Income and living conditions (ilc)

1.1. Contact organisation

National Statistics Office (NSO)

1.2. Contact organisation unit

Living Conditions and Tourism Unit 

1.3. Contact name

Confidential because of GDPR

1.4. Contact person function

Confidential because of GDPR

1.5. Contact mail address

National Statistics Office (NSO),

Lascaris, Valletta VLT 2000, Malta.

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

24 July 2024

2.2. Metadata last posted

1 September 2024

2.3. Metadata last update

24 July 2024

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:

1. Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
2. 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 RAMON, Eurostat's metadata server.

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 are listed below. There is no difference between the national and standard EU-SILC definitions for reference population, private household, and household membership.

 

Definitions of reference population, household, and household membership

Reference population	Private household definition	Household membership
The reference population is composed of all private households and their current members residing in Malta and Gozo at the time of data collection. Persons living in institutions are excluded from the target population.	A private household can be defined as a person living alone or a group of people who live together in the same private dwelling and sharing expenditures, including the joint provision of the essentials of living.	A person is a household member if s/he is usually resident in that particular dwelling and shares in household expenses. Persons who are temporarily absent for reasons of holiday, travel, work, health, education or similar are included as long as the persons do not intend to stay away for more than 6 months.

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 geographical area encompasses Malta and Gozo.  

3.8. Coverage - Time

The EU-SILC survey was launched for the first time in Malta in 2005 and has been carried out on an annual basis ever since.

 

The income reference year for EU-SILC is one calendar year prior to the year of survey. Consequently, for EU-SILC 2024, the income variable data reflects information for calendar year 2023. For the rest of the variables in EU-SILC 2024, the data collected reflects calendar year 2024. In particular, the variables reflect data that was collected between April and July 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
The tax on income and social insurance contributions was collected for the income reference period. Thus, for EU-SILC 2023, HY140 reflects amounts for calendar year 2023.	The income reference year for EU-SILC is one calendar year prior to the year of survey. Thus, similar to HY140, for EU-SILC 2024, the income reference period reflects calendar year 2023.	The variable on regular taxes on wealth is not applicable for Malta.	The bulk of the data collection was carried out between April and July 2024. Thus, the lag between income reference period and current variables spans between 4 to 7 months, depending on the date of interview for each household.

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.

 

At a national level, the Malta Statistics Authority (MSA) Act empowers the NSO to collect, compile, extract, and release official statistics related to demographic, social, environment, economic and general activities, and conditions of Malta. EU-SILC has been carried out in Malta since 2005, under European Regulation (EU) No. 1177/2003. This Regulation establishes criteria which ensure the production of high quality and harmonised results at European level. As from 2020, EU-SILC started to be carried out under and new regulation: Regulation (EU) No. 2019/1700 of the European Parliament and of the Council of Europe as of 10 October 2019, establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples.

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

At National level:

The NSO requests information for the compilation of official statistics according to the articles of the MSA Act – Cap. 422 and the Data Protection Act – Cap. 586 of the Laws of Malta implementing the General Data Protection Regulations (GDPR).

Article 40 of the MSA Act stipulates the restrictions on the use of information while Article 41 stipulates the prohibition of disclosure of information. Furthermore, Section IX of the Act (Offences and Penalties) lays down the measures to be taken in case of unlawful exercise of any officer of statistics regarding confidentiality of data.

Since its inception, the NSO has always assured that all data collected remains confidential and that it is used for statistical purposes only according to the articles and derogations stipulated in the laws quoted above.  The Office is obliged to protect the identify of data providers and refrain from divulging any data to third parties that might lead to the identification of persons or entities.

During 2009, the NSO has set up a Statistical Disclosure Committee to ensure that statistical confidentiality is observed, especially when requests for microdata are received.

Upon employment, all NSO employees are informed of the rules and duties pertaining to confidential information and its treatment. In line with stipulations of the MSA Act, before commencing work, every employee is required to take an oath of secrecy whose text is included in the same Act.

An internal policy on anonymisation and pseudo-anonymisation is in place to ascertain those adequate methods are used for the protection of data which the office collects and shares with the public in its capacity as the National Statistics Office.  The policy is meant to safeguard confidentiality of both personal and business data entrusted to the NSO.  The document provides guidance for all NSO employees who process data on a daily basis as to how anonymisation and pseudo-anonymisation methods should be applied.  The policy applies to all confidential, restricted, and internal information, regardless of form (paper or electronic documents, applications, and databases) that is received, processed, stored, and disseminated by the NSO.

 

At European level:

EU Regulation 223/2009 on European statistics (recital 24 and Article 20(4) of 11 March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.

7.2. Confidentiality - data treatment

At the national level, both microdata and macro data (including tabular data) are treated for confidentiality prior to dissemination. Particularly, microdata is modified to produce a safe file such that anonymisation rules apply. Various techniques are used for modification which are listed below:

• Global recording where several categories of a variable are collapsed into one;
• Local suppression where values are suppressed in unsafe combinations (i.e., replacing by a missing value); and
• Top and bottom coding where larger values (top coding) and smaller values (bottom coding) of ordinal categorical variables or continuous variables are collapsed.

 

The MU Argus software is used to identify confidential cells and to apply the various methods.  Disclosure control methods are normally based on optimisation algorithms subject to a number of sensitive variables included in the dataset.

For dissemination, the NSO follows the same publication rules as recommended by Eurostat, which are listed below:

• An estimate should not be published if it is based on fewer than 20 sample observations or if the item non-response exceeds 50%.
• An estimate should be published with a flag if it is based on 20 to 49 sample observations or if the item non-response exceeds 20% and is lower or equal to 50%.
• An estimate shall be published in the normal way when based on 50 or more sample observations and the item's non-response does not exceed 20%.

According to these rules, estimates based on less than 20 sample counts are not published, thus also ensuring the respondent’s confidentiality. In terms of anonymisation of data, this is based on minimum frequency counts.

8.1. Release calendar

An advance release calendar is maintained by the NSO and published on the NSO website.  The calendar projects three months of News Releases (including the current and two subsequent months).

Throughout 2024, a total of 3 News Releases will be published in relation to EU-SILC:

• EU-SILC: Well-being, Social and Health Indicators
• EU-SILC: Salient Indicators
• EU-SILC: Main Dwellings

8.2. Release calendar access

Please refer to the NSO News Release Calendar and the Eurostat Release Calendar both of which are publicly available. 

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.

At a national level, an internal policy on dissemination is in place to govern the dissemination of official statistics in an impartial, independent, and timely manner, making this data available to all users simultaneously. The NSO website is the primary channel for the dissemination of official statistics. Through the website, users can submit tailored requests for statistical information, access News Releases and publications in an electronic format, as well as access tabular information through the Eurostat website. 

Annual

10.1. Dissemination format - News release

Throughout the year, EU-SILC data is published in subject oriented News Releases containing information about the income distribution, poverty, social exclusion, dwelling, and health and well-being key indicators. The data can be accessed in a tabular format, where the main indicators are detailed by age group and gender, as well as in a chart and map format. Additionally, the use of infographics was introduced as from 2024. In particular, the most important points from the EU-SILC 2023: Estimates of Material Deprivation and Housing Problems news release were disseminated using an infographic for enhanced user visualisation. A detailed commentary relating to the published data as well as methodological notes, including the sampling variability of the main indicators, are also made available. All information is disseminated in English and in Maltese, as part of the NSO’s re-branding strategy that was launched in 2023. News releases with salient results from EU-SILC may be accessed on the News releases page on NSO Malta.

