Sickness absence

Name: Sickness absence
Topic: Labour market and earnings

Division for Labour Market and Wage Statistics

Figures of doctor-certified sickness absence are published on national, county and municipality level.

Figures of self-certified sickness absence are published on national and county level.

The statistics are published quarterly and annually. Figures are usually released 9-10 weeks after the end of the quarter.

Figures from the statistics are reported to OECD.

Collected and revised data are stored securely by Statistics Norway in compliance with applicable legislation on data processing.

Statistics Norway can grant access to the source data (de-identified or anonymised microdata) on which the statistics are based, for researchers and public authorities for the purposes of preparing statistical results and analyses. Access can be granted upon application and subject to conditions. Refer to the details about this at Access to data from Statistics Norway.

The statistics are intended to shed light on sickness absence trends in the aggregate and distributed by industry, sector, hours worked and various personal variables connected with those on sick leave. These are intended to form the basis for developing and evaluating measures aimed at sickness absence ('IA-agreement').

Publication began in 2001 and replaced the partially abridged statistics that had covered the central government and municipal sectors and parts of the private sector.

The statistics are published in collaboration with the Norwegian Labour and Welfare Administration (NAV). Doctor-certified sickness absence is based on the Sick Leave Register (sykemeldingsregisteret) at NAV. Self-certified sickness absence is based on a sample survey carried out by Statistics Norway.

Major users of the statistics are the authorities, employers' and employees' organisations, individual companies and researchers. The statistics are sentral in evaluating the IA-agreement.

Aims that have governed the planning of the statistics are:

1. The possibility for employers and funds-appropriating authorities to make reliable estimates of expenses.

2. The possibility for employers' and employees' organisations to evaluate the efficiency of various measures implemented to reduce sickness absence.

3. The possibility of identifying risk groups in order to implement preventive measures.

4. The possibility of comparison over time as the basis for research in the area.

No external users have access to statistics before they are released at 8 a.m. on ssb.no after at least three months’ advance notice in the release calendar. This is one of the most important principles in Statistics Norway for ensuring the equal treatment of users.

There is one exception: As part of the collaborative effort on sickness absence with Statistics Norway (SSB) and The Norwegian Labour and Welfare Administration (NAV), NAV receives figures before release date on both self-certified and doctor certified sickness absence. SSB and NAV then releases the figures simultaneously.

Many different bodies produce or have produced sickness absence statistics that each covers various sectors of business and industry. The Confederation of Norwegian Business and Industry (NHO) compiled earlier sickness absence statistics covering blue-collar and white-collar workers in mining, manufacturing and general contracting activities. Now NHO has stopped their data collection and receives data from Statistics Norway in stead.

The Norwegian Association of Local and Regional Authorities (KS) compiles sickness absence statistics for permanent employees in nearly all municipalities and county authorities as well as for employees of companies that are associate members of KS. The Ministry of Labour and Government Administration compiles sickness absence statistics for all employees for whom the central government is the employer. A more detailed comparison of the various measurement figures for sickness absence has been made in the report from the Central Statistics on Sickness Absence pilot project. Other absence statistics are also available. For the retail and financial intermediation industries, the Federation of Norwegian Commercial and Service Enterprises and the Norwegian Financial Services Association (FNH) compile statistics respectively.

The Labour Force Survey, which Statistics Norway publishes quarterly, also contains some information about sickness absence. The target figure used in this survey is the number of employees who have been absent during the whole registration week in per cent of employees in total. The figure is given by gender and group of industry. This target-figure is less comparable with those in the other Norwegian statistics. The Central statistics of sickness absence for instance, include persons on partial sick leave and also cases of sickness absence shorter than one week. In addition these statistics also take into account both the working time and the duration of the sickness absence when the portion of sickness absence is calculated.

The statistics are developed, produced and disseminated pursuant to Act no. 32 of 21 June 2019 relating to official statistics and Statistics Norway (the Statistics Act).

The statistics are part of the national program for official statistics, main area Labour and wages, sub-area work environment, sickness absence and work stoppages.

Not relevant

The statistics does not include self-employed or conscripts. Further more it does not include absence related to a child's illness or family and maternity leave.

