Employment and unemployment (Labour force survey) (employ)

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: Statistics Denmark


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. 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

Labour Market, Labour Force Survey
1.5. Contact mail address

Sejrøgade 11, 2100 København Ø, Denmark


2. Statistical presentation Top
Please take note of the abbreviations used in the report 
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
2.1. Data description
Coverage   
Coverage Household concept Definition of household for the LFS Inclusion/exclusion criteria for members of the household Questions relating to employment status are put to all persons aged ...
Danish population aged 15-74 years. Housekeeping Members living together in the same dwelling and (if the household consists of more than eight people) with common housekeeping The sample units are individuals for the core part of the LFS, and only one member in a household is selected for interview, but persons living in all kinds of households are included as the survey covers the total Danish population aged 15-74. For the household part of the survey all persons of the household are interviewed.  15-74

 

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
Registered population, including people living in institutions Family home Term address Term address Family home Most of the time

 

Reference week
Fixed week (data collection refers to one reference week, to which the observation unit has been assigned prior to the fieldwork) Rolling week (data collection always refers to the week before the interview)
YES. The Danish LFS is a continuous survey, thus the quarterly sample is divided into 13 sub-samples of equal size. Persons in each sub-sample are interviewed with reference to a specific week (the reference week).  NO
2.2. Classification system

[not requested for the LFS quality report]
2.3. Coverage - sector

[not requested for the LFS quality report]
2.4. Statistical concepts and definitions

[not requested for the LFS quality report]
2.5. Statistical unit

[not requested for the LFS quality report]
2.6. Statistical population

[not requested for the LFS quality report]
2.7. Reference area

[not requested for the LFS quality report]
2.8. Coverage - Time

[not requested for the LFS quality report]
2.9. Base period

[not requested for the LFS quality report]


3. Statistical processing Top
3.1. Source data
Sampling design & procedure
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)
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

 

Sampling design & procedure
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.

The sample is quarterly based on a stratified sample. The sample size was reduced in the 1st quarter of 2016. Before 2016, individuals who had research protection were not interviewed, but this protection was removed from the first quarter of 2016. This effectively meant an expansion of the number of people that actually can be interviewed. This was compensated by reducing the number of people who were drawn out to the sample. The reduction was implemented successively and the sample size for persons aged 15 to 74 was reduced from 38 979 in the 1st quarter of 2016 to 37,426 in the 4th quarter of 2016. The reduction was fully implemented in the 2nd quarter of 2017. A series of tests on this show, however, that it does not appear to have influenced the figures for the labor market participation. Before 2016 the quarterly sample size was 40 532.

 In total there are 7 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.

 

Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate Size of the theoretical yearly sample
(i.e. including non-response) (i.e. including non-response)
3.1 133 350

  

uarterly sample size & Sampling rate
Overall theoretical quarterly sampling rate Size of the theoretical quarterly sample
(i.e. including non-response) (i.e. including non-response)
 0.77  33 338

 

Use of subsamples to survey structural variables (wave approach)
Only for countries using a subsample for yearly variables
 Wave(s) for the subsample  Are the 30 totals for ILO labour status (employment, unemployment and inactivity) by sex (males and females) and age groups (15-24, 25-34, 35-44, 45-54, 55+) between the annual average of quarterly estimates and the yearly estimates from the subsample all consistent? (Ref.: Commission Reg. 430/2005, Annex I) (Y/N) If not please list deviations List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 377/2008, Annex II)
 NA NA  NA  NA 

 

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), socio-economic status, income, nationality, level of education, status in the unemployment register and region.		  N  Individuals Y  15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-74  NUTS 2  See description

 

Brief description of the method of calculating the yearly weights (please indicate if subsampling is applied to survey yearly variables) 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
 The yearly data is based on four quarters, which are weighted separatly.  Y 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-74  NUTS 2 Sex, age group, socio-economic status, income, nationality, level of education, status in the unemployment register and region.

 

Brief description of the method of calculating the weights for households 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.) Identical household weights 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 74 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
3.2. Frequency of data collection

[not requested for the LFS quality report]
3.3. Data collection
Data collection methods: brief description Use of dependent interviewing (Y/N)? Participation is voluntary/compulsory?
 From 2016 both the core-LFS and the household subsample is based on a mix of modes where CATI is supplemented with CAWI.  Y  Voluntary

 

Final sampling unit collected by interviewing technique (%)
CAPI CATI PAPI CAWI POSTAL - OTHER
 NA  49.79  NA  50.21  NA
3.4. Data validation

[not requested for the LFS quality report]
3.5. Data compilation

[not requested for the LFS quality report]
3.6. Adjustment

[not requested for the LFS quality report]


