The production of quality reports is part of the implementation of the EU-SILC instrument. In order to assess the quality of data at national level and to make a comparison among countries, the National Statistics Institutes are asked to report detailed information mainly on: the entire statistical process, sampling and non-sampling errors, and potential deviations from standard definition and concepts.

This document follows the ESS standard for quality reports structure (ESQRS), which is the main report structure for reference metadata related to data quality in the European Statistical System. It is a metadata template, based on 13 main concepts, which can be used across several statistical domains with the purpose of a better harmonisation of the quality reporting requirements in the ESS.

For that reason the template of this document differs from that one stated in the Commission Reg. 28/2004.

Finally it is the combination of the previous intermediate and final quality reports therefore it is worth mentioning that it refers to both the cross sectional and the longitudinal data.

The EU-SILC (Statistics on Income and Living Conditions, Regulation of the European Parliament, no. 1177/2003) is one of the main sources of data for periodic reports on the social situation of the European Union and the spread of poverty in member countries. The core information of EU-SILC is essentially centered around the issues of income and social exclusion. The project is inspired by a multi-dimensional approach to the problem of poverty, with a particular focus on aspects of material deprivation.

Italy participates in the project with a survey on income and living conditions of households, conducted every year since 2004, providing statistics at both cross and longitudinal (households remain in the sample for four consecutive years). Although the EU-SILC Regulation require only the production of indicators at national level, in Italy the survey was designed to provide reliable estimates at regional level. Since 2007 the survey, in addition to net income, also provides an estimate of the gross income, allowing you to calculate the main economic and social indicators (poverty relative persistence of poverty in the state, dispersion around the line of poverty, income inequality ) before and after taxation and social transfers.
Since 2011 has been modified interview technique and the investigation is carried out through a CAPI (Computer Assisted Personal Interview).

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

In terms of precision requirements, the EU-SILC framework regulation as well the Commission Regulation on sampling and tracing rules refers respectively, to the effective sample size to be achieved and to representativeness of the sample. The effective sample size combines sample size and sampling design effect which depends on sampling design, population structure and non-response rate.

EU-SILC is a complex survey involving different sampling design 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 four groups:

1)BE, BG, CZ, IE, EL, ES, FR, IT, LV, HU, NL, PL, PT, RO, SI, UK and HR 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;

2) DE, EE, CY, LT, LU, 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;

3) DK, MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata;

The Eurostat methodology is accepted by Italy.

Alternative Sampling Error Table View

As comment:

Based of an hypothesis of agreement with the technical phase of construction of the sampling design: the Standard error of the (ARPR) at-risk of poverty rate 60% of median at family level , linearized with Taylor Woodruff Method, using (solely for this year) the Software Regenesees with Ultimate Cluster is 0.37 and the Deff at family level is 1.48

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:

1. – Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample

1. – Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained

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

The definition of the sample population is not different from the definition of the target population, considering the registry office.

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 the Commission Regulation 28/2004:

• 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 successfully contacted/Number of valid addresses 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

• Individual non-response rates (NRp) will be 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) will be 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) was selected, the individual non-response rates will be calculated for ‘the selected respondent’, for all individuals aged 16 years or older and for the non-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.

* All the formulas are defined in the Commission Regulation 28/2004, Annex II

A* = Total sample; B = * New sub-sample

For the Italian 2011 SILC survey the address contact rate (Ra), the proportion of completed household interviews accepted for the database (Rh), the household non-response rate (NRh), the proportion of complete personal interviews within the households accepted for the database (Rp), the individual non-response rates (NRp) and the overall individual non-response rates (NRp_overall) are shown below:

Household non-response rates (NRh) will be computed as follows:  NRh=(1-(Ra * Rh)) * 100

Where Ra is the address contact rate

Rh is the proportion of complete household interviews accepted for the database 

For those Members States where substitutions are made in case of unit non-response, non-response rates will be calculated before and after substitutions.

• Individual non-response rates (NRp) will be computed as follows: NRp=(1-(Rp)) * 100

Where Rp is the proportion of complete personal interviews within the households accepted for the database

• Overall individual non-response rates (*NRp) will be computed as follows: *NRp=(1-(Ra * Rh * Rp)) * 100

For those Members States where substitutions are made in cases of unit non-response, non-response rates will be calculated before and after substitutions.

