unemployed persons in per cent of the labour force (seasonally adjusted) in March (average of February -April)
|March 20191||December 2018 - March 2019|
|1Three-month average named by the middle month.|
|Unemployed persons||98 000||-6 000|
|In per cent of the labour force||3.5||-0.2|
|Employed persons||2 717 000||12 000|
|In per cent of the population||67.8||0.2|
See selected tables from this statistics
Employment and unemployment for persons aged 15-74 years, seasonally adjusted, three-months moving average. Absolute figures in 1000 and in per cent
|Population, not seasonally adjusted||Labour force||Labour force in per cent of the population||Employed persons||Employed persons in per cent of the population||Unemployed persons (LFS)||Unemployed persons(LFS) in per cent of the labour force||Registered unemployed (NAV)1||Registered unemployed + government measures (NAV)1|
|1Theses figures are monthly, and not three-months moving average. They are not corrected for the break in 2018, and NAV figures is therefore not comparable before and after the break.|
|March 2016||3 924||2 776||70.7||2 639||67.3||137||4.9||85||101|
|April 2016||3 927||2 769||70.5||2 637||67.1||133||4.8||84||100|
|May 2016||3 930||2 767||70.4||2 634||67.0||132||4.8||85||101|
|June 2016||3 933||2 770||70.4||2 638||67.1||132||4.8||84||102|
|July 2016||3 936||2 774||70.5||2 642||67.1||133||4.8||84||102|
|August 2016||3 940||2 777||70.5||2 644||67.1||133||4.8||83||102|
|September 2016||3 943||2 781||70.5||2 649||67.2||132||4.8||82||101|
|October 2016||3 947||2 772||70.2||2 643||67.0||130||4.7||83||102|
|November 2016||3 949||2 757||69.8||2 633||66.7||125||4.5||83||102|
|December 2016||3 951||2 753||69.7||2 635||66.7||118||4.3||82||101|
|January 2017||3 953||2 745||69.4||2 632||66.6||113||4.1||81||100|
|February 2017||3 956||2 757||69.7||2 640||66.7||117||4.3||80||99|
|March 2017||3 958||2 761||69.8||2 639||66.7||122||4.4||78||98|
|April 2017||3 960||2 765||69.8||2 641||66.7||124||4.5||77||97|
|May 2017||3 962||2 766||69.8||2 643||66.7||123||4.4||75||96|
|June 2017||3 964||2 756||69.5||2 638||66.5||119||4.3||75||94|
|July 2017||3 967||2 760||69.6||2 644||66.7||115||4.2||74||93|
|August 2017||3 970||2 761||69.5||2 648||66.7||112||4.1||73||92|
|September 2017||3 973||2 766||69.6||2 655||66.8||111||4.0||72||91|
|October 2017||3 975||2 768||69.7||2 659||66.9||110||4.0||70||90|
|November 2017||3 977||2 765||69.5||2 652||66.7||113||4.1||69||87|
|December 2017||3 978||2 768||69.6||2 656||66.8||113||4.1||68||87|
|January 2018||3 980||2 774||69.7||2 663||66.9||111||4.0||66||84|
|February 2018||3 983||2 785||69.9||2 678||67.2||107||3.8||66||84|
|March 2018||3 985||2 787||69.9||2 679||67.2||108||3.9||66||83|
|April 2018||3 986||2 785||69.9||2 680||67.2||105||3.8||66||83|
|May 2018||3 988||2 794||70.1||2 687||67.4||107||3.8||64||81|
|June 2018||3 990||2 805||70.3||2 697||67.6||107||3.8||64||80|
|July 2018||3 993||2 813||70.4||2 701||67.6||112||4.0||67||84|
|August 2018||3 996||2 817||70.5||2 704||67.7||113||4.0||66||82|
|September 2018||3 999||2 816||70.4||2 704||67.6||112||4.0||66||82|
|October 2018||4 002||2 821||70.5||2 710||67.7||112||4.0||65||80|
|November 2018||4 004||2 814||70.3||2 709||67.7||106||3.8||66||81|
|December 2018||4 004||2 809||70.2||2 705||67.6||104||3.7||66||81|
|January 2019||4 005||2 811||70.2||2 703||67.5||107||3.8||65||80|
|February 2019||4 007||2 817||70.3||2 711||67.7||106||3.8||64||79|
|March 2019||4 009||2 815||70.2||2 717||67.8||98||3.5||64||78|
See all figures from this statistics
About the statistics
The seasonally-adjusted figures from the Labour Force Survey provide information on the development of employment and unemployment. In order to reduce uncertainty, the series are three-month moving averages. Changes are therefore calculated from figures published three months earlier The series are published every month.
