VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

The statistic shows the average hourly gross pay for employee jobs in Italy from 2011 to 2019. According to data, the average hourly pay increased from **** euros in 2011 to **** euros as of 2019.

🧠 About the Data

This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

📌 Column Descriptions

📈 Potential Use Cases

• Predictive modeling for salary based on role, experience, and location
• Salary benchmarking per industry or employment type
• Visualizing remote vs onsite salary disparities
• Market research for HR and hiring trends
• Exploratory analysis on global employment models

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Switcher (FRBATLWGT12MMUMHWGJSW) from Dec 1997 to Jun 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

This table comprises yearly figures on the main aspects of employment, wages and working hours in the Netherlands. The information in this table is classified according to Standard Industrial Classification of all Economic Activities (SIC 2008) and can be broken down into: - employee characteristics (age and sex) - job characteristics (type of employment contract and working hours) - company characteristics (size of the firm and collective wage agreements)

Data available from: 2009.

Status of the figures: Figures for the years 2009 to 2023 are final. Figures for 2024 are provisional.

Changes as of 28 April 2025: Provisional figures for 2024 are added. The figures for 2024 are excluding the selections: ‘Type of employment contract: full-time’, ‘Type of employment contract: part-time’ for the branche ‘P Education’

When will new figures be published? The final figures for 2024 will be published in October 2025.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

Israel Paid Hourly Wages: per Employee Job (EJ): Manufacturing data was reported at 67.800 ILS in May 2018. This records a decrease from the previous number of 68.200 ILS for Apr 2018. Israel Paid Hourly Wages: per Employee Job (EJ): Manufacturing data is updated monthly, averaging 63.000 ILS from Jan 2012 (Median) to May 2018, with 77 observations. The data reached an all-time high of 69.800 ILS in Mar 2018 and a record low of 57.100 ILS in Jan 2012. Israel Paid Hourly Wages: per Employee Job (EJ): Manufacturing data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G037: Paid Hourly Wages per Employee Job.

Graph and download economic data for 3-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Movement: Job Stayer (FRBATLWGT3MMAUMHWGJMJST) from Mar 1997 to Jun 2025 about growth, moving average, jobs, 3-month, average, wages, median, and USA.

Number of job vacancies and payroll employees, job vacancy rate, and average offered hourly wage by economic region, last 5 quarters.

The job title, job class code, hours per pay period, number of steps, hourly step rates, and yearly step rates per job classification.

This dataset reveals the employees of the City of Austin that are currently paid under the per hour living wage. This dataset supports measure(s) EOA.B.5 of SD23 . Data Source: Banner This is a data report that did not require a calculation. Measure Time Period: Annually (Fiscal Year) Automated: No Date of last description update: 9/26/22 More information related to measure can be viewed on its story page : https://data.austintexas.gov/stories/s/hynm-5nw6

A structured overview of the average, net, median, and minimum wage in Germany for 2025. This dataset combines original market research conducted by KUMMUNI GmbH with publicly available data from the German Federal Statistical Office. It includes values with and without bonuses, hourly minimum wage, and take-home pay after tax.

Number of job vacancies and average offered hourly wage by one-digit National Occupational Classification (NOC) code, last 5 quarters.

