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.
Milana Rakića No5, 11000 Beolgrade, Republic of Serbia
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 ...
Labor Force Survey
Housekeeping
Under the term household we anticipate: a) any family or other community of persons who declare to live together and jointly spend their income for basic living support (costs of housing, food, etc.), regardless the fact whether in the time of interview all the members appear in the place where the household is situated; b) every person who lives on his / her own (single-person household) and is not member of any other household in some other place, a person living in a separate or divided dwelling, or as a tenant, regardless whether he/she lives in the same room with another tenant or with the members of the lessor’s household; however this person does not spend income jointly with them but only pays for the housing.
Excluded are persons in institutional households (students’ homes, homes for children and young people with developmental disability, homes for socially imperiled children, old / retired people homes, homes for adults with disability, monasteries, nunneries, etc.). For the temporary absent persons (less than 1 year) data are obtained from other family member. Persons absent more than one year are excluded, except in a case when they considerably contribute to the income of the household and do not have other family in the place where they live. For students who live in other town/village within country data are obtained from original households in the case when they are economically dependent from that household.
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
Family home (if economically dependent otherwise term address)
Family home if they considerably contribute to the income of the household and do not have another family, otherwise term address.
Family home
Most of the time
Reference week
Fixed week (data collection refers to one reference week, to which the observation unit has been assigned prior to the fieldwork)
Rolling week (data collection always refers to the week before the interview)
In 2022, each week in a year is regarded as referent week, where referent weeks are fixed. Period of interview last two weeks after referent week.
The period of observation for the first quarter included 13 referent weeks and lasted from January 3, 2022 to April 3, 2022.
For the second quarter the period of observation included 13 referent weeks – from April 4, 2022 to July 3, 2022.
For the third quarter the period of observation included 13 referent weeks – from July 4, 2022 to October 2, 2022.
For the fourth quarter the period of observation included 13 referent weeks – from October 3, 2022 to January 1, 2023.
Participation is voluntary/compulsory?
According to the law it is 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]
3.1. 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 sample is based on a two-stage stratified rotation panel design.
Population Census 2011
2011
Primary sampling units (PSUs) are enumeration districts.
Final sampling units are households.
One month that preceding the quarter in which is 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.)
Primary sampling units (enumeration districts), are selected systematically with probability proportional to size (PPS) within each stratum (territory and settlement type) from EDs list. Size measure for each ED was number of persons, age 15 and more. EDs were sorted within each stratum according to the municipality and serial numbers. Using systematic selection on the sorted list, high level of implicit geographical stratification and effective sample distribution were provided
Final stage units - households were randomly selected, from household list, obtained for each ED.
Enumeration districts (PSUs) for each rotation group are stratified according to the type of settlement (urban and other) and 25 areas (NUTS3 level).
UNA
Sample for each quarter consists of 4 rotation groups (sub-samples), with 2- 2-2 rotation scheme. The overlap between two consecutive quarters is 50%.
Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate
Size of the theoretical yearly sample
(i.e. including non-response)
(i.e. including non-response)
3.3
76960
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)
0.8
19240
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
Initial weight for household is equal to inverse of inclusion probability (this inclusion probability is a product of inclusion probabilities from each stage), and correction for non-response. In order to obtain estimates for population that corresponds to current demographics projections, calibration procedure is performed, relating to the distribution of population according to sex, age (five-year age groups), at the level of territory (level NUTS 3) and distribution of households according to number of household members (six groups), at the level of territory, provided that a household and each person from the relevant household have the same final weigh.
N
Beside population in private households reference population also includes population in collective households.
Y
0-14, 15-19,
..., 70-74, 75+
NUTS 3
number of household members 1,2,…,6+
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
Yearly weights are calculated as an average of 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)
Calibration procedure is performed to provide that a household and each person from the relevant household have the same final weight.
Y
Number of households by size
Gender, age, NUTS 3 level
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?
Two modes of data collection were used in 2022: CAPI (Computer assisted personal interviewing) and CATI (Computer assisted telephone interviewing).
CAPI was applied for the households in the first wave and for the households in the later waves without phone contact. CATI was applied for the households in the 2, 3 or 4 wave with the phone contacts.
In average 41 interviewers per month were engaged in 2022 (33 of them in the field and 8 of them in the call center). Interviewing was done through 16 regional offices.
