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.
LABOUR FORCE STATISTICS DEPARTMENT - Labour Force Statistics Unit
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Devlet Mah. Necatibey Cad. No:114 06650 Çankaya/ANKARA
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 ...
All private households are covered in the survey. Institutional population; including the residents of schools (university students living in dormitories), people living in rest homes, orphans' homes, special hospitals (for people staying more than twelve months) and military barracks (conscripts are excluded from the sample while army forces are included) are not covered in the survey.
Dwelling
Members living together in the same dwelling.
Persons to be included;
15 years old and over
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
Family home
Family home
Family home
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)
All weeks of the year (52 weeks) used as the reference period which the observation unit has been assigned prior to the fieldwork.
Participation is voluntary/compulsory?
Compulsory
2.2. Classification system
[not requested for the LFS quality report]
2.3. Coverage - sector
[not requested for the LFS quality report]
2.4. Statistical concepts and definitions
[not requested for the LFS quality report]
2.5. Statistical unit
[not requested for the LFS quality report]
2.6. Statistical population
[not requested for the LFS quality report]
2.7. Reference area
[not requested for the LFS quality report]
2.8. Coverage - Time
[not requested for the LFS quality report]
2.9. Base period
[not requested for the LFS quality report]
3.1. 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 cluster sampling involving eight subsamples.
National Address Database which is based on Address Based Population Register System is used as the sampling frame.
National Address Database consists of all addresses up to indoor numbers and updated at any moment (continuosly) by the municipalities.
Clusters. Each cluster (block) consists of approximately 100 households (between 80 and 120). First stage sample selection is realized by using the frame of clusters.
Household address
One month before the reference week of the relevant month
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 (blocks) are selected with probability proportional to their household sizes from Province X Urban/Rural classes.
Selection of the second stage units (household addresses) are based on the rotation pattern. When the PSU's are firstly entered to the sample, 20 households are selected systematically and divided into two parts namely the sets A and B. Every quarter 10 of those (one set A or B) are involved in the survey.
At the first stage of sampling, the implicit strata are defined as 81 provinces and urban-rural areas. An urban area is defined as a settlement with more than 20 000 inhabitant, rural area is defined as a settlement with less than 20 000 inhabitant based on current Address Based Population Register System. Not all provinces have a rural area.
158 (81 provinces and urban-rural areas)
2-2-2
Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate
Size of the theoretical yearly sample
(i.e. including non-response)
(i.e. including non-response)
0.93%
234 240 households
Quarterly sample size & Sampling rate
Overall theoretical quarterly sampling rate
Size of the theoretical quarterly sample
(i.e. including non-response)
(i.e. including non-response)
0.23%
58 560 households
Use of subsamples to survey structural variables (wave approach)
Only for countries using a subsample for yearly variables
Wave(s) for the subsample
Are the 30 totals for ILO labour status (employment, unemployment and inactivity) by sex (males and females) and age groups (15-24, 25-34, 35-44, 45-54, 55+) between the annual average of quarterly estimates and the yearly estimates from the subsample all consistent? (Ref.: Commission Reg. 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 II)
NA
NA
NA
NA
Brief description of the method of calculating the quarterly core weights
Is the sample population in private households expanded to the reference population in private households? (Y/N)
If No, please explain which population is used as reference population
Gender is used in weighting (Y/N)
Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)?
Which regional breakdown is used in the weighting (e.g. NUTS 3)?
Other weighting dimensions
Since 2021, independent monthly estimates has been published. That is why quarterly weights is calculated by taking the weighted averages of the relevant months according to the number of weeks in the months.
N
Non-institutional population projections belonging to 15th of middle month, based on Address Based Population Register
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
In the calculation of yearly weights, the initial weights adjusted by the nonresponse of each month which were already calculated during the year are defined as the input weights in the calibration procedure. After bringing together all data the weights are calibrated by the "1st of July of the related year population projections" based on Address Based Population Register. Integrated calibration method is applied in the procedure. Also trimming procedure is applied to avoid the use of extreme weights. In the calibration,"Age group by sex", "NUTS 2 by urban-rural", "NUTS 3", and "Household size" distributions are adjusted iteratively.
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)
Household address is the final sampling unit selected for the LFS. Depending on the weighting procedure explained above identical household weights for all household members are obtained as result of integrated calibration. Therefore, no additional calculations are applied for household weights.
Household size information coming from Address Based Population Register.
