Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
3.2. Classification system
International Standard Classification of Education (ISCED'2011);
International Standard Classification of Occupations (ISCO-08);
The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
For more details on the classification used please, see EU Vocabularies.
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 for 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.
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
3.5. Statistical unit
Statistical units are private households and all persons living in these households who have usual residence in the Member State. Annex II of the Commission implementing regulation (EU) 2019/2242 defines specific statistical units per variable and specifies the content of the quality reports on the organization of a sample survey in the income and living conditions domain pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council.
3.6. Statistical population
The target population is private households and all persons composing these households having their usual residence in the Member State. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.6.1. Reference population
Definitions of reference population, household and household membership
Reference population
Private household definition
Household membership
There is no difference to the standard EU-SILC definition, hence the reference population is defined as all the households and their members living in the government-controlled areas of Cyprus. Population in collective households and institutions is excluded.
No deviation from the standard EU-SILC definition. A private household is a person living alone or a group of persons living together in the same dwelling sharing expenses, including the joint provision of the essentials of living.
The definition of household membership is the one recommended by EUROSTAT. Students (either in Cyprus or abroad) are considered to be members of their parents´ household given they are fully financially supported by them.
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
Government-controlled areas of the Republic of Cyprus.
3.8. Coverage - Time
The EU-SILC survey has been carried out in Cyprus annually since 2005.
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 period for taxes payments/refunds and social insurance contributions was 2023. Tax refunds received during 2023 referred to income received in previous years.
For EU-SILC 2024 the income reference period was 2023.
The reference period for taxes on wealth was 2023.
Since EU-SILC 2024 was carried out the period February to September 2024, the time lag between the income reference period and current variables varied between 2 to 9 months
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.
Article 3 of the national Official Statistics Law, No. 25(I) of 2021 defines the functions of the Statistical Service of Cyprus regarding the production and dissemination of official statistics. Moreover, Article 13, explicitly stipulates the mandate for data collection and introduces a mandatory response to statistical enquiries by stipulating the obligation of respondents to reply to surveys and provide the data required. This relates not only to national but also to European statistics which, by virtue of Article 8 of the said Law, are incorporated in the annual and multiannual programmes of work without any further procedure.
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
Official statistics are released in accordance to all confidentiality provisions of the following:
Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European statistics and its later amendments (especially Chapter 5 on statistical confidentiality).
Notifications about the dissemination of statistics are published in the release calendar, which is available on CYSTAT’s web portal. The annual release calendar, announced during the 4th quarter of the year, includes provisional dates of publication for the following year, which are finalized the week before publication
According to the Dissemination and Pricing Policy of the Statistical Service of Cyprus (section 2.3) CYSTAT΄s main channel for dissemination of statistics is the web portal, which offers the same conditions to everyone and is updated at the same time every working day (12:00 noon). No privileged pre-released access is granted.
In addition to the annual release calendar, users are informed of the various statistical releases through the “Alert” service provided by CYSTAT.
AT RISK OF POVERTY AND SOCIAL EXCLUSION INDICATORS, 2008-2024.
10.3. Dissemination format - online database
Not applicable.
10.3.1. Data tables - consultations
Not applicable.
10.4. Dissemination format - microdata access
Statistical micro-data from CYSTAT’s surveys are accessible for research purposes only and under strict provisions as described below:
Under the provisions of the Official Statistics Law, CYSTAT may release microdata for the sole use of scientific research. Applicants have to submit the request form "APPLICATION FOR DATA FOR RESEARCH PURPOSES" giving thorough information on the project for which micro-data are needed.
The application is evaluated by CYSTAT’s Confidentiality Committee and if the application is approved, a charge is fixed according to the volume and time consumed for preparation of the data. Micro-data may then be released after an anonymization process which ensures no direct identification of the statistical units but, at the same time, ensures usability of the data. The link for the application is attached below.
Link to the application for access to microdata on CYSTAT's website:
Please see Annex – CY_2024_Annex 10-Metadata on benefits
10.6.1. Metadata completeness - rate
Not applicable.
10.7. Quality management - documentation
Not applicable.
11.1. Quality assurance
The quality of statistics in CYSTAT is managed in the framework of the European Statistics Code of Practice which sets the standards for developing, producing and disseminating European Statistics as well as the ESS Quality Assurance Framework (QAF). CYSTAT endorses the Quality Declaration of the European Statistical System. In addition, CYSTAT is guided by the requirements provided for in Article 11 of the Official Statistics Law No. 25(I) of 2021 as well as Article 12 of Regulation (EC) No 223/2009 on European statistics, which sets out the quality criteria to be applied in the development, production and dissemination of European statistics.
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
12.2. Relevance - User Satisfaction
Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 (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.
Since 2008 (with the exception of 2010, 2013 and 2020) CYSTAT carries out an annual online “Users Satisfaction Survey”. The results of the surveys are available on CYSTAT’s web portal at the link attached below.
Overall, there is a high level of satisfaction of the users of statistical data published by CYSTAT.
All the nucleus variables included in the regulation were collected. Moreover, all the variables included in the three-year rolling module on “children’s health and living conditions” and the six-year rolling module on “access to services” were collected. However, the optional variables: HY030G -Imputed rent, RL080 - Remote education, HI130G - Interest expenses and HI140G - Household debts were not collected.
Please also note the following:
- HY110G: included in 2024 CY SILC, but no cases for 2024.