This table replaces table 383-0009. Data in this table are not fully comparable with those previously published. Data by industry included in this table corresponds to S and M levels as well as some complementary details at L and W levels of aggregation. For concepts, methods, sources and details concerning the industry classification system, consult the following link http://www.statcan.gc.ca/imdb-bmdi/5103-eng.htm. Provincial and territorial data are available from 1997. Statistics are available from 1999, year of the creation of the Territory of Nunavut. The estimate of the total number of jobs covers two main categories: paid workers jobs and self-employed jobs. These are jobs held by workers whose base pay is calculated at an hourly rate, or on the basis of a fixed amount for a period of at least a week, or in the form of sales commission, piece rates, mileage allowances and so on. Includes workers drawing pay for services rendered or for paid absences and for whom the employer must complete a T-4 Supplementary form from Canada Revenue Agency. These are jobs held by unincorporated working owners, self-employed persons who do not have a business and persons working in a family business without pay. The number of hours worked in all jobs is the annual average for all jobs times the annual average hours worked in all jobs. According to the retained definition, hours worked means the total number of hours that a person spends working, whether paid or not. In general, this includes regular and overtime hours, breaks, travel time, training in the workplace and time lost in brief work stoppages where workers remain at their posts. On the other hand, time lost due to strikes, lockouts, annual vacation, public holidays, sick leave, maternity leave or leave for personal needs are not included in total hours worked. The number of hours worked for paid workers jobs is the average number of paid workers during the year times the annual average number of hours worked in paid jobs. The number of hours worked for self-employed jobs is the average number of paid or unpaid self-employed workers during the year times the annual average number of hours worked in paid or unpaid self-employed jobs. Self-employed jobs are jobs held by unincorporated working owners, self-employed persons who do not have a business and persons working in a family business without pay. This is the annual average of hours worked for the respective job category mentioned in the variable title. The total compensation for all jobs consists of all payments in cash or in kind made by domestic producers to workers for services rendered. It includes labour income for paid workers and imputed labour income for self-employed workers. Often referred to as labour income, it includes two components— wages and salaries, and supplementary labour income. The wages and salaries include all types of regular earnings, special payments, stock options and bonus payments. Supplementary labour income comprises employers' contributions or payments to a variety of paid workers benefit plans for the health and financial well-being of paid workers and their families. Self-employed income consists of an imputed labour income for self-employed workers. The ratio between total compensation paid for all jobs, and the total number of jobs. The ratio between total compensation for all jobs, and the number of hours worked. The term 'hourly compensation' is often used to refer to the total compensation per hour worked. The ratio of labour income paid to paid workers to the number of hours worked. Total economic activities that have been realized within the country. This combines the North American Industry Classification System (NAICS) codes 11-91. This combines the North American Industry Classification System (NAICS) codes 111, 112. This combines the North American Industry Classification System (NAICS) code 111 excluding 1114. This combines the North American Industry Classification System (NAICS) codes 1151, 1152. This combines the North American Industry Classification System (NAICS) codes 212393, 212394, 212395, 212397, 212398. This combines the North American Industry Classification System (NAICS) codes 213111, 213118. This combines the North American Industry Classification System (NAICS) codes 213117, 213119. This combines the North American Industry Classification System (NAICS) codes 2212, 2213. Special hybrid: corresponds to sections of the North American Industry Classification System (NAICS) code 23. This combines the North American Industry Classification System (NAICS) codes 3112, 3118, 3119. This combines the North American Industry Classification System (NAICS) codes 31213, 31214. This combines the North American Industry Classification System (NAICS) codes 313, 314. This combines the North American Industry Classification System (NAICS) codes 315, 316. This combines the North American Industry Classification System (NAICS) code 324 excluding 32411. This combines the North American Industry Classification System (NAICS) codes 3255, 3256, 3259. This combines the North American Industry Classification System (NAICS) code 327 excluding 3273. This combines the North American Industry Classification System (NAICS) codes 3322, 3329. This combines the North American Industry Classification System (NAICS) codes 3332, 3333. This combines the North American Industry Classification System (NAICS) codes 3343, 3345, 3346. This combines the North American Industry Classification System (NAICS) codes 485, 487. This combines the North American Industry Classification System (NAICS) codes 4852, 4854, 4855, 4859, 487. This combines the North American Industry Classification System (NAICS) codes 4861, 4869. This combines the North American Industry Classification System (NAICS) codes 491, 492. This combines the North American Industry Classification System (NAICS) codes 51112, 51113, 51114, 51119. This combines the North American Industry Classification System (NAICS) codes 51211, 51212, 51219. This combines the North American Industry Classification System (NAICS) codes 521, 5221. This combines the North American Industry Classification System (NAICS) codes 52211, 52219. This combines the North American Industry Classification System (NAICS) codes 523, 526. Corresponds to code 53 of the North American Industry Classification System (NAICS). However, it differs from the Input-Output code BS53 since it excludes the industry of owner-occupied dwellings ( BS5311A). This combines the North American Industry Classification System (NAICS) codes 5312, 5313. This combines the North American Industry Classification System (NAICS) code 532 excluding 5321. This combines the North American Industry Classification System (NAICS) codes 5411, 5412. This combines the North American Industry Classification System (NAICS) codes 5414, 5416, 5417, 5419. This combines the North American Industry Classification System (NAICS) codes 5612, 5619. his combines the North American Industry Classification System (NAICS) code 61 excluding 6113. This combines the North American Industry Classification System (NAICS) codes 6114-6117. This combines the North American Industry Classification System (NAICS) code 62 excluding 624. This combines the North American Industry Classification System (NAICS) codes 6213, 6214, 6215, 6216, 6219. This combines the North American Industry Classification System (NAICS) codes 711, 712. This combines the North American Industry Classification System (NAICS) codes 7131, 7139. This combines the North American Industry Classification System (NAICS) codes 7212, 7213. This combines the North American Industry Classification System (NAICS) codes 8112, 8113, 8114. This combines the North American Industry Classification System (NAICS) codes 812, 814. This combines the North American Industry Classification System (NAICS) codes 8121, 8129. This combines the North American Industry Classification System (NAICS) code 813 excluding 8131. This combines the North American Industry Classification System (NAICS) code 911 excluding 9111. This combines the North American Industry Classification System (NAICS) codes 913, 914. Statistics are available until 1998 inclusively; starting in 1999, data for Northwest Territories and Nunavut are presented separately. This combines the North American Industry Classification System (NAICS) code 112 excluding 1125. Starting in 2014, the crop production industry incorporates the activities related to cannabis. Starting in 2014, the miscellaneous store retailers industry incorporates the activities related to cannabis. The ratio of wages and salaries paid to paid workers to their number of hours worked.

