Employment and unemployment (Labour force survey) (employ)

National Reference Metadata in Single Integrated Metadata Structure (SIMS)

Compiling agency: Statistics Denmark


Eurostat metadata
Reference metadata
1. Contact
2. Metadata update
3. Statistical presentation
4. Unit of measure
5. Reference Period
6. Institutional Mandate
7. Confidentiality
8. Release policy
9. Frequency of dissemination
10. Accessibility and clarity
11. Quality management
12. Relevance
13. Accuracy
14. Timeliness and punctuality
15. Coherence and comparability
16. Cost and Burden
17. Data revision
18. Statistical processing
19. Comment
Related Metadata
Annexes (including footnotes)



For any question on data and metadata, please contact: Eurostat user support

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1. Contact Top
1.1. Contact organisation

Statistics Denmark
1.2. Contact organisation unit

Work and Income Unit
1.5. Contact mail address

Skt. Kjelds Plads 11, 2100 København Ø, Denmark


2. Metadata update Top
2.1. Metadata last certified

9 December 2025
2.2. Metadata last posted

9 December 2025
2.3. Metadata last update

9 December 2025


3. Statistical presentation Top
3.1. Data description

The EU Labour Force Survey (EU-LFS) is the largest European household sample survey. Its main statistical objective is to classify the population of working age (15 years and over) into three mutually exclusive and exhaustive groups: employed persons, unemployed persons, which together represent the ‘labour force’, and the people outside the labour force. 

Abbreviation

Explanation

CV

Coefficient of variation (or relative standard error)

Y/N

Yes / No

H/P

Households/Persons

M?

Member State doesn’t know

NA

Not applicable/ Not relevant

UNA

Information unavailable

NR

Non-response: Member State doesn’t answer to Eurostat request for information. Blank is allowed only in boxes with comments

LFS

Labour Force Survey

NUTS

Nomenclature of territorial units for statistics or corresponding statistical regions in the EFTA and candidates countries

 
3.2. Classification system

Reference can be found in the dedicated webpage at: Eurostat website.
3.3. Coverage - sector

See sections below
3.3.1. Coverage

Individuals living in private households in Denmark
3.3.2. Inclusion/exclusion criteria for members of the household

Members of the household are interviewed in the main respondents' fourth and last wave. Household members are derived from registers as persons belonging to the same familiy. For the definition of family, please find more information (only available in Danish) website.
3.3.3. Questions relating to labour status are put to all persons aged

15-89 years
3.4. Statistical concepts and definitions

 See below
3.4.1. Household concept

Common housekeeping
3.4.2. Definition of household for the LFS

Is the same as the family concept from Danish population registers. For the definition of family, please find more information (only available in Danish) website.
3.4.3. Population concept

Persons with registered address in Denmark
3.4.4. Specific population subgroups

Population concept 

Specific population subgroups

Primary/secondary students

Tertiary students

People working out of family home for an extended period for the purpose of work

People working away from family home but returning for weekends

Children alternating two places of residence

Usual residence (12 months)

Registered address

Registered address

Registered address

Registered address

Registered address
3.5. Statistical unit

The data collection is carried out for a sample of observation units constituted by private households or by persons belonging to private households who have their usual residence in Denmark.
3.6. Statistical population

The statistical population consists of all persons having their usual residence in private households in Denmark.
3.7. Reference area

Not requested for the LFS quality report.
3.8. Coverage - Time

The time period covered by the data is 1995-2023.

Issues concerning comparability over time are discussed in 15. Coherence and comparability
3.9. Base period

Not requested for the LFS quality report.


4. Unit of measure Top

The LFS produces different indicators with different measures:

  • Numbers;
  • Percentages.


5. Reference Period Top

• Quarter

• Year


6. Institutional Mandate Top
6.1. Institutional Mandate - legal acts and other agreements

EU level:

The EU-LFS is based on European legislation since 1973. The principal legal acts, currently in force, are the Regulation (EU) 2019/1700 establishing a common framework for European social statistics, the Commission Delegated Regulation (EU) 2020/256 establishing a multiannual rolling planning, the Commission Implementing Regulation (EU) 2019/2181 regarding items common to several datasets, and the Commission Implementing Regulation (EU) 2019/2240 which specifies the implementation rules, technical items and contents of the EU-LFS.

