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.
Statistics Poland Al. Niepodległości 208, 00-925 Warszawa
1.2. Contact organisation unit
Labour Market Department
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Labour Market Department
Statistics Poland Al. Niepodległości 208, 00-925 Warszawa
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, however we collect also some information on members of household staying in collective household if they are part of target population. The target population covers all persons 15 years old and older with usual residence in Poland. Persons living in institutional households (army, hospital, prison, hostels etc.) for a total period exceeding one year are excluded from the survey. The same applies to persons living permanently or temporarily (for more than one year) in other countries.
Housekeeping
Members living regularly together in the same dwelling, sharing income, household expenditures, food and other essentials for living
The following categories of people are considered as members of a household:
- persons present in a household (registered for a permanent or temporary stay, staying or intending to stay without registering for 12 months and more in a household), -persons absent for duration shorter than 12 months in a household (e.g. persons staying temporarily abroad, living in institutional households or other households in the country for duration shorter than 12 months).
Persons living in institutional households (army, hospital, prison, hostels etc.) for a total period exceeding one year are excluded from the survey. The same applies to persons living permanently or temporarily (for more than one year) in other countries.
15-89 years
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
Term address
Most of the time
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)
Y
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
Two-stage stratified probability sampling of dwelling units
OBS – statistical sampling frame for social surveys
continuously updated
The primary sampling units refer with few exceptions to census clusters in towns and enumeration districts in rural areas
dwelling units
December 2021 (wave 1) or earlier (other waves)
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.)
PSUs are sampled within strata with sampling probability proportional to the number of dwellings in a PSU
In the second stage a total of 55536 dwelling units per quarter are sampled from selected PSU's stratified by size of the municipality
The primary sampling units are stratified by urban/rural division of NUTS2 regions; stratification within NUTS2 regions depends on the size of the place, with rural areas included among the smallest ones
70
The quarterly sample is divided into four subsamples, subject to the rotation scheme 2-(2)-2. In each quarter are surveyed two elementary samples surveyed in the previous quarter, one sample introduced into the survey for the first time and one sample which was introduced into the survey exactly a year before (overlap of 50% between samples in two successive quarters)
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)
1.6% of dwelling units
55536 x 4= 222144 dwellings
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)
0.4% of dwelling units
55536 dwellings
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)
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 computed using a four-step procedure. First the initial design weights are calculated for dwelling units, i.e. the reciprocals of the selection probabilities for the final sampling units in each stratum. Secondly, the weighted response rates are calculated for sampling units stratified a posteriori by six place-of-residence categories in each NUTS2 region. Thirdly, the initial weights are adjusted by the response rates. The final step is a calibration to some constraints which include the population by the urban-rural division, sex, age, NUTS2 region and a constraint forcing equal representation of reference weeks in the quarterly estimates.
N
Reference population covers entire population excluding members of households living temporarily abroad for more than 12 months or living in institutional households
Y
0-4 years, 5-9 years, 10-14 years, 15-17 years, 18-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-89 years, 90 years and more
NUTS2
6 categories of place of residence (the rural area or one of the five town classes) [second step of weighting]; reference week [final step]
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 subsampling is not applied to survey yearly variables. Yearly weights are calculated as averages of quarterly weights
Y
0-4 years, 5-9 years, 10-14 years, 15-17 years, 18-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-89 years, 90 years and more
NUTS2
6 categories of place of residence (the rural area or one of the five town classes); reference week
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)
The final weights for generalizations concerning households since the Ist quarter 2016 are calculated as mean values of final weights attributed to household members. In case when only some persons in a household submitted the questionnaire for the respondents, other members of such household who did not submit the questionnaire for the respondents are given zero final weight. The final weight for a household is the mean value of the final weights of the household members including persons with zero final weight. The final weight is attributed to the person who is the household head, while the estimate of the number of households in a given quarter is calculated by adding such weights for all household heads.
N
Number of household members (household size)
gender, 15 age groups, NUTS2 regions, 6 categories of place of residence of a given dwelling (the rural area or one of five town classes)
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)?
In case of Computer Assisted Methods adoption for data collection, could you please indicate which software is used?
In 2022 LFS data were collected mainly by telephone interviews instead of face-to-face ones using CAPI application (in case of problems with IT applications or equipment paper questionnaires were used). In the 3rd and 4th quarter only the first wave data were collected by face-to-face interviews using CAPI application.