Doctor-certificated sickness absence
The population is all employees aged 16-69 residing in Norway and registered in the A Scheme (a-ordningen) Register classified as ordinary and maritime jobs during the reference week. The Reference week is the weeks each month containing the 16th. During a quarter there are Three Reference weeks, and the employment has to be active in at least one of these weeks to be counted.

Under the A Scheme Act, the reporting duty an employer occurs from 2015 for all his employees with payroll or expenditure allowance above 1000 Norwegian kroner per annum. Before 2015 the Employee register population was limited to employees having a job relation scheduled to more than four hours a week and lasting more than six days. Self-employed are not included in the statistics.

Self-certificated sickness absence
The population for the survey is all businesses operating in Norway with a few exceptions. Because of difficulties in obtaining reliable information about self-certificated sickness absence, the following businesses are kept outside the survey:

• private households with employed persons
• extra territorial organizations
• investment companies
• extraction of crude petroleum and Natural gas
• water transport
• animal husbandry service activities (except veterinary activities).

Doctor-certified sickness absence

The Sick Leave Register is the main source of information for doctor-certified sickness absence. The Norwegian Labour and Welfare Administration (NAV) is responsible for the register.

The Sick Leave Register is based on the local NAV offices registrations of medical certificates ("Sykemeldingsattest IA") as part of its sickness benefits routine. These registrations covers all doctor-certified absences due to a person's own illness. The Sick Leaves are linked to employees by the national identity number.

As from January 2015 the Employee register was replaced by a new register on monthly reporting of employee and payroll information (a-melding) to NAV, Tax Administration and Statistics Norway. Jobs classified as ordinary and maritime in the A Scheme register corresponds to the population of employees in the earlier Employee register. Still, the earlier Employee register was supposed to contain all job relation scheduled to more than four hours a week and lasting more than six days. Under the A Scheme Act the reporting duty for an employer occurs for all his employees with payroll or expenditure allowance above 1000 Norwegian kroner per annum. Due to this new and more strict reporting duty, the A Scheme register have a wider scope than the earlier Employee register, and this has produced a minor level shift from the 1st quarter of 2015.

Self-certified sickness absence

Statistics Norway conducts a sample survey to be able to estimate sickness absence days which are self-certified.

Self-certified sickness absence

The statistics on self-certified sickness absence are based on data reported by a sample of establishments. A random sample of nearly 10 000 establishments is drawn from The Central Register of Establishments and Enterprises (CRE). The establishments are stratified by industry, size and county of work. All the establishments in the population with at least 5 employees are divided into 4 groups depending on the number of employees. The establishments are stratified on most Divisions (2-digit groups) in the Standard Industrial Classification (SIC2007). This ensures a representative sample in 44 groups of industry, sizes of establishment and counties. All establishments in the same strata have the same probability to be drawn. The probability to be drawn increases by size. None of the small establishments (four or less employees) are drawn, while all big establishments (more than about 150 employees) are included. This ensures that the survey covers about 35 per cent of all employees while only 5 per cent of all establishments are surveyed. While the biggest establishments are always surveyed, most others participate for four years at a time. Therefore, we replace almost a quarter of the sample each year. The drawing of the sample is coordinated with several other surveys in Statistics Norway in order to disperse the burden placed on smaller and medium sized establishments.

The questionnaires are sent out the last week of each quarter to the enterprises having establishment(s) in the sample. The personnel office of the enterprise fills in the questionnaire or forwards it to the personnel office of their establishment. The deadline for delivery is about four weeks after receiving the questionnaire. An establishment may contact the Division for Data collection at Statistics Norway if it is impossible to separate the sickness absence figures for the chosen establishment. In this case the enterprise may give figures for the enterprise as a whole.

The data is sent to Statistics Norway electronically via Altinn.

Doctor-certified sickness absence
The Norwegian Labour and Welfare Administration performs a number of controls and corrections on the doctor's certificates especially related to overlapping/inconsistencies in the dates.

Editing is defined here as checking, examining and amending data.

Employees from the A Scheme Register
There are a number of controls in the reporting system. In addition, Statistics Norway makes a number of checks and reworking to arrive at the active employments.

