1.1. Contact organisation
ISTAT Italian National Institute of Statistics
1.2. Contact organisation unit
Directorate of Social Statistics and Welfare
Integrated system for household economic conditions and consumer prices
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Via Cesare Balbo 16, 00184, Rome – ITALY
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
22 April 2025
2.2. Metadata last posted
22 April 2025
2.3. Metadata last update
22 April 2025
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
3.2. Classification system
- International Standard Classification of Education (ISCED'2011);
- International Standard Classification of Occupations (ISCO-08);
- Classification of Economic Activities (NACE Rev.2-2008);
- Common classification of territorial units for statistics (NUTS 2);
- SCL - Geographical code list;
- The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
3.4. Statistical concepts and definitions
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| Same definition as standard EU-SILC |
In accordance with the Commission Implementing Regulation (EU) 2019/2181 (Article 2), IT uses the following household definition: “a person or a group of two or more persons that usually reside together in a housing unit or part of a housing unit and share income or household expenses with the other household members, where sharing household income means contributing to the private household income or benefitting from the private household income, or both’ |
IT does not include live-in domestic or caregiving personnel au pairs that do not share income or expenses |
3.6.2. Population not covered by the data collection
The sub-populations that are not covered by the data collection includes: those who moved out of the country’s territory; or those with no usual residence; or those living in institutions or who have moved to an institution compared to the previous year.
3.7. Reference area
The statistical phenomenon measured relates to the Italian territory and all the regions are covered.
3.8. Coverage - Time
As established by Regulation (EU) 2019/1700 of the European Parliament and of the Council, the reference time varies according to the particular item of information considered. In general, income variables refer to the year N-1; living conditions refer to the time of interview (current) while information on arrears or on main reasons for unmet need make reference to the last 12 months. A longer period of time is considered for information on the duration of specific employment situations (for instance unemployment spell), i.e. Last 5 years from the end of the income reference period.
Data are available for the survey years 2004-2024
3.9. Base period
Not applicable.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| Same definition as standard EU-SILC |
Same definition as standard EU-SILC |
Same definition as standard EU-SILC |
In 2024 data collection, current variables refer to the moment of the interview, that is the period from 17th January to 26th May, 1-6 months after the income reference period. Concerning the previous surveys involved in the longitudinal component, the lag between the income reference period and current variables is about 6 months in 2017, about 7 months in 2018, about 13 months in 2019, about 12 months in 2020 and 11 months after the income reference period in 2021 and about 4-9 months in 2022 and 3-7 months in 2023. |
6.1. Institutional Mandate - legal acts and other agreements
Regulation (EU) 2019/1700 was publish in OJ on 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (IESS). The Annex to the Commission implementing regulation (EU) 2019/2180 of 16 December 2019 specifies the detailed arrangements and content for the quality reports pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council and Regulation (EU) 2019/2242.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by Eurostat. Access to confidential microdata for scientific purposes may be granted on the basis of Commission Regulation 557/2013 and Regulation 223/2009 of the European Parliament and the Council on European statistics.
7.1. Confidentiality - policy
The personal data are processed by Istat for the performance of the public interest tasks entrusted to it (Article 15 of Legislative Decree No. 322/1989),
7.2. Confidentiality - data treatment
The information collected, protected by statistical confidentiality (Article 9 of Legislative Decree No. 322/1989) and subject to the relevant legislation of protection of personal data (Regulation (EU) 2016/679, legislative decree n. 196/2003, and legislative decree n. 101/2018), may be used, also for subsequent processing, by subjects of the National Statistical System, exclusively for statistical purposes. The same data may also be communicated to the European Commission (Eurostat) as well as be communicated for scientific research purposes under the conditions and in the manner provided for by art. 5 ter of Legislative Decree 33/2013.
8.1. Release calendar
More information about released calendar can be found in ISTAT webpage.
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see section 10 - 'Accessibility and clarity'), respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users. Additional information about microdata access is available in EU statistics on income and living conditions - Microdata - Eurostat.
Annual
10.1. Dissemination format - News release
More information can be found in ISTAT webpage also in the dedicated section of income and living condition.
10.2. Dissemination format - Publications
More information you can find in annual report, sdg report and BES report.
10.3. Dissemination format - online database
More information can be found in ISTAT webpage.
