Labour force survey
Updated: 24 November 2022
Next update: 22 December 2022
About the statistics
The purpose of the Labour Force Survey is to provide information on the development in employment and unemployment, and on the relationship to the labour market for different groups.
Concepts and definitions are in accordance with recommendations given by the International Labour Organization (ILO) and EU/Eurostat.
According to the international recommendations persons above a specified age should be classified by their attachment to the labour market in a specified, short period, either a day or a week. In the Norwegian LFS the reference period is one week, and the sample of persons are classified in relation to their situation in that reference week.
Employed persons are persons who performed work for pay or profit for at least one hour in the reference week, or who were temporarily absent from work because of illness, holidays etc. Conscripts are classified as employed persons. Persons engaged by government measures to promote employment are also included if they receive wages. Persons laid off 100 per cent with a continuous duration of until three months are defined as employed, temporarily absent.
Unemployed persons are persons who were not employed in the reference week, but who had been seeking work during the preceding four weeks, and were available for work in the reference week or within the next two weeks (in 1996-2005 one should be available within two weeks following the time of interview, and until 1996 one should be able to start working in the reference week). Persons laid off 100 per cent are defined as unemployed after three continuous months of leave.
Persons in the labour force are either employed or unemployed. The remaining group of persons is labelled not in the labour force.
In addition to the measurement of employment and unemployment according to the international recommendations and definitions, it is also asked a single question in the LFS to all non-employed persons, and to the part-time employed persons, about their main activity. This variable gives the perons' self-perception regarding their activity or status. The purpose is to estimate how many people are in education, homemakers, pensioners etc., and how many have a part-time job besides. We also gain figures on how many of the unemployed people who are attending education. In the tables for those outside the labour force, i.e. neither employed nor unemployed, a group called "looking for work" is specified. They do not satisfy all the requirements to be classified as unemployed, but consider themselves as unemployed.
The potential labour force consist of persons who were not employed in the reference week, but had either been seeking work during the preceding four weeks or were available for work in the reference week or within the next two weeks.
The extended labour force consist of the (ordinary) labour force and the potential labour force.
The labour market slack consist of the unemployed, persons working part-time involuntary and persons in the potential labour force.
Man-hours worked include all actual working hours, i.e. including overtime and excluding absence from work. Persons absent from work are not included in the calculation of actual working hours per week (in average) .
Contractual/usual working hours refer to the weekly number of working hours determined by the working contract. Absence from work because of illness, holidays etc. is not subtracted, and overtime is not included. Employees, whose contractual working hours vary from week to week, give information on both the actual reference week as well as the average of their contractual working hours per week (in the tables published the average numbers are normally used). For employees without contract on working hours, for self-employed and for unpaid family workers, data on their usual weekly working hours are used (as an average of their actual working hours during the last 4 weeks).
Full-time/part-time distinction is based on usual working hours per week. Usual working hours of 37 and more is full-time, in addition to varying working hours or usual working hours between 32 and 36 where the respondent classify this as full-time work. The rest is classified as part-time, i.e. usual working hours less than 32 hours and varying hours or usual working hours between 32 and 36 where the respondent classify this as part-time work. For persons with more than one job, only the usual working hours in the main job is used in the classification of full-time/part-time work.
Overtime is defined as working hours which exceed the contractual working hours for full-time employees, conducted during a specified reference week. The overtime may be compensated by payment or by time off, or be without any compensation. Up to 2006 these questions were included in the survey only during the 2nd quarter each year. As from 2006 onwards they are included each quarter.
A major revision of the LFS in 2006 led to a significant break in the time-series for overtime, i.e. lower estimates than published earlier. This was mostly due to changes in the questionnaire concerning working time.
Statistics on overtime include employees working full-time only. For persons with more jobs, the working hours in the main and the second job are summarized in order to classify them as either part- or full-time workers, as in LFS statistics generally.
For those with more jobs, overtime hours in the main job only are included. By calculating the overtime hours as a share of the total number of man-hours worked among full-time employees, the working hours in the second job are, however, included in the denominator. Persons temporarily absent from work are included in the denominator while calculating the share of employees working overtime, mainly to avoid seasonal variations.
