Employment and unemployment (Labour force survey) (employ)

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

Compiling agency: Statistics Austria


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 Austria
1.2. Contact organisation unit

Directorate Social Statistics
1.5. Contact mail address
Statistics Austria
Guglgasse 13
1110 Vienna
Austria


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 ...
Private households only. Institutional households not covered. Sampling frame covers all dwellings where at least one person has a registered main residence. Housekeeping Unit A private household is either: (a) A one-person household that is a person who lives alone in a separate housing unit or who occupies, as a lodger, a separate room (or rooms) of a housing unit but does not join with any of the other occupants of the housing unit to form part of a multi-person household as defined below; or (b) A multi-person household that is a group of two or more persons who combine to occupy the whole or part of a housing unit and to provide themselves with food and possibly other essentials for living. Members of the group may pool their incomes to a greater or lesser extent.  All household members who have their centre of life in the sampled household during the reference week, independent of residence registration.  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
 Usual residence  Family home  Term address (if living in a private household)  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)                                  
Week assigned to households during sampling, surveying usually within five weeks following the reference week.  N

 

Participation is voluntary/compulsory?
 Compulsory
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) Date of sample selection
 Stratified random sample Register of Residents The register is continuously updated. There is only one sampling unit. The sampling unit is the dwelling with at least one person with main residence. All the people in the selected dwellings are sampled. Sampling is done three months before the start of the survey.

 

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.)
 Random Sampling within Bundesländer (NUTS 2).  There is only one. Stratification is carried out according to Bundesländer (NUTS 2). Number of strata is constant: 9 (number of Bundesländer in Austria)  5

 

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)
 2.4%  90 000 households gross sample size

  

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)
 0.6%  22 500

 

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

The weights are calculated in two steps. In the first step for each record a basic weight which depends on the stratum and the domain is calculated. In the second step the weights are adjusted in such a manner that the resulting distribution is in accordance to the population within the federal provinces (Bundesländer, NUTS 2). Calibration is performed by iterative proportional fitting. The weights are adjusted in such a manner, that the figures correspond to the population with main residence at the beginning of each quarter minus persons in institutional households.

The following specifications are used for calibration:

*) Pbas … total number of persons in private households in NUTS-2 region b (= 1, …, 9), age class a (1 = 0-2 years, 2 = 3-5 years, 3 = 6-9, 4 = 10-14, … (5-year classes) …, 18 = 80-84, 19 = 85+) and sex s (= 1, 2).

*) Pbn … total number of persons in private households in NUTS-2 region b (= 1, …, 9) with nationality n (1 = Austria, 2 = EU-15 without Austria, 3 = EU from 2004 onwards, 4 = European non-EU states, 5 = Turkey, 6 = others).

*) Pbse … total number of persons in private households in NUTS-2 region b (=1, …,9), administrative employment status e (1=standard employment, 2=non-standard employment, 3=self-employment, 4=unemployment, 5=out of labour force) and sex s (=1, 2).

*) Wbg … total number of households in NUTS-2 region b (= 1, …, 9) with g (= 1, 2, …, 5+) residents.

 Y   NA  Y  5 year age groups up to 84 years, except for children aged 0 - 2 and 3 - 5 years are weighted separately; last age class ist 85+. NUTS 2 

Nationality (Austria, EU-15 without Austria, EU from 2004 onwards, European non-EU states, Turkey, others),

 Size of households (1, 2, 3, 4, 5+),

Register based labour status (standard employment, non-standard employment, self-employment, unemployment, out of labour force) 

 

 

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
 Average of quarterly weights Same as in quarterly weights  Same as in quarterly weights  Same as in quarterly weights Same as in quarterly weights

 

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)
 There are no extra weights on household level. All weights are on individual level. Individual weight of each household's reference person is identical with the household weight. 

The number and size of private households is based on the register based census for the census years (2011, 2021, etc.) and the register-based labour market statistics for the years in between.

