About the statistics
Name and topic
Name: Earnings in water supply, sewerage, waste management and remediation activities
Topic: Labour market and earnings
Division for Income and Wage Statistics
Definitions of the main concepts and variables
In the statistics, wages and salaries refer only to cash payments from employer to employee for work rendered. The statistics hence do not include payment in kind, insurance or non-taxable expense allowances and the like.
Gross monthly earnings. Includes basic salaries, variable additional allowances and bonuses. Overtime pay is not included.
Basic monthly salary
Basic monthly salary is the actual payment at the time of the census, and is often described as salary on a scale or regular basic wage. Qualification/skills allowances and other regular personal allowances are included. Wages or salaries can be paid per hour, week, fortnight or month.
Variable additional allowances
Variable additional allowances are associated with special duties, and the figure given is a calculated average per month for the period 1 January to the time of the census. Included are allowances such as shift allowance, allowances for working evenings and nights, call-out allowance, dirty conditions allowance, offshore allowance and other allowances that occur irregularly.
This item includes allowances usually not connected with specific duties and where the payments occur irregularly with respect to the period in which they are earned or to which they apply. Types of payments include commissions, profit sharing, production allowance and gratuities, and are a calculated average per month for the period from the 4th quarter of the previous year to the time of the census.
Overtime pay covers the sum of cash compensation for work carried out beyond contractual working hours, and is a calculated average per month over the period 1 January to the time of the census.
Estimated annual earnings
Annual earnings are an estimate for 12 months of the calendar year and are based on the monthly earnings at the census time. Includes basic paid wages and salaries, variable additional allowances, bonuses, but does not include holiday pay supplement and overtime pay.
Full-time and part-time
Information is collected on all employees regardless of contractual working hours. Employees with a contractual 33 hours or more per week are regarded as full-time employees. Employees in municipalities, in publicly maintained schools and central government are regarded as full-time employees when engaged in a 100 per cent occupation. Employees with less than 33 hours work-time per week or engaged in an occupation of less than 100 per cent are defined as part-time employees.
In the wage statistics employees with less than 33 hours work-time per week or engaged in an occupation of less than 100 per cent are defined as part-time employees. To be able to compare earnings for full-time and part-time employees the earnings for a part-time employee is recalculated to the earnings a full-time employee would receive. This is done by using the ratio of the working hours for each part-time employee and the average working hours for full-time employees in the industry as the factor of recalculation. Monthly earnings per full-time equivalent for part-time employees may then be put together with monthly earnings for full-time employees and thus it is made possible to calculate average monthly earnings for all employees.
Contractual working hours
Contractual working hours is defined as the contractual number of working hours per week, excluding meal breaks. No deductions are made for absences due to holiday, illness, leave of absence or the like. For employees with working hours that varies from one week to another, the average number of hours per week is reported for the year or for the last month.
Age and sex
The national identity number indicates age and sex.
Classification of occupation
The Standard Classification of Occupation (C521), which is the Norwegian version of the International Standard Classification of Occupations , is used in the statistics. This set of occupation codes is established throughout the wage statistics, either through direct input or by encoding from other occupation codes.
A key component of the wage statistics is classification by industry in accordance with the Standard Industrial Classification (SN 2007), which is the Norwegian version of the international Standard Industrial Classification
Education levels are obtained from the register of the Population’s Highest Level of Education (BHU). The classification is by the length of education according to the Standard for Educational Classification http://www.ssb.no/english/subjects/04/90/
Frequency and timeliness
Frequency: Annual per 1 October.
Timeliness: Publication in March the succeeding year.
Statistics files are stored.
Background and purpose
The purpose of the statistics is to provide an overview of wage levels and wage changes for employees in water supply, sewerage, waste management and remediation activities. The wage statistics in the current form were established in 2009.
Users and applications
Major users are the Technical Reporting Committee on the Income Settlement, research and policy institutes, employee and employer organizations, Eurostat, the media, business and industry and individuals. The statistics will be used in Statistics Norway’s National Accounts.
Coherence with other statistics
New annual wage statistics for most industrial sections were established in 1997. The wage statistics are to be uniform and comparable among the industrial sections.
Statistics Act Sections 2-1, 2-2 and 2-3
The population covers all enterprises in Statistics Norway’s Central Register of Establishments and Enterprises in Section E of the Standard Industrial Classification .
Each enterprise covers one or more establishments grouped by industrial category. The wage statistics data are obtained at establishment level for each employee.
Data sources and sampling
Data are obtained via forms or electronic media from the units covered by the sample. Information is obtained on wages, bonuses and commissions, variable additional allowances, overtime, occupation and working hours of the individual employee in the establishment.