Dependent interviewing is used, except for variables by which ILO status is defined and for variables which relate to the specific period.
Data entry application designed and developed by SORS using MS Visual Studio (.net technologies) and MS SQL Server.
Are any LFS data collected from registers (Y/N)?
If Yes, please indicate which variables are collected from registers.
Yes, partially
INCGROSS in a case of item non-response
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
LFS data are used by national and international users.
National users are the Government organizations of the Republic of Serbia such as: the Ministry of Labour and Social Affairs, Ministry of Science and Technology, Ministry of Youth and Sports, National Bank, National Employment Service, local authorities, etc. Other national users are various academic and research institutions (universities and institutes), enterprises, journalists and individual users such as students, scientists, researchers and others.
The National Accounts use LFS data to produce the adequate estimations of the labour input. For this purpose, the variables used are: actual hours of work, section of activity, professional status, type of labour contract etc.
The international users are: Eurostat (for main LFS indicators), UNESCO and UNICEF (for educational data), ILO (for employment and unemployment), World Bank, International Monetary Fund (IMF) and others. Some countries from the region need LFS data for various regional projects.
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
To have insight at situation in the labor market, for policy makers to make future plans and actions.
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?
In LFS individual data are collected at NUTS3 regional level.
At the quarterly level data are published at NUTS2 regional level. At annual level only basic indicators such as rates of activities, employment, unemployment and outside the labor force are published at NUTS3 level.
NUTS3
NA
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
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
1000
1000-3800
NA
NA
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
NA
NA
NA
NA
NA
NA
6.2.1. Sampling error - indicators
Coefficient of variation (CV) Annual estimates Sampling error - indicators - Coefficient of variation (CV), Standard Error (SE) and Confidence Interval (CI)
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
0.4
2.2
4.2
SE
0.2
0.1
1.0
CI(*)
54.77 - 55.64
5.51 - 6.00
22.37 - 26.34
Unemployment-to-population ratio 15-74 (NUTS 2 regions)
CV
SE
CI(*)
Region 1
4.1
0.2
4.66 - 5.46
Region 2
4.3
0.2
4.54 - 5.38
Region 3
4.5
0.3
5.88 - 7.02
Region 4
4.2
0.3
6.16 - 7.27
Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
Coefficient of variation for the number of employed persons (aged 15-74) and the employment rate (aged 15-74) are identical. Since the denominator of the employment rate is the total population and for this particular age group (individuals aged 15-74) it is one of the population totals used in the calibration procedure. Thus the only source of sampling variability is the numerator.
Reference on software used:
Reference on method of estimation:
ReGenesees
Taylor linearization
(*) 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
Q1: 3.92
Q2: 4.20
Q3: 3.96
Q4: 3.78
Annual average: 3.96
UNA
UNA
UNA
UNA
UNA
(a) Mention specifically which regions / population groups are not suitably represented in the sample. (b) Misclassification refers to statistical units having an erroneous classification where both the wrong and the correct one are within the target population.
6.3.1.1. Over-coverage - rate
[Over-coverage rate, please see concept 6.3.1 Coverage error in the LFS quality report]
6.3.1.2. Common units - proportion
[not requested for the LFS quality report]
6.3.2. Measurement error
Errors due to the medium (questionnaire)
Was the questionnaire updated for the 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
In the third quarter of 2022 following 6 variables were added: MIGREAS, SIZEFIRM, WAYJFOUN,CONTRHRS, SHIFTWK, HWWISH. All variables are in accordance with regulation.
Only application.
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
Number of households
Response rates were calculated for each enumeration area and used to adjust the design weights calculated for each cluster.
