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.

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.

Wages in the United States increased 4.46 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Data Overview WageScape's US job listings dataset offers real-time, forward-looking insights into the American labor market. Covering millions of job postings data from various industries and locations, it supports workforce analytics, economic forecasting, and strategic planning, enabling businesses to make data-driven decisions with salary data, skill taxonomy data, and comprehensive company data.

Data Enrichment The dataset includes enriched recruiting data with industry codes, title normalization, company normalization, geographic parsing, firmographic info, and compensation data, enhancing usability and accuracy with precise skill taxonomy data.

Main Attributes • Job Titles: Across various sectors and industries, supported by detailed job postings data. • Job Descriptions: Detailed roles and requirements based on recruiting data. • Salary Data: Expected pay information, sourced from comprehensive salary data. • Locations: From state-level to city-specific details, offering insights into company data. • Company Information: Name, size, revenue, and industry sectors, providing context to company data. • Posting Dates: Timeline of market activity reflecting trends in job postings data. • Job Requirements: Skills, education, and experience needed, informed by skill taxonomy data.

Coverage • Industries: Technology, healthcare, finance, manufacturing, retail, and more, analyzed through company data. • Geographical Reach: National coverage, including metropolitan areas, regional hubs, and smaller towns, enriched with job postings data.

Scale and Quality • Data Volume: Over 4 million job postings monthly, providing extensive recruiting data. • Hiring Organizations: Data from 6+ million organizations, supported by reliable company data. • High Precision: Rigorous validation for accuracy, ensuring dependable salary data.

Use Cases • Workforce Analytics: Analyze trends and dynamics using job postings data and recruiting data for HR decisions. • Economic Forecasting: Predict economic and labor market shifts based on company data and job postings data. • Talent Acquisition: Improve recruitment strategies with detailed recruiting data and insights from skill taxonomy data. • Market Research: Understand industry trends using enriched company data. • Strategic Planning: Inform long-term business strategies with comprehensive job postings data and salary data.

Data Accessibility • Delivery Channels: Available through Data-as-a-Service (DaaS), with access to recruiting data and job postings data. • Customizable Reports: Tailored to specific business needs, incorporating skill taxonomy data. • Integration: Seamless integration into existing systems, supporting detailed company data analysis.

Key Benefits • Real-Time Insights: Up-to-date job information for timely decisions based on job postings data. • Forward-Looking Data: Predict future labor market trends with enriched company data and salary data. • Comprehensive Coverage: Extensive industry and geographic data, including detailed recruiting data. • High Quality and Scale: Millions of postings monthly for robust analysis, supported by skill taxonomy data. • Actionable Insights: Enhance job modeling and workforce strategies with high-quality company data.

Key Points WageScape's dataset is essential for businesses to understand the labor market deeply. With extensive coverage and high-quality company data, it empowers organizations to optimize workforce strategies and maintain a competitive edge, leveraging the latest job postings data, salary data, and skill taxonomy data.

This report was written in collaboration between the Mayor's Office of Innovation and the Rochester Monroe Anti-Poverty Initiative (RMAPI), and released in July 2017. Executive SummaryThe purpose of this report is to explore the demographic and earning disparities in the local workforce in Monroe County. It focuses on people who live in poverty, despite being employed, and aims to help the community better understand major contributing factors preventing residents from becoming self-sufficient. It is meant to augment and contextualize existing data on the state of poverty in Rocheser and to inform the strategy deployed by the Rochester Monroe Anti-Poverty Initiative.This report includes analysis on the correlations between the industries in which Rochester residents in poverty are employed, the wages they earn, and the hours that they work. It also examines these factors by race, gender, educational attainment, and physical ability. Through the analysis of several data sources, inlcuding the U.S. Census Public Use Microdata Sample, the Office of Innovation examines how the intersection of these factors contributes to Rochester's poverty landscape. Key findings outlined in this report include:• Many part-time and seasonal workers live in poverty or are not self sufficient.• Minorities are over-represented in several key service industries.• The industries with over-representation of minorities also tend to be the county’s lowest paying and largest sectors.• Minorities earn less than their white counterparts in nearly every industry sector.• Regardless of educational attainment, the wage gap between whites and minorities persists.The report concludes that wages play a key role in preventing minorities, women, and the disabled from achieving self-sufficiency in Rochester. The recommended next steps for RMAPI are to engage employers and lawmakers in the industry sectors where minorities are both underpaid and over-representated and work towards increasing wages to help meet the goal of increasing self-sufficiency and reducing poverty in Rochester by 50% over the next 15 years.Data Source:2015 Census American Community Survey 5-Year Estimates, Public Microdata SampleData and documentation can be accessed here:https://www.census.gov/programs-surveys/acs/data/pums.html