4. Quality management Top
4.1. Quality assurance

[not requested for the LFS quality report]
4.2. Quality management - assessment

[not requested for the LFS quality report]


5. Relevance Top
5.1. Relevance - User Needs
Assessment of the relevance of the main LFS statistics at national level (e.g. for policy makers, other stakeholders, media and academic research)
 Mainly relevant for international comparison.
5.2. Relevance - User Satisfaction

[not requested for the LFS quality report]
5.3. Completeness
NUTS level of detail   
Regional level of an individual record (person) in the national data set Lowest regional level of the results published by NSI Lowest regional level of the results delivered to researchers by NSI Brief description of the method which is used to produce NUTS-3 unemployment and labour force data sent to Eurostat?
LAU

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, then 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 are not considered sufficiently reliable.

 NUTS2 

For quarterly estimates the lowest published regional level is NUTS-2. For annual averages only aggregated estimates of employment and labour force are published at NUTS-3 level with no cross-tabulations, and estimates of unemployment are not published at this level.

  LAU Register data is considered the most reliable source of unemployment and labour force data at NUTS-3 level. From Q1 2010 also NUTS-3 (REG3D + REG3DW) data from the LFS are transmitted.
5.3.1. Data completeness - rate

[not requested for the LFS quality report]


6. Accuracy and reliability Top
6.1. Accuracy - overall

[not requested for the LFS quality report]
6.2. Sampling error
Publication thresholds   
Annual estimates Annual estimates - wave approach 
(if different from full sample thresholds) 
 Limit below which figures cannot be published  Limit below which figures must be published with warning  Limit below which figures cannot be published Limit below which figures must be published with warning
 2000 4000   NA NA 
6.2.1. Sampling error - indicators
Coefficient of variation (CV) Annual estimates
Sampling error - indicators - Coefficient of variation (CV), Standard Error (SE) and Confidence Interval (CI)       
 

Number of employed persons

Employment rate as a percentage of the population

Number of part-time employed persons

Number of unemployed persons

Unemployment rate as a percentage of labour force

Youth unemployment rate as a percentage of labour force

Average actual hours of work per week(*)
 

Age group: 20 - 64

Age group: 20 - 64

Age group: 20 - 64

Age group: 15 -74

Age group: 15 -74

Age group: 15 -24

Age group: 20 - 64
 CV  0.15 0.15  0.81  1.62  1.6  3.07  0.16
 SE  3968.96  0.11  4252.16  2766.14  0.08  0.35  0.06
 CI(**)  2669649,84-2685208.15  74.53-74.96  513861.35-530529.81  164930.61-175773.89  5.03-5.36  10.85-12.24  35.21-35.43

 

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 estimated number of employed persons divided by the estimated number of persons in the same age group in the population.
The estimation is done using calibration estimation. This estimation ensures the at following population totals are recreated for each of the four panels: gender, age, education, socio-economic status, number of children, ethnicity, register unemployment, income and region of residence.

 

Reference on software used: Reference on method of estimation:
 Andersson, C and Nordberg, L (1998). CLAN – A SAS-program for Computation of Point- and Standard Error Estimates in Sample Surveys. Örebro: Statistics Sweden.  Särndal, C. E., Swensson B. and Wretman J. (1992). Model Assisted Survey Sampling. New York: Springer Verlag.

 

Coefficient of variation (CV) Annual estimates at NUTS-2 Level        
NUTS-2  CV of regional (NUTS-2) annual aggregates (in %)     
Regional Code  Region

Number of employed persons

Employment rate as a percentage of the population

Number of part-time employed persons

Number of unemployed persons

Unemployment rate as a percentage of labour force

Youth unemployment rate as a percentage of labour force

 Average actual hours of work per week(*)
   

Age group: 20 - 64

Age group: 20 - 64

Age group: 20 - 64

Age group: 15 -74

Age group: 15 -74

Age group: 15 -24

Age group: 20 - 64 
 DK01  Hovedstaden  0.49  0.39  1.51  3.05  3.02  5.56  0.29
 DK02  Sjælland  0.89  0.69  2.61  5.35  5.3  7.79  0.44
 DK03  Syddanmark  0.68  0.54  1.91  4.30  4.27  6.67  0.37
 DK04  Midtjylland  0.62  0.49  1.66  3.81  3.79  6.42  0.35
 DK05  Nordjylland  1.03  0.82  2.87  6.24  6.26  10.46  0.55

 

(*) The coefficient of variation for actual hours worked should be calculated for the sum of actual hours worked in 1st and 2nd jobs, and restricted to those who actually worked 1 hour or more in the reference week.