Employees who just had an occasional job in the reference week, and on-call workers, are not asked about overtime, but they are included in the denominator if they were full-time employed in the reference week.
As from 2007 imputation is used concerning the overtime variables in case of proxy interviews and partial non-response.
Underemployed persons consist of part-time employed persons wishing and seeking longer usual working hours and who were able to start with increased working hours within a month. This definition corresponds to that of the unemployed in the meaning that they must be both seeking and available.
Persons who work involuntary part-time consist of employed persons wishing longer usual working hours and who are able to start with increased working hours within a month. This group differs from the underemployed since they don't need to be seeking more increased working hours, it suffices to wishing it and beeing available for it.
Permanence of the job
The employees are asked whether they have a permanent job (a work contract of unlimited duration) or a temporary job (a work contract of limited duration). If the person has more than one job, only the main job is classified by permanence.
There are two kinds of working arrangements outside ordinary hours (Monday to Friday from 6 am to 6 pm).
- Shift work is usually understood as working time outside normally working hours. What counts in the survey as shift work is based on the evaluation of the respondent. For respondents who have not answered the question on shift work, the value is imputed based on the answers they have given with regard to evening, night, Saturday and Sunday work.
- Work outside ordinary hours, not shift work. This refers to work on evenings, nights, Saturdays and Sundays which is not shift work. Evening work is defined as work between 6 pm and 10 pm. Night work is between 10 pm and 6 am. Respondents who report that they work on one or more of these working time schedules outside ordinary hours are also asked about the frequency of this kind of work during a four week period. This is done for each of the working time schedule separately. Based on this, the work outside regularly hours is divided into the categories "regularly" or "sometimes". For evening and night work, the respondent must have this kind of work on at least half of their working days in the four week period to be defined as having regularly evening work and/or regularly night work. For work on weekends, they must work 2-4 Saturdays and/or 2-4 Sundays during the four week period to be defined as having regularly Saturday and/or Sunday work. The respondents who have these kinds of working schedules, but more seldom than indicated above, are defined as sometimes having this kind of work.
Course participation refers to job related courses which the respondent was paid to attend. Only courses received during a period of four weeks are included. Up to 2006 these questions were asked only during the 2nd quarter each year. As from 2006 onwards they are included each quarter to the whole sample. Labour force survey. Education and training.
As from 2006 the definition of age was changed from completed years at the end of the year to completed years at the time of the reference week.
The persons are classified by marital status as unmarried, married and previously married according to information given by the respondents. Previously married includes widows, widowers, separated and divorced persons. In the tables married women include cohabitants.
Immigrants are defined as persons born abroad by foreign-born parents who have emigrated toNorway. In statistics based on the Labour Force Survey (LFS) they are divided in two groups by country of birth:
1) Immigrants from EU/EFTA-countries,North-America,AustraliaandNew Zealand.
2) Immigrants from Eastern Europe except EU, Asia, Africa, Latin America and Oceania except Australia and New Zealand.
The industrial classification is in accordance with the Standard Industrial Classification (NOS D 383), which is based on the EU-standard of NACE Rev. 2.
The occupational classification is based on the International Standard Classification of Occupations 2008 (ISCO-08). The Norwegian standard has been named STYRK-08 (Notater 17/2011).
The educational classification is in accordance with the Norwegian Standard Classification of Education (NOS C617).
Name: Labour force survey (LFS)
Topic: Labour market and earnings
Division for labour market and wage statistics
The whole country.
Quarterly and annual, but monthly publishing of some key figures seasonally adjusted (averages of the last 3 months). Quarterly figures are normally published 5 weeks after the end of the quarter.
The quarterly data files are sent to Eurostat. Tables are sent each month/quarter/year to Eurostat, OECD, ILO and IMF, as well as to the Nordic Yearbook. A selection of the variables are also sent to NSD.
The basic material (survey results from the interviewers) as well as the statistical files (on the basis of revision and estimation procedures) are stored.
The main purpose of the survey is to provide data on employment and unemployment.
The Norwegian LFS started in 1972.
The surveys give information to the labour market authorities and other users about the situation on the labour market, and provide data for labour force research and forecasts, as well as for international organizations and mass media.