10.3.1. Data tables - consultations
| Number of request | Type of request |
|---|---|
| 00001343/2022 | dati reddito provinciale |
| 00001390/2022 | accesso a variabili dataset Eu SILC |
| 00001452/2022 | dati sui redditi a livello comunale |
| 00001653/2022 | serie storica redditi procapite o per nucleo familiare |
| 00001675/2022 | maschi e femmine per classi di reddito |
| 00003981/2022 | tavole reddito delle famiglie 2012-2022 |
| 00004199/2022 | richiesta informazioni su redditi equivalenti |
| 00004376/2023 | EU-SILC 2004-2011 variabile provincia di nascita |
| 00004394/2022 | come calcolare il numero di figli in EU-SILC |
| 00004589/2022 | EU-SILC problemi con merge di diversi moduli |
| 00006128/2022 | Tesi sul reddito dei cittadini |
| 00006787/2022 | Rilascio SILC 2021 |
| 00007571/2022 | dati reddito livello subcomunale |
| 00004661/2022 | informazioni microdati per la ricerca EU-SILC |
| 00002758/2022 | dati reddito famiglie |
| 00003030/2022 | Microdati file per la ricerca trasversali 2016-2020 EU-SILC |
| 00004353/2022 | tesi magistrale su disuguaglianze economiche a livello sub comunale o comunale |
| 00003582/2022 | informazioni su variabili: RB031: Anno di immigrazione. |
| 00003799/2022 | richiesta aperta da utente diverso: informazioni su variabili: RB031: Anno di immigrazione. |
| 00001390/2022 | richiesta aperta da utente diverso: informazioni per accedere alle variabili i) comune o provincia di nascita e ii) background educativo dei genitori dell'intervistato. |
| 00001852/2022 | reddito netto delle famiglie medio nel 2019 |
10.4. Dissemination format - microdata access
Istat provides microdata files free of charge for study and research purposes or for statistical-scientific purposes, in compliance with the regulations in force. The files released are those available at the time of the request and may be subject to statistical revisions.
ISTAT disseminates different types of microdata files in order to respond to different information needs:
Scientific use files are specially released for the purposes of scientific research and relate to statistical surveys on individuals, households and enterprises. These are microdata files, with no direct identifiers, which have been subject to control methods to protect confidentiality.
Scientific use files may be requested exclusively for carrying out specific research projects by researchers belonging to Entities recognised as research institutions by Comstat or included in the list of research Institutions recognised by Eurostat.
Failing such requirement, it is necessary to activate the procedure for the recognition of the relevant Entity as a matter of priority.
Files for Sistan are sets of microdata reserved for the Statistical offices or entities belonging to the National statistical system. These data are collected for statistical purposes and are not subject to further methods of statistical disclosure control.
Such files may be requested for the purpose of:
- implementing the National Statistical Programme;
- processing statistical data in connection with the institutional activities or the geographical area of the applicant. In this case, a brief description of the project must also be submitted.
Personal data with identifiers may be released only in exceptional cases, in which it is absolutely and strictly necessary to achieve the set objectives.
The request for access to the files for Sistan shall clearly state the nature of data, the subject matter and the purpose of the request.
10.5. Dissemination format - other
auditions
10.5.1. Metadata - consultations
For the year 2024, 16 users consulted Adele laboratory (Laboratory for Elementary Data Analysis) which is a “safe” environment where researchers from universities or research institutions or bodies may conduct statistical analyses that require the use of elementary data.
Within the Laboratory, data security and statistical confidentiality are guaranteed by the control of both the working methods and the results of the analyzes conducted by the users.
Once the processing is complete, the output is evaluated in terms of statistical confidentiality by the experts of the ADELE Laboratory. Only results that positively comply with the Rules for the release of results can be issued.
10.6. Documentation on methodology
Methodological documentation can be found in ISTAT webpage.
10.6.1. Metadata completeness - rate
All required concepts are provided
10.7. Quality management - documentation
More information can be found in ISTAT webpage.
11.1. Quality assurance
The initial step of the fieldwork activities aims at facilitating the approach with the household. It involves sending all families in the sample a letter, signed by Istat President, informing them of their involvement in the survey. The letter expresses the salient aspects of the survey, with particular reference to its relevance for the purposes of national and European policies, as well as the regulatory aspects that govern it (obligation to reply, sensitive questions, etc.).
It is known that the household response rate forewarned institutionally about the interview to be carried out is always significantly higher than those who, for whatever reason, have not received it. Furthermore, families can contact a toll-free number that can reassure them about the authenticity of the interview and confirm the accreditation of the interviewers.