Global earned wage access software market size was worth around $22.50 billion in 2022 and is predicted to grow to around $26.74 billion by 2030

In December 2024, inflation amounted to 2.9 percent, while wages grew by 4.2 percent. The inflation rate has not exceeded the rate of wage growth since January 2023. Inflation in 2022 The high rates of inflation in 2022 meant that the real terms value of American wages took a hit. Many Americans report feelings of concern over the economy and a worsening of their financial situation. The inflation situation in the United States is one that was experienced globally in 2022, mainly due to COVID-19 related supply chain constraints and disruption due to the Russian invasion of Ukraine. The monthly inflation rate for the U.S. reached a 40-year high in June 2022 at 9.1 percent, and annual inflation for 2022 reached eight percent. Without appropriate wage increases, Americans will continue to see a decline in their purchasing power. Wages in the U.S. Despite the level of wage growth reaching 6.7 percent in the summer of 2022, it has not been enough to curb the impact of even higher inflation rates. The federally mandated minimum wage in the United States has not increased since 2009, meaning that individuals working minimum wage jobs have taken a real terms pay cut for the last twelve years. There are discrepancies between states - the minimum wage in California can be as high as 15.50 U.S. dollars per hour, while a business in Oklahoma may be as low as two U.S. dollars per hour. However, even the higher wage rates in states like California and Washington may be lacking - one analysis found that if minimum wage had kept up with productivity, the minimum hourly wage in the U.S. should have been 22.88 dollars per hour in 2021. Additionally, the impact of decreased purchasing power due to inflation will impact different parts of society in different ways with stark contrast in average wages due to both gender and race.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Feb 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

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.

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

The Agency Report Table aggregates pay and employment characteristics in accordance with the requirements of Local Law 18 of 2019. The Table is a point-in-time snapshot of employees who were either active or on temporary leave (parental leave, military leave, illness, etc.) as of December 31st of each year the data is available (see Column "Data Year"). In addition, the Table contains snapshot data of active employees in seasonal titles as of June 30th. To protect the privacy of employees, the sign “<5” is used instead of the actual number for groups of less than five (5) employees, in accordance with the Citywide Privacy Protection Policies and Protocols. The Pay and Demographics Report, and the list of agencies included is available on the MODA Open Source Analytics Library: https://modaprojects.cityofnewyork.us/local-law-18/

Each row represents a group of employees with a common agency, EEO-4 Job Category, pay band, employee status and demographic attributes, which include race, ethnicity and gender.

Earned Wage Access Software Market Analysis The global earned wage access software market is set to expand rapidly over the forecast period, driven by increasing financial hardships faced by employees, the rising popularity of on-demand pay solutions, and the growing adoption of cloud-based deployment models. The market size, valued at USD 15.5 billion in 2025, is projected to reach USD 105.4 billion by 2033, exhibiting a CAGR of 25.75%. Key market drivers include the shift towards gig economy and remote work, which necessitates flexible wage access. Other factors contributing to the market growth include the increasing demand for financial wellness tools, the growing adoption of mobile technologies, and the rising awareness of financial inclusion. In terms of segmentation, the market is categorized by deployment (cloud, on-premises), organization size (SMEs, large enterprises), industry vertical (retail, healthcare, manufacturing), wage cycle (weekly, biweekly, monthly), and features (real-time access, no-fee options, integration with payroll systems, financial literacy tools). Key players in the market include M1 Finance, FlexWage Solutions, Paycheck Plus, FloatMe, Instant Financial, DailyPay, Wagestream, PayActiv, Payfare, Gusto Global, Branch, Deel, Earnin, TrueConnect, and Even. North America is the largest regional market, followed by Europe and Asia Pacific. Emerging markets such as Latin America and Africa are expected to witness significant growth in the coming years. The global earned wage access software market size was valued at USD 1.28 billion in 2021 and is projected to reach USD 7.72 billion by 2028, exhibiting a CAGR of 29.9% during the forecast period. The market is driven by factors such as the growing demand for financial wellness programs, the rise of the gig economy, and the increasing popularity of mobile banking. Key drivers for this market are: Employee financial wellness Reduced employee turnover Improved employee productivity Integration with payroll software Increased financial inclusion. Potential restraints include: Rising demand for financial wellness Growing adoption by small and medium-sized businesses Increasing employee financial stress.