10.2. Dissemination format - Publications

The NSO publishes around 230 News Releases a year. All releases are published and disseminated at 1100 hrs as scheduled in the Advance Release Calendar. The calendar is published on the NSO website and includes a three-month advance notice (the current month and the forthcoming two months). It should be noted that the calendar is subject to changes. The responsible unit for the dissemination of all News Releases is the Dissemination Unit.

 

Publications relating to EU-SILC data were published in the format of a whole publication up until 2010. Following that, data publication was then split into subject-oriented News Releases.

10.3. Dissemination format - online database

Until 2023 no information on EU-SILC was available on the NSOs online statistical database StatDb. However, starting in 2024, some of the most important EU-SILC indicators were made accessible in a tabular format, with the data disaggregated by age and gender, and are now available in an online statistical database. Furthermore, detailed EU-SILC statistics may be obtained from Eurostat's website.

10.3.1. Data tables - consultations

Not available.

10.4. Dissemination format - microdata access

EU-SILC data is not available at microlevel as a result of confidentiality preservation. However, EU-SILC anonymised microdata may be provided under strict conditions to a selected number of institutions or persons accredited as research entities or researchers respectively. Microdata can be requested and be accessible after the anonymisation carried out by the Methodology Unit. Further information on access to anonymised microdata is available on the NSO website.

Researchers who require such access need to submit an application form clearly explaining the purpose of their statistical research and justifying their need for access to microdata.  The application form will be evaluated internally and if considered favourably a formal contractual agreement will be drafted to explain the responsibilities of the researcher for the security of the information.  Once the agreement is agreed upon and signed by both parties, access to anonymised microdata will be granted subject to the terms of reference included in the contractual agreement.  Access is normally granted for a definite time period.

10.5. Dissemination format - other

EU-SILC data can also be disseminated based upon an agreement that is made between the NSO and other entity to transmit specific data for research purposes. This agreement is formalized through a legally binding document.

 

Finally, EU-SILC data is also disseminated to Eurostat where 4 longitudinal microdata files are transmitted on an annual basis. 

10.5.1. Metadata - consultations

Not available.

10.6. Documentation on methodology

Work processes and procedures for the compilation of the EU-SILC are documented in a standardised reporting template and aligned to the GSBPM model.  The model covers all phases of the statistical production process, from the initial stages of identifying what statistics are needed and the scope of the particular survey, to the final stages of dissemination and evaluation. GSBPM is only available internally and may be accessed by all NSO employees.

 

An explanation of the definitions, key concepts and methodological notes for users is published in each News Release. When necessary, additional information is provided to the internal users. The NSO website offers an entire section dedicated to the Metadata, as well as an online version of the EU-SILC 2023 questionnaire.

10.6.1. Metadata completeness - rate

All requested concepts are provided, 100%. 

10.7. Quality management - documentation

At the national level, procedures used for data analysis are documented in line with the GSBPM model and are only made available to the NSO staff. EU-SILC SIMS reports, including concepts relating to metadata and quality are available to the users on the NSO website. In addition, methodological notes detailing the computation of main indicators using EU-SILC data are published as part of the news releases. Further information on these computations can be obtained from Sources and Methods on the NSO website. 

11.1. Quality assurance

The NSO ensures the accuracy of data released to the public and prepares clear methodological notes which explain the processes involved in the collection and production of official statistics.

The NSO is committed towards the collection, production, and dissemination of data. As a case in point, in 2018 the Commitment on Confidence in Statistics was approved. With this document, full support for developing the Maltese Statistical System was pledged. This includes producing and disseminating statistics according to the 15 principles of the European Statistics Code of Practice. Furthermore, every 5 to 7 years, the NSO participants in a Peer Review exercise through which the compliance of its operations with the ESS Code of Practice principles are assessed by an expert team. Peer Reviews are part of the European Statistical System (ESS) strategy to implement the ESS CoP.  Each NSI is expected to provide information as requested by a standard self-assessment questionnaire.  Following this an expert team visits the office to meet NSI representatives and main stakeholders.  Peer Reviews result in a compliance report and the listing of a set of Improvement Actions which need to be followed up by the NSI.  The last Peer Review was carried out in 2022.

The NSO has developed an internal Quality Management Framework (QMF) which is built on common requirements of the ESS Code of Practice. A document was prepared to include a set of general quality guidelines spanning over all statistical domains, including relevance, accuracy, timeliness and punctuality, accessibility and clarity, comparability, and coherence.  Assuring methodological soundness is an integral part of the QMF, nonetheless, the document spans also on other areas related to institutional aspects. the QMF was officially launched in 2025.

Annexes:
Quality Management Framework 2025

11.2. Quality management - assessment

The methodological manual provided by Eurostat is constantly being consulted to ensure the full conformity to Eurostat definitions. Moreover, an ESQRS report, which is a detailed standard structure for the collection and dissemination of quality reports, is filled in annually.

The NSO recognises that the production of high-quality statistics from EU-SILC is paramount for policy making purposes. During the past years, many efforts were made to ensure accuracy of results. Great importance is also given to the production of harmonised results. In this regard, every effort is made in order to ensure that all Eurostat's recommendations and all Regulation's requirements are strictly adhered to.  Apart from this a Quality Management Framework (QMF) was developed to improve the quality of the processes and the outputs from surveys lie EU-SILC.

Quality assessment is one of the elements of the Quality Management System in the European Statistical System (ESS). NSO’s quality assessment takes existing information on quality and uses this as an input to evaluate the statistical processes and its outputs against pre-defined standards, identify strengths and weaknesses, and derive the actions required for improvement.

• Relevance is ensured through the comparison of topics covered by the EU-SILC, with those covered by other EU surveys.

• Accuracy is ensured through the computation of standard errors and coefficients of variation. Both are computed as indicators of the sampling error for the total population, and by sex of the respondent. These calculations make it possible to perform a minimum assessment of the capacity of EU-SILC to break results down by this basic demographic variable.

• Timeliness and punctuality are ensured through the comparison of the foreseen and actual calendars for dissemination.

• Accessibility and clarity are ensured through the availability of direct links to Metadata, Sources, and Methodological notes upon the NSO’s website. More specifically, detailed methodological notes are available in each EU-SILC News Release disseminated throughout the year.

Comparability and coherence are two separate quality criteria that are often used interchangeably, having aligned definitions. As a case in point, comparability involves an element of coherence, where outputs relating to the same data items are consistent and thus can be utilised for comparison across a particular time, region, or any other relevant domain. Coherence of EU-SILC data with other statistical sources is assessed through a preliminary screening of questions with respect to the core social variables, to identify what variables are requested, and the valid response options available within the survey. This step is necessary prior to ensuring the coherence of statistical results through the comparison of response distributions. Coherence with other statistical sources involve the analysis of aggregated EU-SILC results against National Accounts, Labour Force Survey, and the Inland Revenue Department.

12.1. Relevance - User Needs

The main users of EU-SILC statistical data include institutions within the European Commission, policy makers (such as ministries dealing with economic affairs, finance, treasury, industry, trade, employment, and environment), business associations, non-governmental organizations, academia (including university and college students and research institutes), media (including journalists, newspapers, radio, TV stations and magazines), the European Central Bank, National Administrations (mainly those in charge of the monitoring of social protection and social inclusion), and other international organizations.