Istat also asks municipalities to take responsibility for sending their own letter to families, signed by the Mayor, which further reaffirms the relevance of the survey.
Finally, the Municipalities themselves are made aware of the survey, so that the families who contact for information on the actual conduct of the survey can be suitably reassured. Particular attention is then paid to the recruitment and training of the interviewers. The directives imposed by Istat, in fact, provide that the interviewers possess certain characteristics of suitability (higher education qualification, previous experience) and that they can begin to carry out interviews only after having participated in the training sessions scheduled before the start of the survey. Each detected family is also associated with the code that unambiguously identifies the interviewer who handled it and many of the quality indicators are detailed at the individual level. Furthermore, as the fieldwork is carried out, a survey result is recorded for each household in the sample. For families who refuse the interview, some information is collected that allows a better characterization of the refusal: time of refusal, the reason for refusal, some synthetic characteristics of the person who refuses.
Before the switch to computer-assisted survey techniques, this information, combined with a summary report on the number of attempts made, with or without contact, and on the duration of the interview, provided the elements for an a posteriori assessment of the quality of the survey and the work of the individual interviewer. The transition first to CAPI and then to CATI, where the aforementioned information flow takes place almost in real time, has allowed the development of an articulated system for monitoring the survey in progress that allows, for example, to intervene with supplements training for the interviewers, remotely via e-mail or by organizing specific debriefing sessions.
The computer-aided survey also allows to make certain information more precise such as the duration of the interview, the number of contacts, and the outcome of the survey itself. In fact, the use of the electronic questionnaire allows to give an outcome even to a single contact or attempted contact and the results of detection arise automatically from the different paths of the electronic questionnaire based on the rules. In this way it is possible to classify the outcomes taking into account the whole "history", such as falls with or without contact, with or without an appointment, without contact attempts (not working), etc. The duration of the interview (or individual contact) is automatically registered by the computer.
11.2. Quality management - assessment
Thanks to the supply, almost contextually to the interview, of additional information with respect to that collected through the questionnaires, the monitoring activity allows to have an updated picture of the progress of the survey and improves the quality of the survey in real time. The processing of monitoring data, also disaggregated by territory and by interviewer, makes it possible to intervene promptly and in a targeted manner on any critical issues encountered. For the CATI survey, the monitoring system includes the following indicators: status of families contacted (in terms of final outcome), main contact rates of families (per interviewer, by type of family and receipt of the letter, by territory, and cumulative total). All the indicators are also declined by day and by solar week. The monitoring cards also include: daily indicators per interviewer on the duration of the interview, contact attempts and proxy interviews, broken down by family type and number of members. The phone call monitoring is mainly aimed at identifying any critical issues in the administration, the most difficult passages of the questionnaire as well as identifying the most frequent resistances expressed by the families interviewed. To evaluate various aspects of the conduct of the interviews and the behavior of the interviewers, it is possible to be present in the room as a non-participating observer, also having the possibility of listening to telephone calls through headphones. At the end of each shift, the most relevant observations are reported in monitoring forms. In particular, the "questionnaire module", with a grid divided into sections and monitoring shifts, allows for the detection, for single questions or series of questions, of difficulties encountered on the concepts, on the formulation of the questions, on the terminology adopted; it is also possible to insert general annotations in the questionnaire: particular technical problems of the software, possible presence of errors/systematic problems in the questionnaire, types of households that create more problems, frequent interruptions of the interviews and/or refusals.
The "interviewer card", with an articulated grid by interviewer and by monitoring shift, makes it possible to report observations on individual interviewer. Finally, the "answer sheet", with a grid articulated by questionnaire sections, allows to write down any questions that arise during the shift and the related answers provided, in order to share clarifications and insights provided with the rest of the staff involved (interviewers, supervisors , Istat personnel competent for the survey). It is considered important to organize de-briefings with the interviewers in order to take advantage of their ongoing experience, reporting the main difficulties encountered in the field work, taking up some doubts in the interpretation of questions or some requests for clarification.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, universities, media, and students.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat. The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use. The use of the ad-hoc modules was less widespread than the use of the nucleus variables. Nevertheless, there was high interest to repeat these modules in order to have the possibility of comparing data over time. Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be available for the main indicators. Finally, users were satisfied with overall quality of the service delivered by Eurostat, which encompasses data quality and the supporting service provided to them.
lang="EN-GB">For more information, please consult the User Satisfaction Survey.
