Managers earned on average the highest monthly salary in Norway. In 2022, people with a manager position earned over 78,000 Norwegian kroner on average on a monthly basis. Professionals were the occupational group with the second highest average monthly salary, followed by technicians, associate professors, and people employed in the armed forces. The lowest average salaries in Norway that year were found among elementary occupations.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

The Title and Salary Listing is a compilation of job titles under the jurisdiction of the Department of Civil Service.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

As of the 2023/24 academic year, graduates from the Massachusetts Institute of Technology (MIT) had a starting salary of 110,200 U.S. dollars, and a mid-career salary of 196,900 U.S. dollars. Top universities in the United States One of the top universities in the United States, Harvey Mudd College, is located in Claremont, California. Not only do graduates earn a high salaries after graduation, they also pay the most. In the academic year of 2020-2021, Harvey Mudd College was one of the most expensive school by total annual cost. The best university in the United States in 2021 belonged to the University of California, Berkeley. The Ivy League The Ivy League is a group of eight private universities in the Northeastern United States. It is not only a collegiate athletic conference, but also a group of highly respected academic institutions. They are usually regarded as the best eight universities in the United States and the world. They are extremely selective with their admissions process. However, these universities are extremely expensive to attend. Despite the high price tag, students who graduate from Princeton University have the highest early career salary out of all Ivy League attendees in 2021. This is compared to the overall expected starting salaries of recent college graduates across the United States, which was less than 35,000 U.S. dollars.

Gross weekly and hourly earnings by level of occupation, UK, quarterly, not seasonally adjusted. Labour Force Survey. These are official statistics in development.

The Census Bureau has created a special subset file from the 1990 Census of Population and Housing data designed to meet the needs of Equal Employment Opportunity (EEO) and affirmative action planning. It contains detailed 1990 Census data dealing with occupation and educational attainment for the civilian labor force, various racial groups, and the Hispanic population. The file consists of four tabulations of the United States civilian labor force. They present EEO data similar to those in the CENSUS OF POPULATION AND HOUSING, 1990 [UNITED STATES]: EQUAL EMPLOYMENT OPPORTUNITY (EEO) FILE (ICPSR 9929), but are expanded to include occupation data by education level, industry group, and earnings. Total population and unemployment data are also available. They are referred to as Tables P1-P4. Table P1 lists occupation by education by sex by race and Hispanic origin. Table P2 lists occupation by earnings by sex by race and Hispanic origin. Table P3 lists occupation by industry by sex by race and Hispanic origin. Table P4 lists population and unemployment by sex by race and Hispanic origin. The collection includes four United States files and 51 separate files, one for each state and Washington, DC. Each state file contains statistics for the state, each county, Standard Metropolitan Statistical Areas (SMSAs), and places with a population of 50,000 or more.

In 2023, the average wage and salary per full-time equivalent employee in the mining industry in the United States was at 126,707 U.S. dollars. The highest wage and salary per FTE was found in the information industry, at 164,400 U.S. dollars.

In 2023, first-line supervisors of housekeeping and janitorial workers were the highest paid workers among the employees in cleaning occupations in the United States, who earned approximately 50,000 U.S. dollars for the year. On the other hand, maids and housekeeping cleaners earned an annual wage of almost 35,000 U.S. dollars as of May 2023, coming bottom of the list of earners.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, sex, and age group, 1997 to 2022.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

In 2023, the highest average monthly wages in Norway were in the mining and quarrying industry, with nearly 87,000 Norwegian kroner. The second highest average wages were in the financial and insurance industry, followed by the electricity, gas, and steam industry. That year, the lowest average salaries were in the accommodation and food service industry, counting only 38,000 Norwegian kroner per month.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅206M+ Job Postings Tracked – Data sourced from 1.8M+ company websites worldwide. ✅7M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at (string, ISO 8601 date-time) – Timestamp when the job was first detected.
• last_seen_at (string, ISO 8601 date-time) – Timestamp when the job was last detected.
• last_processed_at (string, ISO 8601 date-time) – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

Response Example: https://docs.predictleads.com/v3/api_endpoints/job_openings_dataset/retrieve_company_s_job_openings

Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

Graph and download economic data for Employment Cost Index: Wages and Salaries: Private Industry Workers (ECIWAG) from Q1 2001 to Q4 2024 about cost, ECI, salaries, workers, private industries, wages, private, employment, industry, inflation, indexes, and USA.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

Abstract

A. Objectives

To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data

Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered

Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National coverage, 17 administrative regions

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 20 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2008 BLES Survey Sampling Frame (SSF 2008) is an integrated list of establishments culled from the 2006 List of Establishments of the National Statistics Office; and updated 2006 BLES Sampling Frame based on the status of establishments reported in the 2006 BLES Integrated Survey (BITS) and 2006 Occupational Wages Survey. Lists of Establishments from the Department of Trade and Industry (DTI) and Philippine Chamber of Commerce and Industries (PCCI) were also considered in preparing the 2008 frame.

Sampling design: The OWS is a sample survey of non-agricultural establishments employing 20 persons or more where the survey domain is the industry. Those establishments employing at least 200 persons are covered with certainty and the rest are sampled (stratified random sampling). The design does not consider the region as a domain to allow for more industry coverage.

Sample size: For 2008 OWS, number of establishments covered was 6,460 of which, 5,176 were eligible units.

Note: Refer to Field Operations Manual Chapter 2 Section 2.5.

Sampling deviation

Not all of the fielded questionnaires are accomplished. During data collection, there are reports of permanent closures, non-location, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements (three consecutive survey rounds for "can not be located" establishments) of the frame and their count is not considered in the estimation. Non-respondents are made up of refusals, strikes or temporary closures, can not be located (less than three consecutive survey rounds) and those establishments whose questionnaires contain inconsistent item responses and have not replied to the verification queries by the time output table generation commences.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by BLES and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2006 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2008 OWS; results of the 2006 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color.

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the BLES and DOLE Regional Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in determining the correct occupational sheet that should be furnished to the respondent.

Results of the 2006 OWS (Page 12) The results of the 2006 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Note: Refer to questionnaire and List of Monitored Occupations.

Cleaning operations

Data were manually and electronically processed. Upon collection of accomplished questionnaires, enumerators performed field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the Field Operations Manual. The forms were again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertook the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries were returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 78.4%.

Sampling error estimates

Estimates of the sampling errors will be computed and posted in BLES website.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 206M+ Job Postings Tracked – Data sourced from 1.8M+ company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

Response Example: https://docs.predictleads.com/v3/api_endpoints/job_openings_dataset/retrieve_company_s_job_openings

Philippines Employment: Wage & Salary Workers: Private Establishment data was reported at 20,816.000 Person th in Oct 2018. This records an increase from the previous number of 20,721.000 Person th for Jul 2018. Philippines Employment: Wage & Salary Workers: Private Establishment data is updated quarterly, averaging 15,073.500 Person th from Jul 2003 (Median) to Oct 2018, with 62 observations. The data reached an all-time high of 20,816.000 Person th in Oct 2018 and a record low of 11,639.000 Person th in Jul 2003. Philippines Employment: Wage & Salary Workers: Private Establishment data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G013: Labour Force Survey: Employment: by Industry, Occupation and Class.