1.1. Contact organisation
National Institute of Statistics
1.2. Contact organisation unit
Department of Household Surveys
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
16, Libertatii Ave., Sector 5, Bucharest, Romania
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
31 May 2023
2.2. Metadata last posted
31 May 2023
2.3. Metadata last update
28 March 2025
3.1. Data description
The European Union Statistics on Income and Living Conditions (EU-SILC) is a survey-based instrument aiming at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty, social exclusion and living conditions. In addition, it collects module variables every three years, six years or ad-hoc new policy needs modules.
The EU-SILC instrument provides two types of data:
- Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions;
- Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700). Social exclusion and housing condition information is collected mainly at household level while labour, education and health information is obtained for persons aged 16 and over. The core of the instrument is income information at very detailed component level and mainly collected at personal level.
EU-SILC is part of the European statistical system, and in Romania it has been implemented since 2007 and is part of the national statistical system called the Quality of Life Survey (Ancheta asupra calitatii vietii). The Quality of Life Survey is an annual survey. Since 2021 CAPI method (Computer Assisted Personal Interview) is used, which consists in performing interviews with respondents with the use of mobile devices (tablets) that record the provided answers. The information concerning the household as a whole is recorded in the household section of the questionnaire, while the information about the household members at the age of 16 years and more, in the individual section of the questionnaire. The questions include all the mandatory variables from the DocSILC065 (Eurostat guidelines). The tables contain the results for households and persons by main characterstics: socio-economic groups of the population and households in total, macro-regions (NUTS 1), regions (NUTS 2). Additional profiles for persons are: - age; - sex; - level of education.
3.2. Classification system
- International Standard Classification of Education (ISCED'2011);
- International Standard Classification of Occupations (ISCO-08);
- Classification of Economic Activities (NACE Rev.2-2008);
- Common classification of territorial units for statistics (NUTS 2);
- SCL - Geographical code list;
- The recommendations made by the United Nations in the Canberra Group Handbook on Household Income Statistics should also be taken into account.
‘For more details on the classification used please, see EU Vocabularies, Eurostat's metadata server or CIRCABC’.
3.3. Coverage - sector
Data refer to all private households and individuals living in the private households in the national territory at the time of data collection.
The EU-SILC survey is a key instrument for the European Semester and the European Pillar of Social Rights, providing information on income distribution, poverty and social exclusion, as well as various related living conditions and poverty EU policies, such as on child poverty, access to health care and other services, housing, over indebtedness and quality of life. It is also the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.
In accordance with Eurostat requirements, in 2024 there were the following rotational modules: Children (3-year rolling module) and Access to services (6-year rolling module). According to the legislation in force, the survey should collect the data allowing for both the cross-sectional and longitudinal analyses. That is why EU-SILC is carried out with the use of the rotational panel method in the four-year cycle.
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.
Income
The total disposable income of a household is calculated by adding together the personal income received by all of household members plus income received at household level. Missing income information is imputed.
Disposable household income includes:
- all income from work (employee wages and self-employment earnings)
- private income from investment and property
- transfers between households
- all social transfers received in cash, including old-age pensions
Note: Some of the income components are mandatory only from 2007: Imputed rent, Interest paid on mortgage, Employer's social insurance contributions. From the 2007 year on, all countries have to supply gross income information. From 2021 onwards, imputed rent is collected every 3 years as part of the rolling module on ‘Labour and housing’; and all countries have to supply gross and net income information.
Equivalence scale
The total disposable household income is "equivalised" to take into account the impact of differences in household size and composition. The equivalised income attributed to each member of the household is calculated by dividing the total disposable income of the household by equivalisation factors, which can be determined in various ways. Eurostat applies the OECD modified scale, which gives a weight of 1.0 to the first person aged 14 or more, a weight of 0.5 to other persons aged 14 or more and a weight of 0.3 to persons aged 0-13.
Household
A private household’ means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
3.5. 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.
The Quality of Life Survey is carried out throughout the country and only private households are interviewed. The survey did not cover collective accommodation households (such as boarding house, workers’ hostel, pensioners’ house or monastery).
3.6. Statistical population
The target population is private households and all Romanian or foreign persons composing these households having their usual residence in Romania. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
EU-SILC covers only people living in private households (all persons aged 16 and over within the household are eligible for the operation), i.e. persons living in collective households and in institutions are generally excluded from the target population.
SILC covers all Romanian or foreign citizens who have their usual residence in Romania, members of the households selected in the survey sample. The subject of the survey, according to the purpose of the SILC and European regulations, are people aged 16 and over.