12.3. Completeness
All the required variables are transmitted except the following optional variables:
HY030G: Imputed rent (Optional)
RL080: Remote education (Optional)
HI130G: Interest expenses [not including interest expenses for purchasing the main dwelling] (OPTIONAL)
HI140G: Household debts (OPTIONAL
12.3.1. Data completeness - rate
All the required variables are transmitted
13.1. Accuracy - overall
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- Ratio at‐risk‐of‐poverty or social exclusion to population;
- Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
- BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
- DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
Sampling errors of the main indicators are calculated with national methods in order to take into account of the sampling design effects.
More precisely, the R package ReGenesees (R Evolved Generalized Software for Sampling Estimates and Errors in Surveys) was used.
ReGenesees is the outcome of a long term research and development project, aimed at defining a new standard for calibration, estimation and sampling error assessment to be adopted in all large-scale sample surveys routinely carried out by Istat (see Zardetto, D. (2015).
“ReGenesees: An Advanced R System for Calibration, Estimation and Sampling Error Assessment in Complex Sample Surveys”. Journal of Official Statistics, 31(2), 177-203).
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
- Coverage errors: errors due to divergences existing between the target population and the sampling frame.
- Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
- Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
- Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
- Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
- Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage and misclassification:
- Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
- Under-coverage: refers to units not included in the sampling frame.
- Misclassification: refers to incorrect classification of units that belong to the target population
13.3.1.1. Over-coverage - rate
Coverage error: this information in not available
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
|
|
|
| Under-coverage |
|
|
|
| Misclassification |
|
|
|
13.3.1.2. Common units - proportion
Not applicable.
13.3.2. Measurement error
Measurement error for cross-sectional data
| Cross-sectional data |
|
||
|---|---|---|---|
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
| The main source of measurement errors are: (i) memory effect, because the information is collected according to respondents’ memories (official documentation about income is not required; external sources of information, such as administrative registers, are used when available); (ii) omission, because respondents might not be willing to provide correct information about income or other living conditions; (iii) proxy effect, because in some cases individuals are allowed to provide information about other household members; (iv) interviewers, who might provide the respondents with an incorrect interpretation of the questions, or may be wrong when filling the questionnaire. The IT questionnaire of SILC is developed according to EU-SILC regulations and EUROSTAT guidelines. |
The final version of the questionnaire is based on (i) the support of experts working in other research institutes; (ii) a cognitive laboratory on self-employment; (iii) the experience of the previous editions of the survey. Information is collected through three main questionnaires: the first one collects information on the main demographic characteristics of each household member; some information on child care is also included; the second questionnaire collects information at the household level and mainly regards housing conditions and expenditures; the third one collects information at the individual level (about individuals aged 16 and over) and covers many topics ranging from occupational conditions to health and economic conditions.
|
Interviewers are firstly trained and provided with training tools (e.g. instruction manuals or presentations) by Istat. The private company in charge of the fieldwork provides support to the interviewers and controls the quality of their work. The training strategies have been outlined also on the experience of pilot surveys. |
Computer-assisted interviewing (CAPI and CATI) makes it possible to prevent measurement problems and simplify data collection. Besides, most of the processing errors can be checked and corrected during the interview: hard and soft checks are implemented both in the CAPI and CATI questionnaires to reduce inconsistent or incorrect data collection. |
13.3.3. Non response error
Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the Reg.(EU) 2019/2242
- Household non-response rates (NRh) is computed as follows:
NRh=(1-(Ra * Rh)) * 100
Where Ra is the address contact rate defined as:
Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected
and Rh is the proportion of complete household interviews accepted for the database
Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)
• Individual non-response rates (NRp) is computed as follows:
NRp=(1-(Rp)) * 100
Where Rp is the proportion of complete personal interviews within the households accepted for the database
Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database
• Overall individual non-response rates (*NRp) is computed as follows:
*NRp=(1-(Ra * Rh * Rp)) * 100
For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.
2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional
| Address (including phone, mail if applicable) contact rate | Complete household interviews | Complete personal interviews | Household Non-response rate | Individual non-response rate | Overall individual non-response rate | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Ra) | (Rh) | (Rp) | (NRh) | (NRp) | (NRp)* | ||||||||||||
| A | B | C | A | B | C | A | B | C | A | B | C | A | B | C | A | B | C |
| 99.59 | 98.59 | 100.0 | 75.79 | 70.38 | 81.96 | 100.0 | 100.0 | 100.0 | 24.52 | 30.62 | 18.04 | 0.00 | 0.00 | 0.00 | 24.52 | 30.62 | 18.04 |
where
A=total (cross-sectional) sample,
B =New sub-sample (new rotational group) introduced for first time in the survey this year,
C= Sub-sample (rotational group) surveyed for last time in the survey this year.