 

The different classes of users have different needs, and in order to satisfy these needs, the authority is committed to producing the required statistical information within given resource constraints through the publication of News Releases as well as through requests.  Furthermore, different classes of users also have different priorities. Even though all requests for information received by the authority are treated with utmost importance, requests received from policy makers, business associations and media are given the highest priority.

 

At a European level, one of the indicators of EU-SILC data is also used to monitor the EU-2030 targets. Apart from that, EU-SILC is used to feed sectoral or transversal publications and reports such as the Annual Progress Report on the Lisbon Strategy (structural indicators), the Sustainable Development Strategy monitoring report, the Eurostat yearbook, and various pocketbooks. Researchers are also given access to microdata under contract in order to allow further detailed analysis.

12.2. Relevance - User Satisfaction

Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 (repeated in June-July 2022) to obtain better understanding about users’ 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 microdata 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. Users emphasized their strong need for more detailed microdata.

 

For more information, please consult the User Satisfaction Survey.

 

At a national level, the NSO keeps record of the number of news releases and publications disseminated on the website, the users to whom statistical products are provided, as well as the number of ad-hoc requests that are processed every year (tabular data and microdata requests). News releases, and tailor-made statistical outputs are assessed on account of their quality, timeliness, and ability to meet users’ needs.

12.3. Completeness

The Methodological Guidelines and Description of EU-SILC target variables for the reference year 2024 lays down a total of 309 variables. From these, 267 variables are core variables pertaining to the D-file, H-file, R-file, and P-file registers, whereas a total of 42 variables are part of the 3-year, 6-year, and other optional variables. From these 42 variables, 38 variables are required for transmission, whereas 4 variables are classified as optional variables. Consequently, a total of 305 variables (267 core and 38 module variables) should be communicated to Eurostat, whereas 4 variables are optional for transmission.

Out of the core variables, 18 variables (HY051G, HY061G, HY062G, HY064G, HY071G, HY072G, HY074G, PY094G, PY101G, PY111G, PY113G, PY114G, PY121G, PY124G, PY131G, PY141G, PY142G, and PY143G) were not collected since the scheme does not exist at national level. In fact, for these variables, the flag was set to -5. In addition, 12 variables (DB050, DB060, DB062, DB070, HY120G, HY120N, HY121G, HY121N, HY140N, HY145N, HY170G, and HY170N) were also not collected since these variables were not applicable. In fact, these variables were transmitted with flag -2 and 79. Finally, 5 variables (PB060, PB070, PB080, RB065, and RB066) were also not collected since at national level, the selected respondent model is not used, and a four-year panel is adopted. Consequently, these variables were transmitted with flag set to -2 and -5.

Out of the optional variables, HY030G (Imputed Rent) and RL080 (Remote Education) were not collected. Variables HI130G (Interest Expenses) and HI140G (Household Debts) were not applicable at a national level, and thus not collected.

All the information that is collected and compiled for Eurostat may be downloaded from Eurostat’s website.

12.3.1. Data completeness - rate

Data completeness rate measures the ratio of the number of variables provided to the number of variables required by Eurostat, as specified in the EU-SILC guidelines for the respective operation year.

 

Considering the number of variables presented in the methodological document and the actual total number of variables that were transmitted (excluding those that were not applicable to Malta), the data completeness rate stands at 99.4%, with 0.6% being the share of missing statistics. 

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:

1. 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;
2. 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;
3. 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 the 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 considered. 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.

The standard error of the main indicators is calculated in R using the vardomh() function, which estimates the variance of sample surveys within domains using the ultimate cluster method. This approach applies a Jackknife-like technique were clusters (usually primary sampling units) are systematically omitted to create replications, approximating the sampling variance.

The persistent-risk-of-poverty ratio is calculated in SPSS. The dataset is filtered to include only the survey year and weighted using the four-year longitudinal weight. The standard error (SE) is computed with the final value being adjusted by the Kish Effect. The value is then used to obtain the upper and lower confidence intervals. 

 

Main indicators, standard errors, and CI at country level

	AROPE				At risk of poverty				Severe Material and Social Deprivation				Very low work intensity
	Ind. Value (%)	Stand. Errors (%)	95% CI		Ind. Value (%)	Stand. Errors (%)	95% CI		Ind. Value (%)	Stand. Errors (%)	95% CI		Ind. Value (%)	Stand. Errors (%)	95% CI
			L	U			L	U			L	U			L	U
Total	19.7	1.0	17.8	21.6	16.8	0.9	15.1	18.6	4.0	0.5	3.0	4.9	4.5	0.7	3.1	5.8
Male	18.1	1.0	16.1	20.2	15.6	0.9	13.8	17.4	3.2	0.4	2.4	4.1	4.2	0.7	2.8	5.7
Female	21.5	1.1	19.3	23.7	18.3	1.1	16.2	20.4	4.8	0.7	3.4	6.1	4.8	0.8	3.2	6.3
Age 0-17	25.9	2.7	20.6	31.1	24.1	2.7	18.8	29.4	4.8	1.6	1.7	8.0	3.7	1.4	0.9	6.6
Age 18-64	15.2	1.0	13.3	17.1	11.9	0.8	10.3	13.5	3.7	0.5	2.8	4.6	4.7	0.7	3.3	6.0
Age 65+	31.7	1.4	28.9	34.6	29.7	1.4	27.0	32.5	4.1	0.6	2.9	5.2	N.A	N.A	N.A	N.A

Note: Date of extraction: 06 March 2025

 

Persistent-risk-of-poverty ratio over four years to the population, standard error, and CI

	Persistent-risk-of-poverty
	Indicator Value (%)	Standard Error (%)	95% CI
			L	U
Total	13.5	1.1	11.3	15.8

 Note: Date of extraction: 25 February 2025

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 the absence of information for 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.

At national level EU-SILC is carried out by Computer Assisted Telephone Interviewing (CATI) or Computer Assisted Personal Interviewing (CAPI). These modes of data collection reduce these types of non-sampling errors. Furthermore, a large share of income data is derived from registers, reducing the effect of under-reporting. 

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; and
• Misclassification refers to the incorrect classification of units that belong to the target population.

In Malta, coverage errors are attributed to the lack of updates in the population register maintained at the NSO. Up until EU-SILC 2022, the sampling frame in use referred to the population based on the 2011 Census of Population and Housing. Consequently, an element of over- and under-coverage was unavoidable. As a case in point, the foreign component was not adequately covered in the registers based upon the 2011 Census of Population and Housing. However, with the introduction of a new sampling frame based on the 2021 Census of Population and Housing updates from EU-SILC 2023 onwards, this error was mitigated. This is because EU-SILC 2024 was benchmarked with updated demographic estimates derived from the 2021 census.

13.3.1.1. Over-coverage - rate

The database on the 2021 Census of Population and Housing, that is held and maintained by NSO through annual updates, provides a comprehensive frame of all persons and households living in Malta and Gozo. As a result, this database is considered to be the most adequate source to be used for the Maltese EU-SILC sample selection. Provided that the census was carried out in 2021, and that a new sampling frame was introduced for EU-SILC 2023 and EU-SILC 2024 based upon the 2021 census updates, over-coverage has been limited when compared to previous years. This is attributed mainly to the fact that the time lag between the Census period and the EU-SILC reference period has been reduced.

