Employment and unemployment (Labour force survey) (employ)

Coverage
Coverage	Household concept	Definition of household for the LFS	Inclusion/exclusion criteria for members of the household	Questions relating to employment status are put to all persons aged ...
The Lithuanian Labour Force Survey is a private households survey. The population of the survey – all permanent residents of the Republic of Lithuania, living in private households, including those who are temporarily abroad for a period of less than one year. The population also includes foreign nationals who have been living in Lithuania at least a year.	Housekeeping	Members living regularly together in the same dwelling, sharing household expenditures, food and other essentials for living.	Only members actually living in the selected private household are interviewed. Household is a person or a group of persons sharing the same living accommodation and expenditures, including collective provision of vital needs.	15-89

Population concept	Specific population subgroups
Population concept	Primary/secondary students	Tertiary students	People working out of family home for an extended period for the purpose of work	People working away from family home but returning for weekends	Children alternating two places of residence
Usual residence	Family home	Family home	Term address	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)
The reference weeks are distributed uniformly over the 13 weeks of the quarter.	NA

Participation is voluntary/compulsory?
Voluntary

Not Applicable

[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 constructed as follows: having selected a simple random sample of the fixed number of persons of the sampling frame, the members of their households are also included. Even in case turned out that according to the address denoted, a part of people or all of them were not included into the list of the sample, all household members actually living there were interviewed. The cluster sample of persons is thus obtained. All the persons living at the address selected belong to the same cluster. The actual composition of the cluster is indicated during the survey.

The Population register. The Population register has some shortcomings: it suffers from the undercoverage and overcoverage; not all the addresses are included in the frame, part of them are imprecise, and it is impossible to follow the person selected up to his correct address. The Population register for persons of age under the survey coverage with the addresses of residence in towns and at least the name of the village in rural area is used as a sampling frame. This frame is actually used as a frame of addresses.

The sampling frame is being updated every day.

Households

2022Q1: 16/12/2021

2022Q2: 17/03/2022

2022Q3: 23/06/2022

2022Q4: 26/09/2022

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.)
NA	The survey base is the Population register. The sampling unit is the person living in the private household. All household members are surveyed. The sampling plan is a one-stage simple random sample of about 2 000 households per quarter aged 15 years and over.	NA	NA	Each quarter, one-fourth of addresses is new and three-fourth of addresses come from the previous waves. Each dwelling is kept in the survey for four quarters. The rotation scheme is 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)
4.0% population 15 years and older per year	About 32 000 households per year

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)
1.0% population 15 years and older per quarter	About 8000 households per quarter

Use of subsamples to survey structural variables (wave approach)
Only for countries using a subsample for yearly variables
Wave(s) for the subsample	Are the 30 totals for ILO labour status (employment, unemployment and inactivity) by sex (males and females) and age groups (15-24, 25-34, 35-44, 45-54, 55+) between the annual average of quarterly estimates and the yearly estimates from the subsample all consistent? (Ref.: Commission Reg. 2019/2240) (Y/N)	If not please list deviations	List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 2019/2240, Annex I)
NA	NA	NA	NA

Brief description of the method of calculating the quarterly core weights	Is the sample population in private households expanded to the reference population in private households? (Y/N)	If No, please explain which population is used as reference population	Gender is used in weighting (Y/N)	Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)?	Which regional breakdown is used in the weighting (e.g. NUTS 3)?	Other weighting dimensions
The weighting method for the Lithuanian LFS is based on the generalised calibration method introduced by Deville and Särndal in 1992. The initial household design weights are adjusted by the use of auxiliary information relating to population data on 60 municipalities and the intersection of 13 age-groups, sex and urban/rural living place. All household members have the same sampling weight.	Y	NA	Y	13 age groups (0-14,15-19, 20-24, 25-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75+)	Municipalities (LAU)	Urban/rural living place

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 the identical to COEFFQ/4.	Y	Gender, 13 age groups (0-14,15-19, 20-24, 25-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75+)	Municipalities (LAU)	Urban/rural living place

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)
As described above.	N	Number of households, household size.	Gender, 13 age groups (0-14,15-19, 20-24, 25-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75+ ), regional breakdown (LAU), urban/rural living place.	Y