 Yes, see quarterly weights.  See quarterly weights. The individual weights are the same for all members of the household.  Y

The variables used for stratification are the Districts and the urban/rural areas within each district.
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)? In case of Computer Assisted Methods adoption for data collection, could you please indicate which software is used?
First wave: Face-to-Face-Interviews, using CAPI (Computer assisted personal interviews). Second to fifth wave CAWI (Computer assisted web interviewing) or CATI (Computer assisted telephone interviewing). CAWI available from the second quarter of 2021 onwards.  Y  The internally developed software "STATsurv" is used.

 

Are any LFS data collected from registers (Y/N)? If Yes, please indicate which variables are collected from registers.
 Y

INCGROSS,

INCGROSS_F,

REGISTER
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
Description of users with respect to the statistical data
 High relevance of the main LFS statistics and indicators at national level, especially for policy makers, media, but also for other stakeholders, academic research and research institutes.
Indication of the needs and uses for which users want the statistical outputs; information on unmet user needs and any plans to satisfy them in the future
 The main LFS statistics are very important to monitor the developments of the labour market and to carry out long-term and EU-wide comparisons. Data related to the shortage of skilled workes are needed.
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  NUTS 2  NUT 2

Data source on NUTS 3 level for population, employed and economically active population is the LFS, annual average -  calculated by Eurostat.

Data source for unemployed on NUTS 3 level is the administrative data of the Austrian Employment Agency (AMS).
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 average estimates Yearly estimates - wave approach 
 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
 3 000  6 000  5000 14000
Biennial variables estimates Household estimates Household average estimates
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 Limit below which figures cannot be published Limit below which figures must be published with warning
 5 000 14 000   3 000  6 000  3 000  6 000
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)       
 

            Employment rate                                 

Unemployment-to-population ratio                      

Youth unemployment rate as a percentage of labour force
 

Age group: 15 -74

Age group: 15 -74

Age group: 15 -24
 CV 0,150 1,630 4,290
 SE 0,094 0,069 0,471
 CI(**) 63,744 ; 64,108 4,078 ; 4,355 9,972 ; 11,844

 

                                      Unemployment-to-population ratio 15-74 (NUTS 2 regions)                                 
  CV       SE         CI(**)  
AT11 5,25 0,1733 2,952 ; 3,636
AT12 4,14 0,1433 3,189 ; 3,74
AT13 2,76 0,2267 7,727 ; 8,622
AT21 4,72 0,1702 3,275 ; 3,945
AT22 4,88 0,1407 2,618 ; 3,157
AT31 5,73 0,1506 2,338 ; 2,936
AT32 5,19 0,1669 2,868 ; 3,54
AT33 4,57 0,1516 3,01 ; 3,613
AT34 5,74 0,1648 2,567 ; 3,185

 

Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
 We treat the denominator (population) as an estimate with a sample variance but due to calibration (against administrative labour status) this variance can be quite small.

 

Reference on software used: Reference on method of estimation:
 R  Calibrated Rescaled Bootstrap

  (*) 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  From 2004 onwards the sample for the Austrian LFS is drawn from the Austrian Register of Residents. This register was set up in 2002. The sample is drawn three months before the start of the quarter. This results in a time lag of three to six months. Therefore dwellings where persons moved in after the due date for the survey are not covered. Furthermore, undercoverage of migrants can be observed, although the questionnaires are translated into several languages.  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 2021 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)?
 Y  The questionnaire was adapted according to the IESS  Regulation and the implementation of CAWI.   Y  A lot of tests were carried out: cognitive tests, pilot tests, Internal checks.

 

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
Interviewer  Periodical training (at least 1 time per year) (Y/N)  Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
 Y  Y (during annual training, furthermore questions can be asked at any time)
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)
 Y (for CAWI)  Y (for CATI-Interviews)  Y
Questionnaire  Questionnaire in several languages (Y/N)  On-line checks (for computer assisted interviews (Y/N)
 N  Implementation of foreign questionnaires postponed to 2022.
Other / Comments  NA
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  Age, sex, nationality, size of households, NUTS 2 according to register of residents, labour status from administrative data.   Post-stratification
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
 Mode can change any time during the survey; mode is therefore not meaningful.  see CAPI  NA  see CAPI  NA

 

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) (%) 
1 5,58 2,02 1,43
2 5,28 1,78 0,5
3 4,93 1,94 0,73
4 4,57 2,71 0,67
Annual 5,09 2,11 0,83