For employments with position proportion over 1.2, the position proportion is set to 1.2. The total postion proportion for a person is not allowed to exceed 1.6. For persons where this is the case, some of the employments are removed, because we do not believe them all to be active in the reference week.

Until 2014, there was a limit regarding how many employments one person could have.

Self-certified sickness absence
Absolute checks: verification controls in the last stage of the optical data registration to reveal obvious errors from the optical reading, misunderstandings or inaccuracy in the reporting. An example would be wrong totals compared to the partial figures.

Evaluation checks: these checks aim to reveal errors due to extreme values reported or due to incorrect number of employees reported. For this purpose a check variable is being used, sickness absence cases per 100 employees. Large establishments that report extreme values on this variable will be more closely examined. Data from whole enterprises will also be adjusted in proportion to the portion of employees the sampled establishments represent in the enterprise as a whole. Before the final estimation, we also check that the net samples in each industry and size strata are large enough. If the net samples are too small for some strata, they will be added to the "neighbouring" strata in the same industry.

Doctor-certified absence
Persons with a so-called active self-certificate are included in the statistics as ordinary doctor-certified sickness absence.

Measures of sickness absence
The sickness absence rate is sickness absence man-days in per cent of contractual man-days. The proportion of employees with sickness absence at a given date is also calculated. See under 'Definitions' for more information regarding these measures.

Self-certified absence
Sheltered workshops and establishments in the following industries are not a part of the sample: Animal husbandry service activities (except veterinary activities), extraction of crude petroleum and natural gas, water transport, private households with employed persons and extra territorial organizations. In the estimation, employees from these groups are represented by employees from other industries.

Estimation at national level
A model-based ratio estimator is used to inflate sample data of self-certified sickness absence to population level. The ratio model corresponds to a linear regression model without a constant term and with the disturbance term that are proportional with the number of male (female) employees. Auxiliary variables about total number of male (female) employees in each establishment in the population are used. The figures are taken from the A Scheme Register. The dependent variable is the number of self-certified sickness absence man-days for male (female). A weighted least square method is used to fit the regression models. The population totals for each group are predicted by using the groups model estimates and the population sums of the number of employees in each group.

Because the smallest establishments are not sampled, a "Cut-off"-estimator is being used for these strata. This means that the number of male (female) employees in the population of the smallest strata is being multiplied by the ratios for the second smallest strata. This method is being done separately for each industry strata. This method assumes that the ratios in the strata with the smallest establishments are similar to the corresponding ratios in the strata with the second smallest establishments. If the ratios are very different we introduce a bias.

Estimation at county level and institutional sector level
Self-certified sickness absence by county of work and institutional sector is estimated with a different method than the national level estimates, because the sample is not stratified on these variables. For the figures by county and sector, we use model based ratio estimators for different groups.

The estimations of sickness absence by county are done separately for two industry groups, sex and the 19 counties of work. One industry group consists of agriculture, forestry and fishing, and industrial activities; the other group is service activities.

In the estimation of figures by institutional sector, each of the tree sectors 1) central government, 2) counties and municipalities, and 3) private sector and government enterprises are estimated separately for male and for female for groups of industry. Depending on what the sample sizes allow, different groups of industry are used for each of the tree sectors.

The different model-based ratio estimators can add up to figures of totals that are a little different from the official national totals from the design-based ratio estimator. Due to that, we use the county and sector divided figures to make percentage distributions, which the official national totals are broken down with, so that the totals also are right.

Seasonally adjusted data are calculated by using the X-13ARIMA-SEATS software. The adjustment is done indirectly. We separately seasonally adjust the numerator and denominator for gender divided sickness absence rates certified by doctor and for self-certified sickness absence rates by gender.

For total sickness absence, the seasonally adjusted data is available starting from 2015 by

• County (as of 2020)
• Industry (SIC2007)

For more detailed information about the seasonal adjustment of this statistics, please see About seasonal adjustments.

Interviewers and everyone who works at Statistics Norway have a duty of confidentiality. Statistics Norway has its own data protection officer.

Statistics Norway does not publish figures where there is a risk of identifying individual data about persons or households [enter the correct unit here, where applicable].