Not Applicable

Restricted from publication

Divergence of national concepts from European concepts
(European concept or National proxy concept used) List all concepts where any divergences can be found
Is there a divergence between the national and European concepts for the following characteristics?	(Y/N)	Give a description of difference and provide an assessment of the impact of the divergence on the statistics
Definition of resident population (*)	N	NA
Identification of the main job (*)	N	NA
Employment	N	NA
Unemployment	N	NA

Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes in	(Y/N)	Description of the impact of the changes	Statistics also revised backwards (if Y: year / N)	Variables affected	Break in series to be flagged (if Y: year and quarter/N)
concepts and definition	N	NA	N	NA	N
coverage (i.e. target population)	N	NA	N	NA	N
legislation	N	NA	N	NA	N
classifications	N	NA	N	NA	N
geographical boundaries	N	NA	N	NA	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	NA	N	NA	N
sample design	N	NA	N	NA	N
rotation pattern	N	NA	N	NA	N
questionnaire	Y	Improved questions wording and fixed issues with filters.	N	NA	N
instruction to interviewers	Y	Just minor additions.	N	NA	N
survey mode	N	NA	N	NA	N
weighting scheme	N	NA	N	NA	N
use of auxiliary information	N	NA	N	NA	N

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

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?
At first respondents can answer the LFS questionnaire by themselves on the internet (CAWI). If they choose not to do so, interviews are conducted by face-to-face (CAPI) or telephone (CATI) according to the circumstances. The interview normally takes place during the week immediately following the reference week but never later than five weeks after the reference week. The interview is on average about 11 minutes in the first interview and shorter in the subsequent interviews. If the target persons selected to the sample cannot be reached, a proxy may by used on certain conditions.	Y	NA

NUTS level of detail
Regional level of an individual record (person) in the national data set	Lowest regional level of the results published by NSI	Lowest regional level of the results delivered to researchers by NSI	Brief description of the method which is used to produce NUTS-3 unemployment and labour force data sent to Eurostat?
LAU	NUTS 3, employement by municipalities (LAU)	NUTS 3	Used methods are the same as methods used for national level data produce.

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
<1100	1100-4000	<1100	1100-4000

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
<1100	1100-4000	<1100	1100-4000	<1100	1100-4000

Are any LFS data collected from registers (Y/N)?	If Yes, please indicate which variables are collected from registers.
Y	YEARBIR, REGION, REGIONW, NACE3D, ISCO4D

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	0.92	6.46	20.12
SE	0.61	0.38	0.24
CI^(*)	[64.97;67.35]	[5.19;6.69]	[6.9;16.44]

Unemployment-to-population ratio 15-74 (NUTS 2 regions)
	CV	SE	CI(*)
LT01 Sostinės regionas	13.51	0.61	[3.74;6,19]
LT02 Vidurio ir vakarų Lietuvos regionas	6.99	0.59	[6.06;7.58]

Reference on software used:	Reference on method of estimation:
Clan 97-a SAS program for computation of point and standard error estimates in sample survey.	Calibration.

Frame quality (under-coverage, over-coverage and misclassifications^(b))
Under-coverage rate (%)	Over-coverage rate (%)	Misclassification rate (%)	Comments: specification and impact on estimates^(a)
Under-coverage rate (%)	Over-coverage rate (%)	Misclassification rate (%)	Undercoverage	Overcoverage	Misclassification^(b)	Reference on frame errors
UNA	0.68	UNA	UNA	Among not interviewed households, in 218 cases (0,68% of total number of sampled households) the reason was an error or inaccuracy of the frame (imprecise address, the premises at the indicated address are non-residential (a hairdresser’s, shop, etc.), the building was knock down, etc).	UNA

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	The questionnaire updated due to new COMMISSION IMPLEMENTING REGULATION (EU) 2019/2240 of 16 December 2019	Y	Internal check.

Main methods of reducing measurement errors
Error source
Respondent	Letter introducing the survey (Y/N)	Phone call for booking or introducing the survey (Y/N)
	Y	Y
Interviewer	Periodical training (at least 1 time per year) (Y/N)	Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
	Y	Y
Fieldwork	Monitoring directly by contacting the respondents after the fieldwork (Y/N)	Monitoring directly by listening the interviews (Y/N)	Monitoring remotely through performance indicators (Y/N)
	Y	Y	Y
Questionnaire	Questionnaire in several languages (Y/N)	On-line checks (for computer assisted interviews (Y/N)
	Y	Y
Other / Comments	Explanatory notes for interviewers.

Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N)	Variables used for non-response adjustment	Description of method
Y	Sex, age, urban/rural, administrative teritory.	Parameters are estimated using a calibration method. Weights for households classified according to the inequality probabilities of household dependency to the sample. For the calculation of weights, demographic data are used.
Substitution of non-responding units (Y/N)	Substitution rate	Criteria for substitution
N	NA	NA
Other methods (Y/N)	Description of method
N	NA

Rates of non-response by survey mode. Annual average
Survey
CAPI	CATI	PAPI	CAWI	POSTAL
UNA	UNA	NA	UNA	NA

Non-response rates. Annual average (% of the theoretical yearly sample by survey mode)
Quarter	Non-response rate
	Total (%)	of which:
	Total (%)	Refusals (%)	Non-contacts (including people who migrated (or moved) internally or abroad) (%)
1	19.23	51.80	45.80
2	19.44	51.72	45.80
3	18.50	52.82	43.91
4	18.27	52.05	46.26
Annual	18.86	52.10	45.44

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	163
Subsample_Q1_2021	282	123
Subsample_Q2_2021		299	193
Subsample_Q3_2021			392	176
Subsample_Q4_2021	471			259
Subsample_Q1_2022	1396	645
Subsample_Q2_2022		1362	495
Subsample_Q3_2022			1394	638
Subsample_Q4_2022				1353
Total in absolute numbers	2312	2429	2474	2426
Total in % of theoretical quarterly sample	10.62%	11.02%	10.94%	10.89%

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	266
Subsample_Q1_2021	324	55
Subsample_Q2_2021		433	337
Subsample_Q3_2021			404	383
Subsample_Q4_2021	531			342
Subsample_Q1_2022	923	536
Subsample_Q2_2022		863	448
Subsample_Q3_2022			868	528
Subsample_Q4_2022				903
Total in absolute numbers	2044	1887	2057	2156
Total in % of theoretical quarterly sample	9.39%	8.56%	9.10%	9.67%

*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	2022	Many respondents leave INCGROSS variable blank. We will try to improve the collection and imputation of this variable.
Compulsory	324-325	INCGROSS_F	2022	Many respondents leave INCGROSS variable blank. We will try to improve the collection and imputation of this variable.
Compulsory	229	VARITIME	2022	Had some issues with the collection of this variable in 2022. From 2023Q1 fixed it.
Compulsory	259	WAYJFOUN	2022	Had some issues with the collection of this variable in 2022. From 2023Q1 fixed it.

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 )

Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)	Non response rate (%)
LT01 Sostinės regionas	23.70
LT02 Vidurio ir vakarų Lietuvos regionas	16.67

*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
UNA	UNA	UNA	UNA	UNA	UNA	UNA	UNA

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	UNA
Main variables	Imputation rate	Describe method used, mentioning which auxiliary information or stratification is used
INCGROSS	UNA	Administrative data from State Social Fund Board database are used for imputation of missing values for the INCGROSS variable.

Do you apply any seasonal adjustment to the LFS Series? (Y/N)	If Yes, is your adopted methodology compliant with the ESS guidelines on seasonal adjustment? (ref. ESS guidelines on seasonal adjustment - Products Manuals and Guidelines - Eurostat (europa.eu)) (Y/N)	If Yes, are you compliant with the Eurostat/ECB recommendation on Jdemetra+ as software for conducting seasonal adjustment of official statistics. (ref. Jdemetra-officially-recommended-as-software-for-the-seasonal-adjustment-of-official-statistics) (Y/N)	If Not, please provide a description of the used methods and tools
N	NA	NA	NA