(**) The value is based on a CI of 95%. For the rates the CI should be given with 2 decimals.
6.3. Non-sampling error

 [not requested for the LFS quality report]
6.3.1. Coverage error
Frame quality (under-coverage, over-coverage and misclassifications(b))      
Under-coverage rate (%) Over-coverage rate (%) Misclassification rate (%)  Comments: specification and impact on estimates(a)   
 Undercoverage  Overcoverage  Misclassification(b)  Reference on frame errors
 UNA UNA  UNA  UNA  UNA  UNA  UNA 

 

(a) Mention specifically which regions / population groups are not suitably represented in the sample.
(b) Misclassification refers to statistical units having an erroneous classification where both the wrong and the correct one are within the target population.
6.3.1.1. Over-coverage - rate

[Over-coverage rate, please see concept 6.3.1 Coverage error in the LFS quality report]

Statistics Denmark applies registers that are expected to have full coverage. In the Danish LFS the main sampling frame is the Population Register supplemented with other registers for stratification purposes. The Population Register of Statistics Denmark covers all registered residents in Denmark, and the administrative register is currently updated on a daily basis, which we recieve an extract from once a month. In terms of both coverage and updating, as such, this is a high quality sampling frame. After selection the monthly LFS sub-samples are compared with the most updated Population register in order to both verify active status (alive and resident) and to add updated information on dwelling address.
6.3.1.2. Common units - proportion

[not requested for the LFS quality report]
6.3.2. Measurement error
Errors due to the medium (questionnaire)   
Was the questionnaire updated for the 2018 LFS operation? (Y/N) Synthetic description of the update Was the questionnaire tested? (Y/N) If the questionnaire has been tested, which kind of tests has been applied (pilot, cognitive, internal check)?
 N  NA  Y  Internal

 

Main methods of reducing measurement errors 
Error source  
Respondent  Letter introducing the survey (Y/N) Phone call for booking or introducing the survey (Y/N)
 Y  N
Interviewer  Periodical training (at least 1 time per year) (Y/N)  Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
 Y Y 
Fieldwork  Monitoring directly by contacting the respondents after the fieldwork (Y/N) Monitoring directly by listening the interviews (Y/N) Monitoring remotely through performance indicators (Y/N)
 N N  N 
Questionnaire  Questionnaire in several languages (Y/N)  On-line checks (for computer assisted interviews (Y/N)
 Y Y 
Other / Comments The questionaire exists in danish and english. The interviewers are thoroughly educated and we arrange yearly workshops. Soft checks and hard cheks. Many ressources are put into manual error checks of interviews
6.3.3. Non response error

[not requested for the LFS quality report]
6.3.3.1. Unit non-response - rate

IN THIS SECTION INFORMATION REFERS TO THE FINAL SAMPLING UNITS *

Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N) Variables used for non-response adjustment Description of method
 Y Sex, age, socio-economic status, income, nationality, education, status in the unemployment register, region and if the dwelling place has recently changed. The groups for the sampling strata are weighted seperately, based on quarterly updated data from the Danish Population Register. Registers with information containing the whole Danish population are used in order to adjust the data for biases.
Substitution of non-responding units (Y/N) Substitution rate Criteria for substitution
 N NA  NA 
Other methods (Y/N) Description of method
 N NA 

  

Non-response rates by survey mode. Annual average (% of the theoretical yearly sample by survey mode)
Survey
CAPI CATI  PAPI  CAWI  POSTAL
 NA UNA  NA  UNA  NA 

 

Divisions of non-response into categories. Quarterly data and annual average
Quarter Non-response rate
Total (%)             of which:
 Refusals (%)     Non-contacts (including people who migrated (or moved) internally or abroad) (%)        of which people who migrated (or moved) internally or abroad (%)
1 45 5 36  1
2 45 5 35  1
3 44 6 36  1
4 47 7 37  1
Annual  46 6  36   1

 

Units who refused to participate in the survey  (Please indicate the number of the units concerned in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018  591      
Subsample_Q1_2019  522  635    
Subsample_Q2_2019    521  643  
Subsample_Q3_2019      592  852
Subsample_Q4_2019  384      618
Subsample_Q1_2020  227  389    
Subsample_Q2_2020    221  352  
Subsample_Q3_2020      268  468
Subsample_Q4_2020        285
 Total in absolute numbers  1724  1766  1855  2223
 Total in % of theoretical quarterly sample  5.2  5.3  5.6  6.7

 