3.6.1. Reference population
Definitions of reference population, household and household membership
| Reference population |
Private household definition |
Household membership |
|---|---|---|
| The Quality of Life Survey is carried out throughout the country and only individual households are interviewed. The reference population is the population residing in private households. Thus, the survey covered persons with usual residence in Romania, for a period of at least 12 months, members of the households from the selected dwellings. All the provisions related to the inclusion among the household members of certain categories of people were applied according to Regulation 2181/2019. |
Household means the group of two or several persons who are usually residing together, providing |
A person will be considered a usually resident member of the household if they spend most of their daily night-rest there, evaluated over the past 12-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.
The survey does not include institutionalized persons (in homes for the elderly, collective housing), persons who have permanent residence (domicile) in Romania but who have their usual residence abroad.
3.7. Reference area
The survey includes all residents of Romania, members of the households in the research centers (urban and rural) selected from all the counties of the country and from the Municipality of Bucharest.
3.8. Coverage - Time
Annual
Every 3 years rolling modules
Every 6 years rolling modules
In Romania, EU-SILC was implemented from 2007 until now.
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 |
|---|---|---|---|
| January 2023 - December 2023 |
January 2023 - December 2023 |
January 2023 - December 2023 |
The fieldwork period (15 March - 15 April 2024). Therefore, the lag is 3 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.
Law no 226/2009 on the organisation and functioning of official statistics in Romania.
6.2. Institutional Mandate - data sharing
Confidential microdata are not disclosed by INS and 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
Law no 226/2009 on the organisation and functioning of official statistics in Romania, stipulates in Chapter X the confidentiality of statistical data. In this chapter art. 36 refer to Confidentiality of official statistical data and art.37 refer to Protection of statistical data.
Law on the organisation and functioning of official statistics in Romania is available at the INSSE website.
The EU member states, including Romania, apply the provisions of the Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation /GDPR) in the production of official statistics.
In this regard, the staff of National Institute of Statistics (INS), Territorial Directorates of Statistics (DTS) and the staff employed with a contract for the provision of data collection services sign Confidentiality Commitments and are well trained in the observance of the GDPR.
The staff of the National Institute of Statistics, including the people working in the territorial statistical directorates, as well as the persons temporarily involved in the collection (statistical operators) and processing of individual data, shall be under the obligation to observe the confidentiality of these data, during the activity and after the activity is completed. The previously mentioned staff shall not be allowed to use the individual data obtained in the activities that are specific to official statistics for personal purposes or for performing activities to the benefit of a third party.
Also, the INS is fully certified to ensure compliance with the IT security framework of the SSE.
7.2. Confidentiality - data treatment
Ensuring data confidentiality starts with the collection until the dissemination.
All the questionnaires used by INS to collect data have clear statement that the information provided by the respondents are confidential and used only for statistical purposes. For the questionnaires that apply in the households, INS have clear information regarding the confidentiality, provided in the letters addressed to them.
Statistical operators collect SILC data using tablets with a security system and secure access based on a strong user and password, they are not allowed to give the tablet to anyone. Questionnaires for which the interview has been completed, correct and complete, are sent immediately after completion or at least once a day. The electronic questionnaires are sent to the INS via a secure line. All data collected on electronic tables are encrypted during the transmission process.Once submitted, the questionnaires disappear from the tablets.
A limited number of civil servants from INS and DTS have access to the SILC database.
The INS servers are managed by the IT department and are in secure rooms, with limited access, based on an access code assigned only to certain IT experts.
Procedures are implemented for all the activities carried out by the INS, and they are applied by all the staff of the INS and the Territorial Directorates of Statistics.
Statistical data processing (from the data entry to their publication) was done by the INS and Territorial Statistical Directorates staff.
Dissemination of the statistical data is made in compliance with the norms statistical data confidentiality. The data are published in aggregate forms so as to comply with the provisions of confidentiality.
Anonymized microdata are provided only for the purpose of scientific research and for European statistics according to the European Regulations in force.
The microdata access for scientific purposes is strictly regulated and the steps to be followed are published on the INS website.
8.1. Release calendar
INS is responsible with the dissemination of data from its own annual data collection: press releases (30 June 2025 for reference year N-1), publication "CONDITIILE DE VIATA ALE POPULATIEI DIN ROMANIA" (29 August 2025 for reference year N-1) and "DIMENSIUNI ALE INCLUZIUNII SOCIALE IN ROMANIA" (31 October 2025 for reference year N-1) ), online database (mid-November for reference year N-1).
8.2. Release calendar access
Please refer to the Release calendar - Eurostat (europa.eu) publicly available on the Eurostat’s website.
At the national level, more information about the released calendar can be found here:
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 the national level, according to the official statistics law, no. 226/2009, the dissemination of statistical data respects the principle of impartiality according to which statistics must be developed, elaborated and disseminated in a neutral way, and all users must benefit from equal treatment.
Annual
10.1. Dissemination format - News release
Not available.
10.2. Dissemination format - Publications
Annual publication “Conditiile de viata ale populatiei din Romania in 2024” in August 2025. For last year the publication is available online.