For those Members States where a sample of persons rather than a sample of households (addresses) was selected, the individual non-response rates will be calculated for ‘the selected respondent’ (RB245=2), for all individuals aged 16 years or older (RB245=2+3) and for the non-selected respondent (RB245=3).

The computation of item non-response is essential to fulfil the precision requirements concerning publication as stated in the Commission Regulation No 1982/2003. Item non-response rate is provided for the main income variables both at household and personal level.

 Alternative Table View as requested by the previous NRME Template

Administrative data cover 95.3% of recipients of old age benefits, disability benefits and survivor’ benefits.

The total item non-response for total disposable household income is 1.46 per cent (number of observations is 283) and the total number of observations is 19.399 (unit=households). For unadjusted gender pay gap the total item non-response is 5.32 per cent (number of observations is 2155) and the total number of observations is 40.496 (unit=individuals 16 + ).

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

The length of time between data availability and the event or phenomenon that they describe is as requested by  Eurostat  Reg. 28/2004

The number of months from the last day of the reference period to the day of publication of first results is as requested by  Eurostat  Reg. 28/2004

The number of months from the last day of the reference period to the day of publication of complete and final results are as requested by Eurostat  Reg. 28/2004

Time lag between the actual delivery of the data and the target date when it should have been delivered as requested by  Eurostat  Reg. 28/2004

The number of days between the delivery / release date of data and the target date on which they were scheduled for delivery/ release as requested by  Eurostat  Reg. 28/2004

Not requested by Reg. 28/2004

Publication on Income and Living condition are downloadable at

http://www.istat.it/it/archivio/condizioni-economiche-delle-fami

alias

http://www.istat.it/en/archive/households-economic-conditions

or you can contact the Press release Center

Your direct access to the Italian National Institute of Statistics is:  I.STAT

I.Stat is the warehouse of statistics produced by Istat, a complete and homogeneous wealth of information unique for the Italian official statistics.

http://dati.istat.it/?lang=en

Your direct access to the Italian Statistics is I.ISTAT

I.Stat is the warehouse of statistics produced by Istat, a complete and homogeneous wealth of information unique for the Italian official statistics.

http://dati.istat.it/?lang=en

At the following web address, Istat provides Micro-data access

http://www.istat.it/en/information/researchers/microdata-files

The list of methodological documents is available on line throught a search engine.

http://www3.istat.it/dati/catalogo/ricerca.php?tipo=n&ciclo=0&stringa=reddito&settori[]=7&num_collana=

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Documentation on procedures applied for quality management and quality assessment are as requested by  Eurostat  Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

the total duration of the data collection of the sample: 4 months. starting from the transmission of questionnaires to interviewers until their return back.

— basic information on activity status during the income reference period: same to the standard EU-SILC concept;

Components of income

Differences between the national definitions and standard EU-SILC definitions

— total household gross income: same definition as standard EU-SILC;

— total disposable household income: same definition as standard EU-SILC;

— total disposable household income. before social transfers other than old-age and survivors' benefits: same definition as standard EU-SILC;

— total disposable household income. before social transfers including old-age and survivors' benefits: same definition as standard EU-SILC;

— imputed rent: estimated by 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-mkt price/free tenants vs tenants at a mkt 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 mkt rate;

— income from rental of property or land: same definition as standard EU-SILC;

— family/children-related allowances: same definition as standard EU-SILC;

- social exclusion payments not elsewhere classified: same definition as standard EU-SILC;

— housing allowances: same definition as standard EU-SILC;

— regular inter-household cash transfers received: same definition as standard EU-SILC;

— interest. dividends. profit from capital investments in unincorporated businesses: same definition as standard EU-SILC;

— interest paid on mortgages: same definition as standard EU-SILC;

— income received by people aged under 16: same definition as standard EU-SILC;

— regular taxes on wealth: same definition as standard EU-SILC;

— regular inter-household transfers paid: same definition as standard EU-SILC;

— tax on income and social insurance contributions: same definition as standard EU-SILC;

— repayments/receipts for tax adjustments: repayments/receipts for tax adjustments are those paid in the n+1 year, where n is the income reference period. This is consistent with the (optional) definition of taxes as 'taxes due on the incomes of the reference period'.

