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 ...

Persons in private households

Households are register based, on these sources

• Familial ties:
o Married persons and partners (with and without children)
o Unmarried persons with children

• Kinship:
o Grandparents and grandchildren
o Multi-generational families
o Siblings

• Cohabitation:
o Addresses with only two inhabitants
o Addresses with two adult inhabitants and children belonging to one of the adults
o Two persons with joint property, debt, or housing cooperative apartments, including children
o Two persons that moved to the address on the same date

Children without either biological parents or grandparents on the same address are considered foster children if the addresses contain only one other family unit. In cases where there are more than one family unit, the children are considered single-person households. However, if the address consists of children only, i.e. persons younger than 18 years old, they are considered ‘unspecified’.
Students that according to the National Population Register are living on the same address as their parents but are registered as ‘living away from home’ in the Student Loan database, are categorized as single-person households.

As of today, there are two household types the register is unable to identify. These are households that consists of three or more single-person families, and households with two or more multi-person families.

Data
Input data used in the register includes

• The National Population Register
• Data of students receiving support from the Norwegian State Educational Loan Fund
• Conscription data
• Kinship data (register derived from the population register)
• Employee data
• Tax returns (to obtain debt data)
• The Cadastre
• The Land Registry

The different registers were joined by national identity number or by address.

Structure
After importing and processing the input data, we improve upon the information we have on addresses. Here, we add housing unit number from the Cadastre to those who lack that number in the National Population Register, or we correct it for those we identify having an invalid one.

Consequently, we arrange the persons registered in each building according to family ties. This is done on the building level and not on the housing unit level because incorrect registrations of housing unit numbers are prevalent, leading, in many cases, to families being registered in different parts of building complexes. We give married persons, partners, and parents with children a family number, in addition to a family type, e.g. ‘spouses with children’. Unmarried, childless persons receive unique family numbers and are considered one-person families.

Then we identify people living in institutions. These includes persons living on addresses with businesses such as prisons, somatic nursing homes, psychiatric nursing homes, retirement homes, and children homes, in addition to people living in buildings that are registered as nursing homes. These are removed from the register since they are not considered private households. Some of the people who live on addresses like those previously mentioned are employees and therefore kept in the household register. As of today, we are only able to identify around 20–30 % of the total amount of non-private households due to lack of data.

Next, we utilize the Land Registry for finding people with shared real estate and shared housing cooperative apartments, respectively.

Then we assemble the households. The previously constructed family household numbers are converted to household numbers, which is the national id of the oldest person in the group. Most of the already identified family households remain as they are through this section. In some cases, one person may be merged with a multi-person family household, e.g. a single parent with children will merge together with a one-person family of opposite gender, if they are the sole inhabitants of an address; however, one-person families are what predominately changes. The code constructing the households loops through each person on an address for each type of household. ‘More secure’ household types (higher probability of being households in actu) are processed earlier. For example, looping through people registered on an address, we look for persons with shared property before we look for identical moving date.

Finally we code the relations between the members of the households to the reference person of the household based on kinship. We also create a new set of categories for different types of households to make the register more easily comparable to the already extant habituation register.

15-89 years of age

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
Persons in the national population register	Included in the household they de jure live in (which for children normally will be with at least one parent)	No special rules. Persons are sampled from their population register address.	No special rules. Persons are sampled from their population register address.	No special rules. Persons are sampled from their population register address.	Population register address

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)
X

Participation is voluntary/compulsory?
Compulsory

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

One-stage random stratified sampling

Population register and household register

Quarterly updated

Person

21.12.2021

22.03.2022

20.06.2021

20.09.2021

Sampling design & procedure
First (and intermediate) stage sampling method	Final stage sampling method	Stratification (variable used)	Number of strata (if strata change quarterly, refer to Q4).	Rotation scheme (2-2-2, 5, 6, etc.)
One-stage random stratified sampling		age, region, register labour market status	56	8

Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate	Size of the theoretical yearly sample
(i.e. including non-response)	(i.e. including non-response)
1.6 % of the population 15-89 years	84 000 reference persons plus 12 000 household members

Quarterly sample size & Sampling rate
Overall theoretical quarterly sampling rate	Size of the theoretical quarterly sample
(i.e. including non-response)	(i.e. including non-response)
0.4 % of population 15-89 years	21 000 reference persons plus 3000 household members

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. 430/2005, Annex I) (Y/N)	If not please list deviations	List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 377/2008, Annex II)
2 and 6	Yes		All yearly variables