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 aged 15-74 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 .
Man-hours worked include all actual working hours, i.e. including overtime and excluding absence from work.
Age is given in complete years at the end of the survey week.
Registered unemployed is defined as persons who are seeking income-earning work through Norwegian Labour and Welfare Service (NAV) and who are available to carry out such work. In addition, these persons must not have had any work for pay or profit in the preceding two weeks.
Participants of ordinary labour market measures are jobseekers who, during the reference period, took part in a measure aimed for ordinary jobseekers.
Name: Labour force survey, seasonally-adjusted figures
Topic: Labour market and earnings
Division for Labour Market and Wage Statistics
The whole country.
Frequency: Monthly figures Timeliness: Seasonally adjusted figures from the Labour Force Survey (LFS) are normally published 4 weeks after the end of the month. Due to the three-month moving average smoothing of the time series, the figures are in reality presented with a time lag of one month. See the advance release calendar for coming statistics the next 4 months.
At the time of each publication, unemployment and employment figures are being sent to Eurostat (seasonally unadjusted, adjusted and trend figures, by age groups and sex).
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, and data on the labour force participation for the total the population.
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.
No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 08 am. Prior to this, a minimum of three months' advance notice is given in the Statistics Release Calendar.
For more information, see Principles for equal treatment of users in releasing statistics and analyses.
Statistics on unemployed persons at the Employment Offices and government measures to promote employment are compiled by the Norwegian Labour and Welfare Administration (NAV) on the basis of the job applicant register (ARENA).
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 results from the LFS are used in the National Accounts Statistics. For statistics at regional levels (counties and municipalities), the Registerbased Employment Statistics are recommended. These statistics also give figures on employees who are immigrants. Statistics Norway also produces statistics on selected industries based on data from the establishments, containing figures on employment, compensation of employees, value of production and capital formation etc. These statistics contain more detailed information about each industry than it is possible to give from the LFS and the National Accounts Statistics.
As from 2006 persons on lay-off (until 3 months) are no longer classified as unemployed, but as employed persons (temporarily absent from work) in the LFS, while they still are classified as unemployed in the register based statistics. However, if the period of lay-off exceeds 3 months, they are classified as unemployed in the LFS as well.
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 LFS only includes persons who are registered as residents in the population register. Persons working in Norwaywho are not registered as permanent residents or who are planning to stay for less than six months are not included in the employment figure in the LFS. If these people lose their job it does not count as a fall in employment or a rise in unemployment. In national accounts figures (NR), employed non-residents are included in the employment figure as long as they work in an establishment in Norway. If employment decreases in this group it will count as a fall in employment in NR. Statistics Norwaypublishes separate figures for all registered non-residents once a year. See short-term immigrants .
The Statistics Act §§ 2-1, 2-2 and 2-3.
Council Regulation (EC) nos 577/98, 1991/2002 and 2257/2003, and Commission Regulation nos 1575/2000, 1897/2000, 2104/2002, 430/2005 and 377/2008.
The total population aged 15-74 with permanent residence in Norway is covered by the LFS. The target population is based on the Central Population Register, which is the official administrative register of residents. Residents are defined as those expected to live here over 6 months. The LFS does not cover employees who commute from abroad, or people with a residence permit of less than 6 months duration. The observation unit is person.
The main source for the LFS is quarterly, representative sample surveys.
The statistics on registered unemployment and labour market measures are based on data from Norwegian Labour and Welfare Service’s (NAV) administration system, ARENA. The register includes persons registered as unemployed, persons on ordinary labour market measures, and persons with a reduced capacity to work.
The sampling design is a county-stratified systematic sampling (with assumed random sorting) of family clusters from our Central Population Register. The sampling fraction varies somewhat between the 19 counties in Norway, giving smaller counties higher representation. The sample consists of about 12 000 family units or 24 000 persons each quarter. Each family member aged 15-74 participates in the survey, answering questions about their situation during a specified survey week. Each family participates every third months over a 2 year period. This means the survey is designed as a rotating panel, where families suppose to participate 8 times.
For more information, please cf. " Labour Force Survey 2001 (NOS C748). " (NOS C748).
All interviews are done by telephone. As from 1 st quarter 1996 the data are collected weekly, i.e. the LFS became a continuous survey. Up to 1 st quarter 1996 (from 2 nd quarter 1988) the surveys were based on one week each month.
Information from previous interviews are used while asking about any changes in the situation, instead of the same, comprehensive data collection every time. For the coding of industry, information from some registers is also used. 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 (but questions are also asked to get more updated information).