Y (we use for some variables historical data from previous observations which are during the interview verified by the respondent, however, information about each respondent known prior to the interview is not used to determine question routing and wording).
CAPI application is programmed in JAVA.
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
The main Polish users of LFS data are: researchers, science institutes, universities, government and local government institutions, National Polish Bank (NBP).
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
Within the framework of elaborating the annual programme of statistical surveys, the data users report their needs concerning extending or changing the scope of the data collected in the PL-LFS. Only the proposals which are in accordance with the objective and methodology of the carried out survey and will not have too much impact on the increase in the survey costs or burdening respondents (which may negatively influence both the implementation and quality of the survey) are taken into account.
The postulate often submitted by the users of the PL-LFS data is demand for data at the levels lower than NUTS2 region and availability of a larger scope of the data possible to be presented at NUTS2 level. Statistics Poland had been carrying out the work targeted at facilitating obtaining such data and at the same time maintaining the current size of the sample for the survey (also with the use of the small area estimation methods) but they did not result with desired effects, therefore currently the demand cannot be satisfied.
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
NUTS 2
3-year average from the LFS dataset
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
[not requested for the LFS quality report]
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
0.28
3.07
6.56
SE
0.17
0.06
0.71
CI(*)
60.50-61.18
1.70-1.92
9.41-12.19
Unemployment-to-population ratio 15-74 (NUTS 2 regions)
NUTS-2 region (code + name)
CV
SE
CI(*)
PL21 Małopolskie
11.04
0.1649
1.17-1.82
PL22 Śląskie
8.97
0.1166
1.07-1.53
PL41 Wielkopolskie
14.47
0.1846
0.91-1.64
PL42 Zachodniopomorskie
13.35
0.1880
1.04-1.78
PL43 Lubuskie
14.07
0.1353
0.70-1.23
PL51 Dolnośląskie
11.49
0.2582
1.74-2.75
PL52 Opolskie
10.63
0.1775
1.32-2.02
PL61 Kujawsko-pomorskie
11.96
0.2911
1.86-3.00
PL62 Warmińsko-mazurskie
12.91
0.2592
1.50-2.52
PL63 Pomorskie
17.93
0.2147
0.78-1.62
PL71 Łódzkie
11.49
0.2550
1.72-2.72
PL72 Świętokrzyskie
8.75
0.2026
1.92-2.71
PL81 Lubelskie
8.89
0.2628
2.44-3.47
PL82 Podkarpackie
10.34
0.2817
2.17-3.28
PL84 Podlaskie
10.79
0.1722
1.26-1.93
PL91 Warszawski stołeczny
11.05
0.1491
1.06-1.64
PL91 Mazwowiecki regionalny
10.56
0.2509
1.88-2.87
Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
The denominator for the calculation of the CV for the employment rate is the estimate of the employment rate as a percentage of the population
Reference on software used:
SAS Base
Bootstrap method with calibration of bootstrap weights Shao J. and Tu D., The Jackknife and Bootstrap, Springer-Verlag, New York 1995
(*) 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
1.58
UNA
New dwellings underrepresented in the sample-dwellings are selected once a year from the register of housing units and due to differences in time span there is no current information about addresses or flats
Overcoverage consists of dwellings in which inhabitants are not present for a long time, not inhabited or inhabited seasonally, changed into uninhabitable space (for example shop), in liquidation, not found (incorrect address)
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
There were introduced changes in the names of the education levels (resulting from the harmonisation with other our social surveys) and modification of the way of asking about the year of attaining the highest level of education aimed to improve quality of the variable
N
NA
Main methods of reducing measurement errors
Error source
Respondent
Letter introducing the survey (Y/N)
Phone call for booking or introducing the survey (Y/N)
Y
N
Interviewer
Periodical training (at least 1 time per year) (Y/N)
Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
Y
Y
Fieldwork
Monitoring directly by contacting the respondents after the fieldwork (Y/N)
Monitoring directly by listening the interviews (Y/N)
Monitoring remotely through performance indicators (Y/N)
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
place of residence (5 categories of towns and rural area) in the NUTS2 regions
The interview rates R are calculated by the formula: R=(K-N)/K, where K is the number of interviewed dwellings estimated using primary weights and N is the estimate of the number of dwellings that were qualified for the survey but were not interviewed regardless of the reasons. The interview rates are calculated for each of 17 NUTS2 regions in six categories of place of residence. The secondary weights are calculated by dividing the primary weights (reciprocals of selection probabilities for dwellings) by the corresponding R rate (the R rate depends on the NUTS2 region and the category of place of residence of a given dwelling)
