Employment and unemployment (Labour force survey) (employ)

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

Compiling agency: Federal Statistical Office Germany


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)
 



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1. Contact Top
1.1. Contact organisation

Federal Statistical Office Germany
1.2. Contact organisation unit

Department F: Population, Finance and Taxes

Unit H3 - Microzensus
1.5. Contact mail address

Fedaral Statistic Office Germany

Gustav-Stresemann-Ring 11

DE-65189 Wiesbaden


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 ...
 The survey covers private and collective households (military quarters are not assigned to the collective households). Housekeeping Members living together in the same dwelling with common housekeeping  The members of a private household are characterized by the same dwelling and common housekeeping. Concripts on compulsory military service are included in the household of their parents rsp. the household they belong to.The resident population (statistical population) comprises all inhabitants with their main place of residence and their secondary residence in the territory of Germany. Foreign armed forces, members of diplomatic corps and their families are excluded.  15+

 

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 (if private)  Family home 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)                                  
 X  
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)
 The basic concept of sampling methodology is one-stage cluster sampling (area sampling). Explanations on the rotation design applied are given below.   The census data from 2011 is the survey base for the sample.  The annual update of the sampling frame is realized by using information on building licences. These information was available in all Federal States in autumn 2017.  Sampling districts consisting of 9 dwellings on average (area sampling). All buildings are assigned to one of three size classes, depending on the number of dwellings they comprise. Buildings with less than 5 dwellings belong to the first size class. In this size class, each sampling district comprises 12 dwellings on average (usually in neighbouring houses in rural areas). The second size class comprises medium-sized buildings with five to 10 dwellings. Each of these buildings constitutes a sampling district. The buildings in the third size class comprise 11 dwellings or more. In this size class sampling districts are subdivisions of the building, the target size being 6 dwellings. An additional stratum covers the population living in collective households. Collective households are divided into sampling units with a target size of 15 persons. New buildings reported are allocated to the size classes specified above. Compared with the selection based on the 1987 population census, the following modifications have been made: The sample districts formed by buildings with 1 to 4 dwellings have a target number of 6 dwellings (instead of 12). The minimum number of dwellings per building is 9 in the third size class. This means that the sample districts of all building classes have roughly the same size.  Households, persons ans dwellings

 

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.)
 The sampling method applied, i.e. sorting, defining zones, and sampling within the zones, guaranteed an effect similar to stratification. The regional strata (see "stratification") were grouped to 131 "adjustment strata", at least to the extent that an average of 500,000 inhabitants was obtained. Bound expansion is performed at that regional level. Before sampling, the sample districts were sorted in terms of region, i.e. within each stratum they were sorted by regional stratum subgroup, administrative district, community size class, community, and sample district number. "Zones" were formed by 100 consecutive sample districts each. The sample districts of each zone were formed at random by permutation of numbers 0 to 99 by means of a random number generator. Sample districts with the same number, i.e. the same "sampling number", were grouped to form a 1%-sample. Thus the population was divided into 100 1% samples. The random number generator was also used to form at random four successive zones each by permutation of numbers 1 to 4. This permitted to divide every 1% sample into 4 rotation quarters of 0.25%. The 20 1% stock samples were determined at random by sampling from an urn an interval comprising 20 sampling numbers between 0 and 99. Subsequently, the first 1% sample to be used was determined also by sampling from an urn. The subsample for the yearly variables, too, are obtained systematically with a random start.  All households in the sampling districts and all persons in the housholds are to be surveyed.   Stratification is done for the overall microcensus sample and applies also for each subsample (including the LFS):
The sampling districts are stratified by region and size of buildings. The stratification by size of buildings is based on the size classes used to work out the sampling units. There are 243 regions which comprise 200.000 inhabitants on average. The sampling rate is the same in each stratum. The list of sampling districts is sorted within each stratum by sub-region, Kreis (administrative district), the size class of the commune, commune and number of sample district. Within each stratum, an effect similar to stratification is obtained by systematic sampling in a list classified by geographical entity. The list of sampling districts in each stratum is devided into groups of 100 consecutive sampling districts. In each of these groups a number of sampling districts in accordance with the regional sampling ratio – which average to a 1% sample for the microcensus (and roughly 0,4% for the LFS) on the national level - 1% is drawn at random in each of these groups. In order to account for the differing design effects of the individual NUTS2 regions the regional sampling ratio do vary quite a bit. The 1%-sample is allocated to the months and quarters of the year at random. The sample is refreshed each year by sampling districts of new buildings. The building size is utilized in form sample districts, but due to its low numbers they form just one subject-related stratum (instead of 4) per regional stratum.		  131  The rotation system is composed of four waves and the rotation scheme is 2-2-2. Each sampling district remains in the sample either for 2 or 3 years (2 years if the first wave is during Q1-Q3). 4/9 (44,4%) of the LFS-sample districts is replaced each year. Thus, the degree of overlapping between two consecutive yearly samples is 5/9 (55,6%).