Age group by sex, NUTS 2 by urban and rural, NUTS 3
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?
The first interviews are performed by CAPI technique. Interviews in the second and subsequent waves are mainly carried out by CATI.
Y
Harzemli Data Entering Program (Developed by TURKSTAT for CAPI and CATI)
Are any LFS data collected from registers (Y/N)?
If Yes, please indicate which variables are collected from registers.
Y
Birth year, month and date, birth place, sex, nationality, marital status, personal contact number
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 aim of the survey is to reveal clearly the status of the population in terms of labor, to compile detailed information about employment and unemployment and to offer them to the public for the usage of determinants of economic policies and reviewers of country's economy.
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
Demands for basic labor force indicators at provincial level cannot be met. Production of these indicators is planned to start in 2023.
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?
NUTS 3 and further details (province, district etc.) are kept in the original national data set, but these information is neither published nor given to the users since sample is not representative at NUTS 3 level.
Estimation level is NUTS 2 for LFS (at annual basis).
At NUTS 3 level; only LFPR, employment&unemployment rate was published between 2008 and 2013 using small area estimation methods. In 2011 these indicators will not be published from LFS, since 2011 Census results will serve more detailed information on employment and unemployment at NUTS 3 level.
At micro data only NUTS 2 codes are given.
NUTS 3 level results have not sent to Eurostat (published with a press release at national level between 2008 and 2013) since it is not in line with the requested detail (no age and sex breakdown). However, it is available at the TURKSTAT website and also might be sent if it is requested.
The main labour force indicators at provincial level could not be estimated statistically significant because of transition to the continuous labour force survey covering every weeks of the year as reference period (52 weeks) and also the change on urban-rural distinction since 2014. So, it has been decided not to publish related press release considering the misleading results for decision makers.
NUTS3 level main indicators (LFPR, employment and unemployment rates) were calculated by the method “Empirical Best Linear Unbiased Predictor” (EBLUP) based on SAE methods. This method depends on using selected external variables (province populations and other demographic indicators, Social Security Institution data, Turkish Employment Office data etc.) besides the direct LFS estimations and standard errors. The province level results obtained from this study are very close to the original results directly calculated from the survey as the sample size is big enough for the province level estimations, while the sample size is not enough for the province level estimations, the estimation is determined predominantly by the external variables. Detailed methodology could be reached from the web site.
Briefly the study was implemented mainly in three stages:
1. Direct estimation at province level and the calculation of related standard errors by using LFS data. Calculation of direct standard errors is based on JRR approach and takes into account the srs variance, kish factor, overlapping factor and nuts1 level design effects. 2. The determination of the variables for the model by using external data sources. 3. The calculation of the composite estimates.
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
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
500
Limit is 2 500. The explanation is; "sample size is too small for reliable estimates for figures less than two thousand persons in each cell".
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
0.28
1.00
1.66
SE
0.14
0.06
0.32
CI(*)
(49.56-50.11)
(5.69-5.92)
(18.81-20.08)
Unemployment-to-population ratio 15-74 (NUTS 2 regions)
CV
SE
CI(*)
TR10-İstanbul
2.77
0.16
(5.58-6.23)
TR21-Tekirdağ, Edirne, Kırklareli
6.05
0.29
(4.26-5.41)
TR22-Balıkesir, Çanakkale
5.62
0.22
(3.50-4.37)
TR31-İzmir
3.79
0.28
(6.94-8.05)
TR32-Aydın, Denizli, Muğla
5.03
0.25
(4.52-5.51)