The ‘[suppression, rounding up/down, perturbation]¹’ method is used in these statistics to ensure this.

More information can be found on Statistics Norway’s website under Methods in official statistics, in the ‘Confidentiality’ section.

In the autumn of 2001 the authorities and the organisations of employers and employees signed an agreement aiming at reducing the level of sickness absence. One of the means was to offer the employees an expanded period of self-certified sickness absence beyond three days combined with dialogues with the employer. This effort might turn some of the sickness absence from the doctor- to the self - certified kind. Due to this - the survey has to capture a greater part of the sickness absence than earlier, and our questionnaire for the 4th quarter of 2001 was changed accordingly. This change implies more questionnaire-based reporting from the establishments at the expense of register data based on the doctors' reports.

The effect of the A Scheme Register on the sickness absence rate

Introduction of the A Scheme register to replace the Register of Employees and Employers (Aa-register), from the 1st quarter of 2015, gives somewhat changing figures for the number of employees. This is due to a somewhat broader coverage of the group, and a better quality. The transition to the new data has had a small, but still observable effect on the sickness absence rate at the aggregate level. The effect of the transition to the new data is small also when it comes to self-certified and doctor-certified sickness absence separately. But since the self-certificated absence is lower than the doctor-certificated, this small effect provides a greater impact on the numbers of alteration for self-certified absence than for doctor-certified absence, and for the total absence.

Only the seasonal and influenza adjusted sickness absence rates are also adjusted for the break in the time series due to the A-Scheme register from 2015.

We haven’t calculated the size of possible breaks related to the a-scheme for other variables or groups, but the size may vary between groups. Of importance may be the fact that the A Scheme register captures more of the small job relations, than the Aa-register did.

Also, direct reporting of position proportion is now being used, while Aa-register only gave information about contractual working hours, and one assumed that 37.5 hours per week equaled a full position. This may also have affected the figures, especially for industries with a high degree of shift and rotation.

New industry classification

A new industry classification (SIC2007) was introduced from the publication of the first quarter of 2009. Compared with earlier published figures classified by the previous industry standard (SIC2002) there will be a break in the time series.

It is not a one to one link between all the detailed codes of SIC2002 and SIC2007. However, 1 st quarter 2009 all active establishments in the Central Register of Establishments and Enterprises are coded both after SIC2002 and SIC2007. This information is utilized to construct conversion matrixes. These conversion matrixes are used to calculate aggregated SIC2007 divided historical figures from aggregated SIC2002 divided figures.

For the variable doctor-certified sickness absence man-days and for (non-holiday corrected) contractual man-days the conversion calculations are made separately for males and females for the most detailed industry code as possible. For the conversion of all the above variables the conversion percentage distributions are calculated from (non-holiday corrected) contractual man-days 1st quarter 2009.

Self-certified sickness absence man-days figures are converted from SIC2002 to SIC2007 within the industry stratums used in the sampling and estimation before and after the change. This is done separately for males and females based on conversion percentage distributions made from the variable contractual man-days 1 st quarter 2009. Gender and SIC2007 divided figures are converted quarterly for the period 2000 – 2007 under the assumption that the different detailed conversion percentage distributions are constant and equal to the once in 1 st quarter 2009.

The methods for converting sickness absence rates with non-holiday corrected denominator are closely related to the methods we used from Q4 2014 on sickness absence rates with holiday-corrected denominator. However, now we wanted to make the method a bit simpler, more robust, transparent, and consistent between numerator and denominator, for the best possible result for industry-divided sickness absence rates. Previously, the absolute figures were more in focus when we decided upon method of conversion.

Since 2015, the statistics on sickness absence have been largely based on medical certificates from NAV and the a-scheme as sources. Both sources are of good quality, but errors may occur. For the a-scheme, for example, there may be errors in the reporting from obligated parties about employees, while for medical certificates, errors may occur during the completion of forms by the person issuing the sick leave. In addition, errors may occur in the processing of data afterward. Generally, such errors are corrected in the statistics when discovered.

Payment during the employer's period

We are unable to link self-certifications and medical certificates together at the individual level, as we only have data on self-certified sickness absence at the enterprise level for a selection of enterprises. This means that we do not know whether a medically certified absence is the first day the person is sick or, for example, the ninth day. This also means that we cannot accurately distinguish between how many days are paid by the employer and how much is paid by NAV.