Coverage error

Main problems	Population (sub-population)	Size of error	Comments
Over-coverage	Out of scope units	5.9%	Proportion of units accessible via the frame (sampled) which do not belong to the target population (e.g., vacant)
Under-coverage	Foreigners’ population living in private households	5.8 % or 32,050 persons	22.5% of foreigners in the sampling frame is estimated at while in EU-SILC 2024 the share is estimated at 28.3%
Misclassification	NA	NA	NA

13.3.1.2. Common units - proportion

Common Units Proportion is not applicable to Malta since information about all individuals is collected through the survey. Additionally, the information provided during the data collection phase is enhanced through the use of various data registers for different levels of use. These registers include data extracts from the Automated Revenue Management Services (ARMS) and data on social benefits (SABS), wages and NI data (MFSS). These registers are mainly used in order to collect reliable information on household income and housing costs.

13.3.2. Measurement error

Main sources of measurement errors relate to imperfections in the questionnaire, recall errors, under-reporting, errors made by interviewers during data collection, as well as errors made during data analysis.

EU-SILC is mainly carried out by Computer Assisted Telephone Interviewing (CATI) or Computer Assisted Personal Interviewing (CAPI) which help considerably when it comes to reducing these types of errors. In addition, a large share of data on income is derived from registers, which help to reduce the effect of under-reporting.

 

Measurement error for cross-sectional data

Cross-sectional data
Source of measurement errors	Building process of questionnaire	Interview training	Quality control
Measurement errors can occur in different phases and for different reasons.   They can be defined as the bias between the recorded value provided by the respondent (which might not be the actual value) and the true but unknown value of the given variable.    The main sources of such errors are typically the questionnaire and the data collection process in general.	Every year, in preparation for a new SILC wave, revisions are made to the questionnaire. The revisions are made to include the new module and correct for any possible misunderstandings in the way the questions are worded and departures from standard Eurostat definitions. This is done by taking on board any feedback obtained from interviewers and respondents during the previous year’s data collection round and also from Eurostat communications.   Since SILC 2011, the structure of the questionnaire has been revised in an attempt to reduce response burden and interview duration, without compromising on quality. This was done through the introduction of a series of filter questions aimed at respondents who were participating in SILC for the second, third or fourth time. Through these filter questions, respondents were asked whether their situation in terms of things like marital status, citizenship, type of dwelling, number of rooms in the main dwelling etc. has changed from the previous year. When answers to the filter questions are in the negative, the routing of the questionnaire allows respondents to by-pass certain questions since responses can be retrieved from the previous year’s dataset. If on the other hand respondents report that there has been a change, the relevant questions are asked as usual. In this way any redundant questions are filtered out, and the data collection process becomes more efficient. SILC data collection is conducted primarily using a Computer-Assisted Telephone Interviewing (CATI) method; however, data can also be collected using a Computer-Assisted Personal Interviewing (CAPI) method upon household request. Even though CATI reduces response burden and simultaneously increases efficiency and lower costs, the data collected might lack accuracy since the interviewer does not have visibility into certain factors such as the dwelling state and respondent lifestyle. This lack of direct observation can lead to incomplete or biased information, as the interviewer cannot verify or fully understand the conditions and behaviours that might influence the respondent’s answers. Therefore, the data may not accurately reflect the true circumstances of the respondent’s living conditions.   For the CATI surveys, a data collection program, like the one used for CAPI was developed. Essentially the CATI program is the same as the CAPI one but has different routing of questions. Also, more information from the previous year's survey is uploaded into the CATI program since the likelihood that certain variables would have changed is low for these household types. Thus, the survey duration is shortened since the interviewer will have to confirm with the household that the information, we have is still correct rather than asking certain questions from scratch.	The approach, integrity, knowledgeability of SILC definitions and professionalism of interviewers are fundamental in determining the success of the SILC project. Therefore, considerable effort is directed towards the recruitment, training, and monitoring of interviewers. This entire process is co-ordinated by NSO (i.e., no sub-contractors).   Training is carried out through an intensive session. For interviewers working on SILC for the first time, the training session is held in person, at the NSO premises. On the other hand, for interviewers with prior experience working on SILC, the training session is typically held online using Microsoft Teams.   During these training sessions, the questionnaire and corresponding definitions are explained at a high level of detail. Furthermore, assistance related to the data entry program is also provided. In particular, a number of ‘test’ scenarios are simulated to help interviewers understand how the data collection program works. In addition, interviewers are also provided with fictitious ‘test’ households created in each tablet in order to encourage them to experiment the process of inputting data before interviewing the actual households. For old interviewers, a presentation is held outlining changes made to the questionnaire and data entry program, as well as interviewers’ errors identified from the previous year. Furthermore, all interviewers are encouraged to contact our office whenever encountering difficulties.	Since Malta is a small country, the response burden is large. This is increased by the fact that SILC is based on a rotational design where households are asked to participate for four consecutive years. In addition to this, despite an emphasis on the fact that the Malta Statistics Authority Act ensures full confidentiality, there still exists the fear amongst respondents that identification of individuals through their responses may be possible, and the sensitive nature of the questions in SILC tends to make respondents even more wary. Despite these difficulties, a reasonably good level of co-operation and response rate are achieved in EU-SILC. In cross-sectional SILC 2024, despite our best efforts to reduce proxy interviews, a small percentage was recorded. When there are issues and difficulties in the data collection interviewers are allowed to use proxy. In such cases interviewers are to request household members who could not be present during the interview to leave documentation such as pay slips and tax returns with the person who will be responding on their behalf, so that as much as possible the proxy effect does not result in a loss in quality. Furthermore, the availability and use of register data helps offset the proxy effect to some extent. Register data was available for income components like employment & self-employment income, income tax and social benefits, in previous years. For SILC 2024 register data were available. Other registers supply demographic characteristics and partial information on levels of education attained. Register data is incorporated into SILC variables as much as possible, particularly in the case of persons who are interviewed by proxy. This is done through ID card linking. Consequently, the rate of proxy interviews must be evaluated in this context. Monitoring of the interviewing process is carried out through regular audits on a sub-sample of households throughout the data collection period.  Response rates for different interviewers are also monitored throughout the process. In rare instances where audits revealed negligence or inappropriate behaviour from interviewers, immediate disciplinary action was taken.   New interviewers are followed more closely. The quality of the data collected is checked to ensure that the interviewers are performing as required. Any difficulties encountered are also discussed.

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.

 

 Refer to sub-concepts 13.3.3.1 and 13.3.3.2. 

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
0.994	0.984	0.998	0.860	0.798	0.927	1.000	1.000	1.000	14.539	21.470	7.502	0.000	0.000	0.000	14.539	21.470	7.502

 

Where:

A = total (cross-sectional) sample,

B = new sub-sample (new rotational group) introduced for the first time in the survey this year,

C = sub-sample (rotational group) surveyed for the last time in the survey this year.