TR33-Manisa, Afyon, Kütahya, Uşak
5.20
0.22
(3.72-4.56)
TR41-Bursa, Eskişehir, Bilecik
4.05
0.21
(4.70-5.51)
TR42-Kocaeli, Sakarya, Düzce, Bolu, Yalova
3.79
0.23
(5.51-6.39)
TR51-Ankara
3.35
0.23
(6.33-7.22)
TR52-Konya, Karaman
5.50
0.22
(3.52-4.37)
TR61-Antalya, Isparta, Burdur
5.59
0.31
(4.94-6.16)
TR62-Adana, Mersin
4.67
0.31
(6.08-7.31)
TR63-Hatay, Kahramanmaraş, Osmaniye
3.66
0.28
(7.00-8.08)
TR71-Kırıkkale, Aksaray, Niğde, Nevşehir
5.00
0.25
(4.47-5.44)
TR72-Kayseri, Sivas, Yozgat
5.54
0.27
(4.41-5.48)
TR81-Zonguldak, Karabük, Bartın
6.48
0.39
(5.22-6.73)
TR82-Kastamonu, Çankırı, Sinop
10.30
0.37
(2.87-4.32)
TR83-Samsun, Tokat, Çorum, Amasya
5.81
0.27
(4.17-5.24)
TR90-Trabzon, Ordu, Giresun, Rize, Artvin
4.83
0.26
(4.97-6.01)
TRA1-Erzurum, Erzincan, Bayburt
7.70
0.37
(4.12-5.59)
TRA2-Ağrı, Kars, Iğdır, Ardahan
5.78
0.39
(6.00-7.53)
TRB1-Malatya, Elazığ, Bingöl, Tunceli
5.50
0.23
(3.70-4.59)
TRB2-Van, Muş, Bitlis, Hakkari
5.57
0.55
(8.74-10.88)
TRC1-Gaziantep, Adıyaman, Kilis
5.97
0.33
(4.83-6.11)
TRC2-Şanlıurfa, Diyarbakır
5.73
0.29
(4.44-5.56)
TRC3-Mardin, Batman, Şırnak, Siirt
5.89
0.47
(7.04-8.88)
Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
In TurkStat for employment totals and rates, we have different CV values. We apply proc survey means sas module which is based on Taylor approach. We take into account the calculation formulas of standard deviation formula for the total estimates and the standard error formula for the mean estimates of the indicators required in the table.
Reference on software used:
Reference on method of estimation:
SAS Enterprise Guide 5.1
TAYLOR SERIES
(*) 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
11.37
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 order to estimate the impact of the new regulations and IESS on the indicators and use it in comparable back-calculation of the series, questions were added to the existing questionnaire for measuring the definition effect in 2020
Y
Pilot, cognitive and internal tests were made.
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)
Quality control study is regularly realized almost every month of year for around 1500 households which were intervieved in the previous week. In this study; households which are selected randomly from each region and interviewer were called and some critical questions were again asked to the selected household member. This is a very good tool for minimazing measuremet errors in the later months.
6.3.3. Non response error
[not requested for the LFS quality report]
6.3.3.1. Unit non-response - rate
IN THIS SECTION INFORMATION REFERS TO THE FINAL SAMPLING UNITS *
Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N)
Variables used for non-response adjustment
Description of method
Y
Individual nonresponse adjustment is applied on the basis of household. Household nonresponse is adjusted on the basis of clusters. Cluster nonresponse is adjusted on the basis of design domain.
The individual adjustment is made in each household according to number of respondent individual in the household. The household adjustment is made in each cluster by the formula 10/r where r is the number of respondent household. The cluster adjustment is made in each design domain by number of respondent 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
88.35
11.65
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
2.02
4.22
58.29
2
1.89
4.98
59.96
3
1.87
3.07
64.21
4
1.82
3.50
63.20
Annual
1.90
3.98
61.35
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
10
Subsample_Q1_2021
19
9
Subsample_Q2_2021
21
4
Subsample_Q3_2021
9
7
Subsample_Q4_2021
16
20
Subsample_Q1_2022
13
17
Subsample_Q2_2022
49
13
Subsample_Q3_2022
30
13
Subsample_Q4_2022
33
Total in absolute numbers
58
96
56
73
Total in % of theoretical quarterly sample
0.10
0.16
0.10
0.12
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
229
Subsample_Q1_2021
430
306
Subsample_Q2_2021
452
317
Subsample_Q3_2021
391
198
Subsample_Q4_2021
304
235
Subsample_Q1_2022
341
367
Subsample_Q2_2022
446
284
Subsample_Q3_2022
382
178
Subsample_Q4_2022
266
Total in absolute numbers
1304
1571
1374
877
Total in % of theoretical quarterly sample
2.23
2.68
2.35
1.50
Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)
Non response rate (%)
TR10-İstanbul
2.22
TR21-Tekirdağ, Edirne, Kırklareli
2.71
TR22-Balıkesir, Çanakkale
0.90