Annual publication “Dimensiuni ale incluziunii sociale in Romania in 2024” in October 2025. For last year the publication is available online.
10.3. Dissemination format - online database
The disseminated data can be accessed free in database of NIS Romania TEMPO online.
10.3.1. Data tables - consultations
TEMPO Database can be found on the website of NIS Romania.
10.4. Dissemination format - microdata access
Microdata are available for scientific purposes via Safe Center for acces to microdata
10.5. Dissemination format - other
Not available
10.5.1. Metadata - consultations
Metadata consultation can be found on the NSE website.
10.6. Documentation on methodology
Metadatabase can be found on the NIS Romania website.
10.6.1. Metadata completeness - rate
100 %
10.7. Quality management - documentation
No other documentation is available.
11.1. Quality assurance
As the coordinator of the Romanian National Statistical System, NIS has the mission of a satisfies to the greatest extent the need for information for all categories of users of statistical data by collecting, producing and disseminating relevant, accurate statistical data, coherent, timely and accessible, necessary for making decisions regarding economic development and social aspect of the country and regarding the realities of Romanian society. In the last time, INS has made considerable progress in the direction of quality management total and ensuring a culture of quality within the organization. The target in the following years is to obtain methodological and operational performances at levels comparable to the most advanced national statistical institutes from EU Member States. NIS define the quality of the result in accordance with the SSE Quality Definition. In more terms broad, the quality of NIS results is evaluated in terms of "fitness for use". More precisely, the quality of the result is measured in terms of six quality components: relevance, accuracy, timeliness and punctuality, clarity and accessibility, coherence and comparability.
The main aspects regarding quality assurance was the analysis of European and national legislation and the study of the Eurostat methodology for EU-SILC in order to comply with it.
During the design stage of statistical tools, we considered the following issues for designing the questionnaires: the topics included in the questionnaires are have to be compliant with Regulation no. 1700/2019 of the European Parliament and of the Council and the Eurostat methodology ensures data comparability at European level; questions are easy to understand and their sequence is following a logical flow; coverage of all possible answers (there are no answers that cannot be encoded). When designing the questionnaires, it was taken into account that they should be easy to fill out by the interviewer. When developing the survey manual all measures to ensure the quality terms for performing the survey were taken into account.
When developing the survey manual all measures to ensure the quality terms for performing the survey were taken into account.
During the data collection stage, for quality assurance, a thoroughly training of the interviewers and transmission to the participating population of an Information letter regarding the survey organization by the INS were taken into account.
In the data collection phase, the survey responsible in each Territorial Statistical Directorate had, among its attributions, to check the field activities performed by interviewers and survey controller, to replace the interviewers in case deviations from the survey rules were found or if they were not able to continue the data collection due to health problems. Also, to ensure the quality of collected data, the survey responsible was permanently in touch with the INS survey responsible asking, if needed, additional methodological specifications for the specific situations encountered in the field. In case of particular situations encountered during the data collection, INS team provided solutions that were sent simultaneously to all Territorial Statistical Directorates, for the attention of the EU-SILC Survey responsibles.
In order to ensure a good data collection, during the data collection phase, the survey controllers randomly checked the interviewing in households and, at the end of the data collection, they received the questionnaires from interviewers and checked together with them the number of questionnaires (integrity of materials received) and the way the answers in the questionnaires were encoded.
During the data checking, processing and validation process the following were considered:
- checking the integrity of the volume of completed questionnaires and those for which the data was not collected and that form of the full volume of the sample;
- encoding the variables for which classifications are used;
- implementing an IT application to allow data checking at local and central level, in successive stages.
11.2. Quality management - assessment
NIS implement the quality management system based on the approach and elaboration of procedures and mechanisms in accordance with the EFQM/CAF excellence model, for evaluation continues to improve the quality of the organizational system. NIS identify, in a systematic and regular way, the strengths and weaknesses specific to the fields statistics and takes actions to improve and expand the implementation of effective solutions, respectively for the removal of deficiencies, where applicable. Good domestic and other countries' practices will be a valuable source for improvement total quality management in NIS.
While performing the Survey on Income and Living Condition (EU-SILC), the INS respected the following quality criteria:
- The National Institute of Statistics established, on a scientific basis, impartially and independently: variables, indicators, nomenclatures, classifications, methodologies, techniques recording, processing and dissemination of statistical data resulting from EU-SILC;
- For EU-SILC, the most relevant variables were selectedto allow the production and dissemination of statistical data that meet the needs of national and international users;
- Adopting and ensuring, throughout the EU-SILC survey, the measures for the protection of individual dataand their use for statistical purposes, only;
- Applying data verification methods and procedures so that statistical results reflect as accurately as possible the phenomena related to the assessment of the living conditions of the population by highlighting the interconnection and complementarity of various aspects such as: health status, education, occupational status, income, living conditions, economic situation of households;
- Reducing the statistical process as much as possible and publishing the results according to a calendar established at national level. However, due to the small number of staffs, the data dissemination process will be slightly delayed compared to the initial deadline;
- EU-SILC was made transparent, the main characteristics of statistical research and dissemination deadlines being presented in the Annual National Statistical Program approved by a Government Decision, the survey methodology was approved by the Methodological Advisory Committee consisting of: statisticians, academic experts, experts from research institutes, from ministries and public health institutes.