Expert industry market research on the Workers' Compensation & Other Insurance Funds in the US (2005-2031). Make better business decisions, faster with IBISWorld's industry market research reports, statistics, analysis, data, trends and forecasts.

Abstract

This survey provides salaries & wages statistics at the national level. The survey also provides aggregate data by state as well as urban and rural areas. The survey was carried out using the household approach covering all states in Malaysia. Salaries & Wages Survey uses the personal interview method. During the survey period, trained interviewers visit households in selected living quarters (LQs) to collect demographic information on all household members and salaries & wages particulars of household members aged 15 years and over. The main objective is to collect information on monthly salaries & wages form the principal occupation of paid employee in public and private sectors. The main statistics reported are median and mean monthly salaries & wages by sex, ethnic group, educational attainment, strata, state, occupation and industry. The results of these statistics is published in the 'Salaries & Wages Survey Report'.

Starting with the Salaries & Wages Report 2017, the main statistics presented in the report is for the citizens. Meanwhile, the salaries & wages selected statistics consists of non citizens is shown in a separate table.

Geographic coverage

This survey provides estimates at national and state level as well as urban and rural areas.

Geographic coverage notes

National level.

Analysis unit

Household/Individual

Universe

All household members and salaries & wages particulars of household members aged 15 years and over.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Monthly

Sampling procedure

The survey is carried out using probability sampling through household approach comprising Malaysian citizens and non-citizens. The survey is carried out using probability sampling through household approach comprising Malaysian citizens and non-citizens.

Mode of data collection

Face-to-face [f2f]

The compensation software market is projected to reach USD 674.52 million by 2033, growing at a CAGR of 13.25% during the forecast period (2025-2033). The increasing demand for accurate and efficient compensation management solutions, coupled with the rising adoption of cloud-based software, is driving the market growth. The market is segmented by deployment model (cloud-based, on-premises), organization size (large enterprises, SMEs), industry vertical (financial services, healthcare, retail, etc.), compensation practice (base pay management, bonus management, etc.), and features (performance management integration, data analytics and reporting, etc.). North America holds the largest share of the market, followed by Europe and Asia Pacific. The increasing adoption of compensation software by large enterprises in these regions is driving the market growth. Additionally, the presence of key players such as SAP SuccessFactors, Cornerstone OnDemand, Kronos Incorporated, and Workday HCM in these regions is further contributing to the market growth. The growing awareness of the benefits of compensation software, such as improved accuracy and efficiency, is expected to drive the market growth in the future. Key drivers for this market are: 1 Cloud-based deployment models2 Integration with HR platforms3 AIdriven data analytics4 Customization for industry-specific needs5 Mobile accessibility. Potential restraints include: 1 Growing demand for data-driven compensation decisions2 Increasing adoption of cloud-based compensation software3 Focus on enhancing employee experience4 Integration with HR and payroll systems5 Regulatory compliance and transparency.