Medically certified sick leave is a full count, while for self-certified sick leave, there is a response rate of over 90 percent after the obligation to report was introduced in the second quarter of 2003.

Variance (self-certified sickness absence)

There are mainly two uncertainties that can be calculated for self-certified sickness absence. This is a sample survey, so a sampling uncertainty can be calculated. The smallest businesses have also been given special consideration to reduce the reporting burden, which means that we do not have an unbiased estimator. This bias that arises can also be calculated.

The sample on which the survey is based is drawn as a representative sample. This sampling uncertainty can be calculated. For the business sector, it is between +/- 0.01 and 0.07 percentage points, depending on both seasonal variation and industry.

The table below shows the standard error multiplied by 1.96, distributed by gender and industry (SN07) for the sick leave percentage for 2022. You can thus construct your own confidence interval according to gender and industry as desired. You can then say that if all businesses had been surveyed, instead of a sample, one would be within that interval with 95 percent certainty.

In addition to this, there is some uncertainty related to choices in the model. The smallest businesses (fewer than 4 employees) are excluded from the sample but are included in the estimation of the total. Therefore, it is reasonable to assume that the uncertainty for small businesses is greater. Thus, it is not an unbiased estimator, but the bias is quite small because the sample is of a reasonable size, and the consideration of the businesses' reporting burden weighs heavily. In total, uncertainties can lead to an error estimate of up to 0.13 percentage points, but usually around +/- 0.06 percentage points for the smallest businesses.

Nonresponse errors (refer to self-certified sickness absence)

These are errors that may occur when units choose not to respond for various reasons. The effect of nonresponse errors over time has been calculated, and no systematic biases have been found due to different nonresponses.

Processing errors

Processing errors are errors that occur during SSB's data processing. Typical examples of this are that correct figures are assessed as incorrect and are erroneously corrected. Electronic reporting from NAV and the a-scheme for medically certified sickness absence reduces this risk.

Coverage errors

Coverage errors are deviations between the population we want to measure and the one we actually obtain information for.

A significant number of sick notes are recorded that cannot be linked to employment relationships. In relation to all sick notes, this accounted for about 5 percent in 2022. Analyses suggest that the vast majority are notifications that do not apply to reportable employment relationships and are therefore excluded from the data set. By excluding all, however, we get some undercoverage in the statistics.

Up to and including 2014, sickness absence workdays and agreed workdays were calculated using sickness absence days and working days, combined with a calculated job share/percentage. At the individual level, we only had information on whether the employment relationship was full-time, short, or long part-time. We then used data from the Labor Force Survey to calculate job percentages for these three groups. This generalization resulted in errors at the individual level but less so at the aggregated level.

Starting from 2015, we receive data on both the job percentage and the number of hours per week that a full-time position corresponds to at the individual level, from the a-scheme. There are still some challenges in obtaining good quality data on the job percentage.

The challenges are partly due to the fact that many hourly workers do not report a job percentage and partly due to some hourly workers reporting standard values of either 0 or 100. The latter makes it difficult to distinguish a real job percentage of 100 from an incorrect value of 100.

Based on this, SSB has developed a method that provides a better data basis for working hours for statistical production. The method consists of several steps. First, it is determined whether there is a reasonable relationship between the reported job percentage and salary. Where there is a good relationship, which we have for about 85 percent of all employment relationships, the reported job percentage is used.

Where there is no reasonable relationship, a new job percentage is calculated using the number of paid hours or salary. In some cases, the poor relationship is due to a reported job percentage of zero, while in other cases it is due to an incorrect job percentage being reported. Experience after five years with the a-scheme suggests that where there are deviations, the reporting of salary is usually correct.

A revision is a planned change to figures that have already been published, for example when releasing final figures as a follow-up to published preliminary figures. See also Statistics Norway’s principles for revisions.