- Users will have access to the results of the EU-SILC in conditions of equality, simultaneity and without any discrimination,and the results will be accompanied by metadatain which the main characteristics of the survey will be presented (European legislation based on EU-SILC, survey objectives, target population, data collection methods, sampling techniques and extension of applied results etc.).
- The EU-SILC implementation process ensures obtaining consistent and coherent data;
- EU-SILC was made in the conditions of optimal use of resources, to reduce costs taking into account matters such as:
- the establishment of a optimal number of variables in accordance with the needs of national and international information and the elimination of less important variables;
- the use of CAPI questionnaires for all research centres where we had interviewers who had competence in the use of tablets;
payment of interviewers according to the data collected and in a differentiated way for the questionnaires completed in full or in part.
12.1. Relevance - User Needs
The main users of EU-SILC statistical data are policy makers, research institutes, media, and students.
NIS collect, processes and disseminates official statistics to meet users' needs, both in terms of volume and in terms of quality and compliance with broadcasting deadlines, in a way objective, professional and transparent, through which all users are treated fairly and non-discriminatory.
NIS disseminate data and statistical information in an accessible format - agreed by users - and in an appropriate manner. The involvement of mass media ensures easy access to data and information statistics and contribute to the formation of a statistical culture of data users. Opportunities will be created for the intensification of collaboration/consultation with the different categories of users, for the knowledge and satisfaction of their ever increasing and diversified needs.
The main users of EU-SILC statistical data are the following:
- Institutional users like DG EMPL of the Commission and the Social Protection Committee, in charge of the monitoring of social protection and social inclusion, or other Commission services, DG SANTE, DG ENER, DG JRC, DG ECFIN, DG JUST, DG HOME, DG ENV, DG REGIO and other institutional users;
- Statistical users in Eurostat or in Romania to feed sectorial or transversal publications;
- Researchers having access to microdata;
- End users - including the media - interested in living conditions
- Ministry of Labour, Family, Youth and Social Solidarity
12.2. Relevance - User Satisfaction
At national level, the current and future requirements and needs of users will always guide the statistical activities of the INS. The level of user satisfaction is regularly monitored through satisfaction surveys a user’s and through other specific means. A very high proportion (77%) of statistical data users value quality INS products as being at the level of statistics produced at European level. The results of the last user satisfaction survey are available on the INS website (only in Romanian).
However, the satisfaction survey did not specifically target the SILC survey, but the general fields of INS statistics. INS will continue the process of informing users about the quality of statistical products, through elaboration of metadata on the meaning of the indicators it produces and quality reports in European format or user-oriented, for all statistical domains. Over time it has been observed that the SILC has of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use.
At European level, Eurostat carried out an online general User Satisfaction Survey (USS) in the period between April and July 2019 to obtain a better knowledge about users, considering their needs and satisfaction with the services provided by Eurostat.
The survey has shown that EU-SILC is of very high relevance for users. For the majority, both aggregates and micro-data were important or essential in their work irrespective of the purpose of their use.
The use of the ad-hoc modules was less.
Users emphasized their strong need for more detailed micro-data, which is currently not possible. Under the new legal framework implemented from 2021, the NUTS 2 division will be
available for the main indicators.
For more information, please consult the User Satisfaction Survey.
In conclusion, users were satisfied with overall quality of the service delivered by Eurostat.
12.3. Completeness
Romania did not collect the following optional variables:
- RL080: Remote education
- HI130G: Interest expenses
- HI140G: Household debts.
12.3.1. Data completeness - rate
All required variables were transmited.
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.
The INS has analyzed and identified several actions that it will implement in the coming years to get closer to the precision of the European regulation. Among these we can mention, the revision of the periodic national legislation regarding the incomes of the population (Fiscal Code, laws, ordinances of the Government); the inclusion of additional validation rules; a better promotion of data collection for household surveys at the local level to increase the response rate; gradual increase of SILC subsamples.
Further information is provided in section 13.2 Sampling error.
13.2. Sampling error
EU-SILC is a complex survey involving different sampling designs in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation.
Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another.
The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries, we have used different variables to specify strata and cluster information.
In particular, countries have been split into 3 groups:
- BE, BG, CZ, IE, EL, ES, FR, HR, IT, LV, HU, PL, PT, RO, SI, UK and AL, whose sampling design could be assimilated to a two-stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;
- DK, DE, EE, CY, LT, LU, NL, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;
- MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata.