TR31-İzmir
2.95
TR32-Aydın, Denizli, Muğla
1.33
TR33-Manisa, Afyon, Kütahya, Uşak
0.36
TR41-Bursa, Eskişehir, Bilecik
1.18
TR42-Kocaeli, Sakarya, Düzce, Bolu, Yalova
2.15
TR51-Ankara
1.69
TR52-Konya, Karaman
1.40
TR61-Antalya, Isparta, Burdur
2.18
TR62-Adana, Mersin
3.39
TR63-Hatay, Kahramanmaraş, Osmaniye
1.21
TR71-Kırıkkale, Aksaray, Niğde, Nevşehir
2.45
TR72-Kayseri, Sivas, Yozgat
0.83
TR81-Zonguldak, Karabük, Bartın
1.44
TR82-Kastamonu, Çankırı, Sinop
1.59
TR83-Samsun, Tokat, Çorum, Amasya
2.69
TR90-Trabzon, Ordu, Giresun, Rize, Artvin
2.69
TRA1-Erzurum, Erzincan, Bayburt
1.69
TRA2-Ağrı, Kars, Iğdır, Ardahan
2.69
TRB1-Malatya, Elazığ, Bingöl, Tunceli
1.74
TRB2-Van, Muş, Bitlis, Hakkari
0.75
TRC1-Gaziantep, Adıyaman, Kilis
3.00
TRC2-Şanlıurfa, Diyarbakır
2.08
TRC3-Mardin, Batman, Şırnak, Siirt
1.60
* 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
Col_017/18
NATIONAL
100
100
100
100
According to the 2010 Address Based Population Registration System; 99,7 % of population has Turkish Nationality. So, it is not easy to cover non-nationals with a sample survey.
compulsory
Col_039/40
COUNTRYW
C
C
C
C
There are very few people who are working abroad and at the same time considered as household member since Turkey is a very broad country. This may only occur in border cities, but not common. So, this variable is not asked.
compulsory
Col_067/68
HWOVERPU
100
100
100
100
Only total overtime is asked in the questionnaire (paid+unpaid). Since it is not possible to distinguish paid and unpaid overtime. Total overtime is given in HWOVERP and this variable is coded as blank.
compulsory
Col_073/74
HWWISH
100
100
100
100
This variable was dropped out from the questionnaire in 2009 since it was observed that, results were not reliable. Respondents replied this question as they undestand (some give the hours that would like to work in total while others only give the additional hours).
compulsory
Col_104 - Employed
METHODB
.
C
C
C
For employed people all the methods are not asked in same detail, some of them are grouped looking at the frequency (for example, Public Employment Office and Private Employment Offices are combined in one code).
compulsory
Col_108 - Employed
METHODF
.
C
C
C
For employed people all the methods are not asked in same detail, some of them are grouped looking at the frequency (for example, Public Employment Office and Private Employment Offices are combined in one code).
compulsory
Col_111 - Employed
METHODI
.
C
C
C
For employed people all the methods are not asked in same detail, some of them are grouped looking at the frequency (for example, Public Employment Office and Private Employment Offices are combined in one code).
compulsory
Col_113 - Employed
METHODK
.
C
C
C
For employed people all the methods are not asked in same detail, some of them are grouped looking at the frequency (for example, Public Employment Office and Private Employment Offices are combined in one code).
compulsory
Col_114 - Employed
METHODL
.
C
C
C
For employed people all the methods are not asked in same detail, some of them are grouped looking at the frequency (for example, Public Employment Office and Private Employment Offices are combined in one code).
compulsory
Col_115 - Not employed
METHODM
C
.
.
.
compulsory
Col_168
DEGURBA
100
100
100
100
We are planning to transmit this variable to Eurostat from 2021 on.
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
Col_055
TEMPAGCY
100
This variable is not asked since tempopary working agencies are not common in Turkey for the moment.
compulsory
Col_121
REGISTER
100
This question is not asked since the coverage of unemployment benefits is very limited in Turkey. (around 10% of registered unemployed are receiving unemployment benefit at the current situation).
optional
Col_132
COURPURP
100
Questions about attending any courses, seminars, conferences or receive private lessons or instructions outside the regular education system haven't asked since 2014.
optional
Col_133/135
COURFILD
100
Questions about attending any courses, seminars, conferences or receive private lessons or instructions outside the regular education system haven't asked since 2014.
optional
Col_136
COURWORH
100
Questions about attending any courses, seminars, conferences or receive private lessons or instructions outside the regular education system haven't asked since 2014.