 

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)
  0,4% of the sampling disctricts (40% of the microcensus sample) about 300 000 persons rsp. 151 500 households

  

Quarterly 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)
    4/9 (44,4%) of the LFS sampling districts  about 130 000 persons resp 67 500 households

  

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)
 1st and 3rd wave and 4th wave if the reference week is in Q1.  Households in the 4th wave and Q1 are surveyed  because the German microcensus law states that these structural variables are to be gathered from each LFS household each year.  Consistency of ILO labour status in combination with age-brackets and gender between the yearly and quarterly sub-samples has to be met by the extrapolation of the LFS data.  NA  Y

 

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
 

A two-stage adjustment procedure is used:

(a) Due to Covid-19 and technical issue it was not possible to access sufficient information on households who failed to respond.  As a proxy the structure of 2019 LFS-sample was adjusted by regional sampling ratios of 2020. The net-sample was calibrated to this proxy based on education (low, medium, high), nationality (German, non-German), age (65 and under, over 65).  and household size (1person or more). Non-Response weights were calculated as the inverse of the received calibration weight. 

(b) The sample is stratified a posteriori by nationality, sex, age group, region NUTS-2, employment status

  Y  NA  Y  Y NUTS2 Education, nationality, employment status.

 

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
 

A two-stage adjustment procedure is used:

(a) Due to Covid-19 and technical issue it was not possible to access sufficient information on households who failed to respond. As a proxy the structure of 2019 LFS-sample was adjusted by regional sampling ratios of 2020. The net-sample was calibrated to this proxy based on education (low, medium, high), nationality (German, non-German), age (65 and under, over 65).  and household size (1person or more). Non-Response weights were calculated as the inverse of the received calibration weight. 

(b) The sample is stratified a posteriori by number of private households, size of household, employment status, age groups, nationality, sex, region NUTS-2.

  Y Y   

NUTS2

 Education, nationality, employment status, number of private households, size of household

 

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)

In contrast to the procedure of previous years the annual weights are not merely calculated as a mere average of the quarterly weights. Instead the weights are calculated on a differing weighting frame that takes consistency with the quarterly weights for key indicators into account.

  see above  see above  see above  Y
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?
 The field staff of the 14 statistical offices of the federal states comprises interviewers. A full multi-mode-design (CAPI, CATI, CAWI, PAPI) were implemented along with completely new and complex IT tools for survey management and data collection. In addition, technical issues during the system changeover have restricted the data collection since the beginning of 2020. Moreover, the COVID-19 crisis has had a large impact on data collection processes.  N    Participation is compulsory. For some LFS-variables provision of  information is voluntary. LFS-variables with voluntary response are those which are not spezifically stated in the German microcensus law. (mainly the ad-hoc-modul variables).

