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The number of employees of 15401 ZIP Code was 291 for business and finance in 2018.
Jobs and Occupations Datasets Involving 15401 ZIP Code
- API data.edd.ca.gov | Last Updated 2022-05-06T18:16:46.000Z
Short-term Occupational Projections for a 2-year time horizon are produced for the State to provide individuals and organizations with an occupational outlook to make informed decisions on individual career and organizational program development. Short-term projections are revised annually. Data are not available for geographies below the state level, including labor market regions. Data is based on second quarter averages and may be subject to seasonality. Detail may not add to summary lines due to suppression of data because of confidentiality and/or quality.
- API data.ny.gov | Last Updated 2022-06-17T15:56:32.000Z
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.
- API data.edd.ca.gov | Last Updated 2022-06-13T16:54:39.000Z
The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.
- API opendata.utah.gov | Last Updated 2019-04-18T22:44:27.000Z
The Occupational Employment Statistics (OES) program produces employment and wage estimates annually for over 800 occupations. These estimates are available for the nation as a whole, for individual States, and for metropolitan and nonmetropolitan areas; national occupational estimates for specific industries are also available.
- API data.bts.gov | Last Updated 2022-07-06T17:44:55.000Z
Monthly Transportation Statistics is a compilation of national statistics on transportation. The Bureau of Transportation Statistics brings together the latest data from across the Federal government and transportation industry. Monthly Transportation Statistics contains over 50 time series from nearly two dozen data sources.
- API opendata.utah.gov | Last Updated 2019-02-11T22:29:39.000Z
Salt Lake City MSA Occupational Projections 2012-2022
- API data.pa.gov | Last Updated 2019-11-13T21:08:49.000Z
Represents a comprehensive collection of occupational wage data available for Pennsylvania. Data are collected through the Occupational Employment Statistics program in cooperation with the U.S. Department of Labor’s Bureau of Labor Statistics. Occupational wage information can be used as a reference by educators, PACareerLink® staff, career counselors, Workforce Development Boards, economic developers, program planners, and others. Technical Note Occupational wages do not represent a time series. Due to the prescribed production methodology, current occupational wages are not comparable to previously published occupational wages.
- API data.pa.gov | Last Updated 2022-05-11T18:56:23.000Z
New Dataset for 2019 - Current by Quarter is now available - <a href="https://data.pa.gov/Workforce-Development/Quarterly-Census-of-Employment-and-Wages-QCEW-2019/bm6e-y9xf"> Quarterly Census of Employment and Wages (QCEW) 2019 - Current County Labor and Industry</a> The Quarterly Census of Employment and Wages (QCEW) dataset provides information about the number of establishments within a geographic area by industry as well as the average number of employees and average weekly wages paid. QCEW is the universe of employment covered under Pennsylvania’s unemployment insurance laws. QCEW employment is based on the location of the position not where the person resides.
- API mydata.iowa.gov | Last Updated 2021-11-15T14:33:22.000Z
Iowa Vocational Rehabilitation Services (IVRS) mission is to provide expert, individualized services to Iowans with disabilities to achieve their independence through successful employment and economic support. This dataset provides information on closed cases where the individual received services from IVRS. Data includes cases closed after October 1, 2008.
- API data.bayareametro.gov | Last Updated 2019-10-25T20:41:01.000Z
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 firstname.lastname@example.org 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 c