- HY145: the following rule applies: “This variable should be filled when the country has recorded only net income at the component level. If the income at the component level is reported gross or some of the components are reported gross and some net of tax, adjustments will be recorded in the variable HY140G.”
12.3.1. Data completeness - rate
Not applicable.
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.
Please see Annex - CY_2024_Annex 3-Sampling_errors
13.2.1. Sampling error - indicators
The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non-sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.
Please see Annex - CY_2024_Annex A EU-SILC - content tables
13.3. Non-sampling error
Non-sampling errors are basically of 4 types:
Coverage errors: errors due to divergences existing between the target population and the sampling frame.
Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection.
Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting.
Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample.
Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained.
13.3.1. Coverage error
Coverage errors include over-coverage, under-coverage and misclassification:
Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice.
Under-coverage: refers to units not included in the sampling frame.
Misclassification: refers to incorrect classification of units that belong to the target population
Sampling frame and coverage errors
The sampling frame used for the 2024 survey was the list of households from the 2021 Census of Population. The reference date for the Census was October 1, 2021. Consequently, the only housing units not included in the sampling frame were those built after that date. As we move further away from 2021, more coverage errors are encountered. Therefore, the 2024 survey experienced more coverage errors compared to 2023, although still significantly fewer than in the previous waves conducted using the 2021 Census.
13.3.1.1. Over-coverage - rate
Coverage error
Main problems
Population (sub-population)
Size of error
Comments
Over-coverage
158 dwellings
3,2%
149 addresses that were out of scope and 9 addresses were not successfully contacted
Under-coverage
NA
NA
Misclassification
NA
NA
13.3.1.2. Common units - proportion
Not applicable.
13.3.2. Measurement error
Measurement error for cross-sectional data
Cross-sectional data
Source of measurement errors
Building process of questionnaire
Interview training
Quality control
Possible sources of measurement errors are the questionnaire (design, content and wording), the method of data collection, the interviewers and the respondents. As the 2024 EU-SILC round was the 20th in the series, quality was considerably improved due to interviewers’ feedback, continuous data analysis and research.
The questionnaire for EU-SILC was developed on the basis of the EU-SILC Methodological Guidelines 2024. Even though, the questionnaire was well tested and despite the fact that this was the 20th wave of the survey, some questions were still difficult to be answered with precision. Difficulties due to memory lapses were encountered in questions regarding income, housing cost and the main activity each month. In an effort to minimise these problems respondents were requested to prepare pay slips and utility bills when the interviewer was making an appointment. In the case that the respondents could have the pay slips at a later date, then they could send them via email at the central offices. Difficulties were also encountered in distinguishing the various benefits and pensions. In order to overcome these difficulties a part of the training of the interviewers was focused specifically on social benefits and pensions.
As the method of data collection was Computer Assisted Telephone Interviewing (CATI) and Computer Assisted Personal Interviewing (CAPI), many validation and consistency checks were implemented during the interview. This had a positive impact on the quality of the data collected. Additionally, problems usually accounted to the routing of the questionnaire were fully avoided because of CATI and CAPI.
In order to reduce interviewer effects, a training session of 1 week for all the interviewers was organised at the head offices of the Statistical Service. The training was conducted by permanent staff, Statistics Officers responsible for the EU-SILC survey. The aim of the training was to ensure that all interviewers were uniformly trained both in regard to the content of the questionnaire, as well as their behaviour during the interview. It was focused on refreshing the terminology used in the questionnaire and on the understanding of new terminology used for the first time in the questionnaire (e.g. children’s health and living conditions, and access to services). Main emphasis was given on difficult definitions and on explaining the various public benefits, as well as the importance of the accuracy of the information collected. Also, the interviewers had intensive sessions on working with their laptops and the electronic questionnaires in the environment of BLAISE. An interviewer manual was prepared explaining each and every single question of the questionnaire as well as their respective possible answers.
Apart from the 24 interviewers the training sessions were also attended by 8 supervisors. Each one of them was responsible for a group of 3 interviewers. During the fieldwork period the supervisor had meetings with each one of the interviewers in his/her group at least once a week. During these meetings, apart from discussing problems or questions raised during the week, the supervisors also collected (from the interviewers’ laptops) all completed questionnaires. Their main duty during the data collection period was to examine the interviewers’ work and refer back to them for inconsistencies or for problems identified in connection with terminology. Furthermore, the supervisors had to double check some of the answers with respondents by telephone, especially in the case of unusual answers or missing data. Additionally, from 2nd wave onwards, data for households in the survey for 2 years or more were further checked based on the data from previous years. Finally, administrative data were used. The available registers included the pensions (of all types) provided by the Social Insurance Services and the Public Sector, the Guaranteed Minimum Income (GMI) and other social and disability benefits, and the income of the employees in the Public Sector.
13.3.3. Non response error
Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:
1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According to Annex VI of the Reg.(EU) 2019/2242
Household non-response rates (NRh) is computed as follows:
NRh=(1-(Ra * Rh)) * 100
Where Ra is the address contact rate defined as:
Ra= Number of address/selected person (including phone, mail if applicable) successfully contacted/Number of valid addresses/selected person (including phone, mail if applicable) selected
and Rh is the proportion of complete household interviews accepted for the database
Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses (including phone, mail if applicable)
• Individual non-response rates (NRp) is computed as follows:
NRp=(1-(Rp)) * 100
Where Rp is the proportion of complete personal interviews within the households accepted for the database
Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database
• Overall individual non-response rates (*NRp) is computed as follows:
*NRp=(1-(Ra * Rh * Rp)) * 100
For those Members States where a sample of persons rather than a sample of households (addresses, phones, mails etc.) was selected, the individual non-response rates will be calculated for ‘the selected respondent.