National level:

No mandate
6.2. Institutional Mandate - data sharing

Member States shall make available to the Commission (Eurostat) the data and metadata required under the Regulation 2019/2240 using the statistical data and metadata exchange standards specified by the Commission (Eurostat) and the Single Entry Point.

The Commission (Eurostat) shall, in cooperation with Member States, publish the aggregated data on the Commission (Eurostat) website, in a user‐friendly way, as soon as possible and within six months of the transmission deadline for annual and infra‐annual data collection.

Data sharing and exchange between international data producing agencies, for example, a Eurostat data collection or production that is in common with the OECD or the UN.

Anonomized disaggregated data can be shared with national institutions through the Research Service Unit of Statistics Denmark. Please find more information website.


7. Confidentiality Top
7.1. Confidentiality - policy

EU level:

Regulation (EU) No 557/2013 17 June 2013 as regards access to confidential data for scientific purposes and repealing Commission Regulation (EC) No 831/2002. It implements the Regulation (EC) No 223/2009 of the European Parliament and of the Council on European Statistics, which sets criteria for confidentiality of data.

National level:

The Danish Labour Force Survey follows the guidelines of the Data Confidentiality Policy at Statistics Denmark: Data Confidentiality Policy. Statistics Denmark has described the guidelines for the use of data from the LFS. The purpose is to assure quality in the analysis based on the LFS and furthermore inform external users of the LFS on e.g. sampling errors. It is possible to achieve knowledge about publishing limits on yearly and quarterly basis.
7.2. Confidentiality - data treatment

Data is released after checking it does not reveal confidential data. Administrative identifiers, interconnecting statistical identifiers and any other identification data shall be removed (or they shall be modified to an extent where they cannot directly identify the unit to which they relate).


8. Release policy Top
8.1. Release calendar

EU level:

1) the Member States shall transmit pre‐checked microdata without direct identifiers, according to the following two‐step procedure:

  • during the first three years of implementation of this Regulation, as provided for in Article 11(4):
    • for quarterly data: within ten weeks of the end of the reference period,
    • for other data: by 31 March of the following year;
  • from the fourth year of implementation as follows:
    • for quarterly data: within eight weeks of the end of the reference period,
    • for other data regularly transmitted: by 15 March of the following year,
    • for other data concerning ad‐hoc subjects: by 31 March of the following year.

Where those deadlines fall on a Saturday or Sunday, the effective deadline shall be the following Monday. The detailed topic income from work may be transmitted to the Commission (Eurostat) within fifteen months of the end of the reference period.

(2) The Member States shall transmit aggregated results for the compilation of monthly unemployment statistics within 25 days of the reference or calendar month, as appropriate. If the data are transmitted in accordance with the ILO definition, that deadline may be extended to 27 days.

National level:

Data is released nationally 50-60 days following the end of the reference quarter.
8.2. Release calendar access

Access to country release calender.
8.3. Release policy - user access

EU level:

European social statistics are provided on the basis of equal treatment of all types of users, such as policy‐ makers, public administrations, researchers, trade unions, students, civil society representatives including non‐ governmental organisations, and citizens, which can access statistics freely and easily through Commission (Eurostat) databases on its website and in its publications.

National level:

Standard tables for free access are published in the StatBank of Statistics Denmark. Results are disseminated to all users at the same time.


9. Frequency of dissemination Top

Quarterly


10. Accessibility and clarity Top
10.1. Dissemination format - News release

News releases are published for each quarterly dissemination. Additionally, one annual news release is published following the first dissemination of Q4. Additional news releases are produced on an ad-hoc basis.