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
A one-step multiple model-calibration method (e.g. Montanari and Ranalli 2009) is used to calculate quarterly individual weights. There is no additional step to adjust for non-response with one-step approach (Lundström and Särndal 1999). Thus, design weights are directly used as initial weights in the calibration. At first, monthly weights are calculated by applying calibration to data of each month of the quarter of interest, and then quarterly weights are calculated as a weighted average of monthly weights, where the weights are defined as proportional to the number of weeks of the associated month. Model-calibration approach may provide better estimates with higher precision than the usual linear calibration method by enabling to describe the relationship between the output variable and the explanatory variables by a generalized linear model, which also captures linear regression (Wu and Sitter 2001). The probabilities of being employed, unemployed and outside of labour force are predicted via a multinomial logistic regression model, and then these predicted probabilities are used as calibration variables in addition to other auxiliary variables obtained from register data (Oguz-Alper 2018).	Yes		Y	0-14, followed by five year age groups, followed by 75-89. Also 15-17, 18-19, 55-61, 62-66, and 67-74	NUTS2 (6 regions)	- five-year age groups from 15 to 74 cross-classified by gender - other age groups - predicted probabilities of being employed, unemployed and outside of labour force by gender and three age groups (15-24, 25-54 and 55-74) - predicted probabilities of being employed, unemployed and outside of labour force by NUTS 2 - three age groups (15-24, 25-54 and 55-74) by NUTS 2 - gender by NUTS 2 - Register-based employment status (full-time employed, part-time employed, self-employed and others) by gender - Register-based employment status (employed, not employed) by country of origin (not immigrants, immigrants coming from European Economic Area, USA, New Zealand, Canada and Australia, immigrants coming from other countries, stateless or others)

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
Linear calibration of quarterly weights, as described in Deville, J.-C., og Sarndal, C.-E. (1992): Calibration Estimators in Survey Sampling. Journal of the American Statistical Association, 87(418), 376–382.Doi.org . Implemented by using the calmar sas-macro from INSEE.	Y	25-34, 35-44, 45-54, 15-74	NUTS2

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)
Integrative calibration is used to calculate the yearly household weights. Household design weights, which are the same for all individuals in the same household, are used as initial weights in the calibration. With integrative calibration, not only both person and household-level calibration conditions are satisfied, but also all eligible individuals in a household take the same household weight.	Private households (cost sharing)	- number of households by household size groups (1, 2, 3, 4, 5+) - number of register based jobless households	- gender - age (0-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75+) - number of children 0-17 - number of adults 18-59 by gender - number of children 0-17 in register based jobless households - number of adults 18-59 by gender in register based jobless households - LFS employment status (employed, unemployed, outside of labour force) by gender - LFS employment status (employed, unemployed, outside of labour force) by age groups (15-24, 25-34, 45-54) - LFS employed or not employed by age groups 55-64 and 65-74	Y

The variables used for stratification are the Districts and the urban/rural areas within each district.

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
Identification of the main job (*)	N
Employment	N
Unemployment	N

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	N
instruction to interviewers	N
survey mode	N
weighting scheme	N
use of auxiliary information	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?
CATI	Yes	Blaise 5

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?
Commune	County	Commune	Stratified by region in the sampling

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
5 000	10 000	5 000	10 000

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
5 000	10 000	5000	10 000	5 000	10 000

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	2.84	4.07
SE	0.20	0.07	0.41
CI^(**)	69.88 - 70.65	2.23 - 2.49	9.18 - 10.77

Unemployment-to-population ratio 15-74 (NUTS 2 regions)
	CV	SE	CI(**)
NO01 Oslo og Viken	4.34	0.12	2.63 - 3.12
NO02 Innlandet	9.73	0.17	1.44 - 2.12
NO03 Agder og Sør-Østlandet	8.44	0.21	2.08 - 2.90
NO04 Vestlandet	5.87	0.13	1.89 - 2.38
NO05 Trøndelag	9.66	0.18	1.48 - 2.17
NO06 Nord-Norge	9.70	0.16	1.30 - 1.92

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
Unknown level, since the central population register is the only source, and therefore does not have anything it can be compared to.	Unknown level	Unknown level				There is some time lag in the central population register, so for instance persons who have died or emigrated recently might be included in it at the time of sampling to the LFS. We do not have any analysis to show, but the register is updated daily, and the amount of errors are generally assumed to be very low. It is the main source for all population statistics and population administration, and is very carefully monitored and run.