2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.1. Unit non-response - rate
Unit non-response rate for cross-sectional
Address (including phone, mail if applicable) contact rate
Complete household interviews
Complete personal interviews
Household Non- response rate
Individual non- response rate
Overall individual non- response rate
(Ra)
(Rh)
(Rp)
(NRh)
(NRp)
(NRp)*
A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
99,81
99,40
100
89,23
81,70
96,99
100
100
100
10,93
18,79
3,01
0
0
0
10,93
18,79
3,01
where
A=total (cross-sectional) sample,
B =New sub-sample (new rotational group) introduced for first time in the survey this year,
C= Sub-sample (rotational group) surveyed for last time in the survey this year.
Unit non-response rate for longitudinal data
Please see Annex - CY_2024_Annex A EU-SILC - content tables
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 Annex - CY_2024_Annex 2-Item_non_response
13.3.4. Processing error
Description of data entry, coding controls and the editing system
Data entry and coding
(if any used)
Editing controls
Processing errors were reduced because of CATI/CAPI, and the implementation of validation and consistency checks during the data collection phase (BLAISE software). The processing errors were further reduced as the questionnaires were edited and coded by the supervisors prior to finalising the data files for processing. For the households which were in the survey for at least 2 years an additional tool during editing was the preloading of certain variables from the previous survey. Inconsistencies were further examined with interviewers and in many cases with the households directly. The coding requested was minimal, i.e. occupation (2 digits ISCO), economic activity (2 digits NACE rev. 2) and country of birth; and was carried out using drop-down lists.
The finalised data files prepared by supervisors were then processed using SAS programs with various other logical and consistency checks. The main errors found were connected to self-employment income. Errors concerning the recording of the various benefits and pensions under the correct income
variable, according to EU-SILC Methodological Guidelines, were reduced, because of the use of registers.
Before sending the final D-, R-, H- and P- files, data files were further checked using Eurostat’s SILC validation programs.
Please see Annex - CY_2024_Annex A EU-SILC - content tables
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
For the EU-SILC 2024 survey, the final microdata files were transmitted on the 28th of February 2025, i.e. 2 months after the end of the reference year.
14.1.1. Time lag - first result
The first results were published on CYSTAT’s website on the 20th of March 2025, 2.7 months after the end of the reference year. On Eurostat’s website, the first results were published on the 21st of March 2025.
14.1.2. Time lag - final result
On Eurostat’s website, the final results were published on the 21st of March 2025.
Cyprus is classified as one region at the Nuts 2 level
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
In the following tables, we compare the results on income components between EU-SILC 2021, EU-SILC 2022, EU-SILC 2023, and EU-SILC 2024 at both the household and personal levels. More specifically, the percentage of households and persons having received an amount on specific income target variables, as well as their mean value per household, are presented in the two tables that follow.
The financial years 2022 and 2023 (the income reference years of the 2023 and 2024 surveys) exhibited a GDP positive growth rate of 14,5% and 6,5%, respectively. As a result, the social and income indicators were expected to improve.
The gradual phasing out, and the final termination at the end of October 2021, of the ad-hoc social benefits introduced by the government in 2020 to alleviate the consequences of the pandemic. had primarily affected the following variables: HY050G - Family/children related allowances, PY090G - Unemployment benefits, and PY120G - Sickness benefits. Furthermore, they significantly impacted variable PY010G - Employee cash or near-cash income, as most of these measures were implemented to support workforce retention.
In addition, mortgage interest rates increased in 2023, explaining the increase in the mean HY100G of EU-SILC 2024. Moreover, due to the overall rise in rents, income from renting a property or land (HY040G) increased.
The increase in the mean of HY140G observed for the 2024 survey can be explained by the general increase in employee income (PY010G).