The respondent is usually the same person as the observation unit (but proxy interviews are done if it is not possible to get in contact with the observation unit; 14-15 per cent of the interviews are done by asking near family members). Data are collected weekly, i.e. the LFS is a continuous survey (all weeks are covered). Up to 1st quarter 1996 (from 2nd quarter 1988) the survey was based on one reference week each month, and in previous years on one week each quarter. Participation in the survey is compulsory, but compulsory fines are not used.
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 analysis unit is person. The absolute numbers from the LFS are presented in the form of estimated totals for the entire population aged 15-74. The weights or inflation factors vary, but have an average of about 195 for quarterly figures in 2017.
The estimation method uses more demographic data and register information relevant to the connection to the labor 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 (MMK). 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]
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
Seasonally adjusted data are calculated by using the X12-ARIMA method. The adjustment is done indirectly. The time series of employed persons and unemployed persons are seasonally adjusted separately by age over/under 24 years. The time series of man-weeks worked are seasonally adjusted separately in 3 groups: persons 24 year and less, male more then 24 years and female more then 24 years. The series of registered unemployed at the Employment Office and the series of registered unemployed at the Employment Office plus participants in labour market measures seasonally adjusted separately in 4 groups: gender cross classified by age over/under 24 years. All the seasonally adjusted subgroup series are summed up for totals afterwards. The official seasonally adjusted figures divided by age groups are broken down into gender-divided figures by utilizing monthly gender distributions calculated from trend-cycle figures from additional unofficial seasonal adjustments of the LFS.
Regression models in X-12-ARIMA pre-adjust the series, where we define the explanatory variables for holidays not falling on weekdays in the same month in LFS every year and for outliers. The seasonally adjusted time series for employed persons over 24 year and unemployed persons over 24 year 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 1 st , May 17 th , 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 17 th in 2007 and 2012 and on May 1 st 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 the number of normal day in the week between Christmas and New Year's Eve that falls in December/January in the LFS. Also we pre-adjust man-weeks worked series for the number of week days in June the in the LFS falls in July, due to the fact that we don’t divide reference weeks (Monday &– Sunday) between months in the LFS like it is done in the calendar. 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 for that some months represent 4 reference weeks and other 5 reference weeks.
Only 3-months moving averages of the seasonally adjusted time series are published.
For more information, see About seasonal adjustment further down.
New estimation method published in April 2018 uses more registers that will reduce nonresponse 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.
Breaks in the series up to 2001 are described in the publication Labour Force Survey 2001 (NOS C748).
The concepts in LFS are defined in accordance with international recommendations, cf item 4.1, to ensure comparability among the countries.
As from 2006 the LFS was revised, mainly to be in accordance with the EU-requirements based on new ILO recommendations. Some definitions and parts of the questionnaire were changed, and some new variables were added. As from 2006 age is defined as completed years at the time of the reference week, instead of completed years at the end of the year, as earlier. Moreover the lower age limit to be covered by the survey was lowered from 16 to 15, in accordance with the surveys in other countries.
These changes from 2006 led to breaks in the time-series for the LFS estimates. The revised survey gives some lower estimates for unemployment (1 000 persons as an annual average for 2006) and some higher estimates for employment (8 000 in 2006). A break in the time series also occured for the estimates of actual working hours per week and man-hours worked. For more information, please cf. http://www.ssb.no/aku_en/. Quarterly data for 2006 are published according to both the revised and the unrevised LFS, in order to improve the comparability for the figures between 2006 and 2005. The breaks in the time-series were most significant for figures divided by age groups.
In all surveys errors may occur in connection with both the collection and the processing of data. The use of proxy interviews (asking near family members instead of the observation unit) often increase the problems of measurement. As an average the employment is underestimated because of proxy interviews.
The size of the nonresponse 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 nonresponse rate gradually increased to 21 per cent in 2013. After systematic work, the nonresponse has been reduced to around 16 per cent in 2017.
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 4600 in 2017. It corresponds to a coefficient of variation of 3.9 per cent. The standard error for the quarterly average for the number of employed aged 15-74 in LFS is about 8600 people in 2017. The coefficient of variation for employment figures will then be around 0.29 percent. More calculations will later be published in our StatBank.
The regression models pre-adjust some of the observations that are seasonally adjusted. There are uncertainties connected to all methods of both seasonal adjustment and pre-adjustment.
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.
Comments : 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
Comments : 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.
Comments : 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.
Comments : 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.
Comments : 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.
Comments : 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.
Analyses, articles and publications
Unemployment at 3.5 per centPublished 23 May 2019
The unemployment rate was 3.5 per cent in March, adjusted for seasonal variations.Read this article