 

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_2021 Quarter2_2021 Quarter3_2021 Quarter4_2021
Subsample_Q1_2020 147      
Subsample_Q2_2020 95 156    
Subsample_Q3_2020 76 93 158  
Subsample_Q4_2020 70 45 108 148
Subsample_Q1_2021 46 46 68 141
Subsample_Q2_2021   41 40 118
Subsample_Q3_2021     41 91
Subsample_Q4_2021       81
Total in absolute numbers 434 381 415 579
Total in % of theoretical quarterly sample 1,93 1,69 1,84 2,57

 

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_2021 Quarter2_2021 Quarter3_2021 Quarter4_2021
Subsample_Q1_2020 36      
Subsample_Q2_2020 6 23    
Subsample_Q3_2020 68 23 21  
Subsample_Q4_2020 81 25 39 28
Subsample_Q1_2021 53 18 28 34
Subsample_Q2_2021   18 40 29
Subsample_Q3_2021     29 25
Subsample_Q4_2021       27
Total in absolute numbers 307 107 157 143
Total in % of theoretical quarterly sample 1,36 0,48 0,70 0,64

 

Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)  Non response rate (%)
AT11 Burgenland 2,71
AT12 Niederösterreich 4,12
AT13 Wien 8,69
AT21 Kärnten 4,53
AT22 Steiermark 3,75
AT31 Oberösterreich 5,35
AT32 Salzburg 7,14
AT33 Tirol 4,07
AT34 Vorarlberg 3,69

* 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 Implementing Regulation (EC) No 2019/2240)       

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  C193-194  ABSREAS  -

0.17

 -

 -

  According to Flow Charts imputation not allowed.
 Compulsory  C199  WANTWORK  0.04 0.20  0.20 0.18   According to Flow Charts imputation not allowed.
               
               
               
               
               
               

 

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 C316-323 INCGROSS Y Delivered yearly with delay since basic data originates from registers.
Compulsory C324-325 INCGROSS_F Y Delivered yearly with delay since basic data originates from registers.
Compulsory C326 REGISTER Y Delivered yearly with delay since basic data originates from registers.

(*) "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  Overall editing rates are not calculated. The editing rate is monitored per national variable.
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 )
 Y  Overall editing rates are not calculated. The editing rate is monitored per national variable.
 Main variables Imputation rate  Describe method used, mentioning which auxiliary information or stratification is used 
 UNA  UNA  UNA
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
 N NA   NA  NA
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
Measures to improve timeliness and punctuality
 Y
7.2.1. Punctuality - delivery and publication

[not requested for the LFS quality report]


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 (*)  N  However, prospective information (intention to stay at least one year) is not available
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

 Y

  Changes according to the new Framework Regulation IESS 2021.  N (but break factors provided for employment and unemployment figures 2009-2020) Numerous variables affected. N, but notice in footmarks and metadata.
coverage (i.e. target population)  Y  Changes according to the new Framework Regulation IESS 2021.  N Numerous variables affected. N
legislation  Y  Changes according to the new Framework Regulation IESS 2021.  N Numerous variables affected. 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 Biannual variables asked in first wave. N Biannual variables. N (but notice in footmarks and metadata).
rotation pattern N NA NA NA NA
questionnaire Y Changes according to the new Framework Regulation IESS 2021 and the introduction of CAWI in 2021. N (but break factors provided for employment and unemployment figures 2009-2020) All variables affected. N (but notice in footmarks and metadata).
instruction to interviewers Y Changes according to the new Framework Regulation IESS 2021. N All variables affected. N
survey mode Y CAWI introduced in second quarter of 2021. N All variables affected. N (but notice in metadata).
weighting scheme N (only new weighting scheme concerning biannual and 8-years-module variables, to be used in odd years) NA  NA NA NA
use of auxiliary information N NA N NA N
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 There are no total employment data from business statistics, but only for parts of the economy. Structural Business Statistics is a combination of business survey, administrative data and modell based data. LFS is a household survey, the net sample is weighted to fit population figures. In most recent data (2020) of BS total employment in NACE B to N plus S (only subsection 95) is slightly higher than in the LFS (+4.8% employed). NA
Total employment by NACE BS covers employed in enterprises still active at the end of the reference year. LFS gives information on the employed according to ILO definitions, sample is evenly spread over the quarter/year. LFS: NACE as reported by respondents, people working with a temporary employment agency report the NACE they actually work in. Deviations are greatest in NACE section N with LFS employment being 41% lower than in the BS. Further bigger deviations: In NACE section L LFS employment is 25% lower. In NACE section K LFS employment is 21% higher. NA
Number of hours worked  See above Business Statistics (Short Term Statistics) use the actually hours worked in main job resulting from LFS. Only parts of the economy are used, further more LFS data are adapted. Business Statistics publish an index. Comparing NACE sector G, the actual hours worked by employees increased from 2020 to 2021, the second year of the pandemic, in the LFS to a lower extent as in the short-term statistics. Regarding sections H, I, J, M, N in total, LFS data increased at a slightly lower rate than the short-term statistics.    NA