Response rate for households by wave
| Longitudinal data 2020-2024 | Wave 2 | Wave 3 | Wave 4 | Wave 5 | Wave 6 |
|---|---|---|---|---|---|
| 2020 | 2021 | 2022 | 2023 | 2024 | |
| Wave response rate | 47.71 | 54.01 | 58.03 | 70.86 | 75.43 |
| L follow-up rate | 49.36 | 74.77 | 69.45 | 80.87 | 78.07 |
| Follow-up ratio | 48.17 | 100.0 | 68.96 | 93.34 | 84.89 |
| Achieved sample size ratio | 48.17 | 100.0 | 68.96 | 93.34 | 84.89 |
Response rate for persons by wave
| Response rate for persons |
Sample persons / coresidents |
Wave 2 |
Wave 3 |
Wave 4 |
Wave 5* |
Wave 6* |
|---|---|---|---|---|---|---|
| Wave response rate |
Sample persons |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| Co-resident |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
|
| L follow-up rate |
|
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| Achieved sample size ratio |
All persons |
46.50 |
100.9 |
66.50 |
92.75 |
83.76 |
| Sample persons |
46.51 |
100.9 |
66.22 | 91.84 | 82.95 | |
| Co-resident |
. |
101.5 |
150.7 |
245.8 |
141.7 |
|
| Response rate for non-sample persons |
Co-resident |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
Sample and response rate by wave
| Year of the survey | Sample of households | Sample of individuals 16+ | Response rate of the households | Response rate of individuals 16+ |
|---|---|---|---|---|
| Wave 1 | 12304 | 14699 | 68,3 | 100,0 |
| Wave 2 | 10488 | 14112 | 77,1 | 100,0 |
| Wave 3 | 10241 | 13851 | 77,6 | 100,0 |
| Wave 4 | 3734 | 4924 | 73,8 | 100,0 |
| Wave 5* | 3226 | 4559 | 77,1 | 100,0 |
| Wave 6* | 2607 | 3779 | 81,4 | 100,0 |
Annexes:
Unit non response rate by wave
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
see annex 2
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| The household is either visited or called by an interviewer, who performs the data collection with the aid of a personal computer, on behalf of Istat. The information is collected through a CAPI/CATI questionnaire. The data entry procedure is realized through the software implemented by the private company in charge of the fieldwork. The procedure contains automatic controls about the range of variables, main routes of the questionnaire and any logical control referred to the internal inconsistency of the collected information. Every control is set up as “soft” in order to reduce typing errors. Some hard controls are implemented on household members’ demographic characteristics (sex, date of birth, etc.) as provided by the registry office (for the first wave) or previous interviews (from the second wave onwards). |
The main errors detected in the post-data collection process are:
|
Annexes:
re-interview rate
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
6 months
14.1.1. Time lag - first result
3 months
14.1.2. Time lag - final result
3 months
14.2. Punctuality
0 month
14.2.1. Punctuality - delivery and publication
Data were pubblished 3 months after the end of reference year N (N=2024)
15.1. Comparability - geographical
Statistics are comparable at NUTS2 level
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See annex 8.
15.2.1. Length of comparable time series
the series are comparable since 2004
15.2.2. Comparability and deviation from definition for each income variable
| Comparability and deviation from definition for each income variable
F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected. |
15.3. Coherence - cross domain
The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See annex 7.
15.4. Coherence - internal
No lack of coherence to report.
Mean (average) interview duration per household = 24.3 minutes.
Mean (average) interview duration per person = 7.5 minutes.
Mean (average) interview duration for selected respondents (if applicable) = minutes.
17.1. Data revision - policy
According to the European Statistics Code of Practice, the Quality Assurance Framework of the European Statistical System (QAF) and the ESS Guidelines on Revision Policy for PEEIs, Istat is committed to guarantee that principles on which revisions are based are respected. In particular:
- the revisions are pre-announced in the release calendar (see Revision Cards);
- in the moment of publication, the statistics are classified as preliminary, provisional or final;
- the causes of revisions are explained and possible breaks in time series are pointed out;
- both qualitative (revision analysis) and quantitative (revision measures) information on revisions are disseminated;
- revisions due to unexpected errors are disseminated out of the release calendar.