Comparison between EU-SILC 2021, 2022, 2023 and 2024 for all income target variables at household level – EU SILC
Income target variable
2021
2022
2023
2024
% of households having received an amount
Mean (weighted) income per household (EURO)
% of households having received an amount
Mean (weighted) income per household (EURO)
% of households having received an amount
Mean (weighted) income per household (EURO)
% of households having received an amount
Mean (weighted) income per household (EURO)
Total household gross income HY010
100
40.804
100
42.755
100
45.390
100
48.567
Total disposable household income HY020
100
34.227
100
35.699
100
38.147
100
40.834
Total disposable household income before social transfers other than old-age and survivor's benefits HY022
99,3
31.726
99,5
33.760
99,6
35.716
99,5
38.654
Total disposable household income before social transfers including old-age and survivor's benefits HY023
99,1
24.575
93,6
27.721
93,6
29.184
93,6
31.671
Imputed rent HY030G
na
na
na
na
87,6
7.008
na
na
Income from rental of a property or land HY040G
8,6
645
8,1
665
7,8
753
7,6
892
Family/children related allowances HY050G
25,5
632
24,4
514
23,8
530
23,1
579
Social exclusion not elsewhere classified HY060G
1,9
149
1,9
122
1,8
125
1,6
131
Housing allowances HY070G
5,8
89
5
83
5,2
82
5
78
Regular inter-household cash transfer received HY080G
16,1
773
16
786
14,9
737
15,4
750
Interest, dividends, profit from capital investment in unincorporated business HY090G
10,5
259
5,5
253
3,3
274
3,5
662
Interest repayments on mortgage HY100G
11,9
257
12,1
280
12,4
335
12,7
447
Regular taxes on wealth HY120G
65,9
58
66,7
59
66,4
61
66,9
68
Regular inter household cash transfer paid HY130G
17,9
709
16,1
650
15
565
14,6
690
Tax on income and social contributions HY140G
94,9
5.810
95
6.347
94,8
6.618
95,2
6.975
Value of goods produced for own consumption HY170G
6,8
14
6,5
13
6,8
14
6,8
15
Comparison between EU-SILC 2021, 2022, 2023 and 2024 for all income target variables at individual level - EU-SILC
Income
2021
2022
2023
2024
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
Employee cash or near cash income PY010G
51,1
26.498
51,2
28.817
52,2
29.949
53,2
32.090
Non-cash employee income PY020G
8,5
174
7,2
146
7,7
157
9,1
183
Company car PY021G
0,5
20
0,4
18
0,6
19
0,6
28
Employer´s social insurance contribution PY030G
49,2
4.307
49,6
4.675
50,9
4.940
52
5.267
Cash benefits or losses from self-employment PY050G
10,9
2.845
10,4
3.128
9,5
3.550
9,5
3.794
Unemployment benefits PY090G
4,7
817
3,3
466
4,2
945
3,7
665
Old-age benefits PY100G
27
6.242
27,5
6.072
28,3
6.526
28,7
6.981
Survivor benefits PY110G
5,2
909
5,3
987
5,9
1.060
6
1.123
Sickness benefits PY120G
5,6
168
8,3
164
11,4
166
6,7
174
Disability benefits PY130G
2,2
477
2,2
399
2,2
402
2
419
Education-related allowances PY140G
2,7
169
2,7
195
2,6
183
2,2
137
Please see Annex – CY_2024_Annex 8-Breaks in series
15.2.1. Length of comparable time series
Not applicable.
15.2.2. Comparability and deviation from definition for each income variable
Income
Identifier
Comparability
Deviation from definition if any
Total hh gross income
(HY010)
F
Total disposable hh income
(HY020)
F
Total disposable hh income before social transfers other than old-age and survivors' benefits
(HY022)
F
Total disposable hh income before all social transfers
(HY023)
F
Income from rental of property or land
(HY040)
F
Family/ Children related allowances
(HY050)
F
Social exclusion payments not elsewhere classified
(HY060)
F
Housing allowances
(HY070)
F
Regular inter-hh cash transfers received
(HY080)
F
Alimonies received
(HY081)
F
Interest, dividends, profit from capital investments in incorporated businesses
(HY090)
F - Interest paid on mortgages is collected asking directly the amount. Over and above, a double check is carried out with an estimation of the amount, which is calculated on the basis of the following questions: the year the housing loan was taken, the initial amount borrowed, years of repayment of the initial loan, the monthly payment, the outstanding amount at the end of the previous year, the actual total amount paid on the previous year and the interest rate applied for the loan.
Interest paid on mortgage
(HY100)
F
Income received by people aged under 16
(HY110)
F
Regular taxes on wealth
(HY120)
F
Taxes paid on ownership of household main dwelling
(HY121)
F
Regular inter-hh transfers paid
(HY130)
F
Alimonies paid
(HY131)
F
Tax on income and social contributions
(HY140)
F
Repayments/receipts for tax adjustment
(HY145)
F
Value of goods produced for own consumption
(HY170)
F
Cash or near-cash employee income
(PY010)
F
Other non-cash employee income
(PY020)
F
Income from private use of company car
(PY021)
F
Employers social insurance contributions
(PY030)
F
Contributions to individual private pension plans
(PY035)
F
Cash profits or losses from self-employment
(PY050)
F
Pension from individual private plans
(PY080)
F
Unemployment benefits
(PY090)
F
Old-age benefits
(PY100)
F
Survivors benefits
(PY110)
F
Sickness benefits
(PY120)
F
Disability benefits
(PY130)
F
Education-related allowances
(PY140)
F
F= Fully comparable; L= Largely comparable; P= Partly comparable and NC= Not collected.
15.3. Coherence - cross domain
The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.
Please see Annex – Annex - Coherence, 2024
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Please see Annex – CY_2024_Annex 7-Coherence
15.4. Coherence - internal
Not applicable.
The mean interview duration
Mean Interview duration per household
The mean interview duration per household is calculated as the sum of the duration of all household interviews plus the sum of the duration of all personal interviews, divided by the number of household questionnaires completed. Only households accepted for the database have to be considered
Mean Interview duration per person
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.
Mean (average) interview duration per household = 49,7 minutes.
Mean (average) interview duration per person = 15,6 minutes.
17.1. Data revision - policy
A data revision policy is in place at CYSTAT. It is published on CYSTAT’s web portal.
CYSTAT also publishes a list of scheduled revisions (regular or major revisions), also published on its web portal.
17.2. Data revision - practice
There are no revisions to report.
17.2.1. Data revision - average size
Not applicable.
Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section (please see below). Such information is mainly used for the computation of the accuracy measures.
18.1. Source data
Sampling frame and coverage errors
The sampling frame used for the 2024 survey was the list of households from the 2021 Census of Population. The reference date for the Census was October 1, 2021. Consequently, the only housing units not included in the sampling frame were those built after that date. As we move further away from 2021, more coverage errors are encountered. Therefore, the 2024 survey experienced more coverage errors compared to 2023, although still significantly fewer than in the previous waves conducted using the 2021 Census.