The results from the LFS are used in the National Accounts Statistics.
The main reason for the discrepancies between the LFS and the NA is that the LFS measures employment among persons who live in Norway, whereas the NA measures employment in Norwegian owned enterprises.
For statistics at regional levels (counties and municipalities), the register based employment statistics are recommended. These statistics also give figures on employees who are immigrants.
Statistics on unemployed persons at the employment offices and government measures to promote employment are compiled by NAV on the basis of registers of unemployed persons and applicants for work.
The figures on unemployment based on the LFS differ from the figures on unemployed persons registered at the Employment Offices. The LFS-figures also include unemployed persons not registered at the Employment Offices, some of the participants in government measures to promote employment and some of the disabled persons. On the other hand, some of the registered unemployed are not classified as unemployed in the LFS, on the basis of the information given on seeking and availability for work.
The Population and Housing Censuses (each 10th year) give statistics on employment during the last 12 months, in addition to the situation in a specified reference week. As from 2001 onwards employment data based on administrative registers are used. Statistics on labour conflicts and working days lost are compiled by Statistics Norway on the basis of information supplied mostly by the labour and employers's organizations. The Surveys of Level of Living (by Statistics Norway) give information on physical working environment as well as organizational working conditions.
The main source to describe the situation for the immigrants on the labour market is the registerbased statistics on employment and unemployment. By using data from the LFS as a supplement we will achieve still more information regarding this group, for example on actual and desired working hours, temporary employment and patterns of working time. Moreover the LFS has more data than the registers on job seeking and desire for work.
The main problem using the LFS for statistics on immigrants is the size of the sample and the statistical uncertainty. The group of immigrants in Norway is rather heterogeneous regarding their situation on the labour market, and therefore it is required to divide among at least two groups in the presentation of statistics. A further dividing by other variables will require long time-series in order to draw any conclusions.
The Statistics Act of 2019.
Council Regulation (EC) 2019/1700. Commission implementing regulations 2019/2240 and 2019/2241.
The LFS covers everyone who lives in Norway. The survey contains most information of the age group 15-74 years, but it also has a substantial data amount about persons 74 - 89 years. Those under 15 and over 89 are only covered by register information.
The observation units are persons and households.
The main source for the LFS is quarterly, representative samples. The data is collected by interview by telephone.
Inhabitants in all municipalities are randomly selected, on the basis of a register of family units. The sample consists of about 21 000 persons each quarter. Each respondent participates in the survey 8 times during a period of 8 quarters, and are asked about their connection to the labour market.
Additionally, all members of the main respondent's household is interviewed once during these two years. They get a shorter interview. They make up aproximately 3 000 persons, so the total sample consist of about 24 000 persons.
The LFS collects data by telephone interviews.
Some information from previous interviews are re-used. For instance, if the respondent confirms that they have the same job as the last time we talked to them, we do not ask about the respondent's occupation.
Coding of industry is done from information from registers.
Demographic data are collected from the Central Population Register, and data on education are based on a register of individual data collected by Statistics Norway from the educational institutions.
The respondent is the same person as the observation unit.
All weeks of the year are covered with data collection.
As the data collection is made by use of computer-assisted interviews, some procedures for electronic control of the registration of answers are included in the questionnaire, for example concerning the number of working hours during the reference week. In some cases the interviewers become a "warning" by recording an answer, in other cases maximum or minimum values have been set beforehand.
The most common analysis unit is person. The absolute numbers from the LFS are presented in the form of estimated total for the entire population aged 15-74. The weights or inflation factors vary, but have an average of about 195 for quarterly figures.
The estimation method uses more demographic data and register information relevant to the connection to the labour market in order to minimize standard errors and correct more for bias in the response sample in LFS since the nonresponse is not random. New method was launched in April 2018, is also used on LFS data back to 2006 to get the most comparable time series.
The estimation method in LFS is done in several stages, and are called multiple model calibration. Initially, the main labor market status of LFS, which is employed, unemployed, outside the workforce, are modeled consistent with a multinomial logit model, explained with a number of register variables known to all in the population. The register information are registered completely unemployed, on measures and persons with disabilities at NAV, register information on disability pensioners, education level, marital status, family size and immigrant category, country of origin, gender and age, residence, and information from the A scheme and the Tax Recovery Register. The model provides predictive probabilities every month for each main labor market status in LFS for everybody in the population.