Information from Statistics Norway, Central Register of Establishments and Enterprises and the register on the Highest Level of Education of the Population (BHU) are added to the incoming data.
The sample consists of enterprises drawn from the population. The population is basically all active enterprises in the section E, with the exception of small enterprises with less than five employees which are not included in the frame population.
The statistics are based on sampling. The final sample comprises a census part and a sample part. In the census part all enterprises with more than a certain number of employees are included, while the sample part comprises a stratified sampling of small and medium-sized enterprises. Also taken into account are the needs for the statistical basis that the parties in the wage settlement have.
The objective of the sample selection process is basically to get samples that ensure a representative basis for the statistics and avoid burdening all enterprises in the industry with forms to fill in. Another objective is to ensure that the smallest enterprises are the least possible burdened with reporting obligations.
Collection of data, editing and estimations
The time of the census for wage statistics for employees in water supply, sewerage, waste management is 1 October each year. Information about the survey are sent to the participants about two weeks before the census date, with a reply deadline of two or three weeks after the census date. Instructions that concern the establishments in the sample are sent to the enterprises. The time of the census for members in KS is 1 december.
The respondent may submit the statements in electronic form. The electronic reporting is described in a separate document, &“Requirement specification for electronic reporting, variables and file description´´:http://ssb.no/emner/06/05/elinn, which is distributed to software suppliers and enterprises that organize their own payroll systems. A number of suppliers of wage administration systems have made arrangements for electronic reporting. The reporting may also be done through Altinn , form RA-0500.
Control and revision of wage statistics take place on several levels, where most of the operations are automated, with respect to both the actual control and any possible correction.
When receiving forms or files, a simple check is made that certain key variables are correctly filled in. This concern primarily national identity number, occupation, basic paid salaries and contractual working hours per week. The individual variables are checked in more detail in priority order in the subsequent quality control process. The highest priority is given to the variables mentioned, followed by controls of bonuses, commissions and the like, variable additional allowances and overtime.
The numerical data collected from the sample shall represent the average wage level in the industry. The figures from the sample must therefore be weighted. Weighting in the statistics is based on the inverse inclusion probability and post-stratification with regard to industry and employment at the date of the census. The weights are additionally adjusted for any imbalances due to non-response. The purpose of weighting is primarily to obtain an inflation of the sample so that the units in the sample reflect the population.
Comparability over time and space
The statistics in the current form were produced for the first time in 2009, and are comparable from that year. An overview of previous years’ statistics is found on Previous articles.
Sources of error and uncertainty
Measurement errors can mainly occur because the respondent misunderstands what is included in and consequently reported for different kind of wages or because it is very difficult for the respondent to find the information requested. All variables collected and that, directly or indirectly, are included in released statistics are checked, either in logical controls or by absolute limits for what is considered valid. If important data are missing in the received reports, the data are obtained either by returning the form, by a phone call to the respondent or by imputation.
The data that are received are registered either by optical scanning, manual recording or loading files structured according to the electronic requirement specification. Several controls are carried out on the material.
Non-response in the wage statistics is between 2.5 and 5 per cent. The main reasons for non-response are that enterprises no longer have employees because the business has been closed, sold or taken over by new owners, has gone bankrupt or has been merged in the time period between the selection of the sample and the time of the census. There is furthermore a small group that report too late to make it into the statistics. Non-responses that are not randomly distributed can still make the sample biased. Post-stratification adjusts any imbalances arising in the distribution between the stratification variables due to non-response.
Non-response in several of the items collected in the report and used in the wage statistics can normally be logically calculated on the basis of other information given in the report or imputed from earlier years.
Since sampling errors are errors that may arise in areas subject to sampling, such errors are only relevant for the data for other financial mediation. In monetary intermediation and insurance all enterprises are included, so that this issue is avoided.
All sample-based surveys will be burdened with a certain uncertainty. Generally, the results are less certain the fewer the observations they are based on. Uncertainty also depends on wage dispersion and rate of coverage for the various variables in the population from which the sample is drawn. Groups that are based on relatively few observations will easily be affected by so-called extreme observations, or observations that deviate markedly from the group average. Such extreme observations are carefully considered on a case-by-case basis for inclusion in the statistical basis.
Sample bias may arise when the distribution on some variables in different parts of the sample is not the same as the corresponding distribution in the population. Dividing the population into groups (strata) according to certain stratification variables reduces the possibility of imbalances in the sample.
Incorrect industry codes and/or employment data in Statistics Norway’s Register of Establishments and Enterprises during the selection of the sample may result in the establishments being placed in the wrong industry or selection stratum.
These are error types that include possible errors in model assumptions in the statistics.