Moreover, the data was not only collected through household interviews but also supplemented by registers for several income components. These registers included pensions (of all types) provided by the Social Insurance Services and the Public Sector, the Guaranteed Minimum Income (GMI), other social and disability benefits, the student grant, the student's package, and the income of Public Sector employees.
Distribution of all household members by data status - RB250
RB250 - Data status
2024
Total
%
information completed only from interview (11)
2.780
30,9
information completed from both interview and registers (13)
6.215
69,1
information completed from full record imputation (14)
3
0,0
refusal to co-operate (23)
0
0,0
person temporarily away and no proxy possible (31)
0
0,0
no contact for other reasons (32)
0
0,0
information not completed: reason unknown (33)
0
0,0
Total
8.998
100,0
18.1.1. Sampling Design
Type of sampling design
The longitudinal component of EU-SILC 2024 as transmitted to EUROSTAT consists of rotational groups R4 for the years 2021-2024, R1 for the years 2022, 2023 and 2024 and of the rotational group R2 for the years 2023 and 2024. The rotational group R4 for the years 2021-2024 was drawn with the sample of 2021, the rotational group R1 with the sample of 2022 and the rotational group R2 with the sample of 2023.
The cross-sectional component of EU-SILC 2024 included the rotational groups of R1, R2, R3 and R4. The rotational group R3 was the new sub-sample added in 2024.
The sample design was one-stage stratification.
Stratification and sub stratification criteria
Geographical stratification criteria were used for the sample selection. The households were stratified in 9 strata based on District (Urban / Rural), i.e. 1) Lefkosia Urban, 2) Lefkosia Rural, 3) Ammochostos Rural(1), 4) Larnaka Urban, 5) Larnaka Rural, 6) Lemesos Urban, 7) Lemesos Rural, 8) Pafos Urban, 9) Pafos Rural.
(1)Ammochostos Urban is an area not under the effective control of the Government of the Republic of Cyprus.
Sample selection schemes
The sample was selected from each stratum with simple random sampling.
Sample distribution over time
The survey for the year 2021 was carried out from the 1st of February 2021 to the 27th of August 2021, while the survey for the year 2022 was carried out from the 11th of April 2022 to the 30th of September 2022. The survey for the year 2023 was carried out from the 20th of March 2023 to the 30th of September 2023 and the survey for the year 2024 was carried out from the 19th of February 2024 to the 30th of September 2024.
Substitutions
No substitution procedures were applied.
Method of selection of substitutes
Not applicable.
Renewal of sample: rotational groups
The year 2005 was the initial year of the survey. The sample in the first round was divided in 4 sub-samples as it was based on a rotational design of 4 replications with a rotation of one replication per year. Each subsample was separately selected so as to represent the whole population. Every year one sub-sample is dropped and substituted by a new one. For 2021 one specific sub-sample, pre-selected from 2017 (R4) was dropped and substituted by a new one (R4). For 2021 the rotational group 1 (R1), was dropped and substituted by a new one (R1). For 2023 the rotational group 2 (R2), was dropped and substituted by a new one (R2). For 2024 the rotational group 3 (R3), was dropped and substituted by a new one (R3).
The size of each Rotational Group for the 2024 survey (longitudinal component) is shown in the table below:
Used addresses and accepted interviews (R4 - R1 - R2)
2021
2022
2023
2024
Used addresses
Accepted interviews
Used addresses
Accepted interviews
Used addresses
Accepted interviews
Used addresses
Accepted interviews
R4
1.800
1.062
1.075
972
1.003
927
941
903
R1
na
na
1.850
1.207
1.212
1.102
1.135
1.035
R2
na
na
na
na
1.650
1.285
1.296
1.144
Total
1.800
1.062
2.925
2.179
3.865
3.314
3.372
3.082
18.1.2. Sampling unit
The sampling units are private households, which were selected with simple random sampling within each stratum.
18.1.3. Sampling frame
The longitudinal component for the years 2021 to 2024, the 4-year trajectory is illustrated in the figure below:
R1
R2
R3
R4
R1
R2
R3
YEAR
2021
2022
2023
2024
The dataset of the longitudinal component consists in total of 4.774 households. These households are broken down into the original households selected in the first wave of 2021 (N=1.800), the follow-up households of 2022 (N=1.040), the split households of 2022 (N=13), the follow-up households of 2023 (N=975), the split households of 2023 (N=15), the follow-up households of 2024 (N=926) and the split households of 2024 (N=5).