Press releases published over the past year:
10.2. Dissemination format - Publications

In addition to news releases, three publicaion formats can contain LFS figures: Statistisk Tiårsoversigt, Bag Tallene and DST Analyse.
10.3. Dissemination format - online database

Access to LFS tables in Statbank Denmark.
10.3.1. Data tables - consultations

Not requested for the LFS quality report.
10.3.2. Web link to national methodological publication
10.3.3. Conditions of access to data

Aggregated data available to public, microdata available to researchers. 
10.3.4. Accompanying information to data

See section 10.3.3
10.3.5. Further assistance available to users

Further assistance available via phone or email
10.4. Dissemination format - microdata access

Data are accessible in micro-data form, e.g. for researchers. See 7. for confidentiality policy
10.4.1. Accessibility to LFS national microdata (Y/N)

Y
10.4.2. Who is entitled to the access (researchers, firms, institutions)?

Researchers and institutions
10.4.3. Conditions of access to data

Projects, users and institutions must all be approved by Eurostat and/or Statistics Denmark
10.4.4. Accompanying information to data

 Explanations of e.g. breaks in series and general documentation of variables
10.4.5. Further assistance available to users

Further assistance available via phone or email
10.5. Dissemination format - other

Not requested for the LFS quality report.
10.5.1. Metadata - consultations

Not requested for the LFS quality report.
10.6. Documentation on methodology

See section 10.3.2+10.7
10.6.1. Metadata completeness - rate

Not requested for the LFS quality report.
10.6.2. References to methodological notes about the survey and its characteristics

See section 10.3.2+10.7
10.7. Quality management - documentation

National LFS documentation: 

National methodological publication:


11. Quality management Top
11.1. Quality assurance

Statistics Denmark follows the principles in the Code of Practice for European Statistics (CoP) and uses the Quality Assurance Framework of the European Statistical System (QAF) for the implementation of the principles. This involves continuous decentralized and central control of products and processes based on documentation following international standards. The central quality assurance function reports to the Working Group on Quality. Reports include suggestions for improvement that are assessed, decided and subsequently implemented.
11.2. Quality management - assessment

Processes are in place to monitor the quality of the compiled and disseminated statistics through regular checks for coverage, classification and missing data. The internal consistency of data source and across datasets also monitored.


12. Relevance Top
12.1. Relevance - User Needs

Due to register information on employment and unemployment being readily available at a detailed level, the user needs are centered around data for international comparison and variables, that are not available in administrative registers. From 2024 and forward user needs will be assessed through an Expert User Group on Labour Market Satistics.

Users have inquired about more frequent releases on tele work. We currently only publish a yearly table on this. We will increase the frequency to quarterly releases from Q1 2024.
12.2. Relevance - User Satisfaction

Not requested for the LFS quality report.
12.3. Completeness

See sections below
12.3.1. Data completeness - rate

Not requested for the LFS quality report.
12.3.2. NUTS level of detail

NUTS 2:

  • Region Hovedstaden
  • Region Sjælland
  • Rgion Syddanmark
  • Region Midtjylland
  • Region Nordjylland
12.3.2.1. Regional level of an individual record (person) in the national data set

In theory, all regional classifications can be made in the Danish LFS as the Population Register supplies the LFS with information on place of residence; e.g. address and municipality code. However, for the quarterly LFS only estimates at NUTS-2 level are considered reliable. By using annual averages instead of quarterly estimates, aggregated NUTS-3 level estimates of employment and labour force are considered reliable, whereas cross-tabulations of these as well as aggregated NUTS-3 level estimates of unemployment should be made with caution.
12.3.2.2. Lowest regional level of the results published by NSI

NUTS2
12.3.2.3. Lowest regional level of the results delivered to researchers by NSI

LAU


13. Accuracy Top
13.1. Accuracy - overall

The overall accuracy is high, given the large sample size
13.2. Sampling error

Being a survey, the LFS is subject to sampling errors. See Annex tables for more information.
13.2.1. Sampling error - indicators

See sections below
13.2.1.1. Coefficient of variation (CV) Annual estimates %

See Annex table 13.2.1.1 for detailed information
13.2.1.2. Coefficient of variation (CV) Annual estimates at NUTS-2 Level  %

See Annex table 13.2.1.2 for detailed information
13.2.1.3. Description of the assumption underlying the denominator for the calculation of the CV for the employment rate

