Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
6402 - Unit for Labour Force, Migration and Equal Opportunities
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Na padesátém 81 100 82 Praha 10 Czech Republic
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
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 the whole country. Only private households are surveyed.
Housekeeping
Members living together in the same dwelling, sharing household expenditures.
The target population comprises all persons living usually in the selected dwellings, disregarding the type of their stay there (permanent, temporary or non-registered).
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 (12 months)
Family home
Family home
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)
Evenly spread over the 52 weeks of the year, based on a reference fixed week
N
Participation is voluntary/compulsory?
Voluntary
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]
3.1. 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
The sampling plan is stratified two-stage probability sample of dwelling units
Register of Census Areas
2013
Census areas
Dwelling units
2021
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 primary sampling units are census (enumeration) areas that are sampled (by randomised systematic sampling) with probability proportional to size, i.e. a number of dwellings per census area.
In the second stage, dwellings are selected from the initial sample by simple random sampling.
The strata consist of 77 districts, including Prague considered as one district
77
5 waves in total. 20% of sample is replaced at each wave.
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. 2019/2240) (Y/N)
If not please list deviations
List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 2019/2240, Annex I)
1st wave
Yes
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 based on the post-stratification into 13 age groups, sex and the 77 districts. Demographic figures for each quarter 2021 were forecasted from the definitive demographic data for end-of-year 2021 with assuming a migration and natural increase
N
to the total population
Y
13 age groups (0-14,15-19,…,60-64,65-69, 70+)
77 districts
N
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 weights are based on the post-stratification into 6 age groups, sex and the 8 districts.
Y
6 age groups (0-14,15-24,25-34,…,55+)
8 districts, NUTS2
economic activity, 2 phases due to the absence of some stratum
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)
Person's weight as of the household unit
N (base sample of Register of Census Areas)
household size
sex, age, region
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)?
In case of Computer Assisted Methods adoption for data collection, could you please indicate which software is used?
Data are collected in first visits with face-to-face interviews, with repeated interviews are partly made by telephone (20%). Interviewers for the lack of time usuallywrite on a paper questionnaire, then overwrite the notebook.
Case 1: subsample wave n+1 = subsample wave n. During the (n+1)th interview, interviewers try to reach the whole sample of the first wave, including the non-respondents.
Sample of PSU is census enumeration area, then they are distinguished households, number will increase only 0.7 percent.
N
Are any LFS data collected from registers (Y/N)?
If Yes, please indicate which variables are collected from registers.
N
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]
4.1. Quality assurance
[not requested for the LFS quality report]
4.2. Quality management - assessment
[not requested for the LFS quality report]
5.1. Relevance - User Needs
Description of users with respect to the statistical data
Assessment of the relevance of the main LFS statistics at national level (e.g.for policy makers, other stakeholders, media and academic research)
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 at national level are highly relevant for all users (policy makers, other stakeholders, media and academic research). It is viewed as a key economic performance indicator of the domestic economy.
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?
Census area
NUTS 3
LAU for total employment by sex only
No method is used. The all database is sent.
5.3.1. Data completeness - rate
[not requested for the LFS quality report]
6.1. Accuracy - overall
[not requested for the LFS quality report]
6.2. Sampling error
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
0.50%
5.45%
13.38%
SE
0.32%
0.10%
1.14%
CI(*)
64.20%, 65.40%
1.69%, 2.10%
6.28%, 10.80%
Unemployment-to-population ratio 15-74 (NUTS 2 regions)
CV
SE
CI(*)
CZ01
21.82%
0.26%
0.69%, 1.73%
CZ02
18.87%
0.29%
0.95%, 2.07%
CZ03
14.28%
0.25%
1.24%, 2.21%
CZ04
13.83%
0.37%
1.94%, 3.38%
CZ05
12.94%
0.23%
1.34%, 2.25%
CZ06
15.66%
0.21%
0.94%, 1.77%
CZ07
15.65%
0.40%
1.77%, 3.33%
CZ08
12.98%
0.34%
1.97%, 3.32%
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:
R Studio
Svydesign in package Survey
(*) 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
10.6
UNA
Households are selected once a year from the Register of Census Areas. Due to differencies in time span there is not the current information about addresses or flats. The sampling frame contains only private households. Persons living in institutional households are not covered.