 

Coherence of LFS data with registered unemployment  
Description of difference in concept Description of difference in measurement Give references to description of differences
 ILO-Concept on unemployment versus registration at employment agency  Number of unemployed according LFS definitions is lower than registered unemployment (Data on registered unemployment as published by the Austrian Public Employment Service, Arbeitsmarktservice/AMS).  Number of unemployment according to the ILO-Concept is 14% smaller (283 700 vs. 331 700) than the number of unemployed according to the national statistics of AMS. 

 

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)
 LFS is 14% lower.  LFS is 70% higher.  LFS is 25% lower.  LFS is 100% higher.  LFS is 24% lower.  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  

Contrary to LFS National Accounts (NA) count employees working in Austria  but living abroad (domestic versus national concept).
People working in civilian or military service are included in the NA, but excluded from the LFS.

Employees living in institutional homes are included in the NA but excluded from the LFS.
Employees below the age of 15 are excluded from the LFS but included in the NA.

  National Accounts use different data sources, above all administrative data.  Employment in National Accounts is higher than in LFS (3 909 900 in National Accounts vs. 3 690 800 in LFS - 2020 data)  https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/B/std_b_mz-arbeitskraefte-wohnungserhebung_ab_2004.pdf
Total employment by NACE  see above see above   LFS: NACE as reported by respondents, people working with a temporary employment agency report the NACE they actually work in.  see above
Number of hours worked  see above  National Accounts use different data sources, related to the hours worked mainly LFS and STS (short time statistics) data are used. LFS data on hours worked are adapted by National Accounts.   Annual volume of work (actual hours, main and second job) in National Accounts is slightly higher than in LFS (6 715 Mio. hours in National Accounts vs. 6 481 Mio. h in LFS - 2020 data)  see above

 

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
 

https://www.statistik.at/statistiken/arbeitsmarkt/erwerbsstatus/erwerbsstatus

https://www.statistik.at/services/tools/services/publikationen/detail/1308?cHash=99a259b88660ed985d1e9071758238c0

 
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
 https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/B/std_b_mz-arbeitskraefte-wohnungserhebung_ab_2004.pdf
 Access to complete microdata files upon acceptance of specific terms of use (free of charge) through AUSSDA: https://aussda.at/

 

A comprehensive documentation of all the variables, instruction manual and all the questionnaires etc. is available on the internet:

https://www.statistik.at/ueber-uns/erhebungen/personen-und-haushaltserhebungen/mikrozensus .
Further information is also available at AUSSDA: https://data.aussda.at/dataverse/statistikaustria.

  Users having problems with the data can get support from Statistics Austria and AUSSDA.
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  Everybody  All users have to confirm compliance with Statistic Austria's terms of use.  Questionnaire, description of variables and levels,  list of articles on methodology, technical report, further important notes.  Users can contact Statistics Austria and AUSSDA for further support.
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

 https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/B/std_b_mz-arbeitskraefte-wohnungserhebung_ab_2004.pdf
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
 Microcensus micordata is anonymized in the course of the data-management process. Results are published strictly in line with the Bundesstatistikgesetz 2000, as amended.


12. Comment Top

[not requested for the LFS quality report]


Related metadata Top


Annexes Top