The monthly weights in LFS are calibrated using these predicted probabilities and some register variables directly. This means that the weights also become consistent with the population for the number in the population register by gender, different age groups and region, as well as consistent weights for the number of full / part-time wage earners by gender and registered employed (yes/no) cross classified by immigrants in 2 groups.
Multiple model calibration provides some variance reduction, utilizing more efficiently that we have available good help information about everyone in the population from various registries that Statistics Norway has linked.
The initial weights before calibration are the ratio of the number of people in the population to the gross sample per. county (NUTS3), and takes into account that people in different counties have different probabilities of being selected to LFS sample.
For more detailed technical information about the new estimation method, please see Documents 2018/16 [https://www.ssb.no/en/arbeid-og-lonn/artikler-og-publikasjoner/new-estimation-methodology-for-the-norwegian-labour-force-survey]
Week-proportional weighting of the months in quarterly averages
LFS have continuous data collection. In order for all reference weeks to weighted evenly in quarterly averages, we now make week-proportional adjustment of monthly weights in our quarterly averages. That is, the monthly weights are multiplied by 4/13 or 5/13 depending on whether the months in the LFS contain respectively 4 or 5 whole weeks. Weekly proportional weighting of quarterly average has been made on all quarterly figures in our StatBank back to 2006. Before that is the even adjustment off all monthly weights was used, i.e. multiplied by 1/3 for all months.
That the weights are consistent for a register variable, such as gender, means that the sum of the weights in the responses in the LFS equals the number of the population for each category of the register variable, such as the number of men and the number of women in the population
The monthly figures are seasonally adjusted using X13-ARIMA-SEATS.
The seasonal adjustment of employed and unemployed are done indirectly; we seasonally adjust the series for women and men respectively over and under 24 years of age separately. For man-weeks workes, we seasonally adjust the following 3 series separately: persons aged 24 and below, men above 24 years and womenabove 24 years. We get the totals by summing the seasonally adjusted partial series afterwards.
We construct sex distributed seasonally adjusted figures for employed, unemployed and weekly hours worked for people aged 24 and below by using seasonally adjusted series by age and breaking them down by gender using monthly distribution keys. The distribution keys are made of trend numbers from extra runs of X-12ARIMA based on the LFS. We leave it to the seasonal adjustment program to decide whether additive or multiplicative decomposition of the series gives the best results, in addition to the choice of ARIMA model. These optimal picks are usually locked in for one year at a time. We allow X-12ARIMA to recalculate the seasonal components and parameters in the precorrection regressions each month. This is best practice for seasonal adjustment program settings according to Eurostat, and is done to have little revision of the seasonally adjusted time series throughout the year.
We allow the seasonal adjustment program to pre-correct the time series using regression analysis. We have specified right-hand side variables to take into account extreme values and effects of holidays that do not fall on weekdays in the same month in the LFS each year. The seasonally adjusted series for employment and unemployment for those over 24 and the figures for man-hours worked are precorrected if Easter is in March. The figures for man-hours worked are also sensitive to more individual holidays, and are therefore additionally pre-corrected for the effects of Easter Monday in March, May 1, May 17, Ascension Day and Whitsunday 2. In addition, we correct for the number of public holidays on weekdays in December and the number of normal working days in Christmas and the number of weekdays in June which in the LFS end up in July, because in the LFS you never divide reference weeks (Monday-Sunday) between months as the calendar does. We also take into account that two of the above-mentioned holidays can end up on the same date (for example, Ascension Day on 17 May in 2007 and 2012 and on 1 May 2008). All the right-hand side variables counted as deviations are given respective monthly averages. In addition, the variables have been adjusted with a week multiplier to better adjust for the fact that some months in the LFS have 5 reference weeks, while others have 4.
Seasonal adjustment during the corona crisis (March 2020 to March 2022) is done in such a way that the figures from the crisis are not included in the basis for the calculation of the seasonal pattern. This is in line with the recommendations from Eurostat. This handling means that trend figures will initially follow seasonally adjusted figures in the period. In the corona period March 2020 to March 2021, the trend figures are therefore smoothed with a three-month moving centered average.