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
50.73
32.33
NA
NA
NA
Non-response rates. 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
35.81
14.12
16.42
2
34.18
16.18
15.86
3
34.62
16.57
16.60
4
34.76
16.69
16.59
Annual
34.83
15.90
16.37
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_Q2_2020
Subsample_Q3_2020
710
Subsample_Q4_2020
629
864
Subsample_Q1_2021
711
902
1078
Subsample_Q2_2021
833
925
978
Subsample_Q3_2021
859
928
Subsample_Q4_2021
667
931
Subsample_Q1_2022
3658
606
Subsample_Q2_2022
4059
583
Subsample_Q3_2022
4014
631
Subsample_Q4_2022
4125
Total in absolute numbers
6375
7264
7459
7593
Total in % of theoretical quarterly sample
13.66
15.46
15.78
16.03
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_Q2_2020
Subsample_Q3_2020
1234
Subsample_Q4_2020
1341
1194
Subsample_Q1_2021
1260
1119
1021
Subsample_Q2_2021
1340
1172
1135
Subsample_Q3_2021
1630
1492
Subsample_Q4_2021
1644
1398
Subsample_Q1_2022
2197
1358
Subsample_Q2_2022
2478
1549
Subsample_Q3_2022
2476
1519
Subsample_Q4_2022
2367
Total in absolute numbers
7676
7489
7848
7911
Total in % of theoretical quarterly sample
16.44
15.94
16.61
16.71
Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)
Non response rate (%)
PL21Małopolskie
38.47
PL22Śląskie
33.79
PL41Wielkopolskie
37.55
PL42Zachodniopomorskie
45.70
PL43Lubuskie
40.91
PL51Dolnośląskie
41.40
PL52Opolskie
31.08
PL61Kujawsko-pomorskie
30.55
PL62Warmińsko-mazurskie
26.81
PL63Pomorskie
39.60
PL71Łódzkie
35.66
PL72Świętokrzyskie
26.63
PL81Lubelskie
21.06
PL82Podkarpackie
26.80
PL84Podlaskie
26.24
PL91Warszawski stołeczny
40.94
PL92Mazowiecki regionalny
31.06
* 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
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
GALI
11.37%
EDUCFED12
11.48%
compulsory
228
MAINCLNT
44.12%
Answering questions under this variable causes some problems not only for proxy answers but also in case of direct participation
compulsory
248-251
HATYEAR
10.98%
Non responses can be caused by proxy answers and, especially in the case of older people, difficulties with recalling distant dates
(*) "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 Not, please provide a description of the used methods and tools
Y
Y
Y
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. Eurostat/documents) (Y/N)
Y
We are compliant (Y), however the PEEIS are computed by Eurostat not by the national statistical offices.
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)
Y
The dwelling samples surveyed in the quarters 2022 were drawn from the sampling frame subset comprising only dwellings with telephone numbers available
N
UNA
N
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
Y
In each quarter 2022 one additional sample surveyed for the 5th time was used together with the samples foreseen by the rotation scheme in order to assure satisfactory response rate. The above mentioned modifications of quarterly sample design result from the changed organisation of the survey during the COVID – 19 pandemic
N
UNA
N
rotation pattern
N
NA
NA
NA
NA
questionnaire
Y
There was introduced a modification of the way of asking about the year of attaining the highest level of education aimed to improve quality of the variable
N
HATYEAR
N
instruction to interviewers
Y
Adapting the explanatory notes to the changes in the questionnaires
NA
NA
NA
survey mode
Y
In all quarters of 2022 data were collected only by telephone interviews (CATI mode) except for the first wave data in the 3rd and 4th quarter, which were collected by face-to-face interviews (CAPI mode)
N
UNA
N
weighting scheme
Y
In all quarters of 2022 an additional calibration condition has been introduced taking into account the structure of the population by level of education. It was introduced in order to ensure comparability of the survey results with the previous periods
N
UNA
N
use of auxiliary information
N
NA
NA
NA
NA
8.2.1. Length of comparable time series
[not requested for the LFS quality report]
8.3. Coherence - cross domain
Coherence of LFS data with Business statistics data
Description of difference in concept
Description of difference in measurement
Give an assessment of the effects of the differences
Give references to description of differences
Total employment
Business surveys comprise only persons employed in enterprises. LFS in contrary to business statistics covers employment in informal economy and all forms of contracts (contracts of specific work, contract of mandate). LFS does not cover people living in collective households. LFS covers residents of Poland working abroad (if their absence in households is less than 12 months) and business statistics covers persons working in Poland for Polish entities (which are not Polish residents).