Revisions in previously published seasonally adjusted figures can take place when new observations (or revised previous observations) are included in the basis of calculation. The scope of the revision is usually greatest in the most relevant part (last 1–2 years) of seasonally adjusted time series. A corresponding revision in trends is also typical, particularly at the end of the time series. The extent of the revision of trends and seasonally adjusted figures is partly determined by the revision policy, see Section 4 of the European Statistical System (ESS) Guidelines on Seasonal Adjustment on the Eurostat website. For more information on the revision of seasonally adjusted figures, see the ‘About seasonal adjustment’ section in the relevant statistics.

Monthly and quarterly time series are often characterized 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-13ARIMA-SEATS 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.

The quarterly statistics for both self-reported and doctor-reported sick leave clearly show seasonal variation. Lost work days due to both self-reported and doctor-reported sick leave are significantly higher in the winter half of the year (quarters 1 and 4), partly due to more frequent respiratory infections.

Both lost and agreed work days are strongly affected by the number of working days in the quarter, which is the number of weekdays in the quarter minus public holidays.

Series adjusted for seasonality

The following quarterly time series are seasonally adjusted separately for men and for women:

The following quarterly time series are seasonally adjusted separately for men and women:

• Sickness absence man-days certified by doctor
• Self-certified sickness absence man-days
• Scheduled man-days

We publish seasonally adjusted figures for lost work days due to sick leave in relation to agreed work days, which is what we call the sick leave rate. This is divided into self-reported and doctor-reported sick leave and total.

For total sickness absence, the seasonally adjusted data is available starting from 2015 by

• County (as of 2020)
• Industry (SIC2007)

Prior to conducting seasonal adjustments, it is crucial to pre-correct the raw data for calendar effects and outliers. This entails implementing a detailed pre-correction process that utilizes customized models, which are not accessible as standard options within the seasonal adjustment tool.

Regarding sickness absence days, these are pre-corrected using a single seasonally adjusted regression variable that accounts for the share of days lost due to influenza diagnosis in the quarter. This variable is applied separately for gender, county, and industry. It isolates the estimated average impact of irregular influenza absence, including instances such as the swine flu outbreak in the 3rd and 4th quarters of 2009. Self-certified sickness absence is also estimated and pre-corrected with the same influenza variable, based on the number of doctor-certified days lost. Although there is no direct correlation, statistical tests demonstrate that the pre-correction variable has a substantial effect on doctor-certified sickness absence and a weak yet significant effect on self-certified sickness absence.

Calendar adjustment

Calendar adjustment is performed on all series exhibiting a significant and plausible calendar effect using a robust statistical approach, such as regression or the RegARIMA procedure (a regression model wherein the noise term is modeled by an ARIMA model). Calendar regression variables are processed in accordance with Norwegian public holidays.

Methods for trading/working day adjustment

No working day adjustment is made, as working day variables (based on Norwegian conditions) do not significantly affect these quarterly data. The time series are pre-corrected for the number of working days in the quarter, which is calculated by subtracting Norwegian public holidays falling on weekdays from the total number of weekdays (Monday-Friday) in the quarter. This includes public holidays such as Good Friday, Easter Monday, and Ascension Day.

Choice of calendar

A separate Easter variable is not employed due to its lack of significance when data are also pre-corrected for the number of working days in the quarter. Pre-correcting for Easter instead of the number of working days yields poorer results, as determined by conducted tests. A national calendar is utilized in this process.

Treatment of outliers

The series are examined for extreme values, and any identified extremes are explained or modeled using all available information. When a clear understanding of the cause of the extreme values is established (e.g., strikes or consequences of policy changes), they are incorporated as regressors within the model.

Model selection

In order to pre-correct, it is essential to select an ARIMA model, and determining whether or not the data should undergo log transformation is essential. The model is automatically chosen based on established routines within the seasonal adjustment tool.

Selection of Seasonal Adjustment Method

The X-13ARIMA-SEATS method (https://www.census.gov/srd/www/x13as/) is utilized for seasonal adjustment.

Consistency Between Raw Data and Seasonally Adjusted Figures

In certain cases, it may be desirable for the sum (average) of quarterly seasonally adjusted figures for a year to match the sum (average) of quarterly figures from the original raw series. However, no consistency conditions are imposed in this context.