17.2. Data revision - practice
The dissemination of information on revisions reflects the fundamental principles of statistical process and product quality.
In order to ensure the standardization of disseminated information, for each short-term survey, Istat publishes a Revision Card. This Card reports information on the revision policy adopted for raw and seasonally adjusted (if produced) time series and a list of the reasons for the ordinary and extraordinary revisions. The calendar of the complete cycle of ordinary revisions is included.
Consistently with the principle of Clarity, for the main short-term indicators, Istat publishes revision triangles (real-time database) in which different versions of data released over time are collected in tabular form. Each row contains the time series released on a certain date; this permits reading by column the story of the released estimates of a given indicator, from the first to the last available release.
In addition, for each indicator, the results of the revision analysis are made available. The revision analysis is carried out through the main quality indicators that provide measures of the average size, direction, and variability of the revisions, with the aim of improving statistical processes.
Short-term indicator press releases contain both information on the revision measures and on the revision policy. The latter is included in a section the “Methodological note” that is released jointly with the press release in case of ordinary revisions or it is described in an ad-hoc “Information note” accompanying the press release in case of extraordinary revisions.
17.2.1. Data revision - average size
Not applicable
Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.
18.1. Source data
The sampling frame is made up of municipalities registers. The sample is extracted from LAC (Liste Anagrafiche Comunali (i.e. the Italian acronym for lists of municipal registry) for the years 2018-2020; from 2021 onwards the list considers the households already involved in the permanent census of the population and housing, which represents the population at the end of the income reference period.
The sample of the households belonging to the rotational group with DB075=3 was extracted and validated in June 2019.
The sample of the households belonging to the rotational group with DB075= 5 was extracted and validated in June 2020.
The sample of the households belonging to the rotational group with DB075= 6 was extracted and validated in June 2021.
The sample of the households belonging to the rotational group with DB075= 4 was extracted and validated in June 2022.
The sample of the households belonging to the rotational group with DB075= 1 was extracted and validated in January 2023.
The sample of the households belonging to the rotational group with DB075= 2 was extracted and validated in January 2024.
18.1.1. Sampling Design
Two-stage sampling design: The first stage units (or primary sampling units PSU) are the municipalities, and the second stage units (SSU) are the households.
The PSUs are stratified according to their size in terms of the number of residents. Stratification is carried out inside each administrative region. Four municipalities are selected in each stratum.
Municipalities are clusters of households, households are clusters of individuals.
Stratification and sub stratification criteria
Stratification of primary sampling units by the number of inhabitants so that the total number of inhabitants in each stratum is approximately constant (this guarantees self-weighting design in each region).
Municipalities whose sizes are higher than a threshold are self-representing units i.e. are strata themselves and included with certainty in the sample of PSU.
Secondary sampling units are not stratified.
Sample selection schemes
PSU are selected with probability proportional to their size (number of residents) by means of a systematic sampling method by Madow (1949) inside each stratum.
Households are selected with equal probability by systematic sampling in each selected municipality from municipality registers.
No substitution of unit non-response has been applied.
18.1.2. Sampling unit
Household.
18.1.3. Sampling frame
Rotational design is used for households. In 2024 the whole sample is composed of six rotational groups. As shown in the table below, until 2019 each group was included in the sample for four waves of the survey. 2020 is a transition year when the panel duration is extended to 5 years. Group A4 is kept instead of dropped and one-fifth of the sample is renewed with the selection of the rotational group E1. From 2021 onward the six-year duration panel begins to be fully operational.
| A | B | C | D | E | F | G | H | I | |
|---|---|---|---|---|---|---|---|---|---|
| 2019 | A4 | B3 | C2 | D1 | |||||
| 2020 | A5 | B4 | C3 | D2 | E1 | ||||
| 2021 | A6 | B5 | C4 | D3 | E2 | F1 | |||
| 2022 | B6 | C5 | D4 | E3 | F2 | G1 | |||
| 2023 | C6 | D5 | E4 | F3 | G2 | H1 | |||
| 2024 | D6 | E5 | F4 | G3 | H2 | I1 |
18.2. Frequency of data collection
Concerning the previous surveys involved in the longitudinal component, the total fieldwork duration is about 5 months in 2017, about 6 months in 2018, about 4 months in 2019, 5 months in 2020 and 2021 and 4 months in 2022 and 2023.