Average full-time hourly wage paid and payroll employment by type of work, North American Industry Classification System (NAICS) and National Occupational Classification (NOC), 2016 and 2017.

In October 2024, the average hourly earnings for all employees on private nonfarm payrolls in the United States stood at 35.46 U.S. dollars. The data have been seasonally adjusted. Employed persons are employees on nonfarm payrolls and consist of: persons who did any work for pay or profit during the survey reference week; persons who did at least 15 hours of unpaid work in a family-operated enterprise; and persons who were temporarily absent from their regular jobs because of illness, vacation, bad weather, industrial dispute, or various personal reasons.

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Stayer (FRBATLWGT12MMUMHWGJST) from Dec 1997 to May 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

Israel Paid Hourly Wages: EJ: Other Manufacturing data was reported at 54.100 ILS in May 2018. This records a decrease from the previous number of 57.000 ILS for Apr 2018. Israel Paid Hourly Wages: EJ: Other Manufacturing data is updated monthly, averaging 48.400 ILS from Jan 2012 (Median) to May 2018, with 77 observations. The data reached an all-time high of 57.000 ILS in Apr 2018 and a record low of 44.100 ILS in Oct 2013. Israel Paid Hourly Wages: EJ: Other Manufacturing data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G037: Paid Hourly Wages per Employee Job.

New Zealand Minimum Wage Rate: Per Hour: Starting Out data was reported at 18.800 NZD in May 2025. This stayed constant from the previous number of 18.800 NZD for Apr 2025. New Zealand Minimum Wage Rate: Per Hour: Starting Out data is updated monthly, averaging 14.160 NZD from May 2013 (Median) to May 2025, with 145 observations. The data reached an all-time high of 18.800 NZD in May 2025 and a record low of 11.000 NZD in Mar 2014. New Zealand Minimum Wage Rate: Per Hour: Starting Out data remains active status in CEIC and is reported by Employment New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.G069: Minimum Wage Rate.

Israel Paid Hourly Wages: EJ: Manufacturing; Mining And Quarrying data was reported at 68.100 ILS in May 2018. This records a decrease from the previous number of 68.500 ILS for Apr 2018. Israel Paid Hourly Wages: EJ: Manufacturing; Mining And Quarrying data is updated monthly, averaging 63.300 ILS from Jan 2012 (Median) to May 2018, with 77 observations. The data reached an all-time high of 70.100 ILS in Mar 2018 and a record low of 57.400 ILS in Jan 2012. Israel Paid Hourly Wages: EJ: Manufacturing; Mining And Quarrying data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G037: Paid Hourly Wages per Employee Job.