 

Unit non-response rate for longitudinal data

 

Response rate for households by wave

Response rate for household	Wave 2	Wave 3	Wave 4
Wave response rate	85.933	88.865	85.461
L follow-up rate	97.413	94.430	90.344
Follow-up ratio	0.891	0.935	0.909
Achieved sample size ratio	0.891	0.935	0.909

 

Response rate for persons by wave

Response rate for persons	Sample persons/co-residents	Wave 2	Wave 3	Wave 4
Wave response rate	Sample persons	85.24	90.94	88.30
	Co-residents	31.19	30.73	45.99
L follow-up rate	Sample persons	100.00	100.00	100.00
Achieved sample size ratio (persons aged 16 and over)	All persons	0.88	0.93	0.90
	Sample persons	0.85	0.91	0.88
	Co-residents	NA	1.64	1.35
Response rate for non-sample persons	Co-residents	100.00	100.00	100.00

 

Sample and response rate by wave

	Sample of households	Sample of individuals 16+	Response rate of the households	Response rate of individuals 16+
Year of survey
Wave 1	1,639	2,689	74.74	100.00
Wave 2	2,912	5,227	82.86	98.39
Wave 3	4,196	7,766	87.30	98.66
Wave 4	3,831	6,868	86.95	100.00

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

Please see the annex 2.

13.3.4. Processing error

Data entry and coding (if any used)

Editing controls

Possible sources of processing errors include data entry errors and an element of human error in the processing of results. CATI and face-to-face CAPI are the methods of data collection used for Malta’s EU-SILC.    EU-SILC is collected through computer-assisted methods that lessen the possibility of such errors through automatic routing of questions and in-built validations. The programs for both methods (CAPI and CATI) have been designed using Blaise software. Through this program, the user is routed automatically from one question to the next. This automatic routing eliminates the risk of omitting certain questions unintentionally and allows the interviewer to concentrate more on other aspects of the survey.   The program also consists of in-built validations which help to reduce processing errors related to data entry as well as human errors.  These validations involve logic and consistency checks with previous related responses and between questions themselves.  Checks are also carried out for any data entry of extreme values. Pop-up dialog boxes are displayed with error messages whenever an error is encountered.  In some cases, error suppression is allowed in order to cater for exceptional responses.    Thus, the computer-assisted method leaves little room for error and at the same time speeds up the whole process of data collection.  Nevertheless, an element of human error remains and consequently the possibility of data entry errors cannot be excluded entirely.

As a further security measure, interviewers were instructed to take regular backups of encrypted data collected from the respondents. In order to reduce the risk that no backups are done, the CATI and CAPI programmes had a validation that does not allow an interviewer to continue with the data collection if after three days there was no backup done. So, by default the backup was taken every three days. This was done in order to prevent any loss of data that may result in the event of the laptop sustaining damage.

 

 

Description of data entry, coding controls and the editing system 

Re-interview rates	Wave 2	Wave 3	Wave 4
(a) Individuals in interviewed households %	88.866	78.502	74.251
(b) individuals out of scope %	0.729	2.955	5.789
(c) individuals not interviewed for reasons other than their being out of scope %	NA	NA	NA

 

Re-interview rates		Wave 2	Wave 3	Wave 4
Re-interview rates for people leaving their household	Total	2.510	2.580	3.433
	Males	2.815	2.742	3.486
	Females	2.219	2.422	3.381
Re-interview rates for young people (16-35) leaving their household	Total	4.147	5.267	6.175
	Males	4.464	5.410	5.730
	Females	3.780	5.111	6.684

13.3.5. Model assumption error

Model assumption error is not applicable at a national level. Data obtained from registers is used wholly as reported in the source. Any imputations made are based on the figures available in the survey and are not based on models relating to the register used. 

14.1. Timeliness

The timeline for transmitting the 2023 data was adjusted, with the data being required for transmission by 31/12/N and the final delivery expected by February N+1. However, for the transmission of SILC 2023, Malta was granted a derogation allowing this submission to take place by April N+1, however the derogation ended in 2025. For SILC 2024, the data was transmitted by December N, in line with the original plans.

14.1.1. Time lag - first result

The reference period for EU-SILC 2023 concluded on December 31, 2023. The first fully validated data delivery was transmitted in April 2024. Consequently, the time lag was that of approximately 4 months. 

14.1.2. Time lag - final result

The reference period for EU-SILC 2024 concluded on December 31, 2024. The first pre-validated data was transmitted in December 2024. The first fully validated data delivery was transmitted in January 2025.

14.2. Punctuality

The National Statistics Office publishes around 230 News Releases a year, with all releases being published and disseminated at 1100 as scheduled in the Advance Release Calendar. Five news releases relating to SILC are published on a yearly basis. All News Releases were published on time. Furthermore, the microdata consisting of 4 pre-validated longitudinal files is transmitted to the Eurostat by the end of April. This was possible for EU-SILC 2023 based on a derogation.  

14.2.1. Punctuality - delivery and publication

The target date for the delivery of the microdata, consisting of 4 longitudinal files, to Eurostat was set for December 31, 2024. However, the final pre-validated data was sent on December 23, 2024.

At the national level, the EU-SILC Salient Indicators news release communicating income data and poverty indicators is published in April. This is usually done on the same data that the Eurostat publishes indicators online in its database. Consequently, data is made available to the users approximately 4 months after submission to the Eurostat.

15.1. Comparability - geographical

Data collection, data cleaning and data submission are fully regulated by Eurostat; this is done to ensure that the SILC data are fully comparable throughout all participating countries.

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

15.2. Comparability - over time

EU-SILC data has been collected in a consistent manner since 2005. In view of this, until 2022 the data can be compared or reconciled over time.

Following the 2021 Population and Housing Census, a new sampling frame of households and individuals was introduced for the first time to be used as from EU-SILC 2023. As a result, in 2023 there was a break in series in the sampling frame used.

EU-SILC uses regularly updated population and household estimates for the calculation of the cross-sectional weights and the calibration of survey data with population and household estimates. For EU-SILC 2024 an updated version of the population and household estimates was provided internally, and in view of this the cross-sectional weights had to be recalculated. The updates were implemented mainly to reflect the changes captured in the Census 2021. This recalculation, apart from having a direct impact on the population household figures provided during the first transmission, also had an impact on the household distribution by household size. Specifically, the updated estimates resulted in a shift, revealing in a small increase in the proportion of one-person households, coupled with a slight decrease in the proportion of two-person households. This change aligns with the distribution patterns observed in the Census data on which the sampling frame is based. Moreover, the increase in the number of one-person households also influenced the distribution of households by tenure status. This is because the increase in single-person households was mainly observed among those residing in rented accommodations. As a conclusion, the interpretation of the results must be carried out with caution and must be considered in light of the changes in the household distributions by household size and tenure status.

15.2.1. Length of comparable time series

EU-SILC data has been collected in a consistent manner since 2005. 

15.2.2. Comparability and deviation from definition for each income variable

PB060 Personal cross-sectional weight for selected respondent, PB070 Personal design weight for selected respondent, PB080 Personal base weight for selected respondent, RB065 Longitudinal weight (five-year duration), RB066 Longitudinal weight (six-year duration), HY120G Regular taxes on wealth, HY121G Taxes paid on ownership of household main dwelling, HY170G Value of goods produced for own consumption and HY145N Repayments/receipts for tax adjustment) were not collected because they are not relevant at national level.