— cash or near-cash employee income: same definition as standard EU-SILC;

— non-cash employee income: the value of the company car for personal use is the user's cost estimated by the ACI (Automobile Club Italiano);

— employers' social insurance contributions: includes also contribution for Cococo “co-ordinated and continuative collaborators”, a special category of status in employment;

— cash profits or losses from self-employment (including royalties): the standard procedure requires to collect the amount of money drawn out of self-employment activity only when the profit/loss resulting from accounting books or the taxable self-employment income (net of corresponding taxes) are not available. For the Italian EU-SILC, both administrative and survey micro-data are available, through an exact matching of tax and sample records. The income from self-employment is set equal to the maximum value between: (i) the (net) self-employment income resulting from the Tax Report and (ii) the (net) self-employment income reported by the interviewee. In the questionnaire, the self-employment income question is preceded by a 'reminder question' that provides a YES/NO list of the possible personal uses of earnings (consumption and saving). The departure from the standard definition (using both sampling and administrative data) is adopted in order to minimise either tax avoidance in the administrative data or under-reporting in the survey data, depending on which of the two is greater. With respect to the standard one, the procedure adopted for the Italian EU-SILC leads to more comparable data, under the assumption that other countries' self-employment incomes are not underestimated;

— value of goods produced for own consumption: same definition as standard EU-SILC;

— unemployment benefits: same definition as standard EU-SILC;

— old-age benefits: same definition as standard EU-SILC;

survivors' benefits: same definition as standard EU-SILC;

sickness benefits. paid sickness leaves of employees are included in the dependent employment incomes;

— disability benefits: same definition as standard EU-SILC;

— education-related allowances: same definition as standard EU-SILC;

— gross monthly earnings for employees: same definition as standard EU-SILC;

The source or procedure used for the collection of income variables

The sources or procedures used for the collection of income variablesare Paper and pencil interviews (CAPI) for all income variable, including the money drawn out of business by the self-employed and administrative data. Administrative data have been linked to sample data and used for estimating data on employee income, pensions and self-employment incomes.

The form in which income variables at component level have been obtained

All income variables at component level are both net and gross of taxes and social security contribution at source.

The method used for obtaining income target variables in the required form

Gross values are estimated by a new methodology using in conjunction an exact record linkage between survey and fiscal data at 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 those tax and social insurance contributions 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, CUD  employers’ tax statements which include also data on social security contributions, and Pension Register Data. Both the use of administrative data and microsimulation estimates  improves the quality and the amount of information on gross income variables.

As requested by Eurostat

The number of reference periods in time series are 8 from year 2004 to year 2011, every year four longitudinal component and a cross file are given.

Regional domain comparability are due to weightning calibration and coherence.

Comparison of income target variables and number of persons who receive income from each ‘income component’, with external sources

In this section we present the main results of the comparison between IT-SILC data and external data sources for the principal income target variables. In particular, we focus on the following income components: 1) Employee – cash, near cash income, non cash –income (PY010N/G+PY020N/G); 2) Social transfers as the sum of Old-age benefits (PY100N/G), Survival benefits (PY110N/G) and Disability benefits (PY130N/G). Data from National Accounts, Labour Force Survey by Istat, Fiscal Agencies of the Ministry of the Economy and Pensions Register by INPS (National Institute for Social Security) are used as external benchmarks. The table 1 below shows the alignment of total net employee income estimate from IT-SILC respect to the National Accounts aggregate in the year 2010 (the overestimation is below 0.04%). Table 2 shows that the number of employee income earners estimated using IT-SILC approximates the number of employees from Fiscal Agency data (universe of taxable employed income recipients) during 2010.

Differences in applied definitions (i.e. domestic vs resident employment), reference period and coverage of the two data sources can explain well the gap in estimates. By definition, the tax register does not report information on wages and employees arising from the hidden economy, that are only partially included in the survey.