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
38.49
18.34
N/A
N/A
N/A
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
27.40
7.10
20.06
2
26.83
5.90
20.59
3
28.35
5.86
22.15
4
27.68
5.92
21.50
Annual
27.56
6.20
21.07
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_Q4_2020
172
Subsample_Q1_2021
208
125
Subsample_Q2_2021
174
121
Subsample_Q3_2021
172
101
Subsample_Q4_2021
277
128
Subsample_Q1_2022
416
202
Subsample_Q2_2022
383
195
Subsample_Q3_2022
376
241
Subsample_Q4_2022
401
Total in absolute numbers
1073
884
864
871
Total in % of theoretical quarterly sample
5.58
4.59
4.49
4.53
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_Q4_2020
387
Subsample_Q1_2021
647
385
Subsample_Q2_2021
682
430
Subsample_Q3_2021
704
431
Subsample_Q4_2021
795
599
Subsample_Q1_2022
1201
832
Subsample_Q2_2022
1184
829
Subsample_Q3_2022
1305
865
Subsample_Q4_2022
1271
Total in absolute numbers
3030
3083
3268
3166
Total in % of theoretical quarterly sample
15.75
16.02
16.99
16.46
Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)
Non response rate (%)
RS11- Beograd
RS12- Vojvodina
RS21- Šumadija i Zapadna Srbija
RS22- Južna i Istočna Srbija
23.76
31.15
21.84
32.88
* 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
316-323
INCGROSS
46.30%
47.64%
50.64%
51.62%
There are several reasons:
- Persons do not want to reveal their income because they think that this question is to personal;
- In a case of proxy interview some respondents do not know income of other family members;
- Some respodents do not want to reveal their income because it is too low;
- In some companies respodents can not reveal their income to the others.
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
316-323
INCGROSS
49.03%
The same reasons as above.
(*) "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
UNA
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
47.90%
Main variables
Imputation rate
Describe method used, mentioning which auxiliary information or stratification is used
INCGROSS
47.90%
Imputation of gross monthly pay is conducted using the Earnings Register, which is established based on income data from the Tax Authority (TA). Approximately 54% of missing earnings data in the LFS, which corresponds to around 25% of all employees, are imputed using gross earnings data obtained from the Tax Authority.
If it not feasible to impute the value from the Tax records, such as in cases of informal employment, an average value of earnings is imputed based on the occupation and activity at the two-digit level. This is done by considering data from both the tax records and the LFS, following specified criteria. This method allows for the imputation of 44% of missing data for the variable INCGROSS, which represents approximately 20% of all employees who need to respond to the salary question.
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
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. Eurostat/documents) (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]
7.1. 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
N/A
Identification of the main job (*)
N
N/A
Employment
N
N/A
Unemployment
N
N/A
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
coverage (i.e. target population)
N
legislation
N
classifications
N
geographical boundaries
N
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
sample design
N
rotation pattern
N
questionnaire
Y
Six missing variables from reguilation were added in the third quarter of 2022.
For the first and second quarter of 2022 answers were imputed for these six variables for the rotational groups which repeated in the third and the fourth quarter of 2022 based on replies from these quarters (third and fourth). For the 2021 year there were no imputations for these variables.
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
Definition of registered employment: the term employed comprises persons who have formal legal employment contract, i.e. who entered into employment with an employer for definite or indefinite period of time and persons who work on the basis of contract on performing temporary or occasional jobs, persons performing occupations/activities independently or who are founders of enterprises or unincorporated enterprises, as well as persons performing agricultural activities up to 65 years old, and are in the records of Central Register of Compulsory Social Insurance LFS - each person is regarded as employed if in the respective week he/she had some work for remuneration (in money or in kind) for minimum one hour.
Registered employment – based on administrative data. Observation period- the next to last working day in the month. Quarterly data are calculated as the arithmetic mean of the number of employees for three months of the reference quarter; annual average as the arithmetic mean of the number of employees for 12 months.
LFS - survey based on the sample of private households. Observation unit for the LFS is each member of random selected household. Data source for the LFS is the statement which the interviewers collect from interviewed persons.
UNA
Notes on methodology (e.g. in Statistical Yearbook)
Total employment by NACE
Registered employment- cover sections of activity from A to S, according to NACE Rev.2.
LFS – cover all the sections of activity according to NACE rev2 classification.
Registered employment - Statistical unit corresponds to local-kind-of- activity unit (LKAU).
UNA
UNA
Number of hours worked
Business statistic surveys:Hours worked include: hours worked during normal periods of work, periods of paid overtime, short rest periods at the place of work like tea or coffee breaks.
LFS – Usual hours of work – which are the modal of actual hours of work over a long reference period (at least four weeks), excluding weeks of absence. For those with an employment contract are accepted contractual hours plus regular overtime.