At the national level, the variance estimation is performed using Taylor linearization method implemented in Regenesses package in R, by considering the characteristics of the sample design.
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.
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: 6.06%
- Under-coverage: refers to units not included in the sampling frame: 6.25%
- Misclassification: refers to incorrect classification of units that belong to the target population
13.3.1.1. Over-coverage - rate
Coverage error
| Main problems |
Population (sub-population) |
Size of error |
Comments |
|---|---|---|---|
| Over-coverage |
|
6.06 |
|
| Under-coverage |
607396 |
6.25 |
|
| Misclassification |
|
|
|
13.3.1.2. Common units - proportion
Not applicable
13.3.2. Measurement error
Measurement error for cross-sectional data
Cross-sectional data
| Source of measurement errors |
Building process of questionnaire |
Interview training |
Quality control |
|---|---|---|---|
| As in any other survey, there are 3 main sources of measurement errors:
|
We used three types of questionnaires:
The questionnaires were up-dated with the improvements based on the 2023 survey conclusions, 3 year rolling module in Children, 6 year rolling module on Access to services. The structure of questionnaires was the following: The household file included:
The household questionnaire included:
The individual questionnaire:
|
The main challenge for the interviewers was recording the exact incomes for each month separately. In this regard, a handbook was prepared with all available information to assist interviewers in their field activities. Explanations for all questions from all questionnaires were included. A special section included some recommendations regarding behaviour in the presence of respondents and how interviewers should persuade the population to participate in this survey. |
For respondents, the most difficult information to declare was the value of incomes in the previous calendar year, the social insurance contribution and the taxes on wealth. Another difficult answer was related to the housing cost, also the question was preceding by a helping question in which they were asked what kind of housing cost that household is actually paying, in order to be sure the respondent is thinking at the elements of the housing cost are recommended by EU-SILC methodology to be included here. Another aspect which created some problems was the co-relation between the declaration of the marital status/consensual union between partners. There were cases in which one partner declared he is married and his/her partner declared he is in consensual union. These case were solved by taking with priority the idea of a consensual union in the case the partners have not the same family name. Some households found difficult to estimate the rent they would receive if they would rent the dwelling. |
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 |
| 99.90 |
99.78 |
100.0 |
94.02 |
88.04 |
98.72 |
99.89 |
99.91 |
99.77 |
6.07 |
12.16 |
1.28 |
0.11 |
0.09 |
0.23 |
6.18 |
12.23 |
1.51 |
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.
13.3.3.2. Item non-response - rate
The computation of item non-response is essential to fulfil the precision requirements. Item non-response rate is provided for the main income variables both at household and personal level.
Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.
13.3.3.2.1. Item non-response rate by indicator
See Annex RO_2023_Annex 2-Item_non_response_13.3.3.2.1
13.3.4. Processing error
Description of data entry, coding controls and the editing system
| Data entry and coding (if any used) |
Editing controls |
|---|---|
| Data collection monitoring: In the data collection phase, the survey responsible in each Territorial Statistical Directorate had, among its attributions, to check the field activities performed by interviewers and the survey controller, to replace the interviewers in case deviations from the survey rules were found or if they were not able to continue the data collection due to health problems. Also, to ensure the quality of collected data, the survey responsible was permanently in touch with the INS survey responsible asking, if needed, additional methodological specifications for the specific situations encountered in the field. In case of particular situations encountered during the data collection, the INS team provided solutions that were sent simultaneously to all Territorial Statistical Directorates, for the attention of the EU-SILC Survey responsible. Additional clarifications were included directly on the questionnaire for some questions to ensure, once again, that respondents are thoroughly informed about all sources of income they need to specify. This eliminates the possibility that the indicators included in the AROPE component are affected by underreporting errors In order to ensure a good data collection, during the data collection phase, the survey controllers randomly checked the interviewing in households. During the data processing and validation phases the following were considered: •checking the integrity of the completed questionnaires corpus and the number of uncompleted questionnaires, to assure that together they form the full survey sample; •checking the questionnaires and approving the questionnaires without errors; •database checking at central level, creation and calculation of additional variables (for example total income) and generating tables with results using a specific ACAV IT application developed by INS experts, while respecting data confidentiality standards; •creation of the anonymized data files for the Eurostat and their verification schema (longitudinal component and cross-sectional component for the 4 sub-samples). |
The first verification of the data is done by the experts from the SILC team of the DTS (they have the role of supervisors). Questionnaires with errors are rejected by the field operator, who must correct the questionnaire and, if necessary, contact the household again. Three colleagues from INS Romania, holding headquarters roles, monitored data collection and validated the questionnaires, each being responsible for specific research centers (PSUs). In cases where the pace of data collection was low, contact was made with the DTS (Directorates of the Territorial Statistical Offices) responsible, who then reached out to the interviewers to identify the issue. Questionnaires containing errors were rejected, and for those with multiple errors, attention was drawn at the research center level to address the issues, and the interviewers were contacted. One week before the end of the data collection period, we contacted the headquarters of the Territorial Directorates of Statistics where there were centers that still had to send a larger number of questionnaires to ensure that the data collection was proceeding as planned. Monitoring indicators were introduced at the DTS level, i.e. monitoring indicator for EU-SILC being the sample attrition rate, in addition to the non-response rate, which was already being monitored. |
13.3.5. Model assumption error
See Annex RO_2023_Annex 2-Item_non_response_13.3.3.2.1
14.1. Timeliness
SILC cross-sectional and longitudinal data are available in the form of tables 15 months after the end of data collection.