 

Final sampling unit collected by interviewing technique (%)
CAPI CATI PAPI CAWI POSTAL - OTHER
 1,70%  22,80%  34,20%  40,30%  
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)
 

Assessment of the relevance of the main LFS statistics at national level (e.g. for policy makers, other stakeholders, media and academic research)

 In Germany, the Labour Force Survey (together with the Microcensus, in which the LFS data collection is integrated), is one of the main statistics to monitor the German labour market regarding level, structure and trends. The LFS is also of key importance in other subject matter areas including education, migration and integration as well as households and families. Indicators based on LFS data are of high importance in numerous indicator frameworks to monitor labour market policy not only at the European level, but also nationally. Examples include the national sustainable development indicators, the indicators of the national dialogue on well-being and quality of life, the short-term economic indicators, as well as statistical indicator reports on education, quality of employment and other topics. Important policy monitoring indicators based on the LFS include the employment rate, the rate of part-time employment, the underemployment rate, the unemployment rate, the long-term unemployment rate, the rate of young people not in education, employment, or training (NEET), the potential additional labour force, and the hours worked per week.

The main users cover the entire spectrum of users of data from official statistics, including governments, international organisations, employers’ associations and trade unions, researchers, media and the general public, all at national as well as regional level. Institutional users include national and regional governments (e.g. the ministries of labour and social affairs), the German Federal Bank, the Federal Employment Agency, the European Commission (e.g. Eurostat and DG employment, social affairs and inclusion), the International Labour Organisation (ILO), the Organisation for Economic Co-operation and Development (OECD), the European Central Bank (ECB) and other international organisations. Also foreign governments frequently use LFS data for international comparisons. LFS data are widely used by employers’ associations, trade unions, trade associations, and individual enterprises both at national or regional level, for their decision making. Market, social and economic research companies use LFS results both as a basis for analysis and as input for weighting and calibration of commercial surveys. Universities and research institutions make frequent use of LFS data both in form of tables provided by the Federal Statistical Office upon request and via micro data access for research purposes made available via the research data centres of official statistics. Results from LFS data are finally frequently disseminated by general and specialised media, both at national and at regional level and requested by the general public. In 2016, more than 2,000 user requests were received from these user groups. Since 2016 the number of requests can not be withdrawn from the software, as the information service is centralized within the statistical office. Furthermore, the number of request is reducing as more information can be found online.

The data requirements of the users are taken into consideration via different channels and are mirrored in national legislation - including the LFS. The ministries of the Federation and the Federal States can directly influence the list of variables included in the Microcensus through national legislation. The sub-committee “Employment/Labour Market” of the German Statistical Council brings together representatives from major user groups including government ministries, employers’ associations and trade unions, trade associations and chambers, environmental and conservation organisations, universities, the head organisations of the municipalities and the council for social and economic data. The sub-committee “Employment/Labour market” meets biennially and provides users a forum in which new developments are being presented and where new or changed data requirements can be discussed.

 

 
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 NUTS 4 for total employment by sex onlyby NSI Brief description of the method which is used to produce NUTS-3 unemployment and labour force data sent to Eurostat?
 NR NR  NR  NR 
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
 25 000  40 000  NA  NA
6.2.1. Sampling error - indicators
Sampling error - indicators - Coefficient of variation (CV), Standard Error (SE) and Confidence Interval (CI)      
  Number of employed persons Employment rate as a percentage of labour force 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 0.099 0.094 0.288 0.91496299 0.91 0.91 0.089
SE 41487.68 0.063 34620.08 15098.71 0.035 0.139 0.0241
CI (**) 41449035.76 - 41611667.48 0.673 - 0.6757 11963571.60 - 12099282.30 1620605.29 - 1679792.23 3.75 - 3.89 7.17 - 7.71 27.02 - 27.11
Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
 NR

 