Actual hours of work presents number of hours actually worked during the reference week, which exclude the main meal breaks, absence from work for personal reasons, education or training hours which are not connected with job
UNA
UNA
UNA
Coherence of LFS data with registered unemployment
Description of difference in concept
Description of difference in measurement
Give references to description of differences
LFS – According to ILO definition as unemployed persons are considered all persons aged 15-74, who did not have paid job in the referent week, have been actively looking for job in the previous four weeks, and can start to work within two weeks after referent week, or have not actively looking for job because they have already found job at which they will start working within 3 months.
Registered unemployment: Unemployed persons are persons aged 15-65 years, who are registered at National employment office, and who are available immediately to start to work, do not have registered employment, and actively searching for work.
UNA
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)
Total unemployed 15-65
Registered: 404 702
LFS; 298 524
15-24 Men unemployed
Registered: 20 195
LFS: 31 613
25-65 Men unemployed
Registered: 157 879
LFS: 125 324
15-24 Women unemployed
Registered: 21 746
LFS: 21 631
25-65 Women unemployed
Registered: 204 882
LFS: 119 956
NSZ ARS
Beogradska oblast (Grad Beograd) 50 726 64 521
Borski upravni okrug 7 196 6 450
Braničevski upravni okrug 5 586 9 879
Jablanički upravni okrug 22 264 13 054
Južnobački upravni okrug 27 041 21 124
Južnobanatski upravni okrug 16 236 9 827
Kolubarski upravni okrug 6 395 5 931
Mačvanski upravni okrug 17 549 11 375
Moravički upravni okrug 8 522 4 237
Nišavski upravni okrug 28 528 18 082
Pčinjski upravni okrug 19 871 4 150
Pirotski upravni okrug 8 384 3 938
Podunavski upravni okrug 7 348 11 264
Pomoravski upravni okrug 18 363 14 351
Rasinski upravni okrug 16 885 10 600
Raški upravni okrug 42 373 23 363
Severnobački upravni okrug 6 508 4 768
Severnobanatski upravni okrug 5 769 5 668
Srednjobanatski upravni okrug 10 520 6 037
Sremski upravni okrug 9 799 10 782
Šumadijski upravni okrug 22 469 16 256
Toplički upravni okrug 9 225 2 795
Zaječarski upravni okrug 6 848 5 178
Zapadnobački upravni okrug 10 998 5 997
Zlatiborski upravni okrug 19 299 10 332
8.4. Coherence - sub annual and annual statistics
[not requested for the LFS quality report]
8.5. Coherence - National Accounts
Coherence of LFS data with National Accounts data
Description of difference in concept
Description of difference in measurement
Give an assessment of the effects of the differences
Give references to description of differences
Total employment
The LFS covers all employed persons who are residents in Republic of Serbia (national concept), especially those who work in territory of the Country. National Accounts meet the domestic concept including all employed, which are in production boundary activities. It means NA includes all agriculture employment and all Non Observed Economy employment.
National Account (NA) estimate number of employed from different sources such as: official statistical data on the number of formal employed, structure of formal and informal employed from LFS, data on employed persons from financial statement and tax records.
NA use estimate of NA employment for own work tables, only.
NA estimates NA employment on experimental base only, so description has not been published yet.
Total employment by NACE
LFS provides data on the economic activity according Nace Rev. 2 classification on 3 digit level.
LFS provides data based on the respondent statement.
see above
UNA
Number of hours worked
UNA
UNA
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)
N
N
N
N
Y
N
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
LFS Communication quarterly, end of May 2022, end of August 2022, end of November 2022 and end of February 2023. Data are at quarterly level.
LFS Bulletin – once a year, at the end of April 2023. Data represent an annual average.
Statistical yearbook – annually.
Statistical calendar – annually.
9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.
Published data are made available to all interested institutions and media. Also, data can be sent to all interested parties at their request by email or phone.
Y
Y
9.3.1. Data tables - consultations
[not requested for the LFS quality report]
9.4. Dissemination format - microdata access
Accessibility to LFS national microdata (Y/N)
Who is entitled to the access (researchers, firms, institutions)?
Conditions of access to data
Accompanying information to data
Further assistance available to users
Y
Researchers and some international orgnaizations.
Microdata in SPSS format are available on special request with written contract.
Each variable in database contains label and value with belonging description.
Y
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
Detailed methodology is available on the following links: Data.stat Metadat.
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
SORS started to apply Eurostat rules for anonymization of microdata for users from 2020, and these rules are implemented for series from 2008 onward
[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 ...