14.1.1. Time lag - first result
No provisional data
14.1.2. Time lag - final result
First result was published in August.
14.2. Punctuality
The microdata file was transmitted to Eurostat within the term established by Commission Implementing Decision (EU) 2020/2050 granting derogations to certain Member States from the application of Regulation (EU) 2019/1700 of the European Parliament and of the Council establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples.
14.2.1. Punctuality - delivery and publication
There was not a delay between the first data delivery date and the final deadline in the legislation.
The final data were validated in March.
15.1. Comparability - geographical
The SILC survey results are comparable both at national level (NUTS 0) and European level, the survey being designed and developed according to Eurostat methodology for the EU-SILC.
There are no problems of comparability between the regions of the country.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
The length of comparable time series is from 2007, no any series breaks occurred.
15.2.1. Length of comparable time series
2007 - 2024
15.2.2. Comparability and deviation from definition for each income variable
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 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.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
See Annex RO_2023_Annex 7-Coherence_15.3-15.3.2
15.4. Coherence - internal
Not applicable.
Mean (average) interview duration per household = 24 minutes.
Mean (average) interview duration per person = 23 minutes.
Mean (average) interview duration for selected respondents (if applicable) = not applicable
17.1. Data revision - policy
INS applies the Revision Policy based on the European Statistics
Code of Practice and complying with the ESS Guidelines on the Revision Policy of the Principal European Economic Indicators in force.
Statistical data are revised according to the INS Revision Calendar. The revision policy is available on the INS website.
17.2. Data revision - practice
The revised results are promptly transmitted and published in an open and transparent manner, mentioning the reasons for their occurrence.
In the case of unplanned revisions, a justification message is drawn up for users and the amplitude of the error is mentioned
INS inform the statistical data users, in due time, on the significant methodological changes, so that the interpretation of statistical data resulting as consequence of applying the newly implemented methodologies would not entail a false perception of the presented phenomenon.
17.2.1. Data revision - average size
Not the case.
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
Starting with 2015, the household surveys carried out by NSI-Romania are based on the use of Multifunctional Sample of Territorial Areas, so called the master sample EMZOT. It is a database including approximately 1.500.000 dwellings, selected according to probabilistic criteria, serving as sampling frame for all household surveys, in 2015-2024.
For the wave 1, wave 2, wave3 and wave4 (subsample selected in 2021, 2022, 2023 and 2024), a master sample database named “EMZOT” is used. In the first stage, a stratified random sample of 792 areas, Primary Sampling Units (PSUs), was designed after the 2011 Population and Dwelling Census. The PSUs were sampled with probability proportional to the size (number of permanent dwellings). The EMZOT sample has 450 PSUs selected from urban area and 342 PSUs selected from rural area. In the second stage, a fix number of dwellings are systematically selected from each PSU of EMZOT.
18.1.1. Sampling Design
The sampling plan is a two-stage probability sampling of housing units (dwellings).
Stratification concerns only the first stage sampling. There are 88 strata, the criteria used being the area where a certain PSU is located (urban or rural area) and county (NUTS 3 level).
The survey uses the integrated four years rotational panel design, in which one-fourth of the sample is replaced each year. The total sample for the year 2024 is made by the sub-samples S1, S2, S3 and S4.
The sample is not distributed over time.
18.1.2. Sampling unit
The Primary Sampling Unit, corresponding to the selection of the master sample, is a group of Census sections (census enumeration areas EAs).
The Secondary (ultimate) Sampling Unit, corresponding to the selection of the survey sample, is the dwelling.
18.1.3. Sampling frame
Concerning the SILC instrument, three different sample size definitions can be applied:
- the actual sample size which is the number of sampling units selected in the sample
- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview
- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator
Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.
18.2. Frequency of data collection
Frequency of data collection is annually.