Reference on software used: Reference on method of estimation:
 SAS-macro ETOS  Taylor linearization
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 
 DEF0
 Schleswig-Holstein 0.46 0.43 1.29 4.84 4.83 9.23 0.46
 DE60  Hamburg 0.55 0.54 1.73 4.44 4.44 9.56 0.5
 DE91  Braunschweig 1.05 0.85 2.05 5.88 5.9 10.53 0.63
 DE92  Hannover 0.84 0.66 1.69 5.67 5.59 11.25 0.56
 DE93  Lüneburg 0.88 0.74 1.7 5.96 5.93 10.23 0.6
  DE94   Weser-Ems 0.75 0.62 1.5 5.31 5.31 10.45 0.53
 DE50  Bremen 0.93 0.92 2.52 7.52 7.44 14.18 0.9
 DEA1  Düsseldorf 0.58 0.48 1.18 3.28 3.26 6.78 0.34
DEA2 Köln 0.63 0.53 1.32 3.76 3.73 7.44 0.42
DEA3 Münster 0.87 0.72 1.76 5.88 5.88 12.98 0.58
 DEA4 Detmold 0.94 0.81 1.96 6.11 6.11 12.12 0.58
 DEA5 Arnsberg 0.72 0.6 1.49 4.32 4.25 7.27 0.45
 DE71 Darmstadt 0.55 0.48 1.44 4.07 4.04 7.71 0.42
 DE72   Gießen 1.41 1.25 2.69 8.92 8.89 12.95 1
 DE73 Kassel 1.11 0.94 2.5 8.82 8.81 18.34 0.82
DEB1  Koblenz 1.03 0.82 2 8.04 8 13.81 0.64
DEB2  Trier 1.43 1.15 2.66 10.44 10.5 23.76 0.89
DEB3 Rheinhessen-Pfalz 0.76 0.65 1.78 5.26 5.25 9.88 0.52
DE11 Stuttgart 0.5 0.4 1.16 3.83 3.81 7.69 0.38
 DE12  Karlsruhe 0.67 0.57 1.45 4.28 4.26 8.11 0.48
DE13  Freiburg 0.67 0.55 1.49 5.38 5.34 10.44 0.5
DE14 Tübingen 0.84 0.64 1.72 6.4 6.39 13.94 0.61
DE21 Oberbayern 0.57 0.46 1.4 5 4.97 12.08 0.43
 DE22  Niederbayern 1.18 0.97 2.49 10.38 10.41 17.12 0.76
DE23 Oberpfalz 1.53 1.26 3.63 17.07 16.92 65.22 1.09
DE24 Oberfranken 1.39 1.14 3.01 8.86 8.98 19.52 0.82
DE25 Mittelfranken 1 0.87 2.32 7.9 7.89 14.91 0.66
DE26 Unterfranken 1.17 0.97 2.36 9.64 9.53 21.7 0.78
 DE27 Schwaben 0.92 0.76 1.97 8.73 8.7 15.85 0.66
DEC0  Saarland 0.88 0.86 2.38 8.28 8.31 18.35 0.71
 DE30 Berlin 0.49 0.47 1.5 3.62 3.62 9.18 0.44
 DE40 Brandenburg 0.62 0.59 2.39 6.43 6.4 14.84 0.5
DE80 Mecklenburg-Vorpommern 0.61 0.59 1.69 4.75 4.78 11.82 0.45
 DED4 Chemnitz 1.03 0.86 2.69 6.94 6.93 20.2 0.54
DED2 Dresden 0.92 0.81 2.22 7.14 7.16 21.69 0.53
 DED5 Leipzig 1.31 1.15 3.29 8.48 8.42 16.86 0.78
DEE0 Sachsen-Anhalt 0.6 0.55 2.13 4.83 4.82 11.99 0.4
 DEG0  Thüringen 0.55 0.49 1.86 4.91 4.86 11.3 0.42
 (*) 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]
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 2019 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)?
 NR NR  NR  NR 

 