Units who were not contacted (including people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018  2745      
Subsample_Q1_2019  2774  2596     
Subsample_Q2_2019    2822  2646   
Subsample_Q3_2019      2887  2546
Subsample_Q4_2019  3240      2866
Subsample_Q1_2020  3315  3143    
Subsample_Q2_2020    3259  3184  
Subsample_Q3_2020      3293  3248
Subsample_Q4_2020        3765
 Total in absolute numbers  12074  11820  12010  12425
Total in % of theoretical quarterly sample  36.2  35.5  36.0  37.3

 

of which people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018  79      
Subsample_Q1_2019  68  50    
Subsample_Q2_2019    67  64  
Subsample_Q3_2019      69  81
Subsample_Q4_2019  96      79
Subsample_Q1_2020  82  81    
Subsample_Q2_2020    96  76  
Subsample_Q3_2020      105  99
Subsample_Q4_2020        103
 Total in absolute numbers  325  294  314  362
 Total in % of theoretical quarterly sample  1.0  0.8  0.9  1.1

 

Non-response rates. Annual average (% of the theoretical yearly sample)
NUTS-2 region (code + name)  Non response rate (%)
 Region Hovedstaden  51
 Region Midtjylland  43
 Region Nordjylland  43
 Region Sjælland  46
 Region Syddanmark  42

* If the final sampling unit is the household it must be considered as responding unit even in case of some household members (not all) do not answer the interview
6.3.3.2. Item non-response - rate
Item non-response (*) - Quarterly data (Compared to the variables defined by the Commission Regulation (EC) No 377/2008)       

Variable status

 Column Identifier Quarter 1 Quarter 2 Quarter 3 Quarter 4 Short comments on reasons for non-available statistics and prospects for future solutions

Compulsory / optional
compulsory Col_028 SIGNISAL C C C C  Respondents included in the filter are coded with 'don't know'. 
compulsory Col_047/48 MSTARTWK 16.5 15.3 14.6 15.8  
compulsory Col_210 EDUCVOC . . 4.6 4.6  

 

Item non-response - Annual data (Compared to the variables defined by the Commission Regulation (EC) No 377/2008)    
Variable status Column Identifier This reference year Short comments on reasons for non-available statistics and prospects for future solutions
optional Col_132 COURPURP 100 Not collected as optional
optional Col_133/135 COURFILD 100 Not collected as optional 
optional Col_136 COURWORH 100 Not collected as optional 

(*) "C" means all the records have the same value different from missing.
6.3.4. Processing error
Editing of statistical item non-response
Do you apply some data editing procedure to detect and correct errors? (Y/N) Overall editing rate (Observations with at least one item changed / Total Observations )
 Y  Approximately 40 percent
6.3.4.1. Imputation - rate
Imputation of statistical item non-response
Are all or part of the variables with item non response imputed? (Y/N) Overall imputation rate (Observations with at least one item imputed / Total Observations )
 N  NA
 Main variables Imputation rate  Describe method used, mentioning which auxiliary information or stratification is used 
 N  0  NA
6.3.5. Model assumption error

[not requested for the LFS quality report]
6.4. 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. http://ec.europa.eu/eurostat/web/research-methodology/seasonal-adjustment) (Y/N) If Yes, are you compliant with the Eurostat/ECB recommendation on Jdemetra+ as software for conducting seasonal adjustment of official statistics. (ref. http://ec.europa.eu/eurostat/web/ess/-/jdemetra-officially-recommended-as-software-for-the-seasonal-adjustment-of-official-statistics) (Y/N) If Not, please provide a description of the used methods and tools
Y  Y  Y Method = X12-Arima Tool = JDemetra+
6.5. Data revision - policy
Do you adopt a general data revision policy fully compliant with the ESS Code of Practice principles? (in particular see the 8th principle) (Y/N) Are you 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/N)
 Y: Data are final when they are published. Only the monthly LFS is revised.  Y
6.6. Data revision - practice

[not requested for the LFS quality report]
6.6.1. Data revision - average size

[not requested for the LFS quality report]


7. Timeliness and punctuality Top
7.1. Timeliness
Restricted from publication
7.1.1. Time lag - first result
Restricted from publication
7.1.2. Time lag - final result
Restricted from publication
7.2. Punctuality
Restricted from publication
7.2.1. Punctuality - delivery and publication
Restricted from publication


8. Coherence and comparability Top
8.1. Comparability - geographical

Divergence of national concepts from European concepts

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

    
Is there a 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 
8.1.1. Asymmetry for mirror flow statistics - coefficient