The corona crisis is modeled with consistent level shift (LS) specification for each month from March 2020 through March 2022.
The trend numbers represent the longer-term tendency in the data, including the business cycle. The trend can often be slightly revised when new observations are incorporated, especially towards the end of the time series, called revision uncertainty. The final trend is calculated by smoothing the seasonally adjusted figures. The program selects a moving trend average based on statistical properties of the data. For monthly series, either a 9-, 13- or 23-term Henderson moving average will be selected, and associated asymmetric variants towards the end. For detailed information on trend filters in X-12ARIMA, see for example the Australian Bureau of Statistics website or chapter 12.6 of the Handbook on Seasonal Adjustment.
See the "About seasonal adjustment" section below for more information about the seasonal adjustment.
We use international standards for definitions, which means that the results of the Norwegian LFS can be compared to results in other countries.
There are times series breaks between 1995 and 1996, and between 2005 and 2006.
The estimation method which we started to use in 2018 uses more registers that will reduce non-response bias and sample uncertainty. This leads to a slightly lower level of employed and almost correspondingly higher levels for people outside the workforce than the previous method. The total number of unemployed changes only a little. To get the most comparable figures, the time series are revised back to 2006 in our StatBank. However, the adjustments vary slightly between groups and over time.
There is a time series break between 2020 and 2021 because of a restructuring of the LFS in 2021. As a consequence, the number of persons aged 15-74 was reduced by 26 700 in the new LFS. Furthermore, it led to a break in the number of employed people estimated to 21 900 and a break in the number of unemployed people to 5 400 (both estimates, the latter not significant). The number of persons outside the labour force was therefore reduced by 54 000 (residual determined). The method used in the break estimation is documented in a separate paper.
Since the LFS population was changed from 2021 to excluding residents in collective households, there are also breaks in the shares of employed persons and persons in/outside the labour force. Taken this and the above-mentioned break estimates into account, the new LFS show 0.8 percentage points higher employment rate. The new LFS show a 0.9 percentage points higher share of persons in the labour force, and a 0.9 percentage points lower share of persons outside the labour force. The unemployment rate in the new LFS is 0.1 percentage points higher than in the old.
The tabel below show break estimates for emploued and unemployed (4th quarter 2020) for more age groups than the one mentioned above.
|Employed (1 000)||Unemployed (1 000)|
Tables 13618 and 13619 contain comparable time series across the break between 2020 and 2021. Theses tables contain figures for the labour force, employed persons and unemployed, where the figures for the period 2009-2020 are adjusted according to the new LFS level using the same method as described above. The tables 13618 and 13619 also contain population figures for the period 2009-2020 in accordance with the new LFS population from 2021 on, e.g. persons living in private households. Consequently, they contain labour force and employed persons in per cent of the population back to 2009.
Table 13332 also contain break adjusted figures. These figures are in addition seasonally adjusted.
In all surveys errors may occur in connection with both the collection and the processing of data.
The size of the non-response as a percentage of the gross sample has varied widely since the LFS began in 1972. The 20 first years were generally around 10-12 percent. In the years 1992-1997 it was particularly low, only 6-8 percent. Subsequently, the non-response rate gradually increased to 21 per cent in 2013. After systematic work, the non-response has been reduced to around 14 per cent in 2018.
Correction for total non-response is done in the estimating procedure. Partial non-response is adjusted for some variables.
The standard error for the quarterly average for the number of unemployed aged 15-74 is around 4 600 in 2022. It corresponds to a coefficient of variation of 4.8 per cent. The standard error for the quarterly average for the number of employed aged 15-74 in LFS is about 8 500 people in 2022. The coefficient of variation for employment figures will then be around 0.3 per cent.