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  Y. But few
Interviewer  Periodical training (at least 1 time per year) (Y/N)  Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
Y Y in regional offices
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  M?
Questionnaire  Questionnaire in several languages (Y/N)  On-line checks (for computer assisted interviews (Y/N)
 Y  Y
Other / Comments  
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    wave, nationality (German, non-German), age (65 and under, over 65), education (low, medium, high)   Due to Covid-19 and technical issue it was not possible to access sufficient information on households who failed to respond. As a substitute the structure of the 2019 gross sample was utilized, skewing it according to regional sampling ratios. Compensation factors are calculated by raising the net sample to the gross sample using regression analysis and the marginal totals of the compensation variables. This is done at different regional levels. The compensation factors calculated are inserted into the files of persons of responding households, according to the household characteristics. They are then used as input for the second step of the raising: the bound expansion using key data from the continuous population updating procedure.
Substitution of non-responding units (Y/N) Substitution rate Criteria for substitution
 N  NA  NA
Other methods (Y/N) Description of method
 N  NA

  

Rates of non-response by survey mode. Annual average
Survey
CAPI CATI  PAPI  CAWI  POSTAL
         

 

Non-response rates by survey mode. Annual average (% of the theoretical yearly sample by survey mode)
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 54 UNA UNA  UNA 
2 55 UNA  UNA  UNA 
3 45 UNA  UNA  UNA 
4 33 UNA  UNA  UNA 
Annual 46.8 UNA  UNA  UNA 

  

 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 UNA UNA UNA UNA
Subsample_Q1_2019 UNA UNA UNA UNA
Subsample_Q2_2019 UNA UNA UNA UNA
Subsample_Q3_2019 UNA UNA UNA UNA
Subsample_Q4_2019 UNA UNA UNA UNA
Subsample_Q1_2020 UNA UNA UNA UNA
Subsample_Q2_2020 UNA UNA UNA UNA
Subsample_Q3_2020 UNA UNA UNA UNA
Subsample_Q4_2020 UNA UNA UNA UNA
 Total in absolute numbers UNA UNA UNA UNA
 Total in % of theoretical quarterly sample UNA UNA UNA UNA

 

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 UNA UNA UNA UNA
Subsample_Q1_2019 UNA UNA UNA UNA
Subsample_Q2_2019 UNA UNA UNA UNA
Subsample_Q3_2019 UNA UNA UNA UNA
Subsample_Q4_2019 UNA UNA UNA UNA
Subsample_Q1_2020 UNA UNA UNA UNA
Subsample_Q2_2020 UNA UNA UNA UNA
Subsample_Q3_2020 UNA UNA UNA UNA
Subsample_Q4_2020 UNA UNA UNA UNA
 Total in absolute numbers UNA UNA UNA UNA
 Total in % of theoretical quarterly sample UNA UNA UNA UNA

 

 

 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)
Sub-sample Quarter1 2020 Quarter2 2020 Quarter3 2020 Quarter4 2020
Sub-sample Q4 2018 UNA UNA UNA UNA
Sub-sample Q1 2019 UNA UNA UNA UNA
Sub-sample Q2 2019 UNA UNA UNA UNA
Sub-sample Q3 2019 UNA UNA UNA UNA
Sub-sample Q4 2019 UNA UNA UNA UNA
Sub-sample Q1 2020 UNA UNA UNA UNA
Sub-sample Q2 2020 UNA UNA UNA UNA
Sub-sample Q3 2020 UNA UNA UNA UNA
Sub-sample Q4 2020 UNA UNA UNA UNA
Total in absolute numbers UNA UNA UNA UNA
Total in % of theoretical quarterly sample UNA UNA UNA UNA

 

 

 

Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)  Non response rate (%)
DE11-Stuttgart UNA
DE12-Karlsruhe UNA
DE13-Freiburg UNA
DE14-Tübingen UNA
DE21-Oberbayern UNA
DE22-Niederbayern UNA
DE23-Oberpfalz UNA
DE24-Oberfranken UNA
DE25-Mittelfranken UNA
DE26-Unterfranken UNA
DE27-Schwaben UNA
DE30-Berlin UNA
DE50-Bremen UNA
DE60-Hamburg UNA
DE71-Darmstadt UNA
DE72-Gießen UNA
DE73-Kassel UNA
DE80-Mecklenburg-Vorpommern UNA
DE91-Braunschweig UNA
DE92-Hannover UNA
DE93-Lüneburg UNA
DE94-Weser-Ems UNA
DEA1-Düsseldorf UNA
DEA2-Köln UNA
DEA3-Münster UNA
DEA4-Detmold UNA
DEA5-Arnsberg UNA
DEB1-Koblenz UNA
DEB2-Trier UNA
DEB3-Rheinhessen UNA
DEC0-Saarland UNA
DED4-Chemnitz UNA
DED2-Dresden UNA
DED5-Leipzig UNA
DEE0-Sachsen-Anhalt UNA
DE40-Brandenburg UNA
DEF0-Schleswig-Holstein UNA 
DEG0-Thüringen UNA 