Table 1 - Employee income

Table 2 - Employees

Due to lack of harmonization, National Accounts data are not directly comparable with IT-SILC estimates on self-employment incomes.  In table 3 are compared the IT-SILC 2011 estimate of number of self-employment incomes earners with the self-employed of other sources. Notice that in LFS a worker is classified as an independent on the basis of his/her main activity. With respect to NA, the estimate of self-employed units in term of full time equalised workers are presented. The IT-SILC estimate is referred to the number of people whose earnings from self-employment may have been temporary and/or from a secondary working activity.

Table 3 – Self-employed

(*) full time equivalent unit of workers

Finally, in tables 4 and 5 are reported data on social expenditure and beneficiaries for three kind of functions (ESSPROS) put all together: old-age, survival and disability. In both cases, IT-SILC 2010 estimates are quite close to the administrative data. The closeness of the estimates between IT-SILC and administrative data is due to the massive use of administrative information on the construction of the Eu-Silc target variables: PY100G/N, PY110G/N and PY130G/N.

Table 4 – Social benefits payment (old-age, survivors and disability functions)

(***) Severance payments  (lump-sum) are excluded

Table 5 – Social benefits recipients

(***) Severance recipients are excluded

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

The sampling frame is composed by the registers of the municipalities.

The sample of the households belonging to the rotational group with DB075=4 was extracted in July 2008 and validated within September 2008.

The sample of the households belonging to the rotational group with DB075=1 was extracted in July 2009 and validated within September 2009.

The sample of the households belonging to the rotational group with DB075=2 was extracted in July 2010 and validated within September 2010.

The sample of the households belonging to the rotational group with DB075=3 was extracted in July 2011 and validated within July 2011, because the survey is a CAPI and the extraction is derived from LAC (the Italian acronym for lists of municipal registry, see http://www.istat.it/it/archivio/16574 ) .

The sampling frame is updated in continuous way by the municipalities in interactive modality.

Concerning the SILC instrument, three different sample size definitions can be applied:

- the actual sample size which is the number of sampling units selected in the sample

- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview

- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator

Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.

Achieved sample size

Mode of data collection

A description of the mode of data collection used in your country. Please mention if you use mixed mode of data collection.

The mean interview duration

The mean interview duration per household is calculated as the sum of the duration of all household interviews plus the sum of the duration of all personal interviews, divided by the number of household questionnaires completed. Only households accepted for the database have to be considered.

Average interview duration = 40.5 minutes

Not requested by Reg. 28/2004

Not requested by Reg. 28/2004

The set of procedures employed to modify statistical data to enable it to conform to national or international standards or to address data quality differences when compiling specific data sets are applied at the end of the weighting procedure, after the non-response adjustments.

The external known totals are the following:

 For the entire sample:

1) Distribution of the population by sex and fourteen 5-years age-groups at NUTS I level (year t-1). The age groups are: 0-15, 16-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75+ at the end of the income reference period (year t-1);

2) Distribution of the population by sex and five age-groups at NUTS II level (year t-1). The age groups are: 0-15, 16-25, 26-45, 46-65, 65+ at the end of the income reference period (year t-1).

3) Distribution of non-national population at NUTS I level by sex; by age in two classes: 0-17, 18+ at the end of the income reference period (year t-1).

4) Distribution of the population by demographic size of the municipality at Nuts I level (year t-1) (six classes).

5) Number of households at NUTS II level at the time of the survey (year t); Number of households with non-national components at NUTS I level (year t).

For the entering rotational sub-group (at first wave):

1) Distribution of the population by sex and five age-groups at NUTS I level. The age groups are: 0-15, 16-25, 26-45, 46-65, 65+ at the end of the income reference period (year t-1).

2) Amount of non-national population at NUTS I level distinct in two classes: 0-17, 18+ at the end of the income reference period (year t-1).

 (year t-1).

3) Distribution of the population by demographic size of the municipality at Nuts I level (year t-1) (three classes).

4) Number of households at NUTS I level at the time of the survey (year t)                                                                 

For the other sub-groups:

1) Population at NUTS I level (year t-1)

2) Number of households at NUTS I level (year t);

All the Ad-hoc Module variables are fully comparable.