(*) "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
N
Seasonal adjustment of Labour Force Statistics carries out by using TRAMO-SEATS methodology based on ARIMA (Autoregressive Integrated Moving Average) model estimation developed by the Banco de Espana and also suggested by Eurostat. The software that is used for the application of this method is TRAMO-SEATS for Windows (TSW). Seasonally adjusted figures of labour force statistics have been produced by indirect approach. Namely, labour force, employed persons according to economic activities and unemployed persons are firstly seasonally adjusted and then aggregated to derive seasonally adjusted employment and unemployment rates.
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
NA
Identification of the main job (*)
N
NA
Employment
Y
ILO definition on unpaid family workers are followed, they're not covered as employed if they didn't work in the reference week even one hour. Three month criterion defined by Eurostat is not followed for unpaid family workers. Individuals in the household only producing goods intended mainly for sale considered as employed. Farmers who only produce for own-consumption are considered as employed if the amount of this production is considerable within total household consumption (if the total amount of this product is at least 51% of total food expenditure).
Unemployment
Y
For national calculation of unemployment passive job search methods are covered besides the active ones.
8.1.1. Asymmetry for mirror flow statistics - coefficient
[not requested for the LFS quality report]
8.2. Comparability - over time
Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes in
(Y/N)
Description of the impact of the changes
Statistics also revised backwards (if Y: year / N)
Variables affected
Break in series to be flagged (if Y: year and quarter/N)
concepts and definition
N
NA
NA
NA
NA
coverage (i.e. target population)
N
NA
NA
NA
NA
legislation
N
NA
NA
NA
NA
classifications
N
NA
NA
NA
NA
geographical boundaries
N
NA
NA
NA
NA
Changes at MEASUREMENT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes to
(Y/N)
Description of the impact of the changes
Statistics also revised backwards (if Y: year / N)
Variables affected
Break in series to be flagged (if Y: year and quarter/N)
sampling frame
N
NA
NA
NA
NA
sample design
N
NA
NA
NA
NA
rotation pattern
N
NA
NA
NA
NA
questionnaire
N
NA
NA
NA
NA
instruction to interviewers
N
NA
NA
NA
NA
survey mode
N
NA
NA
NA
NA
weighting scheme
N
NA
NA
NA
NA
use of auxiliary information
N
NA
NA
NA
NA
8.2.1. Length of comparable time series
[not requested for the LFS quality report]
8.3. Coherence - cross domain
Coherence of LFS data with Business statistics data
Description of difference in concept
Description of difference in measurement
Give an assessment of the effects of the differences
Give references to description of differences
Total employment
Business statistics data mainly cover registered employment while the rate of people who are employed without any social security is though high in Turkey. LFS also covers unregistered employed. Thus, it is not possible to compare total employment figures gathered from LFS and business statistics. To make correct comparision registered employed should be selected from LFS also. SBS surveys also do not cover the following activities in Turkey case (According to NACE Rev 1.1); Section A - Agriculture, Hunting and Forestry, Section B - Fishing, Section L - Public Administration and Defence; Compulsory Social Security, Section P - Activities of Household, Section Q - Extra Territorial Organizations and Bodies, Section O - A part of the section O (except 91-Activities of Membership Organisation N.E.C.)
Main difference in measurement comes from the reference terms. While business statistics cover long reference periods, LFS uses only one week as reference.
Effect is very high in total since 32,2 % of total non-agricultural employment is unregistered and unregistered part of the employment mostly could not be captured by the business surveys.
NA
Total employment by NACE
In some institutionalized sectors (mining, electricty, gas and water, education, health etc.) rate of unregistered employed is very low and results are more comparable. But, for example in manufacturing sector the rate of unregistered employment is very high and these data are not coherent.
SBS does not cover all activities as described in concept difference.
Effect differs among sectors as explained in the concept. In institutionalized sectors effect of the difference is lower than other sectors.
NA
Number of hours worked
No significant difference in the calculation of number of hours worked.
NA
NA
NA
Coherence of LFS data with registered unemployment
Description of difference in concept
Description of difference in measurement
Give references to description of differences
People who apply to Employment Office (ISKUR) to find a job and has no job at that moment in the minimum wage level are considered as unemployed and the application is kept in the registers during one year unless a job is found for him/her. After one year this application is dropped out (people may apply again).
Measurement methods are completely different. In LFS people even worked under the minimum wage (unregistered people) are considered as employed.