 

Comparability and deviation from definition for each income variable

Income	Identifier	Comparability	Deviation of definition (if any)
Total hh gross income	(HY010)	P	NA
Total disposable hh income	(HY020)	P	NA
Total disposable hh income before social transfers other than old-age and survivors' benefits	(HY022)	P	NA
Total disposable hh income before all social transfers	(HY023)	P	NA
Income from rental of property or land	(HY040)	P	NA
Family/ Children related allowances	(HY050)	P	NA
Social exclusion payments not elsewhere classified	(HY060)	P	NA
Housing allowances	(HY070)	P	NA
Regular inter-household cash transfers received	(HY080)	P	NA
Alimonies received	(HY081)	P	NA
Interest, dividends, profit from capital investments in incorporated businesses	(HY090)	P	NA
Interest paid on mortgage	(HY100)	P	NA
Income received by people aged under 16	(HY110)	P	NA
Regular taxes on wealth	(HY120)	NC	NA
Taxes paid on ownership of household main dwelling	(HY121)	NC	NA
Regular inter-household transfers paid	(HY130)	P	NA
Alimonies paid	(HY131)	P	NA
Tax on income and social contributions	(HY140)	P	NA
Repayments/receipts for tax adjustment	(HY145)	NC	NA
Value of goods produced for own consumption	(HY170)	NC	NA
Cash or near-cash employee income	(PY010)	F	NA
Other non-cash employee income	(PY020)	F	NA
Income from private use of company car	(PY021)	F	NA
Employer’s social insurance contributions	(PY030)	F	NA
Cash profits or losses from self-employment	(PY050)	F	NA
Pension from individual private plans	(PY080)	F	NA
Unemployment benefits	(PY090)	F	NA
Old-age benefits	(PY100)	F	NA
Survivor’s benefits	(PY110)	F	NA
Sickness benefits	(PY120)	F	NA
Disability benefits	(PY130)	F	NA
Education-related allowances	(PY140)	F	NA

Note: 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 refer to the degree to which the statistical processes, by which they were generated, use the same concepts, and harmonised methods. EU-SILC follows international standard classifications to further ensure coherence: ISCO, NACE, ISCED, and Degree of Urbanisation.

The sets of weights available in EU-SILC datasets are obtained using calibration techniques which ensure basic coherence of estimates obtained from EU-SILC micro datasets and demographic counts. As already mentioned, there was a change in the weighting and adjustment methodology in 2023 since variable ‘foreigner’ was introduced as a calibration variable in the weighting system.

Further coherence analysis is carried with other surveys by the National Statistics Office having the same reference period for benchmarking purposes. Sources include other domains such as National Accounts, Labour Force Survey, and Social Protection Accounts. Annual aggregates provided by the Inland Revenue Department (IRD) were also used to verify income from employment, interests, and dividends. These tests ensure that the data being submitted falls in line with the aggregate values provided from these sources, thus ensuring coherence throughout. Should coherence not be established, the data are rechecked, and anomalies found will be corrected until the data falls in line.

15.3.1. Coherence - sub annual and annual statistics

Not applicable.

15.3.2. Coherence - National Accounts

When analysing SILC cross-sectional data, National Accounts figures are used as benchmarks and serve as checks through the data cleaning process. Discrepancies between figures provided by National Accounts and those retrieved from SILC are justified by generic differences (reference population, delineation of household sector in NA, treatment of quasi corporation and data measurements) and specific differences related to the definition of the household income.

 

Coherence with National Accounts for income variables

EU-SILC variables	National Accounts item	Coverage rate	EU-SILC growth rate	National accounts growth rate
Employee income: PY010G Employee cash or near cash income + PY021G Company car	D11/rec Wages and salaries	1.1	3.5	8.5
Income from self-employment: PY050G Cash benefits or losses from self-employment	B3g Mixed income, gross	0.6	2.9	14.2
Social benefits other than social transfers in kind: HY050G Family/children related allowances + HY060G Social exclusion not elsewhere classified + PY090G Unemployment benefits + PY100G Old-age benefits + PY110G Survivor’ benefits + PY120G Sickness benefits + PY130G Disability benefits + PY140G Education–related allowances + HY070G Housing allowances	D62/rec: Social benefits, other than social transfers in kind	1.0	18.3	5.9
Social contributions and taxes on income paid: HY140G Tax on income and social contributions	D61/use: net social contributions + D51/use: taxes on income	0.8	3.6	3.5
Total disposable household income HY020	B6 Gross disposable income	0.8	6.7	16.0

15.4. Coherence - internal

All outputs in the dataset are coherent and reconcilable. 

At national level EU-SILC is carried out by Computer Assisted Telephone Interviewing (CATI) or Computer Assisted Personal Interviewing (CAPI). For both CATI and CAPI, the questionnaire has been adapted such that to incorporate logical rules in questions. A series of validations that alert interviewers to inconsistencies during data collection were applied. This method has many advantages as it results in shorter interview duration and in the reduction of data entry errors during fieldwork.

 

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 are to be considered. The mean interview duration per household for EU-SILC 2024 was calculated at 43 minutes.

The mean interview duration per person is calculated as the sum of the duration of all personal interviews, divided by the number of personal questionnaires completed. Only persons accepted for the database have to be considered. The mean (average) interview duration per person for EU-SILC 2024 was calculated at 11 minutes. 

17.1. Data revision - policy

Revision of data is compliant with the ESS Code of Practice principles.

At the NSO, there is currently an internal policy governing revision that occur for all statistics produced.  This revisions policy aims at safeguarding a coordinated revisions system across statistical domains. The policy takes account of the need and causes for revisions; time and frequency of revisions; data and other statistical products affected by such revisions; and length of periods revised.

17.2. Data revision - practice

As from EU-SILC 2023, a new sampling frame of households and individuals based upon the 2021 Population and Housing Census was used. No revisions on past time series data were made since tests revealed that there has been little impact on statistical significance on the core EU-SILC indicators.

For EU-SILC 2024, the sampling frame was also based upon the 2021 Population and Housing Census. Even though an updated version of the population and housing estimates was provided internally resulting in shifts in one-person and two person households, and in the tenure status, there was no need for any data revisions to take place. However, the interpretation of results must be carried out with caution and must be considered in light of the changes in the household distributions by household size and tenure status. 

17.2.1. Data revision - average size

No revisions were made upon previous EU-SILC data, following the methodological changes that took place for EU-SILC 2024. 

 

18.1. Source data

The database based on the 2021 Census of Population & Housing, that is held and maintained by NSO through annual updates, provides a comprehensive count of all persons living in Malta and Gozo. As a result, this database is considered to be the most adequate source to be used for the Maltese EU-SILC sample selection and served as sampling frame for the new waves as from EU-SILC 2023. Previously, the 2011 Census of Population & Housing including annual updates was used.

Information about all individuals is collected through a survey.  Additionally, the information provided during the data collection phase is enhanced through the use of various data registers for different levels of use. These registers include data extracts from the Automated Revenue Management Services (ARMS) and data on social benefits (SABS), wages and NI data (MFSS). These registers are mainly used in order to collect reliable information on household income and housing costs.

Detailed information concerning the 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. 

18.1.1. Sampling Design

Type of sampling design

The type of sampling design adopted for EU-SILC in Malta is the one-stage sampling design (or single-stage sampling design). Simple random sampling is used each year to select the new panel of dwellings to be added to the sample to be interviewed. Thus, in cross-sectional SILC 2024, the complete sample was made up of the 3 panels chosen in each of the three years from 2021 to 2023, together with the new panel chosen to be interviewed for the first time in 2024. For households in the three old panels, SILC 2024 was the second, third or fourth (and last) time they were being contacted to complete the survey.