Consistency Between Aggregates/Definitions for Seasonally Adjusted Figures

For some series, consistency is enforced between seasonally adjusted totals and sub-aggregates. Furthermore, there may be a relationship between different series, such as gross value added equating to output minus intermediate consumption.

Equality between seasonally adjusted sub-aggregates and over-aggregates is enforced, with this equality automatically achieved through the indirect method.

Direct or Indirect Method

A direct method is employed when the time series for a total and its sub-aggregates are all seasonally adjusted separately. Conversely, an indirect method is utilized for the total if the corresponding sub-aggregates' time series are directly seasonally adjusted and subsequently aggregated to the total level.

In this case, the indirect method is used, with the components being directly seasonally adjusted using the same approach and software. The totals are then calculated by aggregating the seasonally adjusted components.

Time Horizon for Model Estimation and Calculation of Correction Factors

The entire time series (from 2020 Q2) is utilized for calculating the model and correction factors for the whole country as well as self-certified and doctor-certified sickness absence.

For industry and county, the time series from 2015 onwards is employed for model estimation.

General revision policy

In the context of seasonal adjustment, revisions may occur due to the introduction of new observations or alterations in raw data. Various methods exist for addressing these revisions during the release of statistical information.

Seasonally adjusted data undergo revisions in compliance with well-defined, publicly accessible revision procedures and release calendars.

Concurrent versus current adjustment

Partial continuous adjustment is employed, where models—including any log transformations, seasonal and trend filters, calendar variables, and outliers—are identified on an annual basis. Meanwhile, respective regression parameters and factors are continuously re-estimated whenever new or revised raw data become available.

Time Horizon for Publication of Revised Figures

When seasonal factors are re-estimated, the entire series is revised. Consequently, users may observe that published figures appear to change; however, this is an inherent characteristic of an estimated series.

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, the quality is evaluated trough iterative processes using a combination of graphical tools and statistical tests.

The table below provides some indicators of the quality of the seasonal adjusment:

Quality indicators (Excel)

For more information on the quality indicators in the table see: metadata on methods: seasonal adjustment

Seasonal Adjustment of Short Time Series

All series possess sufficient length to optimally execute seasonal adjustment routines.

Treatment of Challenging Time Series

Any series presenting difficulties when using standard options within the seasonal adjustment tool are addressed through specialized methods.

Impact of the A Scheme on Sickness Absence Rate

The A scheme replaced the Aa register from the 1st quarter of 2015, leading to slightly different employee figures. This discrepancy results from a somewhat broader coverage of the group, as well as improved quality. The employment percentage for each job from the Aa scheme was directly utilized in calculating agreed daily hours. Previously, only agreed working hours from the Aa register were available, necessitating a rough assumption of full-time hours (37.5 hours per week for all) to construct daily hours. The transition has had a minor yet discernible effect on the sickness absence rate, even at the aggregate level. This effect has been modeled and pre-corrected in the seasonal adjustment tool.

The primary focus in disseminating sickness absence statistics centers on seasonally and influenza-adjusted sickness absence. In addition to seasonal and influenza adjustments, these figures are also adjusted for the break in the A-ordningen in 2015. Statistics Norway contends that these figures provide the most accurate representation of the underlying development in sickness absence from quarter to quarter. For users seeking influenza- and breach-adjusted figures only, these are published in StatBank.

Addressing the Coronavirus Crisis in 2020

The coronavirus crisis impacts sickness absence statistics in multiple ways that must be considered when interpreting the data. In the seasonally adjusted figures, Eurostat's guidelines have been adhered to, which assert that the effect of the coronavirus crisis should not be incorporated into the basis for the seasonal pattern. Consequently, for the time being (2020), it is assumed that the seasonal pattern remains unchanged, and systematic seasonal variation is corrected for based on data preceding the coronavirus crisis.

Data availability

• Raw and seasonally adjusted data are available.
• Historical data are available to enable revision analysis.

Comments: Both seasonally adjusted data and seasonal, calendar, influenza and A-Sheme break adjusted data are available in our StatBank.

Press releases

In addition to raw data, at least one of the following series is released: pre-treated, seasonally adjusted, seasonally plus working day adjusted, trend-cycle series.

For each series, some quality measures of the seasonal adjustment are presented.