18.3. Data collection
Mode of data collection
|
|
1-PAPI |
2-CAPI |
3-CATI |
4-CAWI |
5-PAPI proxy |
6-CAPI-proxy |
7-CATI-proxy |
8-CAWI proxy |
9-other |
|---|---|---|---|---|---|---|---|---|---|
| % of total |
|
100% |
|
|
|
|
|
|
|
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 |
|---|---|---|
| The source for the collection of income variables was CAPI interviews for all income variables, including the money drawn out of business by the self-employed. We did not used administrative records. The use of the justificative documents regarding the incomes was the respondents’ decision. |
The majority of income components were recorded net and the gross variables were obtained by adding at the net values, the value of income tax retained at source and social contributions paid (in the case of wages, we add the value of other sums retained at source, too). |
The only income components calculated in the process of data editing were:
|
18.4. Data validation
The Survey Solution system is used for data collection on CAPI questionnaire. The data processing flow is as follows:
At the level of the statistical operator:
- the primary validation of the data is performed when completing the questionnaire on the tablet due to the logical control conditions that ensure: checking the logical flows from the questionnaires, observing the correlations between the answers to different questions, identifying and eliminating illogical answers;
- corrections are made or explanations are given in case of questionnaires with errors / warnings;
At the level of survey's supervisor (regional / county statistical directions):
- the mode of completing the questionnaires is verified (it is rejected the questionnaires to the interviewer or it is validated);
- the variables are coded from the required format (text);
- integrity check is performed and data is validated.
At the central level:
- the data are validated and it is verified the conformity with the sample of Quality of Life survey; following this inventory it is established whether the sample has been fully investigated, whether there are cases of response or non-response;
- the data processing procedure continues with the verification of the variables from a qualitative and quantitative point of view. Is calculated the frequencies of the variables and checks whether the target population has answered the questions.
18.5. Data compilation
The Romanian NIS specific sampling design for household surveys is a two-stage one.
The sampling design relied on the building up, in the first stage, of a sample frame i.e. the
Multifunctional Sample of Territorial Areas ( so-called EMZOT sample) comprising 792 research
centres (i.e. primary sampling units), across the counties and sectors of Bucharest municipality
(NUTS 3 level). Current sampling frame was carried out based on the data from the Population
and Housing Census 2011 round. The next one and will be setup on between 2024-2025 using
PHC round 2021 data.
In the second sampling stage, inside each primary sampling unit are selected, based on a
systematic selection scheme, the secondary sampling units, and represented by dwellings.
Each secondary sampling unit involves the inclusion in the sample of all households and
implicitly of all persons in the household.
The survey results are weighted considering the non-response adjustments and by using total
number of households and residential population in the private households, both data available
yearly at 1 st of January.
18.5.1. Imputation - rate
Not applicable
18.5.2. Calculation of weighting factors and weight adjustments
See the Annex - RO_2023_ Annex 5 – Weighting procedure
18.5.3. Estimation and imputation
Imputation procedure used
The value of imputed rent was estimated at the household level (and included in the personal file for only one person per household) from the household budget survey (HBS), using the stratification method. The HBS includes arround 37000 households and it is conducted continuosly during each year.
The following information was collected in the individual questionnaire:
- the type of the car;
- the model;
- the registration year;
- number of months in 2023 the car was at the disposal of the person for private use;
The company car value was calculated as:
Company car value = number of months*selling price*[1 – 100*(2023 - registration year)/10]/12
The selling prices of the cars by type of car and producer were taken into account.
18.6. Adjustment
Not applicable.
18.6.1. Seasonal adjustment
Not applicable.
No comment.
RO_2024_Annex 2-Item_non_response_13.3.3.2.1
RO_2024_Annex 3-Sampling_errors_13.2
RO_2024_Annex 4-Data_collection_18.3
RO_2024_ Annex 5 – Weighting procedure
RO_2024_Annex 7-Coherence_15.3-15.3.2
RO_2024_Annex 8-Breaks in series_15.2-updated
RO_2024_Annex 9-Rolling module
RO_2024_Annex A EU-SILC - content tables
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.
EU-SILC is part of the European statistical system, and in Romania it has been implemented since 2007 and is part of the national statistical system called the Quality of Life Survey (Ancheta asupra calitatii vietii). The Quality of Life Survey is an annual survey. Since 2021 CAPI method (Computer Assisted Personal Interview) is used, which consists in performing interviews with respondents with the use of mobile devices (tablets) that record the provided answers. The information concerning the household as a whole is recorded in the household section of the questionnaire, while the information about the household members at the age of 16 years and more, in the individual section of the questionnaire. The questions include all the mandatory variables from the DocSILC065 (Eurostat guidelines). The tables contain the results for households and persons by main characterstics: socio-economic groups of the population and households in total, macro-regions (NUTS 1), regions (NUTS 2). Additional profiles for persons are: - age; - sex; - level of education.
28 March 2025
Statistical concepts and definitions for EU-SILC are specified in Regulation (EU) 2019/1700, Commission Implementing Regulation (EU) 2019/2181, and Commission Implementing Regulation (EU) 2019/2242. Additional information is available in the EU statistics on income and living conditions (EU-SILC) methodology and in the methodological guidelines and description of EU-SILC target variables (see CIRCABC).