* 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_023 PROXY 14.7 12.2 10.6 .  
compulsory Col_101 - Employed SEEKTYPE 99.9 100 99.9 99.9  
compulsory Col_101 - Not employed SEEKTYPE 11 . . .  
compulsory Col_102 - Employed SEEKDUR 19.2 19.4 25.6 25.3  
compulsory Col_112 - Employed METHODJ . . C C  
compulsory Col_112 - Not employed METHODJ C C . .  
compulsory Col_113 - Employed METHODK . . C C  
compulsory Col_113 - Not employed METHODK C . C C  
compulsory Col_114 - Employed METHODL . . C C  
compulsory Col_114 - Not employed METHODL C C C C  
compulsory Col_162/163 INTWEEK 58.1 10.5 . .  

 

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
compulsory Col_049 WAYJFOUN 17.5  
compulsory Col_053 TEMPREAS 45.9  
compulsory Col_100 SEEKREAS 100  
compulsory Col_118 - Employed AVAIREAS 100  
compulsory Col_118 - Not employed AVAIREAS 100  
compulsory Col_120 NEEDCARE 22.7  
compulsory Col_121 REGISTER 100  
optional Col_136 COURWORH 100  

(*) "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 )
 N  NA
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 
 NA  NA  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  N  N

Although a sufficiently long time series is available from the Labour Force Survey, the monthly results do not show sufficiently stable seasonal patterns. For this reason, it is still not (yet) possible to use standard seasonal adjustment procedures. Those would lead to a highly volatile seasonally adjusted time series, which would be very difficult to interpret. Such a procedure would also risk resulting in implausible seasonal effects.

Therefore, as practiced in other Member States in a similar situation, until a more stable seasonal pattern is observed, Eurostat and the Federal Statistical Office of Germany publishes a trend estimate (trend-cycle component) as a substitute for seasonally adjusted unemployment figures. The trend-cycle component is estimated on the basis of the non-adjusted monthly LFS-data. The use of trend estimation removes not only seasonal effects, but also irregular effects as well as variations due to sampling errors and methodological biases from the series. Due to the trend estimation, the resulting series are highly smoothed. The possible drawback of this procedure is that month-to-month movements in the unemployment data might be smoothed out and that there might be significant revisions in case of turning points.

The trend-cycle-component is being estimated in parallel with two different procedures: On the one hand the procedure BV 4.1 (‘Berliner Verfahren’) is applied, which was developed by the Federal Statistical Office. On the other hand the procedure Census X-13-ARIMA is applied, which is recommended at international level. The estimations according to BV 4.1 are taken care of by the Federal Statistical Office, while the trend-cycle component according to X-13-ARIMA is estimated by Eurostat using the software package JDemetra+. The monthly press release shows the results according to the X-13-ARIMA procedure, the results according to BV 4.1 are available at the online data basis Genesis-Online of the Federal Statistical Office (table code 13231; https://www-genesis.destatis.de/genesis/online?operation=sprachwechsel&language=en).

More  information  are  available  under  https://www.destatis.de/EN/Methods/Quality/_node.html


 

 

 
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  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   In the German LFS only people living in households are part of the frame. Homeless people and other people without registered residence (e.g. people living in huts, caravans) are out of the frame.
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  Y  The microcensus law (“Gesetz zur Durchführung einer Repräsentativstatistik über die Bevölkerung und die Arbeitsmarktbeteiligung sowie die Wohnsituation der Haushalte”) from December 2016 regulates the new rotational scheme of the LFS starting in 2020 and the items of the LFS questionnaire – additionally to the other microcensus subsamples.  N  NA  N
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  Y

 Up until 2019 the LFS sample size was identical to the microcensus. Starting in 2020 the LFS is a 40% subsample of the microcensus.