Not existing or not inhabited flats remain in the Register of Census Areas.
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 2022 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
Y
Y
cognitive, internal check
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)
N
Y
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)
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
N
NA
NA
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
UNA
UNA
UNA
NA
UNA
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
30.65
16.68
13.96
2
31.15
16.92
14.23
3
31.10
16.84
14.25
4
31.27
17.17
14.09
Annual
31.04
16.91
14.13
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_2022
Quarter2_2022
Quarter3_2022
Quarter4_2022
Subsample_Q1_2021
1221
Subsample_Q2_2021
1233
1251
Subsample_Q3_2021
1166
1224
1260
Subsample_Q4_2021
1072
1152
1204
1223
Subsample_Q1_2022
964
1113
1168
1213
Subsample_Q2_2022
996
1165
1243
Subsample_Q3_2022
913
1072
Subsample_Q4_2022
1071
Total in absolute numbers
5656
5736
5712
5822
Total in % of theoretical quarterly sample
16.68
16.92
16.84
17.17
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_2022
Quarter2_2022
Quarter3_2022
Quarter4_2022
Subsample_Q1_2021
828
Subsample_Q2_2021
784
788
Subsample_Q3_2021
903
845
827
Subsample_Q4_2021
980
902
852
834
Subsample_Q1_2022
1238
953
901
843
Subsample_Q2_2022
1335
983
896
Subsample_Q3_2022
1269
955
Subsample_Q4_2022
1250
Total in absolute numbers
1250
4823
4832
4778
Total in % of theoretical quarterly sample
14.96
14.23
14.25
14.09
Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)
Non response rate (%)
CZ01 Praha
37.07
CZ02 Stredni Cechy
24.72
CZ03 Jihozapad
31.25
CZ04 Severozapad
39.01
CZ05 Severovychod
33.06
CZ06 Jihovychod
26.77
CZ07 Stredni Morava
30.29
CZ08 Moravskoslezsko
26.55
* 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
148
PROXY
13.8
13.8
13.8
13.9
Unassigned code for children under 15. It will be modified from 2023.
Compulsory
205
AVAIREAS
26.8
27.9
25.6
26.6
Will be improved from 2023.
Item non-response (*) - Annual data (Compared to the variables defined by the Commission Implementing Regulation (EC) No 2019/2240)
Variable status
Column
Identifier
This reference year
Short comments on reasons for non-available statistics and prospects for future solutions
Compulsory
237
NEEDCARE
24.0
Not stated answers arise from respondents.
(*) "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, Supervisors in regions check text description of codes and chosen classification code.
(In the electronic questionnaire there are integrated checkings to control logic links of variables.)
2%
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
NA
Main variables
Imputation rate
Describe method used, mentioning which auxiliary information or stratification is used
GENHEALTH, GALI
100
hot-deck imputation using stratification by age group, sex, ISCED, MAINSTAT and NUTS2
INCGROSS
100
hot-deck imputation using stratification by age group, sex, 2-digit ISCO and NUTS2
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 Not, please provide a description of the used methods and tools
Y
Y
Y
Demetra 2.2.2
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. Eurostat/documents) (Y/N)
Y
Y, by option
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]
7.1. 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
[not requested for the LFS quality report]
7.2.1. Punctuality - delivery and publication
[not requested for the LFS quality report]
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
NA
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
NA
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
BP excludes persons on maternity leave, persons working under “Contract for work”, employees of Ministry of Interior and Defence, majority of state officials.
It is not possible to compare it.
Unknown
UNA
Total employment by NACE
BP classifies NUTS according to the residence of the company
It is not possible to compare it.
Unknown
UNA
Number of hours worked
BP uses paid hours
BP uses paid hours instead of actually or usually worked hours.