| HB010 | HB020 | start_day | start_month | start_year | end_day | end_month | end_year |
|---|---|---|---|---|---|---|---|
| 2024 | IT | 16 | 01 | 2024 | 26 | 5 | 2024 |
18.3. Data collection
Mode of data collection
| IT | 2-CAPI | 3-CATI |
|---|---|---|
| Mode of coll | Mode of coll | |
| 55.4 | 44.6 |
Description of collecting income variables
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| All income variables are collected by personal interview (PAPI until 2010 and CAPI since 2011 onwards, CAPI and CATI since 2015) and integrated with administrative data. Particularly, administrative data have been linked to sample data at micro level and used for estimating data on labour income, income from rental, pensions and other social benefits |
All income variables are available both net and gross of taxes and social security contribution at source |
|
| The source or procedure used for the collection of income variables |
The form (gross, net) in which income variables at component level have been obtained |
The method used for obtaining target variables in the required form |
|---|---|---|
| All income variables are collected by personal interview (PAPI until 2010 and CAPI since 2011 onwards, CAPI and CATI since 2015) and integrated with administrative data. Particularly, administrative data have been linked to sample data at micro level and used for estimating data on labour income, income from rental, pensions and other social benefits |
All income variables are available both net and gross of taxes and social security contribution at source |
Income target variables are collected net of taxes and social security contributions and both administrative and survey micro-data are used in the production process. As regards the net-gross conversion of income variables, the implemented methodology jointly uses an exact record linkage between survey and fiscal data at the micro-level and a microsimulation model (Siena Microsimulation Model SM2-EU-SILC). The integration of microsimulation with register data has the advantage of using administrative data for the validation of microsimulation results. On the other hand, SM2-EU-SILC estimates tax and social insurance contributions that are not covered by register data. Four main register data are used: 730 tax returns used by employees and pensioners, UNICO tax returns used primarily by self-employed workers, CU employers’ tax statements which include also data on social security contributions, and Pension Register Data. Both the use of administrative data and microsimulation estimates improve the quality and the amount of information on gross income variables |
18.4. Data validation
Many external administrative sources of data are used for checking and validating the data obtained by the respondents. The estimates stemming from national accounts are benchmark values used to validate Silc estimates.
18.5. Data compilation
Data editing
Starting from 2011, computer-assisted data collection prevents from many errors as the electronic questionnaire automatically manages the interview process checking the data and making it possible to directly solve the inconsistencies with the respondent help.
However, data editing phase still remains an essential step for several reasons:
- It could not be sustainable to include checks between variables collected in different and distant part of the electronic questionnaire but it could be much more preferable to reconcile the information in the subsequent phase.
- truthful and plausible information could eventually be statistically implausible with the overall distribution (anomalous data / outliers)
- data collected at different survey editions for the same survey unit can show inconsistencies. The preload in the electronic questionnaire of the information collected in the t-1 survey and the request to the respondent to confirm it or not has drastically reduced this kind of errors
- data collected through an interview are subsequently integrated with a multiplicity of data from administrative sources, resulting in possible inconsistencies that need to be reconciled
Imputation procedure used
The imputation procedure for each quantitative variable is implemented by using the IMPUTE module of the software Iveware, as recommended by EUROSTAT.
The imputation procedure for the qualitative variables is based on a ‘hot deck’ stochastic technique that imputes each missing or inconsistent answer by replacing it with a correct value, taken from the ‘nearest donor’ (i.e. from a record randomly selected within a group of statistical units similar to the one that presents missing or erroneous answers).
Imputed rent
It is estimated through a semilogarithmic regression (log of the rent, avoiding the re-trasformation bias) with self-selection correction à la Heckman. In the first stage, we run distinct probit models for owners/renters at a below-the-market price/free tenants vs tenants at a market price. Seniority is included between regressors, but its effect is depurated (parameter from regression equal to 0) in estimating predicted values for sub-populations other than tenants at a market rate.
Company car
The monetary value of company cars is deducted from the accrued value of the vehicle according to the average depreciation rate from the purchase price to the market value at the reference period. When there is no information on the purchase price and/or the market value at the reference period, the value retrieved from time t-1 is used (for 5/6 of the sample, to say the “re-interviewed”).
18.5.1. Imputation - rate
No additional information is available.
18.5.2. Calculation of weighting factors and weight adjustments
Please see annex 5.
Annexes:
Annex 5 - Weighting procedure
18.5.3. Estimation and imputation
See annex 6.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
No comments.