The sample results for the longitudinal component of 2021-2024, the 4-year trajectory are shown in the table that follows:
Sample size, addresses and household interviews (R4)
2021
2022
2023
2024
Follow-up Households
Split Households
Follow-up Households
Split Households
Follow-up Households
Split Households
n
%
n
%
n
%
n
%
n
%
n
%
n
%
Addresses in initial sample
1.800
100,0
1.040
100,0
13
100,0
975
100,0
15
100,0
926
100,0
5
100,0
Addresses used for the survey
1.500
83,3
1.040
100,0
13
100,0
975
100,0
15
100,0
926
100,0
5
100,0
Addresses out of scope
300
16,7
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
Addresses used
1.500
100,0
1.040
100,0
13
100,0
975
100,0
15
100,0
926
100,0
5
100,0
Addresses successfully contacted
1.455
97,0
1.040
100,0
13
100,0
975
100,0
15
100,0
926
100,0
5
100,0
Addresses not successfully contacted
45
3,0
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
Addresses successfully contacted
1.455
100,0
1.040
100,0
13
100,0
975
100,0
15
100,0
926
100,0
5
100,0
Household questionnaire completed
1.062
73,0
959
92,2
13
100,0
913
93,6
14
93,3
899
97,1
4
80,0
Refusal to cooperate
317
21,8
65
6,3
0
0,0
53
5,4
1
6,7
24
2,6
1
20,0
Entire household away for the duration of fieldwork
6
0,4
4
0,4
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
Household unable to respond
48
3,3
12
1,2
0
0,0
9
0,9
0
0,0
3
0,3
0
0,0
Other reasons for not completing the Household questionnaire
22
1,5
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
Household questionnaire completed
1.062
100,0
959
100,0
13
100,0
913
100,0
14
100,0
899
100,0
4
100,0
Interviews accepted for database
1.062
100,0
959
100,0
13
100,0
913
100,0
14
100,0
899
100,0
4
100,0
Interviews rejected for database
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
0
0,0
The table below is a breakdown of addresses and persons present in each year of the 4-year longitudinal component:
Households and persons (R4)
2021
2022
2023
2024
Addresses used for the survey
1.500
1.053
990
931
Addresses successfully contacted
1.455
1.053
990
931
Accepted household interviews
1.062
972
927
903
Persons
2.739
2.439
2.306
2.250
Persons 16+
2.260
2.042
1.944
1.904
Personal interviews
2.260
2.042
1.944
1.904
Achieved sample size
The table below presents analytically the accepted personal interviews, as well as the accepted household interviews, within the 4-year longitudinal component.
Sample Size and Accepted Interviews longitudinal component(R4)
R4
2021
2022
2023
2024
Persons 16 years and over
2.260
2.042
1.944
1.904
Sample persons
2.260
1.977
1.829
1.762
Co-residents
0
65
115
142
Number of accepted personal questionnaires
2.260
2.042
1.944
1.904
Accepted household interviews
1.062
972
927
903
The tables below refer to the cross-sectional component of the year 2024.
Sample size and allocation criteria
As the sample is based on a rotational design of 4 replications with a rotation of one replication per year, the selection of one new sub-sample was required. More specifically, for 2024 one sub-sample of 2020 survey was dropped (R3), and a new sub-sample (R3) was separately selected in the same manner as in 2005, so as to represent the whole population. Due to the non-response of 2023 survey and the number of non-existent or not successfully contacted addresses, the initial sample of 2024 survey was 4.978 households. The status of our sample for the 2024 round in each rotational group is as follows:
Total
R1
R2
R3
R4
Status of sample
4.978
1.119
1.278
1.650
931
Population and sample distribution
DISTRICT
N
N
NUMBER OF HOUSEHOLDS 2024
DISTRIBUTION OF THE SAMPLE
TOTAL
URBAN
RURAL
TOTAL
URBAN
RURAL
TOTAL
380.456
258.121
122.335
4.978
3.341
1.637
LEFKOSIA
145.835
110.994
34.841
1.951
1.469
482
AMMOCHOSTOS
22.125
0
22.125
308
0
308
LARNAKA
63.050
37.015
26.035
848
491
357
LEMESOS
107.239
81.252
25.987
1.355
1.032
323
PAFOS
42.207
28.860
13.347
516
349
167
For the data collection 24 interviewers were appointed, 8 for Lefkosia district, 6 for Larnaka/ Ammochostos, 8 for Lemesos and 2 for Pafos. The sampled households were grouped as much as possible in small areas and according to their rotational group. For households in the 1st wave each interviewer had to interview on average 15 households per week, while for households in the 2nd, 3rd or 4th wave they had to interview on average 18 households.
The 2024 sample results are shown in the table below:
Sample
No
Addresses in initial sample
4.978
Addresses used for the survey
4.829
Addresses out of scope
149
Addresses used
4.829
Addresses successfully contacted
4.820
Addresses not successfully contacted
9
Addresses successfully contacted
4.820
Household questionnaire completed
4.301
Refusal to cooperate
403
Entire household away for the duration of fieldwork
7
Household unable to respond
77
Other reasons for not completing the Household questionnaire
32
Household questionnaire completed
4.301
Interviews accepted for database
4.301
Interviews rejected for database
0
The 149 addresses that were out of scope of the survey correspond to vacant accommodation, or buildings used as secondary residences or for business purposes, or demolished housing units. Furthermore, 9 addresses were not successfully contacted. Out of the 4.820 addresses successfully contacted, 4.301 households completed the Household questionnaire and were all accepted for the database and meets the precision requirements set by the Reg. (EU) 2019/1700, Annex II. Thus, the achieved sample size was 4.301 households, 10.733 persons in total and 8.998 persons aged 16 or over. In order to achieve this, the number of households of the new sub-sample selected was 1.650.
Achieved sample size
The table below presents the achieved samples of persons aged 16 years and over, as well as of households, within each rotational group:
Sample Size and Accepted Interviews
Total
R1
R2
R3
R4
Persons 16 years and over
8.998
2.206
2.355
2.553
1.904
Number of accepted personal questionnaires
8.998
2.206
2.355
2.553
1.904
Accepted household interviews
4.301
1.035
1.144
1.219
903
Substitutions
No substitution procedures were applied.
Method of selection of substitutes
Not applicable.