Further details are provided in items 5, 15.1.1.1, 15.2.2 and 18.3.
Income
The total disposable income of a household is calculated by adding together the personal income received by all of household members plus income received at household level. Missing income information is imputed.
Disposable household income includes:
- all income from work (employee wages and self-employment earnings)
- private income from investment and property
- transfers between households
- all social transfers received in cash, including old-age pensions
Note: Some of the income components are mandatory only from 2007: Imputed rent, Interest paid on mortgage, Employer's social insurance contributions. From the 2007 year on, all countries have to supply gross income information. From 2021 onwards, imputed rent is collected every 3 years as part of the rolling module on ‘Labour and housing’; and all countries have to supply gross and net income information.
Equivalence scale
The total disposable household income is "equivalised" to take into account the impact of differences in household size and composition. The equivalised income attributed to each member of the household is calculated by dividing the total disposable income of the household by equivalisation factors, which can be determined in various ways. Eurostat applies the OECD modified scale, which gives a weight of 1.0 to the first person aged 14 or more, a weight of 0.5 to other persons aged 14 or more and a weight of 0.3 to persons aged 0-13.
Household
A private household’ means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
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 Quality of Life Survey is carried out throughout the country and only private households are interviewed. The survey did not cover collective accommodation households (such as boarding house, workers’ hostel, pensioners’ house or monastery).
The target population is private households and all Romanian or foreign persons composing these households having their usual residence in Romania. Private household means a person living alone or a group of persons who live together, providing oneself or themselves with the essentials of living.
EU-SILC covers only people living in private households (all persons aged 16 and over within the household are eligible for the operation), i.e. persons living in collective households and in institutions are generally excluded from the target population.
SILC covers all Romanian or foreign citizens who have their usual residence in Romania, members of the households selected in the survey sample. The subject of the survey, according to the purpose of the SILC and European regulations, are people aged 16 and over.
The survey includes all residents of Romania, members of the households in the research centers (urban and rural) selected from all the counties of the country and from the Municipality of Bucharest.
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 |
|---|---|---|---|
| January 2023 - December 2023 |
January 2023 - December 2023 |
January 2023 - December 2023 |
The fieldwork period (15 March - 15 April 2024). Therefore, the lag is 3 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.
The INS has analyzed and identified several actions that it will implement in the coming years to get closer to the precision of the European regulation. Among these we can mention, the revision of the periodic national legislation regarding the incomes of the population (Fiscal Code, laws, ordinances of the Government); the inclusion of additional validation rules; a better promotion of data collection for household surveys at the local level to increase the response rate; gradual increase of SILC subsamples.
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 Romanian NIS specific sampling design for household surveys is a two-stage one.
The sampling design relied on the building up, in the first stage, of a sample frame i.e. the
Multifunctional Sample of Territorial Areas ( so-called EMZOT sample) comprising 792 research
centres (i.e. primary sampling units), across the counties and sectors of Bucharest municipality
(NUTS 3 level). Current sampling frame was carried out based on the data from the Population
and Housing Census 2011 round. The next one and will be setup on between 2024-2025 using
PHC round 2021 data.
In the second sampling stage, inside each primary sampling unit are selected, based on a
systematic selection scheme, the secondary sampling units, and represented by dwellings.
Each secondary sampling unit involves the inclusion in the sample of all households and
implicitly of all persons in the household.
The survey results are weighted considering the non-response adjustments and by using total
number of households and residential population in the private households, both data available
yearly at 1 st of January.
Starting with 2015, the household surveys carried out by NSI-Romania are based on the use of Multifunctional Sample of Territorial Areas, so called the master sample EMZOT. It is a database including approximately 1.500.000 dwellings, selected according to probabilistic criteria, serving as sampling frame for all household surveys, in 2015-2024.
For the wave 1, wave 2, wave3 and wave4 (subsample selected in 2021, 2022, 2023 and 2024), a master sample database named “EMZOT” is used. In the first stage, a stratified random sample of 792 areas, Primary Sampling Units (PSUs), was designed after the 2011 Population and Dwelling Census. The PSUs were sampled with probability proportional to the size (number of permanent dwellings). The EMZOT sample has 450 PSUs selected from urban area and 342 PSUs selected from rural area. In the second stage, a fix number of dwellings are systematically selected from each PSU of EMZOT.
Annual
SILC cross-sectional and longitudinal data are available in the form of tables 15 months after the end of data collection.
The SILC survey results are comparable both at national level (NUTS 0) and European level, the survey being designed and developed according to Eurostat methodology for the EU-SILC.
There are no problems of comparability between the regions of the country.
The length of comparable time series is from 2007, no any series breaks occurred.