In business statistics number of persons employed is measured at the end of the reference period and the number of employees as average in FTE. In LFS generally it is counted as number of persons in a given quarter.
UNA
UNA
Total employment by NACE
LFS covers all NACE groupings, STS industry, construction, retail trade, repairs and other services (but for example without financial ones); SBS covers industry, construction, distributive trade, services (but without not market ones e.g. health, education).
NACE code in LFS depends on the knowledge of respondent regarding the economic activity of firm in which this respondent works. In business statistics the NACE code is based on declaration of the company.
UNA
UNA
Number of hours worked
NA- differences not present or not known.
Hours actually worked but not recognized by employer are not counted in business statistics in contrary to LFS.
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
Registered unemployment is measured according to national legislation and its definition differs from ILO unemployment concept. Labour offices use the definition of the unemployed according to the Act of20 April 2004 on Promotion of Employment and Labour Market Institutions (uniform text Journal of Laws 2022 item 690, with later amendments) in which as unemployed are classified: persons aged 18and more and who have not reached the retirement age: women 60 years, men – 65 years (in case of LFS the possible age is 15-74 years, person may be in retirement and be unemployed), are not employed and not performing any other kind of paid work, capable of work and ready to take full-time employment or in the case of disabled persons ― able and ready to take work comprising no less than a half of working time (in case of LFS looking actively for any work matters), not attending school with the exception of schools for adults (or taking extra curriculum exam covering this school as well as those studying at the stage II sectoral vocational school and post-secondary school, providing full-time, evening or weekend education) or tertiary schools in part-time programme (in case of LFS person looking for work even if she/he is student/pupil can be unemployed), registered in the local labour office, appropriate for their (permanent or temporary) place of residence (in case of LFS person doesn`t have to be registered in powiat labour office),and seeking employment or any other income-generating work (in case of LFS person must actively look for work), with additional provisions concerning the sources of income, included in the law.
Registered unemployment: number of unemployed persons on the end of the period - that is on the end of the month or end of the quarter. LFS: there are an average number of unemployed persons through the period (quarter).
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)
III quarter 2022
801.7 - registered unemployment
502 - unemployment LFS
Difference: 299.7
IV quarter 2022
812.3 - registered unemployment
499 - unemployment LFS
Difference: 313.3
all data in thousands
III quarter 2022
44.4 - registered unemployment
71 - unemployment LFS
Difference: -26.6
IV quarter 2022
45.6 - registered unemployment
69 - unemployment LFS
Difference: -23.4
all data in thousands
III quarter 2022
318.0 - registered unemployment
204 - unemployment LFS
Difference: 114.0
IV quarter 2022
330.3 - registered unemployment
210 - unemployment LFS
Difference: 120.3
all data in thousands
III quarter 2022
55.2 - registered unemployment
57 - unemployment LFS
Difference: -1.8
IV quarter 2022
54.8 - registered unemployment
58 - unemployment LFS
Difference: -3.2
all data in thousands
III quarter 2022
384.0 - registered unemployment
169 - unemployment LFS
Difference: 215.0
IV quarter 2022
381.6 -registered unemployment
162 - unemployment LFS
Difference: 219.6
all data in thousands
UNA
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
We use domestic concept and national concept. According to national concept there is a difference with LFS concerns persons in own-use production work – they are excluded from LFS but included in NA. According to domestic concept there is the same difference as described above and there are also the following differences.: LFS data used in national accounts are adjusted by excluding Polish residents working abroad (residents working outside the economic territory) and by including foreigners working for the Polish employers (non residents working inside the economic territory).The data on foreigners working in Poland are taken from the annual survey Z-06 on employment, wages and salaries, and hours worked. The survey is carried out by the Statistics Poland. The survey covers all enterprises of national economy, in which the number of persons employed is more than 9 persons.