If the reader wants an indication of the size of the standard error for quarterly figures and annual averages for other variables or group divisions, see the table below. These indications are only guiding, and can not be interpreted as precise calculations for any variable. Changes between two surveys will usually have the same absolute uncertainty as the two figures which are compared.
|Indication of the standard error|
|Estimated value||Quarterly figures||Annual figures|
|Absolute figures||As per cent of estimated value||Absolute figures||As per cent of estimated value|
|10 000||1 100||11,0||700||7,0|
|20 000||1 600||8,0||1 100||5,5|
|30 000||1 900||6,3||1 300||4,3|
|40 000||2 200||5,5||1 500||3,8|
|50 000||2 500||5,0||1 700||3,4|
|60 000||2 700||4,5||1 800||3,0|
|70 000||2 900||4,1||1 900||2,7|
|100 000||3 500||3,5||2 300||2,3|
|200 000||4 800||2,4||3 200||1,6|
|300 000||5 800||1,9||3 900||1,3|
|400 000||6 600||1,7||4 400||1,1|
|500 000||7 200||1,4||4 800||1,0|
|1 000 000||9 100||0,9||6 100||0,6|
|1 700 000||9 600||0,6||6 400||0,4|
|2 000 000||9 100||0,5||6 100||0,3|
Monthly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.
For more information on seasonal adjustment: metadata on methods: seasonal adjustment
Due to fixed and movable holydays and holidays in July and December the figures of man-weeks worked vary across the year. Also the number of employed persons and unemployed persons vary across the year, especially for the young people. Among other matters this is because of summer substitute and young people searching for summer jobs.
Seasonally adjusted series
We seasonally adjust 2 age-divided series (more/less than 24 year) separately for employed persons and unemployed persons, and add the two series afterwards.
For man-weeks worked we seasonally adjust 3 gender- and age-divided series: persons 15 – 24 years, males 25 – 74 years and females 25 – 74 years.
Due to no sample uncertainty, Registered unemployed and registered unemployed + government measures are also seasonally adjusted as a supplement to the LFS. The Register-figures are seasonally adjusted separately for 4 gender- and age divided (more/less than 24 year) series.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.
Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjustment for both the number of working days/trading days and for that the composition of days can vary from one month to another.
We perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, with RegARIMA.
Methods for trading/working day adjustment
Comments : LFS is surveying persons attachment to the labour market in whole reference weeks. The reference weeks are not split to follow the exact months of the calendar. Therefore, all the monthly data files include as many Mondays as Tuesdays and so on. The monthly data files include either 4 or 5 reference weeks, and the inflation factors adjust fore that. Due to this the number of working days in a month is only affected by holidays and national days off.
Correction for moving holidays
Correction based on an estimation of the duration of the moving holidays effects, specifically adjusted to Norwegian circumstances.
The seasonally adjusted time series for man-weeks worked are pre-adjusted if Easter is in March.
Due to sensitivity to single holidays or common days off, the man-weeks worked series in addition are pre-adjusted for 2 nd Easter day, May 1st, May 17th, Whit Monday and Ascension Day. (We also take account of the effect when two of these days occur on the same date, for instance Ascension Day on May 17th in 2007 and 2012 and on May 1st. in 2008).
In addition we pre-adjust man-weeks worked series for the number of common days off that falls on week days in December, and for the number of normal days in the week between Christmas and New Year's Eve that falls in December/January in the LFS. All regression variables are deseasonalized by subtracting the long term monthly average. Also the regression variables are fine tuned by a week multiplier to take account of that some months represent 4 reference weeks and other 5 reference weeks.
National and EU/euro area calendars
Use of the Norwegian calendar
Comments : We also take account of the fact that LFS don’t divide reference weeks (Monday – Sunday) between months, like in the ordinary calendar. For instance the holiday May 1 st may in the LFS be in April. Correspondingly may New Year's Day be in December in the LFS.
Treatment of outliers
Outliers, or extreme values, are abnormal values of the series.
Outliers are detected automatically by the seasonal adjustment tool. The outliers are removed before seasonal adjustment is carried out, and then reintroduced into the seasonally adjusted data.
Comments : Only additive outliers are detected automatically.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
Automatic model selection by established routines in the seasonal adjustment tool.
Comments : The pickmdl procedure in Version 0.3 of X-12-ARIMA is used with the standard method (=first) in the yearly identification of ARIMA models. (Then these automatically identified models are hard coded in the specification files.)