Errors due to the medium (questionnaire)
Was the questionnaire updated for the 2021 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	New IESS	Y	pilot, cognitive, internal check

Main methods of reducing measurement errors
Error source
Respondent	Letter introducing the survey (Y/N)	Phone call for booking or introducing the survey (Y/N)
	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)
	N	N	Y
Questionnaire	Questionnaire in several languages (Y/N)	On-line checks (for computer assisted interviews (Y/N)
	Y	Y
Other / Comments

Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N)	Variables used for non-response adjustment	Description of method
N
Substitution of non-responding units (Y/N)	Substitution rate	Criteria for substitution
N
Other methods (Y/N)	Description of method
Y	A separate adjustment for nonresponse is not applied to Norwegian LFS. Treatment of unit nonresponse is done via calibration. Quarterly weights are calculated in one step as such that the design weights are adjusted by using external controls obtained from register data. If there is a high association between the output variable and the auxiliary variables, both nonresponse bias due to unit nonresponse and variance may be reduced (e.g. Little 1986; Nguyen and Zhang 2020). Register based employment status, used as a calibration variable among other register variables, explains LFS employment very well while available auxiliary variables do not explain unemployment as good as they do employment. However, the analysis done with Norwegian LFS data has empirically shown that the differences between point estimates with one-step and two-step, including an extra step for nonresponse adjustment, approaches are negligible. The latter has increased variances without reducing nonresponse bias (Oguz-Alper 2018).

Rates of non-response by survey mode. Annual average
Survey
CAPI	CATI	PAPI	CAWI	POSTAL
NA	24.9 %	NA	NA	NA

Non-response rates by survey mode. 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	22.6	2.8	16.8
2	24.1	2.8	18.4
3	26.0	3.1	19.7
4	27.2	3.2	20.9
Annual	24.9	2.9	18.9

Units who refused to participate in the survey (Please indicate the number of the units concerned in the cells where the wave is mentioned)
Subsample	Quarter1_2022	Quarter2_2022	Quarter3_2022	Quarter4_2022
Subsample_Q2_2020	84	x	x	x
Subsample_Q3_2020	160	81	x	x
Subsample_Q4_2020	83	150	86	x
Subsample_Q1_2021	66	82	163	100
Subsample_Q2_2021	66	96	77	153
Subsample_Q3_2021	80	65	84	89
Subsample_Q4_2021	72	69	87	78
Subsample_Q1_2022	62	69	80	80
Subsample_Q2_2022	x	54	59	97
Subsample_Q3_2022	x	x	90	80
Subsample_Q4_2022	x	x	x	63
Total in absolute numbers	673	666	726	740
Total in % of theoretical quarterly sample	2.8%	2.8%	3.1%	3.2%

Units who were not contacted (including people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample	Quarter1_2022	Quarter2_2022	Quarter3_2022	Quarter4_2022
Subsample_Q2_2020	424	x	x	x
Subsample_Q3_2020	1163	439	x	x
Subsample_Q4_2020	498	1165	458	x
Subsample_Q1_2021	455	488	979	482
Subsample_Q2_2021	433	550	505	1008
Subsample_Q3_2021	425	485	557	530
Subsample_Q4_2021	365	496	570	530
Subsample_Q1_2022	336	417	575	553
Subsample_Q2_2022	x	374	516	652
Subsample_Q3_2022	x	x	442	558
Subsample_Q4_2022	x	x	x	486
Total in absolute numbers	4099	4414	4602	4799
Total in % of theoretical quarterly sample	16.8%	18.4%	19.7%	20.9%

