In the fourth quarter of 2023, paid workers and employees in rural Vietnam earned on average around *** million Vietnamese dong per month. Meanwhile, the average monthly salary for paid workers and employees in urban Vietnam was higher, at approximately *** million Vietnamese dong. Regional income disparity Income inequality in Vietnam is reflected in the noticeable pay gap between urban and rural areas. This urban-rural disparity can be attributed to various determinants, including the salary gap between agricultural and non-farm sectors in Vietnam, as agriculture remains the backbone of the Vietnamese rural economy. Although the country has experienced positive economic growth and decreasing poverty rate within the last few years, the higher poverty rate in rural areas suggests that more can be done to tackle inequality between rural and urban Vietnam. COVID-19 and the Vietnamese labor market Vietnam’s effective response and success in the fight against the coronavirus came at the expense of its economy. In 2021, Vietnam’s unemployment rate reached *** percent, being the highest value recorded within the last decade, with many companies forced to lay off employees during the outbreak. Nevertheless, the labor market in Vietnam is expected to recover, as the Vietnamese economy remains resilient and is expected to grow again post COVID-19.

The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey 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 by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

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 depicts the salaries of IT professionals by region in the United States as of October 2010. The average salary of IT professionals in New England amount to 80.8 thousand U.S. dollars.

Graph and download economic data for Employed: Percent of hourly paid workers: Paid total at or below prevailing federal minimum wage: State wage and salary workers: 16 years and over (LEU0204927500A) from 2000 to 2024 about paid, minimum wage, salaries, workers, hours, 16 years +, federal, wages, percent, employment, and USA.

Located in the north of the country, Lombardy had the highest mean gross salary in 2024, while workers in Basilicata earned the lowest average wages nationwide. The figure for Lombardy amounted to ****** euros, around *** euros more than in Lazio, where the capital Rome is situated, as reported by Job Pricing. Trentino-South Tyrol was the region with the second-highest average gross salary, ****** euros per year. The last positions of the raking were occupied by the southern regions, with an average wage of ****** euros. High wages and large pay gap  According to the same source, employees working in banking and financial services had some of the largest salaries in Italy. However, men earned roughly ** percent more than women (****** euros versus ****** euros). Similarly, the annual gross salary in the insurance industry was ** percent higher in favor of men. Low-wage workers The south of Italy was also the place registering the highest percentage of low paid employees. These are employees with an hourly salary of less than ********** of the median salary over the total number of employees. More specifically, in the south and on the islands, the share of low-wage employees was **** and **** percent, respectively. In the northern regions, the share amounted to only *** percent.

As of the third quarter of 2024, in Mexico, Nuevo León and Mexico City reported the highest average monthly salary, reaching over ****** Mexican pesos each. Additionally, Baja California Sur and Baja California ranked third and fourth in terms of highest incomes, with an average monthly salary of ****** and ****** Mexican pesos, respectively.

Wages In the Euro Area increased 3.40 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Euro Area Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Overview This dataset provides insights into salary distributions across various job classifications, enabling a deeper understanding of compensation trends across industries, experience levels, and geographical locations. It serves as a valuable resource for HR professionals, job seekers, researchers, and policymakers aiming to analyze pay scales, wage gaps, and salary progression trends.

Data Sources The data is aggregated from multiple employment and compensation reports, salary surveys, and publicly available job postings. It has been cleaned, standardized, and structured to ensure consistency and usability for analytical purposes.

Features Job Title: Specific title of the job (e.g., Data Analyst, Software Engineer, Marketing Manager).

Job Classification: Broad category of jobs (e.g., IT, Finance, Healthcare, Education).

Industry: The sector in which the job belongs (e.g., Technology, Banking, Retail).

Experience Level: Categorized as Entry-level, Mid-level, or Senior-level.

Education Requirement: Minimum qualification required for the job role.

Average Salary (INR/USD/Other Currency): The median or mean salary for a particular job classification.

Salary Range: The minimum and maximum salary offered for a role.

Location: Country or region where the job is based.

Employment Type: Full-time, Part-time, Contract, or Remote.

Company Size: Small, Medium, or Large enterprises.

Potential Use Cases Salary Benchmarking: Compare salary expectations across industries and job roles.

Career Planning: Identify lucrative career paths based on salary trends.

Wage Gap Analysis: Examine salary disparities by gender, location, or experience level.

Cost of Living Adjustments: Assess salaries relative to regional economic conditions.

HR and Recruitment Strategies: Optimize compensation packages to attract top talent.

Acknowledgments The dataset is compiled from various salary reports and job market research sources. Special thanks to contributors and organizations providing employment data for analysis.

License This dataset is shared for educational, research, and analytical purposes. Please ensure compliance with relevant data usage policies before any commercial applications.

Get Started The dataset can be explored using Python (Pandas), R, SQL, or visualization tools like Tableau and Power BI. Sample notebooks and analyses are available in the Kaggle notebook section.

Explore private sector employees' monthly wage groups based on citizenship and gender in Bahrain. Access detailed data on Bahraini and non-Bahraini employees, male and female workers, and total labor statistics.

Bahraini, Male, Total, Non-Bahraini, Female, Labor, Private Sector, Wage, salary, Monthly wage group, Gender, Nationality, Bahrain Labor force data

BahrainFollow data.kapsarc.org for timely data to advance energy economics research.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that include salary and compensation data. The dataset consists of salary data by employee reported by State Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Wages In the Euro Area increased to 2589 EUR/Month in the fourth quarter of 2024 from 2571 EUR/Month in the third quarter of 2024. This dataset provides the latest reported value for - Euro Area Average Monthly wage per person - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

Graph and download economic data for Hours of Wage and Salary Workers on Nonfarm Payrolls: Private Sector (DISCONTINUED) (PRSCA) from 1964 to 2021 about payrolls, salaries, sector, nonfarm, workers, hours, wages, private, and USA.

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

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data.virginiabeach.gov

2405 Courthouse Dr.

Virginia Beach, VA 23456

Entity

Employee Salaries

Point of Contact

Human Resources

Attributes

Column: Department

Description: 3-letter department code

Column: Department Division

Description: This is the City Division that the position is assigned to.

Column: PCN

Description: Tracking number used to reference each unique position within the City.

Column: Position Title

Description: This is the title of the position (per the City’s pay plan).

Column: FLSA Status

Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA)

“Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.

“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA.

Column: Initial Hire Date

Description: This is the date that the full-time employee first began employment with the City.

Column: Date in Title

Description: This is the date that the full-time employee first began employment in their current position.

Column: Salary

Description: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.

Frequency of dataset update

Monthly

Graph and download economic data for Median Family Income in West Census Region (MEFAINUSWEA646N) from 1953 to 2023 about West Census Region, family, median, income, and USA.

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.

Comparison of the average salary of State of Oklahoma employees and the market average salary beginning in fiscal year 2005.

Pay Information for calendar year 2023 for the employees of the State of Missouri by their Agency of employment, Position Title or Employee name.

Average full-time hourly wage paid and payroll employment by type of work, economic region and National Occupational Classification (NOC), 2016 and 2017.