. Weight adjustments for non-response at household level |
Base weights are adjusted for total non-response at the household level: a correction factor for unit non-response is calculated as the inverse of the response rate at the stratum level. |
3. Any other weight adjustments |
|
|
3.6 Adjustment |
Weight adjustments to external data sources (calibration) |
Final weights are obtained applying a calibration of household weights to external data sources, so that calibrated weights are able to exactly reproduce a set of known totals for key variables of interest.
External data sources used for calibration are:
- at Nuts level 1: resident population by geographical area, sex and age-class; foreign resident population by geographical area and sex; resident population and households by geographical area and type of municipality; resident population 15+ by geographical area and professional condition
- at Nuts level 2: resident population and households by region
|
|
4. Quality management |
4.1 Quality assurance |
Since the ‘90s, Istat adopted a systematic approach to ensure quality in both statistical information and service to the community.
For this purpose, the Italian National Institute of Statistics has defined a quality policy, providing itself with appropriate tools as well as management changes to carry it out.
Istat quality policy aims at the improvement of statistical outputs and processes through the development of appropriate methodologies and tools as well as an appropriate scientific and technical support, provided to the staff directly involved in the production and dissemination of statistical information.
Istat quality policy is coherent with the European framework developed by Eurostat, taking up its main principles and definitions, stated in the European Statistics Code of Practice, and useful to ensure and strengthen the accountability and governance of the European Statistical System and of the National Statistical Systems.
For details: https://www.istat.it/en/organisation-and-activity/institutional-activities/quality-commitment
|
|
4.2 Quality management - assessment |
The HBS process was submitted to Quality statistical self-assessment for the 2014 edition. |
|
5. Relevance |
5.1 Relevance - User Needs |
The Italian HBS represents the informative base for:
- quarterly estimates of household final consumption expenditure
- official estimates of relative and absolute poverty
- annual weighting of the Consumer Price Index basket
- measure of inflation by household expenditure classes
More in general, HBS data are used, at the institutional level, to study, evaluate and promote measures to improve household economic conditions and to overcome economic household disadvantages and disparities.
International bodies and organizations collect HBS data essentially to offer time comparisons between household living conditions in different countries or geographical areas and to focus user attention on specific expenditure categories of particular relevance (e.g. household expenditures on energy).
Researchers, at different levels, are interested in HBS data to monitor both the evolution of household spending behaviors and their living conditions. |
|
5.2 Relevance - User Satisfaction |
Main steps to promote user satisfaction are:
- dissemination of HBS aggregated data and metadata on the Istat data warehouse (http://dati.istat.it/)
- release of HBS microdata files
- dissemination of the annual Report "Household consumption expenditure" containing main survey estimates
More in general, Istat is constantly interested in understanding who the users of the statistics it produces are, what the information needs are, whether they match production and if the statistics produced satisfy users.
To this aim, together with the analysis of user requests received through the Web Contact Center service, tools for direct consultation have been developed, such as the annual online survey of customer satisfaction, as well as indirect tools, such as analysis of accesses and of users' browsing paths on the web site.
|
5.3 Completeness |
Variable Name
|
Label
|
Delivered
|
HA02
|
Survey year(s)
|
X
|
HA04
|
Identification number of the household
|
X
|
HA06
|
Stratum
|
X
|
HA07
|
Primary sampling unit
|
X
|
HA08
|
Region of Residence
|
X
|
HA09
|
Degree of Urbanisation
|
X
|
HA10
|
Sample weight
|
X
|
HC03
|
Sex of Reference Person
|
X
|
HC04
|
Age (in completed years) of reference person
|
X
|
HC04A
|
Year Of Birth of Reference Person
|
X
|
HC04B
|
Passing of birthday of Reference Person on the date of the first interview
|
X
|
HC04C
|
Date of First Interview of Reference Person
|
X
|
HC05
|