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
22 April 2025
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the, content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
The statistical phenomenon measured relates to the Italian territory and all the regions are covered.
Description of reference period used for incomes
| Period for taxes on income and social insurance contributions |
Income reference periods used |
Reference period for taxes on wealth |
Lag between the income ref period and current variables |
|---|---|---|---|
| Same definition as standard EU-SILC |
Same definition as standard EU-SILC |
Same definition as standard EU-SILC |
In 2024 data collection, current variables refer to the moment of the interview, that is the period from 17th January to 26th May, 1-6 months after the income reference period. Concerning the previous surveys involved in the longitudinal component, the lag between the income reference period and current variables is about 6 months in 2017, about 7 months in 2018, about 13 months in 2019, about 12 months in 2020 and 11 months after the income reference period in 2021 and about 4-9 months in 2022 and 3-7 months in 2023. |
According to Reg. (EU) 2019/1700 Annex II, precision requirements for all data sets are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country or in a NUTS 2 region. For the income and living conditions domain, the estimated standard errors of the following indicators are examined according to certain parameters set:
- Ratio at‐risk‐of‐poverty or social exclusion to population;
- Ratio of at‐persistent‐risk‐of‐poverty over four years to population;
- Ratio at‐risk‐of‐poverty or social exclusion to population in each NUTS 2 region.
Further information is provided in section 13.2 Sampling error.
The data involves several units of measure depending upon the variables. Income variables are transmitted to Eurostat in national currency. For more information, see methodological guidelines and description of EU-SILC target variables available on CIRCABC
Data editing
Starting from 2011, computer-assisted data collection prevents from many errors as the electronic questionnaire automatically manages the interview process checking the data and making it possible to directly solve the inconsistencies with the respondent help.
However, data editing phase still remains an essential step for several reasons:
- It could not be sustainable to include checks between variables collected in different and distant part of the electronic questionnaire but it could be much more preferable to reconcile the information in the subsequent phase.
- truthful and plausible information could eventually be statistically implausible with the overall distribution (anomalous data / outliers)
- data collected at different survey editions for the same survey unit can show inconsistencies. The preload in the electronic questionnaire of the information collected in the t-1 survey and the request to the respondent to confirm it or not has drastically reduced this kind of errors
- data collected through an interview are subsequently integrated with a multiplicity of data from administrative sources, resulting in possible inconsistencies that need to be reconciled
Imputation procedure used
The imputation procedure for each quantitative variable is implemented by using the IMPUTE module of the software Iveware, as recommended by EUROSTAT.
The imputation procedure for the qualitative variables is based on a ‘hot deck’ stochastic technique that imputes each missing or inconsistent answer by replacing it with a correct value, taken from the ‘nearest donor’ (i.e. from a record randomly selected within a group of statistical units similar to the one that presents missing or erroneous answers).
Imputed rent
It is estimated through a semilogarithmic regression (log of the rent, avoiding the re-trasformation bias) with self-selection correction à la Heckman. In the first stage, we run distinct probit models for owners/renters at a below-the-market price/free tenants vs tenants at a market price. Seniority is included between regressors, but its effect is depurated (parameter from regression equal to 0) in estimating predicted values for sub-populations other than tenants at a market rate.
Company car
The monetary value of company cars is deducted from the accrued value of the vehicle according to the average depreciation rate from the purchase price to the market value at the reference period. When there is no information on the purchase price and/or the market value at the reference period, the value retrieved from time t-1 is used (for 5/6 of the sample, to say the “re-interviewed”).
The sampling frame is made up of municipalities registers. The sample is extracted from LAC (Liste Anagrafiche Comunali (i.e. the Italian acronym for lists of municipal registry) for the years 2018-2020; from 2021 onwards the list considers the households already involved in the permanent census of the population and housing, which represents the population at the end of the income reference period.
The sample of the households belonging to the rotational group with DB075=3 was extracted and validated in June 2019.
The sample of the households belonging to the rotational group with DB075= 5 was extracted and validated in June 2020.
The sample of the households belonging to the rotational group with DB075= 6 was extracted and validated in June 2021.
The sample of the households belonging to the rotational group with DB075= 4 was extracted and validated in June 2022.
The sample of the households belonging to the rotational group with DB075= 1 was extracted and validated in January 2023.
The sample of the households belonging to the rotational group with DB075= 2 was extracted and validated in January 2024.
Annual
6 months
Statistics are comparable at NUTS2 level
See annex 8.