Data from Labour Force Survey by excluding residents working abroad and by including foreigners working for the Polish employers and by including persons in own-use production work.
I quarter 2022
LFS data:16714 hous.
NA*: 16825.2 thous.
difference: -111.2 thous.
II quarter 2022
LFS data:16770 thous.
NA*: 16903.4 thous.
difference: -133.4 thous.
III quarter 2022
LFS data:16690 thous.
NA*: 16822.7 thous.
difference: -132.7 thous.
IV quarter 2022
LFS data:16796 thous.
NA*: 16952.2 thous.
difference: -156.2 thous.
Annual average
LFS data:16742 thous.
NA*: 16875.9 thous.
difference: -133.9 thous.
*provisional data
UNA
Total employment by NACE
No difference in concept.
NA
NA
NA
Number of hours worked
The total number of hours worked of all persons employed according to the “domestic concept” consists of: total number of hours worked by employees from the survey LFS (excluding residents working outside the economic territory) and the number of hours worked by foreigners working in resident units (statistical survey Z-06 conducted by enterprises). In the LFS, the average number of hours worked per week is calculated as the ratio of the sum of hours worked in the reference week (the actual number of hours) to the number of persons working in the reference week. The following method of estimation of the number of hours worked by employed is adopted: number of total hours worked by employed persons working in the quarter is multiplied by the number of employed persons and the average number of hours worked in the reference week and the average number of weeks in the quarter.
Data from Labour Force Survey by excluding residents working abroad and by including foreigners working for the Polish employers and by including persons in own-use production work.
UNA
UNA
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)
X
8.6. Coherence - internal
[not requested for the LFS quality report]
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
1. Quarterly information on the labour market - short publications with main results from LFS, prepared every quarter after calculating the LFS results (about 2 months after the reference period)
2. "Labour Force Survey in Poland" (for given quarter of the year) - the main publication including more specific results, precision indices, methodological issues etc.; published quarterly
9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.
methodological information (e.g. sampling schemes, weighting, classifications used, main changes in methodology etc.), analytical part
other assistance available e.g. possibility to obtain guidance or some information during telephone conversation or through e-mail
9.3.1. Data tables - consultations
[not requested for the LFS quality report]
9.4. Dissemination format - microdata access
Accessibility to LFS national microdata (Y/N)
Who is entitled to the access (researchers, firms, institutions)?
Conditions of access to data
Accompanying information to data
Further assistance available to users
Y
researchers, science institutes, universities, goverment institutions, National Polish Bank (NBP)
Institutions must obtain individual for each case consent of Statistics Poland in order to get access to these data. The institutions must justify the need to obtain access to microdata – give the aim of data use. All data are anonymized before dissemination to these institutions.
Structure of data
phone 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
Popiński W., Development of the Polish Labour Force Survey, Statistics in Transition – Journal of the Polish Statistical Association, Vol. 7, No.5, 2006, pp. 1009-1030.
"Methodological report Labour Force Survey".
Publication "Labour Force Survey in Poland" (for a given quarter of the year) - contains a section on methodology of LFS in Poland.
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 users receive only anonymized microdata. LFS data are anonymized by cutting off the address data, telephones, names of people and aggregating values for some variables. The three IT environments are separated: for developers, testing and production. Personal data are only available in production environment and only for authorized persons.
[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, however we collect also some information on members of household staying in collective household if they are part of target population. The target population covers all persons 15 years old and older with usual residence in Poland. Persons living in institutional households (army, hospital, prison, hostels etc.) for a total period exceeding one year are excluded from the survey. The same applies to persons living permanently or temporarily (for more than one year) in other countries.
Housekeeping
Members living regularly together in the same dwelling, sharing income, household expenditures, food and other essentials for living
The following categories of people are considered as members of a household:
- persons present in a household (registered for a permanent or temporary stay, staying or intending to stay without registering for 12 months and more in a household), -persons absent for duration shorter than 12 months in a household (e.g. persons staying temporarily abroad, living in institutional households or other households in the country for duration shorter than 12 months).