Labor Force Survey
Housekeeping
Under the term household we anticipate: a) any family or other community of persons who declare to live together and jointly spend their income for basic living support (costs of housing, food, etc.), regardless the fact whether in the time of interview all the members appear in the place where the household is situated; b) every person who lives on his / her own (single-person household) and is not member of any other household in some other place, a person living in a separate or divided dwelling, or as a tenant, regardless whether he/she lives in the same room with another tenant or with the members of the lessor’s household; however this person does not spend income jointly with them but only pays for the housing.
Excluded are persons in institutional households (students’ homes, homes for children and young people with developmental disability, homes for socially imperiled children, old / retired people homes, homes for adults with disability, monasteries, nunneries, etc.). For the temporary absent persons (less than 1 year) data are obtained from other family member. Persons absent more than one year are excluded, except in a case when they considerably contribute to the income of the household and do not have other family in the place where they live. For students who live in other town/village within country data are obtained from original households in the case when they are economically dependent from that household.
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
Family home (if economically dependent otherwise term address)
Family home if they considerably contribute to the income of the household and do not have another family, otherwise term address.
Family home
Most of the time
Reference week
Fixed week (data collection refers to one reference week, to which the observation unit has been assigned prior to the fieldwork)
Rolling week (data collection always refers to the week before the interview)
In 2022, each week in a year is regarded as referent week, where referent weeks are fixed. Period of interview last two weeks after referent week.
The period of observation for the first quarter included 13 referent weeks and lasted from January 3, 2022 to April 3, 2022.
For the second quarter the period of observation included 13 referent weeks – from April 4, 2022 to July 3, 2022.
For the third quarter the period of observation included 13 referent weeks – from July 4, 2022 to October 2, 2022.
For the fourth quarter the period of observation included 13 referent weeks – from October 3, 2022 to January 1, 2023.
Participation is voluntary/compulsory?
According to the law it is compulsory.
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 sample is based on a two-stage stratified rotation panel design.
Population Census 2011
2011
Primary sampling units (PSUs) are enumeration districts.
Final sampling units are households.
One month that preceding the quarter in which is 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.)
Primary sampling units (enumeration districts), are selected systematically with probability proportional to size (PPS) within each stratum (territory and settlement type) from EDs list. Size measure for each ED was number of persons, age 15 and more. EDs were sorted within each stratum according to the municipality and serial numbers. Using systematic selection on the sorted list, high level of implicit geographical stratification and effective sample distribution were provided
Final stage units - households were randomly selected, from household list, obtained for each ED.
Enumeration districts (PSUs) for each rotation group are stratified according to the type of settlement (urban and other) and 25 areas (NUTS3 level).
UNA
Sample for each quarter consists of 4 rotation groups (sub-samples), with 2- 2-2 rotation scheme. The overlap between two consecutive quarters is 50%.
Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate
Size of the theoretical yearly sample
(i.e. including non-response)
(i.e. including non-response)
3.3
76960
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)
0.8
19240
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
Initial weight for household is equal to inverse of inclusion probability (this inclusion probability is a product of inclusion probabilities from each stage), and correction for non-response. In order to obtain estimates for population that corresponds to current demographics projections, calibration procedure is performed, relating to the distribution of population according to sex, age (five-year age groups), at the level of territory (level NUTS 3) and distribution of households according to number of household members (six groups), at the level of territory, provided that a household and each person from the relevant household have the same final weigh.
N
Beside population in private households reference population also includes population in collective households.
Y
0-14, 15-19,
..., 70-74, 75+
NUTS 3
number of household members 1,2,…,6+
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
Yearly weights are calculated as an average of 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)
Calibration procedure is performed to provide that a household and each person from the relevant household have the same final weight.
Y
Number of households by size
Gender, age, NUTS 3 level
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
N/A
Identification of the main job (*)
N
N/A
Employment
N
N/A
Unemployment
N
N/A
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
coverage (i.e. target population)
N
legislation
N
classifications
N
geographical boundaries
N
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
sample design
N
rotation pattern
N
questionnaire
Y
Six missing variables from reguilation were added in the third quarter of 2022.
For the first and second quarter of 2022 answers were imputed for these six variables for the rotational groups which repeated in the third and the fourth quarter of 2022 based on replies from these quarters (third and fourth). For the 2021 year there were no imputations for these variables.