The denominator for the calculation of the CV for the employment rate is the estimated employment rate. The estimated employment rate is calculated as the estimated number of employed persons divided by the estimated number of persons in the same age interval in the population. The estimation is done using calibration estimation. This estimation ensures, that the following population totals are recreated by the weighting for each of the four panels: gender, age, education, region of residence and register-based labour market status.
13.2.1.4. Reference on software used

Andersson, C and Nordberg, L (1998). CLAN – A SAS-program for Computation of Point- and Standard Error Estimates in Sample Surveys. Örebro: Statistics Sweden.
13.2.1.5. Reference on method of estimation

Särndal, C. E., Swensson B. and Wretman J. (1992). Model Assisted Survey Sampling. New York: Springer Verlag.
13.3. Non-sampling error

Not requested for the LFS quality report.
13.3.1. Coverage error

See Annex table 13.3.1 for detailed information
13.3.1.1. Over-coverage - rate

See in the 13.3.1. Coverage error section in Annex.
13.3.1.2. Common units - proportion

Not requested for the LFS quality report.
13.3.1.3. Misclassification errors – detection of mismatches of identifiers

See in the 13.3.1. Coverage error section in Annex.
13.3.1.4. Misclassification errors –description of the main misclassification problems encountered in collecting the data and the methods used to process misclassifications

References to Annex File.
13.3.2. Measurement error

See Annex table 13.3.2 for detailed information
13.3.2.1. Errors due to the media (questionnaire)

See Annex table 13.3.2.1 for detailed information
13.3.2.2. Main methods of reducing measurement errors

See Annex table 13.3.2.2 for detailed information
13.3.3. Non response error

Not requested for the LFS quality report.
13.3.3.1. Unit non-response - rate

See Annex table 13.3.3.1 for detailed information
13.3.3.1.1. Methods used for adjustments for statistical unit non-response

See Annex table 13.3.3.1.1 for detailed information
13.3.3.1.2. Non-response rates. Annual averages (% of the theoretical yearly sample)

See Annex table 13.3.3.1.2 for detailed information
13.3.3.1.2.1. Non-response rates. Annual averages (% of the theoretical yearly sample) – NUTS-2 level

See Annex table 13.3.3.1.2.1 for detailed information
13.3.3.1.3. Units who did not participate in the survey

See Annex table 13.3.3.1.3 for detailed information
13.3.3.2. Item non-response - rate

See sections below
13.3.3.2.1. Item non-response (INR) in % * - Quarterly data (Compared to the variables defined by the Commission Regulation (EC) No 2019/2240)

See Annex table 13.3.3.2.1 for detailed information
13.3.3.2.2. Item non-response (INR) in % * - Annual data (Compared to the variables defined by the Commission Regulation (EC) No 2019/2240)

See Annex table 13.3.3.2.2 for detailed information
13.3.3.2.3. Item non-response for INCGROSS

See Annex table 13.3.3.2.3 for detailed information.

We have extracted/imported a high percentage of INCGROSS data from 2022 and therefore we do not impute any data on INCGROSS.

1,48 ?
13.3.4. Processing error

See sections below
13.3.4.1. Editing and imputation process

See Annex table 13.3.4.1 for detailed information
13.3.4.2. Outliers treatment and other data editing procedures for INCGROSS

See Annex table 13.3.4.2 for detailed information
13.3.5. Model assumption error

Not requested for the LFS quality report.


14. Timeliness and punctuality Top
14.1. Timeliness

See Annex table 14.1 for detailed information
14.1.1. Time lag - first result

Not requested for the LFS quality report.
14.1.2. Time lag - final result

Not requested for the LFS quality report.
14.2. Punctuality

All disseminations and news releases have been published on time.
14.2.1. Punctuality - delivery and publication

Not requested for the LFS quality report.


15. Coherence and comparability Top
15.1. Comparability - geographical

See section 15.1.2. Divergence of national concepts from European concepts
15.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested for the LFS quality report.
15.1.2. Divergence of national concepts from European concepts

 

(European concept or National proxy concept used) List all concepts where any divergences can be found

 NA

NA 

Is there any divergence between the national and European concepts for the following characteristics?