The new panel, amounting to 1,609 households for SILC 2024, is selected randomly from a register of persons and households which is based on the Census of Population and Housing that was held in 2021. This database is maintained and updated on a regular basis by the NSO. The remaining total sample of households for SILC 2024 numbered 3,831 households of which 1,439 households were interviewed for the first time in SILC 2023, 1,289 households were interviewed for the first time in SILC 2022, and 1,103 households were interviewed for the first time in SILC 2021.

Data collection was carried out between April 2024 and July 2024. The data collection was in full swing between April and July. The addition time period was allotted to increase the response rate which in turn would yield in better statistical results. 

 

Stratification and sub-stratification criteria

Stratification and sub-stratification criteria are not applicable since stratified sampling is not adopted for SILC in Malta.

 

Sample size and allocation criteria

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; or
• 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.

As stipulated in the Council Regulation, each Member State is required to achieve a minimum effective sample size of households and eligible persons (persons aged 16+) for the cross-sectional component of EU-SILC.  For Malta, the minimum effective sample size amounts to 3,000 households, which corresponds to a minimum of 7,000 persons aged 16 and over.

The database based on the 2021 Census of Population & Housing, that is held and maintained by NSO through annual updates, provides a comprehensive count of all persons living in Malta and Gozo.  As a result, this database is considered to be the most adequate source to be used for the Maltese SILC sample selection and served as sampling frame for the new wave selected for cross-sectional SILC 2024.

In 2024, the gross sample size for the Maltese cross-sectional SILC was 5,440 households. Of these, 104 households were ineligible for the survey (i.e. addresses that did not actually exist, could not be located, non-residential addresses or permanently vacant dwellings). Consequently, 5,336 households were approached for the interview. Of these, 4,538 completed the survey. These households comprised of 10,901 residents, of whom 9,476 were aged 16 and over.

The table below shows the number of households in the 2024 EU-SILC reconciled component and the number of persons aged 16 and over.

Wave	Number of households for which an interview is accepted for database	Sample persons (aged 16+)	Co-residents (aged 16+)
2021	1,225	2,689	0
2022	2,413	5,088	80
2023	3,663	7,541	190
2024	4,538	9,149	327
Total	11,839	24,467	597

---

 

18.1.2. Sampling unit

The sampling population for EU-SILC in Malta is composed of all private households consisting of persons who share their income and expenses.  The simple random sample of households is selected from a register of persons and households, based on the Census of Population and Housing 2021, which is regularly maintained.  Sample selection is followed by a data collection period during which the selected households are contacted, and personal interviews are carried out with persons living within these households. 

18.1.3. Sampling frame

The integrated, or rotational, design has been adopted for Malta’s EU-SILC. This design with 4 sub-samples complies with Eurostat recommendations with respect to both cross-sectional and longitudinal operations. The system of rotational panels implies that each year the oldest panel is dropped and replaced by a new panel of households.  In this way, each group of households is included in the sample for four waves of the survey and information is collected over a period of four consecutive years.

A single-stage sampling design is used for EU-SILC in Malta.

18.2. Frequency of data collection

Data collection was carried out between April 2024 and July 2024. The data collection was in full swing between April and July. The additional time period was allotted to increase the response rate which in turn would yield in better statistical results. 

18.3. Data collection

The method of data collection in Malta for EU-SILC 2024 is entirely through CATI, with an element of CATI proxy interviews when this was unavoidable. The CAPI method was used only for a few households. The following is a distribution for types of interviews in cross-sectional SILC 2024 for household members aged 16 years and over.

 

Mode of data collection

Face to face interview (CAPI) (% of total)	Telephone interview (CATI)  (% of total)	Face to face interview (CAPI) with proxy (% of total)	Telephone interview (CATI) with proxy (% total)
0.6	72.0	0.1	27.3

This implies that 27.4 per cent of surveys were conducted through proxy interviews. Consequently, despite our best efforts to reduce proxy interviews, a relatively high percentage was recorded. In view of difficulties related to response burden and the sensitivity of SILC questions, in some cases interviewers can use proxy and telephone interviews to reduce non-response.  In such cases interviewers are to request household members who could not be present during the interview to leave documentation such as pay slips and tax returns with the person who will be responding on their behalf, so that as much as possible the proxy effect does not result in a loss in quality.

Furthermore, the availability and use of register data helps offset the proxy effect to some extent. Register data is available for income components like employment & self-employment income, income tax and social benefits. Other registers supply demographic characteristics and partial information on levels of education attained. Register data is incorporated into SILC variables as much as possible, particularly in the case of persons who are interviewed by proxy. This is done through ID card linking.  Consequently, the rate of proxy interviews must be evaluated in this context.

 

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
Data for the Maltese EU-SILC was collected mainly using the CATI method (see Section 3.3. Data collection). The CAPI method was used for few households. This was complemented by the use of register data from various government departments, as described below. Data on social benefits were extracted from a register called System of Social Assistance and Benefits (SABS) database, owned by the Ministry for Family and Social Solidarity (MFSS).  This register includes the details of all individuals who are eligible to receive some form of social benefit and the value of the benefit received by each individual. The list of benefits as defined by the MFSS was merged to fit in with Eurostat definitions and income values from the same reference period as that covered by EU-SILC 2024 were used.   Social benefits obtained from the SABS database are: PY090G – unemployment benefits; PY100G – old-age benefits; PY110G – survivor’s benefits; PY120G – sickness benefits; PY130G – disability benefits; HY050G – family / children related allowances; HY060G – social exclusion not elsewhere classified; and HY070G – housing allowances (only energy benefits were obtained from SABS).   PY140G (education related allowances) and part of HY070G (housing allowances) are the only variables not available in the SABS database. The education related variables are collected from the households as part of the SILC interview.   As from EU-SILC 2010, it became possible to use register data on income from work through the Department of Inland Revenue. Thus, the variables PY010G (employee cash or near cash income) and PY050G (cash benefits or losses from self-employment) for previous surveys were compiled through a combination of register data and survey responses. By combining both sources, a better coverage for these two variables was ensured while consistency with data from previous years was also maintained. As from EU-SILC 2013 it was also possible to use a combination of register data from the Department of Inland Revenue and survey data for the computation of taxes in the variable HY140G (tax on income and social contributions). However, as from SILC 2017, there was no Inland Revenue data available, and these variables relied solemnly in social security data. As from EU-SILC 2020, it became possible once again the use of register data on income from work through the Department of Inland Revenue.   Moreover, SABS database only covers persons who receive social benefits as a result of means testing while the IRD database does not include interests & dividends for persons taxed at source on such income.	All the income components expect for education benefits (PY140G), pensions from individual private plans (PY080G), and part of housing allowance (HY070G), were obtained from administrative data.	Income variables are collected from administrative sources. Provided that these administrative sources contain data for the entire population, the data is linked using ID Cards to extract information for individuals in EU-SILC. The data then undergoes various manipulations such as aggregation, and the identification and removal of duplicates. Additionally, very often several components from the administrative data are combined in order to obtain the final target variable.

18.4. Data validation

Several work processes are carried out during data collection in order to ensure that data are collected in a proper manner. Measures that are regularly implemented include: checks on the questionnaires, interviewer audits, follow-ups on non-responding households, etc.

Initial quality checks carried out on the input data revolve around the IT requirements (source, format, duplicate entries, and missing entries), and consistency with past data.