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

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	The LFS data includes total employment (employees, self-employed with employees, self-employed without employees, family workers). All persons who worked at least one hour or more for pay or profit in the reference week are considered to have been employed. The Business statistics data includes only employees who working under written employment contract. Also employees number excludes persons on parental, maternity or paternity leave who are calculated in LFS.	The LFS is a sample survey. Data are collected from private households. In Business statistics the statistical unit is an enterprise, institution or organization.	In 2022, the Business Statistics shows 7.9% more employees than the LFS.	No official publication.
Total employment by NACE	Identical to total employment.	Identical to total employment.	In 2022, the highest number of employees difference by activity in the LFS compared to the Business statistics was observed in H, L activities. The lowest difference in C activity was about 1.8% and in R activity (5.5%)	No official publication.
Number of hours worked	The main differences are: in the LFS hours usually worked (the typical length of a working week over a longer period of time) and average number of hours actually worked in the main job and second job (hours worked in the reference week) are collected; in Business statistics average number of hours actually worked per employee per week are calculated; in Business statistics average number of hours actually worked per employee per week in main and second job are collected; in Business statistics number of average number of hours actually worked per employee per week in individual enterprises are excluded.	UNA	In comparison of the employees number of hours actually worked in 2022, the Business statistics shows 0.32% less than the LFS.	No official publication.

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)
In 2022, the total number of registered unemployed persons was by 43.25 percent higher than the LFS estimate.	UNA	UNA	UNA	UNA	Alytaus county -33.86% Kauno county -51.55% Klaipedos county -31.06% Marijampoles county -25.41% Panevezio county -18.55% Siauliu county -43.71% Taurages county -49.91% Telsiu county -65.38% Utenos county -15.95% Vilniaus county -52.81%

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 main differences between the employment definitions of Lithuanian LFS and ESA 2010 are: - according to ESA 2010 employment includes also non-residents working for resident producer units while LFS does not include them; - according to ESA 2010 employment does not include residents who are working abroad while LFS does.	For using in National Accounts, the Lithuanian LFS data is adjusted as much as possible to ESA 2010 definition. NA estimates are calculated on LFS database.	Difference 2.2%	No official publication.
Total employment by NACE	Identical to total employment	Identical to total employment	Identical to total employment	No official publication.
Number of hours worked	No difference	NA	NA	No official publication.

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	Y	N	N	N	N

1.1. Contact organisation

1.2. Contact organisation unit

1.3. Contact name

1.4. Contact person function

1.5. Contact mail address

1.6. Contact email address

1.7. Contact phone number

1.8. Contact fax number

2.1. Data description

2.2. Classification system

2.3. Coverage - sector

2.4. Statistical concepts and definitions

2.5. Statistical unit

2.6. Statistical population

2.7. Reference area

2.8. Coverage - Time

2.9. Base period

3.1. Source data

3.2. Frequency of data collection

3.3. Data collection

3.4. Data validation

3.5. Data compilation

3.6. Adjustment

4.1. Quality assurance

4.2. Quality management - assessment

5.1. Relevance - User Needs

5.2. Relevance - User Satisfaction

5.3. Completeness

5.3.1. Data completeness - rate

6.1. Accuracy - overall

6.2. Sampling error

6.2.1. Sampling error - indicators

6.3. Non-sampling error

6.3.1. Coverage error

6.3.1.1. Over-coverage - rate

6.3.1.2. Common units - proportion

6.3.2. Measurement error

6.3.3. Non response error

6.3.3.1. Unit non-response - rate

6.3.3.2. Item non-response - rate

6.3.4. Processing error

6.3.4.1. Imputation - rate

6.3.5. Model assumption error

6.4. Seasonal adjustment

6.5. Data revision - policy

6.6. Data revision - practice

6.6.1. Data revision - average size

7.1. Timeliness

7.1.1. Time lag - first result

7.1.2. Time lag - final result

7.2. Punctuality

7.2.1. Punctuality - delivery and publication

8.1. Comparability - geographical

8.1.1. Asymmetry for mirror flow statistics - coefficient

8.2. Comparability - over time

8.2.1. Length of comparable time series

8.3. Coherence - cross domain

8.4. Coherence - sub annual and annual statistics

8.5. Coherence - National Accounts

8.6. Coherence - internal

9.1. Dissemination format - News release

9.2. Dissemination format - Publications

9.3. Dissemination format - online database

9.3.1. Data tables - consultations

9.4. Dissemination format - microdata access

9.5. Dissemination format - other

9.6. Documentation on methodology

9.7. Quality management - documentation

9.7.1. Metadata completeness - rate

9.7.2. Metadata - consultations

11.1. Confidentiality - policy

11.2. Confidentiality - data treatment