(Y/N)

Give a description of difference and provide an assessment of the impact of the divergence on the statistics

Definition of resident population (*)

Y

The definition of the resident population in the Danish LFS is harmonised with the Danish population register. The permanent address is therfore defined as the place where you with some regularity sleep, when you are not abroad because of holidays, business trips, or the place where you have your belongings. The definition does therefore not explicity include the minimum of 1 year, as stated in the Explanatory notes. This solution complies with art. 2(d) of EP and Council Regulation (EC) No 763/2008

Identification of the main job (*)

N 

NA

Employment

N 

NA 

Unemployment

N 

NA 

 
15.2. Comparability - over time

See sections below
15.2.1. Length of comparable time series

Time series are comparable since 2008.
15.2.1.1. Length of time series

Not requested for the LFS quality report.
15.2.1.2. Length of comparable time series

Not requested for the LFS quality report.
15.2.2. Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)

See Annex table 15.2.2 for detailed information
15.2.3. Changes at MEASUREMENT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)

See Annex table 15.2.3 for detailed information
15.3. Coherence - cross domain

Not requested for the LFS quality report.
15.3.1. Coherence - sub annual and annual statistics

Not requested for the LFS quality report.
15.3.2. Coherence - National Accounts

 

Description of difference in concept

Description of difference in measurement

Give an assessment of the effects of the differences

Give references to description of differences

Total employment

The Danish statistics on National Accounts
-counts foreign residents who work in DK as employed, whereas residents who work abroad are left out of employment.
-makes some corrections for "moonlighting" in selected industries.
-makes some adjustments regarding information on wage and employment to harmonize the National Accounts with the accounting  and production statistics.

NA employment is measured almost entirely through registers. Except the estimation on moonlighting and subdivisions og construction employment, which are edjustments based on surveys.

Overall level higher in NA. Higher level of employees in NA, but lower level om self-employed in the LFS

 

Paper published on the national homepage in English:

 

Total employment by NACE

See comment above

See comment above

Quite large differences between sectors, largely because of the difference in sources

 NA

Number of hours worked

See comment above. The big difference stems from the unit. The working time accounts compile hours to a huge total, and have no individuals. This creates some differences over time.

The way to compare the NA and LFS is to calculate the total number of hours worked in the LFS, since the NA only works on aggregate level and not on the level of individual persons’ working hours per week.

Higher level of hours worked in the LFS - approximately 4 percent.

 NA
15.3.3. Which is the use of LFS data for National Account Data?

 

Which is the use of LFS data for National Account Data?   

Country uses LFS as the only source for employment in national accounts.

Country uses mainly LFS, but replacing it in a few industries (or labour status), on a case-by-case basis

Country doesn’t make use of LFS, or makes minimal use of it

Country combines sources for labour supply and demand giving precedence to labour supply sources (i.e. LFS)

Country combines sources for labour supply and demand not giving precedence to any labour side

Country combines sources for labour supply and demand giving precedence to labour demand sources i.e. employment registers and/or enterprise surveys)

 N

 N

 Y

 N

 N

 N
15.3.4. Coherence of LFS data with Business statistics data

 

 

Description of difference in concept

Description of difference in measurement

Give an assessment of the effects of the differences

Give references to description of differences

Total employment

Business statistics are based on register, where the employers register the number of employed. LFS is based on self-reported working status by the respondents.

Business statistics are based on register, where employers register the number of employed. LFS is based on self-reported working status by the respondents.

Expected because of the differences between self-reported information in LFS and registerinformation

 UNA

Total employment by NACE

Business statistics are based on register, where the employers register the number of employed. LFS is based on self-reported working status by the respondents. This also applies looking at NACE level.

Business statistics are based on register, where employers register the number of employed. LFS is based on self-reported working status by the  respondents. This also applies looking at NACE level.

Expected because of the differences between self-reported information in LFS and registerinformation

 UNA

Number of hours worked

Business statistics are based on register, where employers register the number of paid
hours. LFS is based on self-reported working time by the respondents.

Business statistics are based on register, where employers register the number of paid hours. LFS is based on self-reported working time by the respondents.