The EU-SILC dataset is further validated through several checking rules during the analysis stage. Through this process, trained statisticians identify misleading information in the dataset. The final transmission files are also validated through a validation program which is provided by Eurostat specifically for this purpose.

Furthermore, final aggregated outputs are also compared against different sources, including the

National Accounts, Labour Force Survey, the Social Security Department database, and the Inland Revenue Department database.

18.5. Data compilation

The methodology of how the main indicators are computed using SILC data are published in the News Release relating to Salient Indicators. Information with regards to such computations can be found using the following link.

 

Imputation Procedure

Imputation is normally done by making use of already existing information in conjunction with several methods. For respondents taking part for the second, third or fourth time, imputation is done by using data collected in the previous years. This method is preferred since it ensures consistency with the previous years' data. When considering new respondents or when information from previous years is not available, information from other persons or households with similar characteristics is used. In cases where these two methods are not possible, mathematical imputation methods, such as regression-based techniques, are used.

Estimation of imputed rent values directly from EU-SILC data is not possible. This is due to the fact that the proportion of tenants renting at market prices in Malta is rather low to enable the estimation of rent figures at reliable quality levels. Based on 2021 Census data, the National Accounts Unit of the NSO compiled a table of average imputed rent values for dwellings classified by size and type. These values were then attached to the EU-SILC datasets and used as estimates for the imputed rent. The basis for these estimates has changed from SILC 2022 and SILC 2013, since previously the imputed rent values were based on the 2011 and 2005 Census data respectively.

The annual value for a company car fringe benefit is estimated according to methodology used by the Inland Revenue Department (IRD) for tax purposes. Through the SILC questionnaire, respondents who have such a benefit are asked to specify the car make, model, year of registration, engine type, whether they are compensated for fuel costs and the number of months they made use of the vehicle during the income reference year. The car value can then be computed by using information provided by the Price Statistics Unit at NSO. Finally, the annual fringe benefit value is estimated by scaling down the car value by a percentage which can be derived from the variables collected in the questionnaire as per IRD specifications.

 

Weighting Procedure

For information about the weighting procedure see Annex 5.

18.5.1. Imputation - rate

Refer to sub-concept 18.5.

Income data which is derived directly from administrative registers (e.g., social benefits and employee income) is less subject to item non-response.

Furthermore, the level of item non-response in non-income variables is minimal.

18.5.2. Calculation of weighting factors and weight adjustments

The weighting procedure used can be found attached in Annex 5: Weighting Procedure

18.5.3. Estimation and imputation

The estimation and imputation procedure can be found attached in Annex 6: Estimation and Imputation

18.6. Adjustment

Not applicable.

18.6.1. Seasonal adjustment

Not applicable.

No comments.

ANNEX SILC

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:

1. Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
2. 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.

24 July 2024

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 geographical area encompasses Malta and Gozo.  

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
The tax on income and social insurance contributions was collected for the income reference period. Thus, for EU-SILC 2023, HY140 reflects amounts for calendar year 2023.	The income reference year for EU-SILC is one calendar year prior to the year of survey. Thus, similar to HY140, for EU-SILC 2024, the income reference period reflects calendar year 2023.	The variable on regular taxes on wealth is not applicable for Malta.	The bulk of the data collection was carried out between April and July 2024. Thus, the lag between income reference period and current variables spans between 4 to 7 months, depending on the date of interview for each household.

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

The methodology of how the main indicators are computed using SILC data are published in the News Release relating to Salient Indicators. Information with regards to such computations can be found using the following link.

 

Imputation Procedure

Imputation is normally done by making use of already existing information in conjunction with several methods. For respondents taking part for the second, third or fourth time, imputation is done by using data collected in the previous years. This method is preferred since it ensures consistency with the previous years' data. When considering new respondents or when information from previous years is not available, information from other persons or households with similar characteristics is used. In cases where these two methods are not possible, mathematical imputation methods, such as regression-based techniques, are used.

Estimation of imputed rent values directly from EU-SILC data is not possible. This is due to the fact that the proportion of tenants renting at market prices in Malta is rather low to enable the estimation of rent figures at reliable quality levels. Based on 2021 Census data, the National Accounts Unit of the NSO compiled a table of average imputed rent values for dwellings classified by size and type. These values were then attached to the EU-SILC datasets and used as estimates for the imputed rent. The basis for these estimates has changed from SILC 2022 and SILC 2013, since previously the imputed rent values were based on the 2011 and 2005 Census data respectively.

The annual value for a company car fringe benefit is estimated according to methodology used by the Inland Revenue Department (IRD) for tax purposes. Through the SILC questionnaire, respondents who have such a benefit are asked to specify the car make, model, year of registration, engine type, whether they are compensated for fuel costs and the number of months they made use of the vehicle during the income reference year. The car value can then be computed by using information provided by the Price Statistics Unit at NSO. Finally, the annual fringe benefit value is estimated by scaling down the car value by a percentage which can be derived from the variables collected in the questionnaire as per IRD specifications.

 

Weighting Procedure

For information about the weighting procedure see Annex 5.

The database based on the 2021 Census of Population & Housing, that is held and maintained by NSO through annual updates, provides a comprehensive count of all persons living in Malta and Gozo. As a result, this database is considered to be the most adequate source to be used for the Maltese EU-SILC sample selection and served as sampling frame for the new waves as from EU-SILC 2023. Previously, the 2011 Census of Population & Housing including annual updates was used.

Information about all individuals is collected through a survey.  Additionally, the information provided during the data collection phase is enhanced through the use of various data registers for different levels of use. These registers include data extracts from the Automated Revenue Management Services (ARMS) and data on social benefits (SABS), wages and NI data (MFSS). These registers are mainly used in order to collect reliable information on household income and housing costs.

Detailed information concerning the 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. 

Annual

The timeline for transmitting the 2023 data was adjusted, with the data being required for transmission by 31/12/N and the final delivery expected by February N+1. However, for the transmission of SILC 2023, Malta was granted a derogation allowing this submission to take place by April N+1, however the derogation ended in 2025. For SILC 2024, the data was transmitted by December N, in line with the original plans.

Data collection, data cleaning and data submission are fully regulated by Eurostat; this is done to ensure that the SILC data are fully comparable throughout all participating countries.

EU-SILC data has been collected in a consistent manner since 2005. In view of this, until 2022 the data can be compared or reconciled over time.

Following the 2021 Population and Housing Census, a new sampling frame of households and individuals was introduced for the first time to be used as from EU-SILC 2023. As a result, in 2023 there was a break in series in the sampling frame used.

EU-SILC uses regularly updated population and household estimates for the calculation of the cross-sectional weights and the calibration of survey data with population and household estimates. For EU-SILC 2024 an updated version of the population and household estimates was provided internally, and in view of this the cross-sectional weights had to be recalculated. The updates were implemented mainly to reflect the changes captured in the Census 2021. This recalculation, apart from having a direct impact on the population household figures provided during the first transmission, also had an impact on the household distribution by household size. Specifically, the updated estimates resulted in a shift, revealing in a small increase in the proportion of one-person households, coupled with a slight decrease in the proportion of two-person households. This change aligns with the distribution patterns observed in the Census data on which the sampling frame is based. Moreover, the increase in the number of one-person households also influenced the distribution of households by tenure status. This is because the increase in single-person households was mainly observed among those residing in rented accommodations. As a conclusion, the interpretation of the results must be carried out with caution and must be considered in light of the changes in the household distributions by household size and tenure status.