In the list of ARIMA models that are checked in procedure pickmdl, 3 extra ARIMA models are included in order to possibly take better account of the data acquisition in the LFS where interviewees are interviewed every 3 months for 2 year. The extra ARIMA models are:
The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.
Automatic decomposition scheme selection.
Choice of seasonal adjustment approach
X-12-ARIMA Version 0.3
Consistency between raw and seasonally adjusted data
Impose the equality over the year of seasonally adjusted data to original data (e.g. sum or average).
Consistency between aggregate/definition of seasonally adjusted data
In some series, consistency between seasonally adjusted totals and the original series is imposed. For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.
Impose the equality between aggregated series and the component series.
The labour force is not seasonally adjusted. The labour force, seasonally adjusted is defined as the sum of employed persons, seasonally adjusted and unemployed persons, seasonally adjusted.
The equality is imposed by indirect approach.
Direct versus indirect approach
Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.
Indirect approach where the seasonal adjustment of components occurs using the same approach and software, and then totals are derived by aggregation of the seasonally adjusted components.
Horizon for estimating the model and the correction factors
When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.
The whole time series is used to estimate the model and the correction factors
Due to a major revision of the LFS in 2006, we now let the time series start in 2006.
General revision policy
Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.
Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and release calendar.
Revisions second months in the quarter .
The figures from the first months in the quarter are preliminary. They are re-estimated, at the same time as the data from the second months in the quarter are estimated, supplemented with the extra interviewees that we got after the preliminary deadline and with more up to date or better auxiliary register variables. For the re-estimation, also more up to date auxiliary information about the whole population from the Register of Employee and the Central Population Register are utilized.
Quarterly revisions . The figures from the first two months in the quarter are preliminary. They are re-estimated at the end of the quarter supplemented with the extra interviewees that we get after the preliminary deadlines and with better auxiliary register variables. For the re-estimation, also more up to date auxiliary information about the whole population from the Register of Employee are utilized.
Yearly revisions . After seasonal adjustment, the levels are corrected to make the annual averages of the seasonally adjusted figures equal to the corresponding unadjusted figures from the LFS. The annual averages of a year are not ready until we publish the 4 th quarter figures. At that point we use new factors of level correction. Until the 4 th quarter publication is available, preliminary level correction factors from last year have to be used. For the estimation of the 4 th quarter also more up to date auxiliary information about the whole population from the annual Tax Register are utilized.
Concurrent versus current adjustment
Partial concurrent adjustment
The model, filters and calendar regressors are re-identified once a year and the respective parameters and factors re-estimated every time a new or revised data becomes available.
Additive outliers are detected automatically and parameters re-estimated every time new data becomes available.
Horizon for published revisions
The entire time series is revised in the event of a re-estimation of the seasonal factors.
Evaluation of seasonally adjustment data
Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.
For more information on the quality indicators in the table, see: metadata on methods: seasonal adjustment
Seasonal adjustment of short time series
All series are sufficiently long to perform an optimal seasonal adjustment.
As from 2006 the LFS was revised, among other things the lower age limit covered by the survey was lowered from 16 to 15. In order to avoid breaks in the series, we start all the time series in 2006.
Treatment of problematic series
Problematic series are treated in a special way only when they are relevant. The remaining series are treated according to normal procedures.
Due to large random variation (sample uncertainty) and small seasonal variation, we do not seasonally adjust all the main variables of the LFS gender divided. This is the case for unemployment, employment and for Employed persons man-weeks worked for persons 15-24 years. Afterwards, gender divided figures are constructed from seasonally adjusted figures by utilizing monthly gender distributions calculated from trend-cycle figures from additional unofficial seasonal adjustments of the LFS.
Three months moving averages of seasonally adjusted data and trend are available.
All metadata information associated with an individual time series is available.
Historical data are available to enable revision analysis.
Easily retrieve and integrate Statistics Norway’s seasonally adjusted LFS data with your own systems with API.
In order to reduce the uncertainty, the presented series are three months moving averages of the seasonally adjusted figures. For instance the figure from September is the average of the estimates from August - October.
See previous releases for historical data.
Three months averages of seasonally plus working day adjusted series and trend-cycle series are released.
For each series, some quality measures of the seasonal adjustment are presented.