Renewal of sample: rotational groups
The sample in the first round was divided in 4 sub-samples as it was based on a rotational design of 4 replications with a rotation of one replication per year. Each sub-sample was separately selected so as to represent the whole population. Every year one sub-sample is going to be dropped and substituted by a new one. Thus, for 2024 one specific sub-sample, pre-selected from 2020 (R3), was dropped and substituted by a new one (R3). The new sub-sample was also separately selected, so as to represent the whole population.
Total
R1
R2
R3
R4
Addresses in initial sample
4.978
1.119
1.278
1.650
931
Household Questionnaire completed
4.301
1.035
1.144
1.219
903
Interviews Accepted for database
4.301
1.035
1.144
1.219
903
18.2. Frequency of data collection
CYSTAT collects EU-SILC data annually. The table that follows gives an overview of the cumulative sample development during the fieldwork period from the 19h of February 2024 to the 30th of September 2024.
Sample distribution over time
Period
Addresses in initial sample
Addresses out of scope
Addresses used
Addresses not successfully contacted
Non-response
Household Questionnaire Completed
19/02 – 03/03
569
24
545
0
33
512
19/02 – 17/03
1.285
51
1.234
0
77
1157
19/02 – 31/03
1.849
68
1.781
0
109
1672
19/02 – 14/04
2.520
85
2.435
0
162
2273
19/02 – 28/04
3.170
98
3.072
0
206
2866
19/02 – 19/05
3.722
102
3.620
0
234
3386
19/02 – 02/06
4.182
111
4.071
0
273
3798
19/02 – 16/06
4.577
128
4.449
0
350
4099
19/02 – 30/06
4.799
136
4.663
0
409
4254
19/02 – 30/09
4.978
149
4.820
9
519
4301
18.3. Data collection
Mode of data collection
The main modes of data collection for the EU-SILC survey for 2024 were CATI (96,0%) and CAPI (4,0%). Paper Assisted Personal Interviewing (PAPI) was only used in the extreme case of a technical problem with the interviewer’s laptop. CAPI was used only for some of the interviews of the 1st wave, while for the rest of the sample (2nd, 3rd and 4th wave) CATI was used. For the 1st wave, the first conduct with the households was made by home visits where the interviewees had the option to choose between CAPI or CATI (by providing a valid phone number) at a suitable for them time. For the rest of the sample, the phone numbers collected the previous year were used for CATI.
Of all completed personal questionnaires 13.3% were filled with proxy interviews. For these cases, we preferred to have a personal questionnaire filled with a proxy interview rather than a refusal.
The following table shows the results for the mode of collection.
1-PAPI
2-CAPI
3-CATI
4-Self-administered
5-CAWI
6- PAPI-proxy
7-CAPI-proxy
8-CATI-proxy
9- Self-administered-proxy
10-CAWI proxy
% of total
0,0
3,6
83,1
0,0
0,0
0,0
0,4
12,9
0,0
0,0
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 on income variables were mainly collected by Computer Assisted Telephone Interviewing (CATI) and Computer Assisted Personal Interviewing (CAPI). Each and every income component was separately collected. Additionally, income data from registers were used. More specifically registers were used for income from pensions and social benefits. In addition, registers were used for the income of employees in the Civil Service.
The instructions to the interviewers were to collect each income component as gross and to record separately taxes on income at source and social insurance contributions. In the very few cases where gross income was impossible to collect, net income was recorded.
In the cases where gross income or taxes on income at source or social insurance contributions were impossible to collect, at least net value was collected for the specific income component. It was then converted to gross by applying the existing tax system and social insurance contributions rules.
Please see Annex - CY_2024_Annex 4-Data_collection
18.4. Data validation
Please see point 13.3.4.
18.5. Data compilation
Please find below a description of the weighting and imputation procedures.
18.5.1. Imputation - rate
Imputation is the process used to assign replacement values for missing, invalid or inconsistent data that have failed edits. This includes automatic and manual imputations; it excludes follow-up with respondents and the corresponding corrections (if applicable). The unweighted imputation rate for a variable is the ratio of the number of imputed values to the total number of values requested for the variable.
This metadata concept complements the information provided in the point 18.5 and 13.3.4.
Please see Annex - Imputation and estimation, 2024
18.5.2. Calculation of weighting factors and weight adjustments
Please see Annex – Weighting, C & L data, 2024
18.5.3. Estimation and imputation
Please see Annex - Imputation and estimation, 2024
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
For information on the quality of the rolling module, please see Annex 9 - Rolling module.
Please find in the annexes the Cyprus 2024 EU-SILC questionnaire.
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).
Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
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.
Government-controlled areas of the Republic of Cyprus.
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 period for taxes payments/refunds and social insurance contributions was 2023. Tax refunds received during 2023 referred to income received in previous years.
For EU-SILC 2024 the income reference period was 2023.
The reference period for taxes on wealth was 2023.
Since EU-SILC 2024 was carried out the period February to September 2024, the time lag between the income reference period and current variables varied between 2 to 9 months
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.
Please find below a description of the weighting and imputation procedures.
Sampling frame and coverage errors
The sampling frame used for the 2024 survey was the list of households from the 2021 Census of Population. The reference date for the Census was October 1, 2021. Consequently, the only housing units not included in the sampling frame were those built after that date. As we move further away from 2021, more coverage errors are encountered. Therefore, the 2024 survey experienced more coverage errors compared to 2023, although still significantly fewer than in the previous waves conducted using the 2021 Census.
Moreover, the data was not only collected through household interviews but also supplemented by registers for several income components. These registers included pensions (of all types) provided by the Social Insurance Services and the Public Sector, the Guaranteed Minimum Income (GMI), other social and disability benefits, the student grant, the student's package, and the income of Public Sector employees.