  N  NA  Y
rotation pattern  Y  The LFS switched from a yearly rotation to a 2-(2)-2 schema, meaning that each sampling district will be interviewed in 2 subsequent quarters, pause for two quarters and then again be querried for 2 quarters. Yearly variables are surveyed for each sampling district in the first and third wave and the forth wave under the condition that the reference week is in Q1.
The rotation patern of the LFS differs therefore from the rotation pattern of the microcensus and the SILC and ICT subsamples.		  N  NA  Y
questionnaire  N  NA NA  NA  NA
instruction to interviewers  Y  Due to Covid-19 interviewers were instructed to realize more interviews in the form of CATI instead of CAPI  N  NA  Y
survey mode  Y  A full multi-mode-design was implemented along with completely new and complex IT tools for survey management and data collection. These changes have led to a break in the time series.
In addition, technical issues during the system changeover have restricted the data collection since the beginning of 2020. Moreover, the COVID-19 crisis has had a large impact on data collection processes. These were the two main factors resulting in low response rates and a biased sample for the data collected in 2020.		  N NA  Y
weighting scheme  N  Due to the significant changes in the LFS design, the weighting procedure has also been subject to changes. As described in 3.1 yearly weights are no longer an average of the quarterly weights and are calculated with a differing weighting frame, while consistency with the extrapolation of key quarterly indicators (employment status) is taken into account. The new frames for quarterly and yearly weights are explained in detail in 3.1.  N  NA  Y
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 UNA UNA UNA UNA
Total employment by NACE UNA UNA UNA UNA
Number of hours worked UNA UNA UNA UNA

 

Coherence of LFS data with registered unemployment  
Description of difference in concept Description of difference in measurement Give references to description of differences
Criteria for ILO-unemployment and registered unemployment are different. Registered unemployed can have a job with less than 15 working hours; then in compliance with ILO they have to be classified as employed. Criteria for registered unemployment are: Working less then 15 hours per week, registered at the employment agency, available for the employment agency. Registration at the employment agency is not an ILO-criterion. Registered unemployed can only be persons who registered with the public employment agency. ILO-unemployment is measured by a survey; it is independent of being registered or not.
Unemployed persons, 65 years and older, who are looking for a job, cannot be registered. The actively search for a job, which is a condition for both statistics to be counted or registered, is construed in different manners		 Körner, T./Puch, K.: Der Mikrozensus im Vergleich mit  anderen Arbeitsmarktstatistiken. Ergebnisunterschiede und ihre Hintergründe seit 2011. WiSta 4/2015;  Körner, T./Puch, K.: Coherence of German Labour Market Statistics, Statistik und Wissenschaft, Band 19, 2011.

 

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)
- 1 038 000 (1 038 000 less unemployed then registered) + 14 000 (14 000 more unemployed then registered) - 563 000 (563 000 less unemployed then registered)  + 35 000 (35 000 more unemployed the registered)   - 523 000 (523 000 less unemployed then registered)  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 Employed persons living in collective households (not in the LFS); Treatment of persons with a job on long-term absence (only included in LFS if duration of absence <3 months or continued receipt of at least 50% of employment-related income);  Employees below the age threshold of the LFS Measurement differences compared to sources used by National Accounts (LFS underestimation of marginal part-time employment); Adjustments in National Accounts (informal employment)

Total number of employed is lower in the LFS (- 2.85 million persons in 2020) than in the National Accounts

 

 

Joint OECD/ Eurostat questionnaire on national accounts employment and hours worked (January/February 2006);

Reconciliation table between LFS and NA estimates of   employment (see Differences between employment figures of Labour Force Survey and national accounts estimates - German Federal Statistical Office (destatis.de)