 Expected because of the differences between self-reported information in LFS and registerinformation

 UNA
15.3.5. Coherence of LFS data with registered unemployment

 

Description of difference in concept

Description of difference in measurement

Give references to description of differences

The Danish concept of registered unemployment has been split in two in 2010. What was previously the main concept is now a subgroup called "net unemployment". The total concept (gross unemployment) includes those in certain labour market activation programmes. Although the total is therefore close to the LFS total, the concept is even further from the ILO definition, as many ILO-employed are among the gross unemployed.

 FTE's vs persons

 Paper published at dst.dk/unemployment
15.3.6. Assessment of the effect of differences of LFS unemployment and registered unemployment

Give an assessment of the effects of the differences

Overall effect

Men under 25 years

Men 25 years and over

Women under 25 years

Women 25 years and over

Regional distribution (NUTS-3)

 The LFS unemployment was 5.1 and the gross uemployment was 2.8 per cent.

 The LFS unemployment was 11.8 and the gross unemployment was 1.0. This is due to the fact that young persons rarely have the right to receive unemployment benefits.

 The LFS unemployment was 3.9 and the gross unemployment was 2.9

 The LFS unemployment was 11.3 and the gross unemployment was 1.0. This is due to the fact that young persons rarely have the right to receive unemployment benefits.

 The LFS unemployment was 4.2 and the gross unemployment was 3.3

 NA
15.3.7. Comparability and deviation for the INCGROSS

See Annex table 15.3.7 for detailed information
15.4. Coherence - internal

Not requested for the LFS quality report.


16. Cost and Burden Top

See sections below
16.1. Number of staff involved in the LFS in central and regional offices, excluding interviewers. Consider only staff directly employed by the NSI.

Not requested for the LFS quality report.
16.2. Duration of the interview by Final Sampling Unit

See Annex table 16.2 for detailed information


17. Data revision Top
17.1. Data revision - policy

Data is not revised
17.1.1. Is the general data revision policy fully compliant with the ESS Code of Practice principles? (in particular see the 8th principle) (Y/N)

Y: Data are final when they are published.
17.1.2. Is the country revision policy compliant with the ESS guidelines on revision policy for PEEIs? (ref. http://ec.europa.eu/eurostat/documents/3859598/5935517/KS-RA-13-016-EN.PDF)

Y
17.2. Data revision - practice

Not requested for the LFS quality report.
17.2.1. Data revision - average size

Not requested for the LFS quality report.


18. Statistical processing Top
18.1. Source data

Data for the LFS is a mix of survey data and data from administrative and statistical registers.
18.1.1. Sampling design & Procedure frame

 

Sampling design (scheme; simple random sample, two stage stratified sample, etc.)

Base used for the sample (sampling frame) 

Last update of the sampling frame (continuously updated or date of the last update)

Primary sampling unit (PSU) 

 Final sampling unit (FSU)

Date of sample selection

 One stage stratified sample

The Population Register and other registers (enhanced with information from the labour market register and the income register).

 Continuosly

 NA

Individuals. 

Two months before each reference quarter. 
18.1.2. Sampling design & Procedure method

First (and intermediate) stage sampling method

  Final stage sampling method

Stratification (variable used)

Number of strata (if strata change quarterly, refer to Q4).

Rotation scheme (2-2-2, 5, 6, etc.)

One stage stratified sample

 NA

 

Our stratification is overall based on employment and unemployment. The variables used for the delimitation are gross income, net unemployment from the unemployment register, socioeconomic status and age. Persons aged 15-64 years that were registered as unemployed in a specific quarter prior to the survey quarter are selected with a higher probability than their relative proportion of the total population.

From January 1st 2021 the LFS is adapted to a new EU framework regulation. Until the year 2020 the LFS has been collected at the individual level for 15-74-year-olds. From 2021 the population has changed to also include the age group from 75-89 years. On a quarterly basis the sample has thus increased from 34,320 people to 36,020 people. The weighting scheme has also been changed to also include the age group 75-89 years.

 

In total there are 8 strata. The sizes of different strata are adjusted according to which combination reduces the standard error the most for the variable labour market status.

The rotation scheme is 2-(2)-2. 
Simultaneous to the fourth interview round the rest of the respondent’s household is also interviewed.