Marital status of the Reference Person
|
X
|
HC051
|
Reference person living with a partner in the same household
|
X
|
HH011
|
Net current monthly household income
|
NO
|
HH012
|
Income in kind from employment
|
NO
|
HH023
|
Income in kind from non-salaried activities
|
NO
|
HH032
|
Imputed rent
|
X
|
HH095
|
Monetary net income (total monetary income from all sources minus income taxes)
|
NO
|
HH099
|
Net income (total income from all sources including non-monetary components minus income taxes)
|
NO
|
HI11
|
Main source of income
|
X
|
HI12
|
Main source of income (primary/secondary)
|
X
|
HE00
|
ALL-ITEMS
|
X
|
HE01
|
FOOD AND NON-ALCOHOLIC BEVERAGES
|
X
|
HE02
|
ALCOHOLIC BEVERAGES, TOBACCO AND NARCOTICS
|
X (No narcotics)
|
HE03
|
CLOTHING AND FOOTWEAR
|
X
|
HE04
|
HOUSING, WATER, ELECTRICITY, GAS AND OTHER FUELS
|
X
|
HE042
|
Imputed rentals for housing
|
X
|
HE05
|
FURNISHING, HOUSEHOLD EQUIPMENT AND ROUTINE HOUSEHOLD MAINTENANCE
|
X
|
HE06
|
HEALTH
|
X
|
HE07
|
TRANSPORT
|
X
|
HE08
|
COMMUNICATION
|
X
|
HE09
|
RECREATION AND CULTURE
|
X
|
HE10
|
EDUCATION
|
X
|
HE11
|
RESTAURANTS AND HOTELS
|
X
|
HE12
|
MISCELLANEOUS GOODS AND SERVICES
|
X
|
[HJ] variables
|
Cross border consumption expenditure
|
All variables in this list are not delivered
|
[HQ] variables
|
Household's consumption in Quantities
|
All variables in this list are not delivered
|
HB05A
|
Household size
|
X
|
HB051
|
Number of persons aged less than or equal to 4
|
X
|
HB052
|
Number of persons aged from 5 to 13
|
X
|
HB053
|
Number of persons aged from 14 to 15
|
X
|
HB054
|
Total number of persons aged from 16 to 24
|
X
|
HB055
|
Number of persons aged from 16 to 24 who are students
|
X
|
HB056
|
Number of persons aged from 25 to 64
|
X
|
HB057
|
Number of persons aged more than or equal to 65
|
X
|
HB061
|
Equivalent size (OECD scale)
|
X
|
HB062
|
Equivalent size (modified OECD scale)
|
X
|
HB074
|
HBS Household Type
|
X
|
HB075A
|
Household Type
|
X
|
HB0761
|
Number of persons aged 16-64 in the household who are working
|
X
|
HB0762
|
Number of persons aged 16-64 in the household who are unemployed or who are economically inactive
|
X
|
HC23A
|
Main activity status of Reference Person (self-defined)
|
X
|
HC24
|
Socio-economic situation of reference person
|
X
|
HD01
|
Household Tenure Status
|
X
|
HD20
|
Number of members economically active
|
X
|
|
|
5.3.1 Data completeness - rate |
Groups of HBS 2020 variables
|
Total number of Variables
per sub-group
|
Number of delivered Variables
per sub-group
|
%
|
Basic variables at household level
|
|
|
|
[HA] Identification, weighting, demographic characteristics
|
7
|
7
|
100%
|
[HC] Basic demographic characteristics of the reference person
|
7
|
7
|
100%
|
[HH] Income
|
6
|
2
|
33.3%
|
[HI] Main source of the household's income
|
2
|
2
|
100%
|
[HE] Household’s consumption expenditure
|
476
|
461
|
96.8%
|
[HJ] Cross border consumption expenditure
|
14
|
0
|
0%
|
[HQ] Household's consumption in Quantities
|
87
|
0
|
0%
|
Derived variables at household level
|
|
|
|
[HB] Household size and Type
|
14
|
14
|
100%
|
[HD] Activity and economic situation
|
2
|
2
|
100%
|
Basic variables at member level
|
|
|
|
[MA] Identification, weighting, demographic characteristics
|
2
|
2
|
100%
|
[MB] Basic demographic characteristics of household members
|
14
|
14
|
100%
|
[MC] Education
|
3
|
3
|
100%
|
[ME] Activity
|
7
|
6
|
85,7%
|
[MF] Income
|
1
|
0
|
0%
|
TOTAL
|
642
|
520
|
81%
|
|
|
6. Accuracy and reliability |
6.1 Accuracy - overall |
Unit response rate
|
|
Item response rate
|
|
|
6.2 Sampling error |
The size of the sampling error depends on the sample size: the higher the sample size, the higher the accuracy. In the past, in comparison to other EU household surveys, e.g. Labour Force Survey (LFS) or Statistics on Income and Living Conditions (EU-SILC), the HBS sample sizes attained have been rather low. Furthermore, the effective sample size can be even smaller as a result of the way the sample has been designed. |
6.2.1 Sampling error - indicators |
1. Achieved sample size |
25,668 |
2. Comments on Sampling errors and measures to reduce them |
|
|
6.3 Non-sampling error |
The main sources of non-sampling error are coverage, measurement and non-response errors. |
|
6.3.1 Coverage error |
Over-coverage is provided for: non-existent or uninhabited dwellings; population no longer living in the country/municipalities (due, for instance, to recent change of address or to wrong inclusion (recent emigration)).
|
|
6.3.1.1 Over-coverage - rate |
|
6.3.1.2 Common units - proportion |
|
6.3.2 Measurement error |
- Description of efforts made in questionnaire design and testing.