NA
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 number of registered unemployed is lower when compared with the LFS results altough it covers the one year (registered unemployed was around 2.1 million at the end of 2015, while number of unemployed at annual basis was 3.6 million according to the LFS results). The main reason is the lack of legal requirement for unemployed people to register themselves to ISKUR. Also, the coverage of unemployment benefits is limited in Turkey. In order to receive unemployment benefits from ISKUR, it is necessary to work before (at least 600 days during the last three-year period) and only people who was dismissed by the employer could receive unemployment benefits.
Difference exists in all groups without any significant disparity.
Difference exists in all groups without any significant disparity.
Difference exists in all groups without any significant disparity.
Difference exists in all groups without any significant disparity.
Difference exists in all groups without any significant disparity.
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
1) Labour force statistics do not cover conscripts (since they're not covered as household member during their compulsory military service), but ESA includes conscripts under the general government services, 2) Non-residents working with resident producer units (not included in labour force statistics but included in employment as defined in the ESA), 3) A correction for regular employees on temporary leave is made in National Accounts data (LFS covers these regular employees as employed if total duration of absence is less than 3 months or they're continuing to receive 50% or more of their wage and salary).
National Accounts employment data has three components. 1- LFS (persons at work during the reference week), 2- ESA95 requirements are also covered in NA employment data. Such as; -foreign civilans settled in the country for a period of one year or more, - soldiers, - foreign military personel working with international military organizations located within the geoagraphic territory of the country,- members of the country's armed forces stationed in the rest of the world,- civil cervants who are working as a staff of diplomatic mission on abroad. 3- Secondary jobs. National Accounts employment data basicly based on LFS. In addition another data sources were used by NA. Such as, Social Security Institutions, Ministry of Defence, Ministry of Froeign Affairs etc.
There is no big difference between LFS and National Accounts data.
UNA
Total employment by NACE
No big difference
No big difference
UNA
UNA
Number of hours worked
In NA;
No big difference
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
Y
N
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
Press Releases - Monthly, Quarterly and Annually (Annual results are given at NUTS 2 level) Labour Force Statistics Micro Data Set - Annually and Quarterly
9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.
Press Releases are published on the web site both in TR and EN at the same time. Press releases are automatically sent to the users who were subscribed to the web site. Main headlines of the press releases are also available on teletext page of the national television broadcast. Users can also reach certain headlines depending on their interest via mobile phones. If requested short messages are sent to the users on the general topics of the press release.
Detailed time-series tables are available on the web site. Users can also reach Labour Force Database from the web site and could extract tables depending on their requests.
Anonymised micro data are also available on CD Rom beginning from 2004. Other micro data series of labour force survey (for the period 2000-2003) shall be provided upon request by the bilateral protocols.
Press releases and publications are supported with graphics and metadata.
To request statistical information, users may come to our offices both in Central Office in Ankara and to the regional offices located in 26 provinces. The personnel responsible for this service try to resolve/fulfil this request at the moment using the available sources; when an immediate answer cannot be given, they take this request and by sending it to the related department (using a standardized form) they proved to be covered this request as soon as possible. Users can also transmit their requests using e-mail, post and fax. Users can contact with the sector expert if it is necessary.
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
Institutions and organizations covered under the Official Statistics Programme (OSP), Other official institutions and organizations in Turkey, Universities and other higher educational institutions, Research based establishments and institutions, International organizations at which Turkey is a member.
a) Researcher fills out the “Micro Data Request Form” via internet, and print out the form and get approval of his/her institution organization. After that, he/she directly or through the Regional Offices applies to the Presidency,
b) The demand of researcher is reviewed by the “Evaluation Board” and researcher is informed by the Head of PDDD in 15 days.
c) In case of acception of the demand, the “Protocol” including the necessary conditions regarding the data access and confidentiality for micro data is signed between the Presidency and researcher or his/her institıtion.
Metadata of the micro dataset which covers; background, purpose, coverage, method, concepts and definitions, classifications, questionnaire, data structure and data information.
Technical support is provided via telephone or e-mail for use and analysis of the data sets.
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
Inclusion/exclusion criteria for members of the household
Questions relating to employment status are put to all persons aged ...
All private households are covered in the survey. Institutional population; including the residents of schools (university students living in dormitories), people living in rest homes, orphans' homes, special hospitals (for people staying more than twelve months) and military barracks (conscripts are excluded from the sample while army forces are included) are not covered in the survey.
Dwelling
Members living together in the same dwelling.
Persons to be included;
15 years old and over
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
Family home
Family home
Family home
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)
All weeks of the year (52 weeks) used as the reference period which the observation unit has been assigned prior to the fieldwork.