Persons living in institutional households (army, hospital, prison, hostels etc.) for a total period exceeding one year are excluded from the survey. The same applies to persons living permanently or temporarily (for more than one year) in other countries.
15-89 years
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
Term address
Most of the time
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)
Y
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
Two-stage stratified probability sampling of dwelling units
OBS – statistical sampling frame for social surveys
continuously updated
The primary sampling units refer with few exceptions to census clusters in towns and enumeration districts in rural areas
dwelling units
December 2021 (wave 1) or earlier (other waves)
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.)
PSUs are sampled within strata with sampling probability proportional to the number of dwellings in a PSU
In the second stage a total of 55536 dwelling units per quarter are sampled from selected PSU's stratified by size of the municipality
The primary sampling units are stratified by urban/rural division of NUTS2 regions; stratification within NUTS2 regions depends on the size of the place, with rural areas included among the smallest ones
70
The quarterly sample is divided into four subsamples, subject to the rotation scheme 2-(2)-2. In each quarter are surveyed two elementary samples surveyed in the previous quarter, one sample introduced into the survey for the first time and one sample which was introduced into the survey exactly a year before (overlap of 50% between samples in two successive quarters)
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)
1.6% of dwelling units
55536 x 4= 222144 dwellings
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)
0.4% of dwelling units
55536 dwellings
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)
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 computed using a four-step procedure. First the initial design weights are calculated for dwelling units, i.e. the reciprocals of the selection probabilities for the final sampling units in each stratum. Secondly, the weighted response rates are calculated for sampling units stratified a posteriori by six place-of-residence categories in each NUTS2 region. Thirdly, the initial weights are adjusted by the response rates. The final step is a calibration to some constraints which include the population by the urban-rural division, sex, age, NUTS2 region and a constraint forcing equal representation of reference weeks in the quarterly estimates.
N
Reference population covers entire population excluding members of households living temporarily abroad for more than 12 months or living in institutional households
Y
0-4 years, 5-9 years, 10-14 years, 15-17 years, 18-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-89 years, 90 years and more
NUTS2
6 categories of place of residence (the rural area or one of the five town classes) [second step of weighting]; reference week [final step]
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 subsampling is not applied to survey yearly variables. Yearly weights are calculated as averages of quarterly weights
Y
0-4 years, 5-9 years, 10-14 years, 15-17 years, 18-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, 65-89 years, 90 years and more
NUTS2
6 categories of place of residence (the rural area or one of the five town classes); reference week
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)
The final weights for generalizations concerning households since the Ist quarter 2016 are calculated as mean values of final weights attributed to household members. In case when only some persons in a household submitted the questionnaire for the respondents, other members of such household who did not submit the questionnaire for the respondents are given zero final weight. The final weight for a household is the mean value of the final weights of the household members including persons with zero final weight. The final weight is attributed to the person who is the household head, while the estimate of the number of households in a given quarter is calculated by adding such weights for all household heads.
N
Number of household members (household size)
gender, 15 age groups, NUTS2 regions, 6 categories of place of residence of a given dwelling (the rural area or one of five town classes)
Y
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)
Y
The dwelling samples surveyed in the quarters 2022 were drawn from the sampling frame subset comprising only dwellings with telephone numbers available
N
UNA
N
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
Y
In each quarter 2022 one additional sample surveyed for the 5th time was used together with the samples foreseen by the rotation scheme in order to assure satisfactory response rate. The above mentioned modifications of quarterly sample design result from the changed organisation of the survey during the COVID – 19 pandemic
N
UNA
N
rotation pattern
N
NA
NA
NA
NA
questionnaire
Y
There was introduced a modification of the way of asking about the year of attaining the highest level of education aimed to improve quality of the variable
N
HATYEAR
N
instruction to interviewers
Y
Adapting the explanatory notes to the changes in the questionnaires
NA
NA
NA
survey mode
Y
In all quarters of 2022 data were collected only by telephone interviews (CATI mode) except for the first wave data in the 3rd and 4th quarter, which were collected by face-to-face interviews (CAPI mode)
N
UNA
N
weighting scheme
Y
In all quarters of 2022 an additional calibration condition has been introduced taking into account the structure of the population by level of education. It was introduced in order to ensure comparability of the survey results with the previous periods