- Description of interviewer training.
- Availability of a database on interviewers
- Training course for interviewers
- Supervision of interviewers by assessing completeness and accuracy of filled-in questionnaires
- Monitoring response rates per interviewer during data collection
- Establishing an Help Desk for support to interviewers during field operations
- Interviewer-questionnaire matching by identification codes
- Interviewers filling-in questionnaires on performed interviews
- Telephone re-interviews to verify that interviews were performed
- Ex post evaluation of interviewers performance based on indicators
- Debriefing with interviewers on data collection problems
|
|
6.3.3 Non response error |
1. Reasons for non-response |
Unit non-response can be:
- ‘voluntary’, because respondents refuse to answer the questionnaire due to hostility, lack of interest, lack of availability, suspicion,…;
- ‘non-voluntary’, because respondents cannot be able to provide the information due to disease, dialect, mourning, foreign language, temporarily absence,.... |
2. Characteristics of non-respondents. |
|
3. Achieved Household response rates (%) |
37.4% |
4. Efforts to reduce non-response |
During data collection, each base non-responding household is replaced with a substitutive household having similar socio-demographic characteristics, in order to maintain the sample representativeness, and to minimize the impact of unit non-response as well. No more than five substitutions are admitted for every non-responding household.
Other quality actions undertaken to control unit non-response are:
- Survey presentation letter signed by the President of Istat
- Telephone contacts to make an appointment with the interviewer
- Interviewer identification badge
- Delivery of a brochure containing statistical information on the survey
- Description of survey objectives provided by the interviewer
- Special care in writing clear instructions to fill-in diaries
- Establishing a toll free line or telephone number for further explanations
- Guarantees on statistical confidentiality
|
5. Adjustment of weights in order to reduce non-response. |
A correction factor for unit non-response is calculated as the inverse of the response rate at the stratum level. |
6. Comments regarding non-response errors |
|
|
6.3.3.1 Unit non-response - rate |
1.Unit non-response rate overall and at an appropriate level of detail |
62.6% |
2. Use of substitute Households to replace non-responding households |
Gross sample size |
66,350 |
Number of eligible units |
64,293 |
Number of units successfully contacted before substitution |
25,854 |
Number of units successfully contacted after substitution |
36,075 |
Number of responding households before substitution |
11,455 |
Number of responding households after substitution |
12,605 |
Response rate before substitution |
42.9% |
Response rate after substitution |
33.6% |
3. Qualitative assessment of the bias associated with nonresponse. |
Quality actions undertaken:
- Quantitative control on received questionnaires
- Collecting identification codes of non-responding households
- Collecting non-response reasons during data collection
- Collecting interviewer identification codes for non-responding households
- Control on interview outcome codes (completed, refused, non-contacted, duplicated, etc.)
- Ad hoc analysis to evaluate effects of unit non response
|
|
6.3.3.2 Item non-response - rate |
1.Item non-response rate in the survey |
|
2. Qualitative assessment of the bias associated with nonresponse |
|
|
6.3.4 Processing error |
Comments regarding Processing Error |
|
|
6.3.4.1 Imputation - rate |
Percentage of imputed values of all possible values |
|
|
6.3.5 Model assumption error |
|
6.4 Seasonal adjustment |
|
6.5 Data revision - policy |
Comments regarding Data Revision Policy |
|
|
6.6 Data revision - practice |
Comments regarding Data Revision Practice |
|
|
6.6.1 Data revision - average size |
|
7. Timeliness and punctuality |
7.1 Timeliness |
Data Collection Year |
Data Published Year |
2020 |
2021 |
|
7.1.1 Time lag - first result |
Time lag for the first published results, in terms of months
|
Not applicable |
|
7.1.2 Time lag - final result |
Time lag for the final published results, in terms of months |
160 days |
|
7.2 Punctuality |
|
7.2.1 Punctuality - delivery and publication |
Target date when data should have been delivered (scheduled date of dissemination of national results)
|
09 June 2021 |
Actual delivery of the data (dissemination of national results) |
09 June 2021 |
Number of days (time lag) between the delivery/release date of data and the target date on which they were scheduled for delivery/release. |
0 days |
|
8. Coherence and comparability |
8.1 Comparability - geographical |
Comments regarding Comparability - geographical |
The Italian HBS data are comparable with HBS data from EU countries. |
|
8.1.1 Asymmetry for mirror flow statistics - coefficient |
|
8.2 Comparability - over time |
The Italian HBS data are comparable for the period 2014-2020. In fact, since 2014, the "new" HBS has replaced the "old" HBS, carried out between 1997 and 2013. The present survey design differs deeply from the previous one: in particular, expenditure reference periods have been enlarged and the most updated ECoicop has been adopted. Therefore, it has been necessary to reconstruct the time series of the main expenditure aggregates since 1997. Time comparisons between 2014 estimates and previously disseminated estimates can be made only using reconstructed data, available exclusively on the Istat data warehouse (see par. 9.3). No comparisons are possible between the Italian HBS data disseminated by Eurostat for years 2015 and 2020 and data previously disseminated. |
