Graph and download economic data for Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over (LEU0254503000A) from 2000 to 2024 about occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

In 2022, the top paying state for date entry keyers in the United States was the District of Columbia, where this workforce earned an annual mean wage of approximately ****** U.S. dollars. The state with the second highest annual mean wage for data entry keyers was Massachusetts, where those employed within this industry earned ****** U.S. dollars.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over (LEU0254556400A) from 2000 to 2024 about second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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

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.

In 2023, the best paying industry in the United States for data entry keyers was in the federal postal service. The second best paying industry was in natural gas distribution, where data entry keyers earned an annual wage of approximately ****** U.S. dollars in 2023.

Current Employment by Industry (CES) data reflect jobs by "place of work." It does not include the self-employed, unpaid family workers, and private household employees. Jobs located in the county or the metropolitan area that pay wages and salaries are counted although workers may live outside the area. Jobs are counted regardless of the number of hours worked. Individuals who hold more than one job (i.e. multiple job holders) may be counted more than once. The employment figure is an estimate of the number of jobs in the area (regardless of the place of residence of the workers) rather than a count of jobs held by the residents of the area.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

Economically Active Population Survey: Average wages of the main job by period, type of working day, time that they have been working in their current job and decile. Annual. National.

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.

Number of job vacancies and payroll employees, job vacancy rate, and average offered hourly wage by two-digit North American Industry Classification System (NAICS) code, last 5 quarters.

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)

Rigidity in real hiring wages plays a crucial role in some recent macroeconomic models. But are hiring wages really so noncyclical? We propose using employer/employee longitudinal data to track the cyclical variation in the wages paid to workers newly hired into specific entry jobs. Illustrating the methodology with 1982-2008 data from the Portuguese census of employers, we find real entry wages were about 1.8 percent higher when the unemployment rate was 1 percentage point lower. Like most recent evidence on other aspects of wage cyclicality, our results suggest that the cyclical elasticity of wages is similar to that of employment. (JEL E24, E32, J31, J64)

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

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

Israel Avg Monthly Wages per Employee Job: Agriculture, Forestry and Fishing data was reported at 7,074.623 ILS in Aug 2018. This records a decrease from the previous number of 7,340.232 ILS for Jul 2018. Israel Avg Monthly Wages per Employee Job: Agriculture, Forestry and Fishing data is updated monthly, averaging 6,536.251 ILS from Jan 2012 (Median) to Aug 2018, with 80 observations. The data reached an all-time high of 7,553.610 ILS in Dec 2017 and a record low of 5,699.000 ILS in Feb 2012. Israel Avg Monthly Wages per Employee Job: Agriculture, Forestry and Fishing 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.G034: Average Monthly Wages per Employee Job: Current Price.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by permanent and temporary employees, union coverage, gender, and age group, monthly.

Average pay comparison of male and female wages by job classification (except Library job classes). The data contains weighted average hourly pay rates for women and men and the average of all employee wages in the class.