[not requested for the LFS quality report]
8.2. Comparability - over time
Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes in (Y/N) Description of the impact of the changes Statistics also revised backwards (if Y: year / N) Variables affected Break in series to be flagged (if Y: year and quarter/N)  
concepts and definition  N  NA  NA  NA  NA
coverage (i.e. target population)  N  NA  NA  NA  NA
legislation  N  NA  NA  NA  NA
classifications  N  NA  NA  NA  NA
geographical boundaries  N  NA  NA  NA  NA

 

Changes at MEASUREMENT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes to (Y/N) Description of the impact of the changes Statistics also revised backwards (if Y: year / N) Variables affected Break in series to be flagged (if Y: year and quarter/N)
sampling frame  N  NA  NA  NA  NA
sample design  N  NA  NA  NA  NA
rotation pattern  N  NA  NA  NA  NA
questionnaire  N  NA  N  NA  NA
instruction to interviewers  N  NA  NA  NA  NA
survey mode  N  NA  NA  NA  NA
weighting scheme  N  NA  NA  NA  NA
use of auxiliary information  N  NA  NA  NA  NA
8.2.1. Length of comparable time series

[not requested for the LFS quality report]
8.3. Coherence - cross domain
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

 

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

 

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 is higher - the gross uemployment in 2019 was 3.7 per cent while LFS unemployment was 5.0. LFS unemployment is much higher: 10.3 compared to 1.5 per cent for gross unemployment. This is due to the fact that young persons rarely have the right to receive unemployment benefits. LFS unemployment was 3,9. Gross uemployment was 3.8.  LFS unemployment is much higher: 9.7 compared to 1.5 per cent for gross
unemployment. This is due to the fact that young persons rarely have the right
to receive unemployment benefits.		 LFS unemployment was 4.5. Gross uemployment was 4.3.  NA
8.4. Coherence - sub annual and annual statistics

[not requested for the LFS quality report]
8.5. Coherence - National Accounts
Coherence of LFS data with National Accounts 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 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:

http://www.dst.dk/ext/2751346028/0/arbe/International-definitions-related-to-employment--pdf
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. Paper published on the national homepage in English:

http://www.dst.dk/ext/2751346028/0/arbe/International-definitions-related-to-employment--pdf

 

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 not 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 
8.6. Coherence - internal

[not requested for the LFS quality report]


9. Accessibility and clarity Top
9.1. Dissemination format - News release

[not requested for the LFS quality report]
9.2. Dissemination format - Publications
Please provide a list of type and frequency of publications

 Each quarter we have three publications - one highlighting the unemployment, one with a random subject and one where DK is compared til EU. 

- The LFS Quarter (kvt.) presents the newest quarterly main figures
- The LFS Theme (tema) presents a special theme in national context
- The LFS European (europæisk) presents comparisons with the other EU countries
- The LFS Year (år) presents the newest yearly main figures
9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.    
Web link to national methodological publication Conditions of access to data Accompanying information to data Further assistance available to users
www.dst.dk/aku

www.dst.dk/en/Statistik/dokumentation/documentationofstatistics

 The whole population has access to the Statbank Denmark with LFS tables

Statbank Denmark: https://www.dst.dk/en

  Explanations of eg breaks and dokumentation  Kontaktperson is available via email and phone
9.3.1. Data tables - consultations

[not requested for the LFS quality report]
9.4. Dissemination format - microdata access
Accessibility to LFS national microdata (Y/N) Who is entitled to the access (researchers, firms, institutions)? Conditions of access to data Accompanying information to data Further assistance available to users
 Y  Researchers and ministries  Special rules for researcher's access to micro data  Explanations of eg breaks, dokumentation and guidelines  Kontaktperson is available via email and phone
9.5. Dissemination format - other

[not requested for the LFS quality report]
9.6. Documentation on methodology
References to methodological notes about the survey and its characteristics

http://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/labour-force-survey 

http://www.dst.dk/en/Statistik/dokumentation/metode/aku-arbejdskraftundersoegelsen
9.7. Quality management - documentation

[not requested for the LFS quality report]
9.7.1. Metadata completeness - rate

[not requested for the LFS quality report]
9.7.2. Metadata - consultations

[not requested for the LFS quality report]


10. Cost and Burden Top
Restricted from publication


11. Confidentiality Top
11.1. Confidentiality - policy

[not requested for the LFS quality report]
11.2. Confidentiality - data treatment
Please provide information on the policy for anonymizing microdata in your country

The labour Force survey follows the guidelines of the Data Confidentiality Policy at Statistics Denmark: [Data Confidentiality Policy](http://www.dst.dk/ext/502998790/0/formid/Data-Confidentiality-Policy-at-Statistics-Denmark--pdf).

Statistics Denmark has described some 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.


12. Comment Top

[not requested for the LFS quality report]


Related metadata Top


Annexes Top