8.2.1 Length of comparable time series |
|
8.3 Coherence - cross domain |
1. Comparison with EU-SILC
Eurostat will calculate various indicators based on the HBS micro-data and compare these with similar indicators based on EU-SILC data. These indicators include:
- At-risk-of-poverty threshold (EUR)
- At-risk-of-poverty rate (%)
- Relative at-risk-of-poverty gap
- Income quintile share ratio S80/S20
- Gini coefficient
2. Comparison with HICP
Eurostat will calculate the structure of Consumption Expenditure at 2-digit COICOP level using HBS micro-data and compare these with similar values based on HICP data
3. Additional comments regarding cross-domain coherence
|
|
|
8.4 Coherence - sub annual and annual statistics |
|
8.5 Coherence - National Accounts |
Eurostat will calculate the structure of Consumption Expenditure at 2-digit COICOP level using HBS micro-data and compare these with similar values based on NA data
|
8.6 Coherence - internal |
|
9. Accessibility and clarity |
9.1 Dissemination format - News release |
|
9.2 Dissemination format - Publications |
|
9.3 Dissemination format - online database |
|
9.3.1 Data tables - consultations |
|
9.4 Dissemination format - microdata access |
https://www.istat.it/it/archivio/180356 |
|
9.5 Dissemination format - other |
|
9.6 Documentation on methodology |
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9.7 Quality management - documentation |
The Istat Information system on quality of statistical production processes, SIQual (http://siqual.istat.it/SIQual/lang.do?language=UK), contains information on the execution on Istat statistical production processes and on activities developed to guarantee quality of the produced statistical information. For further details on the Italian HBS: http://siqual.istat.it/SIQual/visualizza.do?id=8889007&refresh=true&language=EN |
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9.7.1 Metadata completeness - rate |
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9.7.2 Metadata - consultations |
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10. Cost and Burden |
1. Cost to the NSI (?) |
Not avaible. |
2. Burden on the Household (Hours) |
The average initial interview time is 31 minutes and the average final interview time is 41 minutes. In 2020, the burden on households in terms of time spent for the interviews is bigger than in previous survey editions as a consequence of the changes introduced in data collection techniques due to the Covid-19 pandemic (see par. 3.3, point 3). |
3. Measures to reduce Costs and Burden |
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4. Recent efforts to improve efficiency and comment on the extent to which information and communication technology has been used |
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11. Confidentiality |
11.1 Confidentiality - policy |
Several national legal acts guarantee the confidentiality of data requested for statistical purposes. In Italy, according to art. 9, paragraph 1 of the Legislative Decree n. 322 of 1989 (concerning the statistical system), statistical data cannot be disseminated but in aggregated form, in order to make it impossible to identify the person to whom the information relates. The data collected can only be used for statistical purposes.
Official statistics must also safeguard the rights, basic freedoms, and dignity of respondents, in particular with regard to the right of confidentiality and personal identity.
Istat assures the protection of personal data according to the General Data Protection Regulation (Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, repealing Directive 95/46/EC) and, as national legislation, Italian Data Protection Code (Legislative Decree no. 196/2003) and 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.
In order to make statistical secrecy and protection of personal data effective, Istat is currently taking appropriate organizational, logistical, methodological and statistical measures in accordance with internationally established standards.
Moreover, Legislative Decree n. 322 of 1989, art. 6 and 6 bis provides that the exchange of microdata and personal data within the National Statistical System (Sistan) is possible if it is necessary to fulfil requirements provided by the National Statistical Programme.
Finally, in implementation of art. 5-ter of the legislative decree 14 March 2013, no. 33, the new "Guidelines for the access for scientific purposes to the elementary data of the National Statistical System" establish the conditions under which the bodies and offices of the National Statistical System can allow researchers to access their own elementary data for scientific purposes.
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11.2 Confidentiality - data treatment |
Actions performed to ensure data confidentiality:
- Methods of risk evaluation for tables: risk evaluation by applying a threshold rule
- Methods of protection for tables: cell suppression; table redesign
- Methods of protection microdata: global recording; local suppression
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12. Comment |
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