The Occupational Employment Statistics (OES) program conducts a semiannual survey designed to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. The Bureau of Labor Statistics produces occupational employment and wage estimates for approximately 415 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and selected 5- and 6-digit North American Industry Classification System (NAICS) industrial groups. The OES program surveys approximately 200,000 establishments per panel (every six months), taking three years to fully collect the sample of 1.2 million establishments. To reduce respondent burden, the collection is on a three-year survey cycle that ensures that establishments are surveyed at most once every three years. The estimates for occupations in nonfarm establishments are based on OES data collected for the reference months of May and November. The OES survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OES survey make these estimates possible. The OES features several arts-related occupations, particularly in the Arts, Design, Entertainment, Sports, and Media Occupations group (Standard Occupational Classification (SOC) code 27-0000). Several featured occupation groups include the following: Art and Design Workers (SOC 27-1000) Art Directors Fine Artists, including Painters, Sculptors, and Illustrators Multimedia Artists and Animators Fashion Designers Graphic Designers Set and Exhibit Designers Entertainers and Performers, Sports and Related Workers (SOC 27-2000) Actors Producers and Directors Athletes Coaches and Scouts Dancers Choreographers Music Directors and Composers Musicians and Singers Media and Communication Workers (SOC 27-3000) Radio and Television Announcers Reports and Correspondents Public Relations Specialists Writers and Authors Data for years 1997 through the latest release and can be found on the OES Data page. Also, see OES News Releases sections for current estimates and news releases. Users can analyze the data for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. As well, OES Charts are available. Users may also explore data using OES Maps. If preferred, data can also be accessed via the Multi-Screen Data Search or Text Files using the OES Databases page.

Workers’ compensation and other insurance funds businesses have experienced significant changes in recent years, largely driven by economic fluctuations and shifts in investment income. The crash of the US economy in 2020 due to pandemic-related restrictions placed immense pressure on the industry. Business formation plunged and unemployment soared, resulting in a diminished customer base for insurance funds and a steep drop in revenue. Regardless, the Federal Reserve's injection of liquidity into the financial system propelled stock prices upward, boosting investment income for insurance providers. This increase in investment income provided some relief for providers, enabling them to cover expenses and sustain profits despite revenue losses. The relaxation of COVID-19 restrictions spurred economic recovery in 2021, driving unemployment down and corporate profit up. This positive economic climate increased demand for insurance services and enhanced investment income due to robust stock market conditions. However, since 2022, inflation has wreaked havoc, causing businesses and organizations to slash investments in insurance funds amid soaring prices. More recently, rising interest rates have reduced downstream demand due to the emergence of recessionary fears, but revenue and profit have expanded because of growing returns on fixed-income products. Overall, revenue for workers’ compensation and other insurance funds has inched downward at a CAGR of 0.2% over the past five years, reaching $56.6 billion in 2025. This includes a 0.5% rise in revenue in that year. Looking ahead, providers are poised for moderate growth over the next five years. As the US economy stabilizes, with solid GDP growth and potential increases in business formation and employment, the customer base for insurance funds is likely to expand. These favorable economic conditions should bolster consumer confidence and investment in the stock market, leading to greater investment income for the industry. Nonetheless, larger players are expected to dominate, given their ability to invest in cutting-edge technologies like AI for predicting claim risks and optimizing business operations. Smaller providers may face intensified internal competition, prompting some to exit the market, while others could focus on niche offerings or invest in technological advancements to remain viable and competitive. Overall, revenue for workers’ compensation and other insurance funds is expected to expand at a CAGR of 1.3% over the next five years, reaching $60.3 billion in 2030.

The global salary management system software market is estimated to reach XX million by 2023, growing at a CAGR of XX% during the forecast period (2019-2023). The market growth is primarily driven by the increasing need for efficient and accurate payroll processing, compliance with regulatory requirements, and the growing adoption of cloud-based solutions. The market is segmented by type (cloud-based, on-premises), application (large enterprises, SMEs), and region (North America, Europe, Asia Pacific, Middle East & Africa). Cloud-based solutions are expected to witness significant growth due to their cost-effectiveness, flexibility, and scalability. Large enterprises are the primary users of salary management systems, but SMEs are expected to adopt these solutions at a rapid pace. North America is the largest market for salary management systems, followed by Europe and Asia Pacific.

Graph and download economic data for TROS Future Wages and Benefits; Percent Reporting No Change for Texas (TROSFWGSNSAMFRBDAL) from Jan 2007 to Feb 2025 about benefits, wages, percent, TX, and USA.

The United States Buy Now Pay Later Services Market is Segmented by Channel (Online and POS) and by Product Category (Kitchen Appliances, Other Consumer Electronics, Fashion and Personal Care, Healthcare, and Other Product Categories). The report offers market size and forecast values for the US Buy Now Pay Later Services Market in USD billion for the above segments.