 
18.1.3. Yearly sample size & Sampling rate

See Annex table 18.1.3 for detailed information
18.1.4. Quarterly sample size & Sampling rate

See Annex table 18.1.4 for detailed information
18.1.5. Use of subsamples to survey structural variables (wave approach)

Yearly variables are collected from all respondents in the LFS.
18.2. Frequency of data collection

Not requested for the LFS quality report.
18.3. Data collection

 

Data collection methods: brief description

Use of dependent interviewing (Y/N)?

In case of Computer Assisted Methods adoption for data collection, could you please indicate which software is used?

From 2016 both the core-LFS and the household subsample is based on a mix of modes where CATI is supplemented with CAWI.

 Y

UNICOM Intelligence:

ISO 27001 certified and compliant with the standards under the Danish Government's IT
18.3.1. Final sampling unit collected by interviewing technique (%)

See Annex table 18.3.1 for detailed information
18.3.2. Info from registers

Are any LFS data collected from registers (Y/N)?

If Yes, please indicate which registers.

 Y

Multiple registers on employment, unemployment, income, population, address, education
18.3.3. Description of data collection and reference period for INCGROSS

See Annex table 18.3.3 for detailed information
18.3.4. Description of percentiles and bands used for INCGROSS

See Annex table 18.3.4 for detailed information
18.4. Data validation

Arithmetic and qualitative controls are used in the validation process, including comparison with other data. Before data dissemination, the internal coherence of the data is checked.
18.5. Data compilation

Not requested for the LFS quality report.
18.5.1. Imputation - rate

Reference to Annexes.
18.5.1.1. Editing and imputation process for INCGROSS

There are no imputations made for INCGROSS
18.5.2. Brief description of the method of calculating the quarterly core weights

Brief description of the method of calculating the quarterly core weights

Is the sample population in private households expanded to the reference population in private households? (Y/N)

If No, please explain which population is used as reference population

Gender is used in weighting (Y/N)

Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)?

Which regional breakdown is used in the weighting (e.g. NUTS 3)?

Other weighting dimensions

 Due to the mentioned stratification, the strata are weighted separately:
Weighting is performed by using a combination of sex, age group (15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-74, 75-89), socio-economic status, income, nationality, level of education, status in the unemployment register and region.

 N

 Indivduals

 Y

 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-74, 75-89

 NUTS 2

NA
18.5.3. Brief description of the method of calculating the yearly weights (please indicate if subsampling is applied to survey yearly variables)

The yearly weights are calculated as one fourth of the qarterly weights.
18.5.4. Brief description of the method of calculating the weights for households

Brief description of the method of calculating the weights for households

Any external reference for number of households etc.?

Which factors at household level are used in the weighting (number of households, household size, household composition, etc.)

Which factors at individual level are used in the weighting (gender, age, regional breakdown etc.)?

Are the household weights identical for all household members? (Y/N)

Households responding to the labour force survey is grossed up to align with known population totals – at household as well as individual level. The weights are calculated using regression estimation.

 The population register

 Mobility defined by whether a member of the household has moved in a time period (number of households in 2 categories)

Average age of household members between 15 and 89 years of age (number of households in 3 categories)

Ethnicity (number of households in to 2 categories)

Average income (number of households in 4 categories)

 Type of family (number of persons in 6  categories)

Size of household (number of children below 15 year of age in 4 categories)

Age combined with gender (number of persons in 6 categories)

 N

 
18.6. Adjustment

Not requested for the LFS quality report.
18.6.1. Seasonal adjustment

 

Do you apply any seasonal adjustment to the LFS Series? (Y/N)

If Yes, is your adopted methodology compliant with the ESS guidelines on seasonal adjustment? (ref. ESS guidelines on seasonal adjustment - Products Manuals and Guidelines - Eurostat (europa.eu) (Y/N)

If Yes, are you compliant with the Eurostat/ECB recommendation on Jdemetra+ as software for conducting seasonal adjustment of official statistics. (ref) (Y/N)

If Not, please provide a description of the used methods and tools

 Y

 Y

 Y

Method = X12-Arima Tool = JDemetra+


19. Comment Top


Related metadata Top


Annexes Top
LFS Annex 2023 [LFS_QR_Multiple+1.0_upd]