Participation is voluntary/compulsory?
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
Two stage stratified cluster sampling involving eight subsamples.
National Address Database which is based on Address Based Population Register System is used as the sampling frame.
National Address Database consists of all addresses up to indoor numbers and updated at any moment (continuosly) by the municipalities.
Clusters. Each cluster (block) consists of approximately 100 households (between 80 and 120). First stage sample selection is realized by using the frame of clusters.
Household address
One month before the reference week of the relevant month
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 (blocks) are selected with probability proportional to their household sizes from Province X Urban/Rural classes.
Selection of the second stage units (household addresses) are based on the rotation pattern. When the PSU's are firstly entered to the sample, 20 households are selected systematically and divided into two parts namely the sets A and B. Every quarter 10 of those (one set A or B) are involved in the survey.
At the first stage of sampling, the implicit strata are defined as 81 provinces and urban-rural areas. An urban area is defined as a settlement with more than 20 000 inhabitant, rural area is defined as a settlement with less than 20 000 inhabitant based on current Address Based Population Register System. Not all provinces have a rural area.
158 (81 provinces and urban-rural areas)
2-2-2
Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate
Size of the theoretical yearly sample
(i.e. including non-response)
(i.e. including non-response)
0.93%
234 240 households
Quarterly sample size & Sampling rate
Overall theoretical quarterly sampling rate
Size of the theoretical quarterly sample
(i.e. including non-response)
(i.e. including non-response)
0.23%
58 560 households
Use of subsamples to survey structural variables (wave approach)
Only for countries using a subsample for yearly variables
Wave(s) for the subsample
Are the 30 totals for ILO labour status (employment, unemployment and inactivity) by sex (males and females) and age groups (15-24, 25-34, 35-44, 45-54, 55+) between the annual average of quarterly estimates and the yearly estimates from the subsample all consistent? (Ref.: Commission Reg. 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 II)
NA
NA
NA
NA
Brief description of the method of calculating the quarterly core weights
Is the sample population in private households expanded to the reference population in private households? (Y/N)
If No, please explain which population is used as reference population
Gender is used in weighting (Y/N)
Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)?
Which regional breakdown is used in the weighting (e.g. NUTS 3)?
Other weighting dimensions
Since 2021, independent monthly estimates has been published. That is why quarterly weights is calculated by taking the weighted averages of the relevant months according to the number of weeks in the months.
N
Non-institutional population projections belonging to 15th of middle month, based on Address Based Population Register
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
In the calculation of yearly weights, the initial weights adjusted by the nonresponse of each month which were already calculated during the year are defined as the input weights in the calibration procedure. After bringing together all data the weights are calibrated by the "1st of July of the related year population projections" based on Address Based Population Register. Integrated calibration method is applied in the procedure. Also trimming procedure is applied to avoid the use of extreme weights. In the calibration,"Age group by sex", "NUTS 2 by urban-rural", "NUTS 3", and "Household size" distributions are adjusted iteratively.
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)
Household address is the final sampling unit selected for the LFS. Depending on the weighting procedure explained above identical household weights for all household members are obtained as result of integrated calibration. Therefore, no additional calculations are applied for household weights.
Household size information coming from Address Based Population Register.
Age group by sex, NUTS 2 by urban and rural, NUTS 3
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
Y
ILO definition on unpaid family workers are followed, they're not covered as employed if they didn't work in the reference week even one hour. Three month criterion defined by Eurostat is not followed for unpaid family workers. Individuals in the household only producing goods intended mainly for sale considered as employed. Farmers who only produce for own-consumption are considered as employed if the amount of this production is considerable within total household consumption (if the total amount of this product is at least 51% of total food expenditure).
Unemployment
Y
For national calculation of unemployment passive job search methods are covered besides the active ones.
Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes in
(Y/N)
Description of the impact of the changes
Statistics also revised backwards (if Y: year / N)
Variables affected
Break in series to be flagged (if Y: year and quarter/N)
concepts and definition
N
NA
NA
NA
NA
coverage (i.e. target population)
N
NA
NA
NA
NA
legislation
N
NA
NA
NA
NA
classifications
N
NA
NA
NA
NA
geographical boundaries
N
NA
NA
NA
NA
Changes at MEASUREMENT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes to
(Y/N)
Description of the impact of the changes
Statistics also revised backwards (if Y: year / N)
Variables affected
Break in series to be flagged (if Y: year and quarter/N)