Unknown
UNA
Coherence of LFS data with registered unemployment
Description of difference in concept
Description of difference in measurement
Give references to description of differences
LFS is in accordance with ILO definition. Registered unemployment involves all persons who registered with Labour Office due to seeking a job and do not work, however the condition of not working is not officially checked.
LFS-unemployed category includes persons who have not contacted Labour Offices (unregistered) and excludes persons who might have contacted Labour Office but do not satisfy ILO conditions. LFS also provides a figure of registered unemployed that is regularly compared with actual figures of Labour Offices.
Register is an exhaustive source of persons interested in help or support from public services. LFS depends on response rate and reliability of data collected from respondents.
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)
Since 1999 fugures on the registered unemployed are higher than LFS – unemployed figures. It is connected with the relatively high rate of refusals in the Czech Republic.
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
NA use population of residents instead of population usually living in private households, foreigners are added from administrative sources, black economy and self-suppliers are estimated.
Employment and Unemployment in the Czech Republic as Measured by the Labour Force Sample Survey – quarterly publication (Czech/ English, electronic version) Labour Market in the Czech Republic – time series 1993-2021 (previous year resp.) – yearly publication (Czech/English, electronic version) Labour Market for each region (NUTS-3) of the Czech Republic 2022 - yearly publication (Czech/English, electronic version) Anthology of statistical analysis – yearly contribution on specific topic of the ad hoc module
9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.
1) through the CZSO info service on the basis of an agreement - file format - xlsx, csv + methodological description - on the basis of an agreement sent via storage;
Regional comparison - Statistical data from the labour force sample survey in the regions of the Czech Republic (in booklet format for answers or other users.).
Further assistance is possible by phone or e-mail. Contacts to professional staff (TEL, EMAIL) are available upon request via the CZSO infoservis (infoservis@czso.cz; infoservis@csu.gov.cz).
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
State enterprise, researcher and educational enterprise
research
general data colletion information
consultation
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
UNA
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]
Restricted from publication
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 sequential address identification is exchanged for a random identification number
[not requested for the LFS quality report]
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 the whole country. Only private households are surveyed.
Housekeeping
Members living together in the same dwelling, sharing household expenditures.
The target population comprises all persons living usually in the selected dwellings, disregarding the type of their stay there (permanent, temporary or non-registered).
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 (12 months)
Family home
Family home
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)
Evenly spread over the 52 weeks of the year, based on a reference fixed week
N
Participation is voluntary/compulsory?
Voluntary
Not Applicable
[not requested for the LFS quality report]
[not requested for the LFS quality report]
[not requested for the LFS quality report]
[not requested for the LFS quality report]
Not Applicable
[not requested for the LFS quality report]
Not Applicable
[not requested for the LFS quality report]
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
The sampling plan is stratified two-stage probability sample of dwelling units
Register of Census Areas
2013
Census areas
Dwelling units
2021
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 primary sampling units are census (enumeration) areas that are sampled (by randomised systematic sampling) with probability proportional to size, i.e. a number of dwellings per census area.
In the second stage, dwellings are selected from the initial sample by simple random sampling.
The strata consist of 77 districts, including Prague considered as one district
77
5 waves in total. 20% of sample is replaced at each wave.
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. 2019/2240) (Y/N)
If not please list deviations
List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 2019/2240, Annex I)
1st wave
Yes
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 based on the post-stratification into 13 age groups, sex and the 77 districts. Demographic figures for each quarter 2021 were forecasted from the definitive demographic data for end-of-year 2021 with assuming a migration and natural increase
N
to the total population
Y
13 age groups (0-14,15-19,…,60-64,65-69, 70+)
77 districts
N
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 weights are based on the post-stratification into 6 age groups, sex and the 8 districts.
Y
6 age groups (0-14,15-24,25-34,…,55+)
8 districts, NUTS2
economic activity, 2 phases due to the absence of some stratum
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)
Person's weight as of the household unit
N (base sample of Register of Census Areas)
household size
sex, age, region
N
Not Applicable
Restricted from publication
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
NA
Identification of the main job (*)
N
NA
Employment
N
NA
Unemployment
N
NA
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)