Distribution of all household members by data status - RB250
RB250 - Data status
2024
Total
%
information completed only from interview (11)
2.780
30,9
information completed from both interview and registers (13)
6.215
69,1
information completed from full record imputation (14)
3
0,0
refusal to co-operate (23)
0
0,0
person temporarily away and no proxy possible (31)
0
0,0
no contact for other reasons (32)
0
0,0
information not completed: reason unknown (33)
0
0,0
Total
8.998
100,0
Annual
For the EU-SILC 2024 survey, the final microdata files were transmitted on the 28th of February 2025, i.e. 2 months after the end of the reference year.
Cyprus is classified as one region at the Nuts 2 level
In the following tables, we compare the results on income components between EU-SILC 2021, EU-SILC 2022, EU-SILC 2023, and EU-SILC 2024 at both the household and personal levels. More specifically, the percentage of households and persons having received an amount on specific income target variables, as well as their mean value per household, are presented in the two tables that follow.
The financial years 2022 and 2023 (the income reference years of the 2023 and 2024 surveys) exhibited a GDP positive growth rate of 14,5% and 6,5%, respectively. As a result, the social and income indicators were expected to improve.
The gradual phasing out, and the final termination at the end of October 2021, of the ad-hoc social benefits introduced by the government in 2020 to alleviate the consequences of the pandemic. had primarily affected the following variables: HY050G - Family/children related allowances, PY090G - Unemployment benefits, and PY120G - Sickness benefits. Furthermore, they significantly impacted variable PY010G - Employee cash or near-cash income, as most of these measures were implemented to support workforce retention.
In addition, mortgage interest rates increased in 2023, explaining the increase in the mean HY100G of EU-SILC 2024. Moreover, due to the overall rise in rents, income from renting a property or land (HY040G) increased.
The increase in the mean of HY140G observed for the 2024 survey can be explained by the general increase in employee income (PY010G).
Comparison between EU-SILC 2021, 2022, 2023 and 2024 for all income target variables at household level – EU SILC
Income target variable
2021
2022
2023
2024
% of households having received an amount
Mean (weighted) income per household (EURO)
% of households having received an amount
Mean (weighted) income per household (EURO)
% of households having received an amount
Mean (weighted) income per household (EURO)
% of households having received an amount
Mean (weighted) income per household (EURO)
Total household gross income HY010
100
40.804
100
42.755
100
45.390
100
48.567
Total disposable household income HY020
100
34.227
100
35.699
100
38.147
100
40.834
Total disposable household income before social transfers other than old-age and survivor's benefits HY022
99,3
31.726
99,5
33.760
99,6
35.716
99,5
38.654
Total disposable household income before social transfers including old-age and survivor's benefits HY023
99,1
24.575
93,6
27.721
93,6
29.184
93,6
31.671
Imputed rent HY030G
na
na
na
na
87,6
7.008
na
na
Income from rental of a property or land HY040G
8,6
645
8,1
665
7,8
753
7,6
892
Family/children related allowances HY050G
25,5
632
24,4
514
23,8
530
23,1
579
Social exclusion not elsewhere classified HY060G
1,9
149
1,9
122
1,8
125
1,6
131
Housing allowances HY070G
5,8
89
5
83
5,2
82
5
78
Regular inter-household cash transfer received HY080G
16,1
773
16
786
14,9
737
15,4
750
Interest, dividends, profit from capital investment in unincorporated business HY090G
10,5
259
5,5
253
3,3
274
3,5
662
Interest repayments on mortgage HY100G
11,9
257
12,1
280
12,4
335
12,7
447
Regular taxes on wealth HY120G
65,9
58
66,7
59
66,4
61
66,9
68
Regular inter household cash transfer paid HY130G
17,9
709
16,1
650
15
565
14,6
690
Tax on income and social contributions HY140G
94,9
5.810
95
6.347
94,8
6.618
95,2
6.975
Value of goods produced for own consumption HY170G
6,8
14
6,5
13
6,8
14
6,8
15
Comparison between EU-SILC 2021, 2022, 2023 and 2024 for all income target variables at individual level - EU-SILC
Income
2021
2022
2023
2024
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
% of persons 16+ having received an amount
Mean (weighted) income per household (EURO)
Employee cash or near cash income PY010G
51,1
26.498
51,2
28.817
52,2
29.949
53,2
32.090
Non-cash employee income PY020G
8,5
174
7,2
146
7,7
157
9,1
183
Company car PY021G
0,5
20
0,4
18
0,6
19
0,6
28
Employer´s social insurance contribution PY030G
49,2
4.307
49,6
4.675
50,9
4.940
52
5.267
Cash benefits or losses from self-employment PY050G
10,9
2.845
10,4
3.128
9,5
3.550
9,5
3.794
Unemployment benefits PY090G
4,7
817
3,3
466
4,2
945
3,7
665
Old-age benefits PY100G
27
6.242
27,5
6.072
28,3
6.526
28,7
6.981
Survivor benefits PY110G
5,2
909
5,3
987
5,9
1.060
6
1.123
Sickness benefits PY120G
5,6
168
8,3
164
11,4
166
6,7
174
Disability benefits PY130G
2,2
477
2,2
399
2,2
402
2
419
Education-related allowances PY140G
2,7
169
2,7
195
2,6
183
2,2
137
Please see Annex – CY_2024_Annex 8-Breaks in series