*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	Column	Identifier	Quarter 1	Quarter 2	Quarter 3	Quarter 4
							We only list variables which can have item non-response and still be accepted by the Edamis portal. All numbers are in percent of the target population.
Compulsory	164-167	citizenship	0	0	0	0
Compulsory	168-170	countryb	0	0	0	0
Compulsory	171-173	cobfath	0	0	0	0
Compulsory	174-176	cobmoth	0	0	0	0
Compulsory	186-188	yearesid	1.48	1.50	2.13	1.36
Compulsory	189-191	countrpr	0	0	0	0.15
Compulsory	197	numjob	0	0	0	0
Compulsory	199	wantwork	0	0	0	0
Compulsory	200	seekreas	0.26	0	0.56	0.24
Compulsory	201	wantreas	0.17	0.06	0.13	0.07
Compulsory	203	wishmore	0	0	0	0
Compulsory	204	availble	0	0	0	0
Compulsory	205	avaireas	0	0	0	0
Compulsory	207-209	countryw	0	0	0	0
Compulsory	210-212	regionw	0	0	0	1.36
Compulsory	214	stapro	0.02	0	0	0.02
Compulsory	215-217	nace3d	0.46	0.44	0.61	0.59
Compulsory	218-221	isco4d	1.63	1.54	1.68	1.52
Compulsory	222	ftpt	0.02	0	0	0.02
Compulsory	223	temp	0.12	0.21	0.29	0.38
Compulsory	224	tempdur	41.71	35.39	35.68	40.18	Almost all of the non-response is 'I don't remember', so we don't quite know what to do in order to improve the situation.
Compulsory	227	ftptreas	0.45	0.22	0.27	0.65
Compulsory	236	seekdur	10.74	10.82	9.48	11.34
Compulsory	238	stapro2j	0.34	0	0	0
Compulsory	239-240	nace2j2d	1.49	1.84	2.27	2.99
Compulsory	241	mainstat	0.15	0.08	0.14	0.17
Compulsory	242-244	hatlevel	0.42	0.38	0.54	0.44
Compulsory	253-256	ystartwk	1.09	1.17	1.26	1.03
Compulsory	257-258	mstartwk	0	0	0	0
Compulsory	262	existpr	0.39	0.67	0.52	0.57
Compulsory	263-266	yearpr	7.02	7.75	8.42	8.08
Compulsory	267-268	monthpr	5.93	7.42	6.08	6.72
Compulsory	269-270	leavreas	0.29	0.14	0.22	0.21
Compulsory	277-279	contrhrs	0.01	0	0	0
Compulsory	280-282	hwusual	0.02	0	0	0.02
Compulsory	283-284	absholid	0.16	0.08	0.07	0.08

*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	213	homework	0.08
Compulsory	229	varitime	0.14
Compulsory	225	tempreas	0.09
Compulsory	226	tempagcy	0.07
Compulsory	237	needcare	8.72
Compulsory	230	supvisor	3.40
Compulsory	252	hatwork	1.25
Compulsory	260-261	findmeth	30.95
Compulsory	271	stapropr	5.14
Compulsory	272-273	nacepr2d	6.60
Compulsory	274-276	iscopr3d	6.20
Compulsory	310	educfed12	1.58
Compulsory	311-312	educlev12	1.17
Compulsory	313	educnfe12	1.80
Compulsory	314	genhealth	2.36
Compulsory	315	gali	0.60
Compulsory	316-323	incgross	3.26
Compulsory	324-325	incgross_f	3.26
Compulsory	228	mainclnt	13.99
Compulsory	231-232	sizefirm	20.03	Coding error for dependent interviews. Will be fixed.
Compulsory	233	lookoj	0.32
Compulsory	234-235	hwwish	0.02
Compulsory	245-247	hatfield	3.38
Compulsory	248-251	hatyear	4.35
Compulsory	259	wayjfoun	0.17

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

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
Y	Y	Y

Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)	Non response rate (%)
NO02 Innlandet	25.8
NO06 Trøndelag	24.1
NO07 Nord-Norge	26.2
NO08 Oslo og Viken	24.7
NO09 Agder og Sør-Østlandet	24.7
NO0A Vestlandet	24.6

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
Main variables	Imputation rate	Describe method used, mentioning which auxiliary information or stratification is used

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	Business statistics is register based and counts the number of registered jobs, whereas the LFS is based on interviews and counts maximun two jobs per person.		Business statistics shows a much lower number of employed.	SSB statistikk/naeringenes-okonomiske-utvikling
Total employment by NACE	Business statistics covers NACE B-J, L-N, S. LFS has no limitations on NACE
Number of hours worked	No difference in how to calculate the hours, but the business statistics does not cover the whole economy		Business statistics shows a much lower number of worked hours.

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)
The registered unemployment rate is lower than the LFS rage	LFS is higher	LFS is higher	LFS is higher	LFS is higher	Too low figueres to give usable information

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	LFS counts persons who live in Norway no matter where they work, whereas the National Accounts count work performed in Norway no matter where the worker lives.		In 2021 the LFS shows 52 000 fewer employed than the National Accounts
Total employment by NACE	No difference
Number of hours worked	LFS counts hours worked by persons who live in Norway, whereas the National Accounts count hours performed in Norway regardless of where the worker lives

Which is the use of LFS data for National Account Data?
Country uses LFS as the only source for employment in national accounts.	Country uses mainly LFS, but replacing it in a few industries (or labour status), on a case-by-case basis	Country not make use of LFS, or makes minimal use of it	Country combines sources for labour supply and demand giving precedence to labour supply sources (i.e. LFS)	Country combines sources for labour supply and demand not giving precedence to any labour side	Country combines sources for labour supply and demand giving precedence to labour demand sources (i.e. employment registers and/or enterprise surveys)
		x

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
Yes	Researchers from pre-approved institutions	Approved research plan for a specific project	Code books, metadata	The LFS team is available to answer questions from researchers

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