Körner, T./Marder-Puch, K.:  Der Mikrozensus im Vergleich mit anderen Arbeitsmarktstatistiken. WiSta 4/2015;

Körner, T./Puch, K.: Coherence of German Labour Market Statistics, Statistik und Wissenschaft, Band  19, 2011.
Total employment by NACE The National Accounts use the enterprise concept, the LFS the local unit/establishment concept; differences regarding employment, see above Methodological differences (LFS captures industry as indicated by respondents; National Accounts rely on register information). The main differences are: the LFS shows a higher share of employed persons (in NACE A3) industry, construction; the National Accounts show higher shares in the service sector, especially in business services  Körner, T./Puch, K.: Coherence of German Labour Market Statistics, Statistik und Wissenschaft, Band 19, 2011
Number of hours worked No conceptual significant differences regarding the concept of hours actually worked; differences regarding employment, see above The calculation of hours worked within the framework of the National Accounts is based on a differentiated component wise accounting concept, where calendar effects, collectively agreed standards, business cycle influences as well as personal and other components are considered. The accounting model uses a total of 20 different statistics, including the LFS (which nevertheless plays only a limited role); LFS underestimation of marginal part-time employment; LFS underestimation of absences during the reference week, e.g. due to holidays;

Hours actually worked per year: LFS: 1544* ; National accounts: 1330,51

 

 

1 published 02.06.2021		 Körner, T. ; Wolff, L. (2016), Do the Germans really work six weeks more than the French ? – Measuring working time with the Labour Force Survey in France and Germany, Journal of Official Statistics 32, p. 405-431; Frosch M. et al. , Quality issues regarding the measurement of working time with the LFS - Findings from the Task Force on Measurement of Absences and Working Time, Paper presented at the 11th LFS Methodology Workshop, Cardiff, Wales, 2016.

 

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  N  N  N  Y

 

 
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
 Yearly: First results in "Wirtschaft und Statistik", press conference, press publication, press releases, publications with detailed tables ("Fachserien"), further results in  articles in the monthly "Wirtschaft und Statistik", detailed results in Destatis-web and Genesis-online" (online data bank).
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

Link to the national web page (national language(s)):https://www.destatis.de/DE/Themen/Arbeit/Arbeitsmarkt/_inhalt.html
and https://www.destatis.de/DE/Methoden/Qualitaet/Qualitaetsberichte/Arbeitsmarkt/einfuehrung.html


Link to the national web page (English):  https://www.destatis.de/EN/Themes/Labour/Labour-Market/_node.html and https://www.destatis.de/EN/Methods/Quality/QualityReports/Labour-Market/einfuehrung.html		 NSI's homepage, telephone, online databank "Genesis", (all free of costs),  campus file (anonymised microdata free of costs), scientific use file (only for researchers in Germany, 95 Euro), email, fax (both free of costs up to a certain number of tables rsp. size of data file), online data-shop (downloads), workplaces for guest scientists Quality report of the German Microcensus, Documentations, glossaries, articles Telefone hotline, user conferences, documentation at GESIS

 

 
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
Micro data are only available only for the national microcensus;  LFS data can be generated Generelly everyone Through research data centres, the Federal Statistical Office and the statistical offices of the Länder provide exclusively institutions of higher education or other institutions tasked with independent scientific research with various forms of access to selected stocks of official statistics for scientific purposes. Persons or institutions that do not belong to the relevant scientific community are given access to official statistical data through the information services of the statistical offices of the Federation and the Länder https://www.forschungsdatenzentrum.de/en/household/microcensus  Institutions of higher education or other institutions tasked with independent scientific research: Consulting by research data centres
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
 N
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

There following anonymisation guidelines apply:

  1. On-Site-Access: https://www.forschungsdatenzentrum.de/de/geheimhaltung
  2. Off-Site-Access: Characteristic coarsening as required


12. Comment Top

[not requested for the LFS quality report]


Related metadata Top


Annexes Top