13 datasets found
  1. T

    Vital Signs: Jobs by Wage Level - Region

    • data.bayareametro.gov
    application/rdfxml +5
    Updated Jan 18, 2019
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    (2019). Vital Signs: Jobs by Wage Level - Region [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Jobs-by-Wage-Level-Region/dzb5-6m5a
    Explore at:
    json, csv, application/rdfxml, application/rssxml, tsv, xmlAvailable download formats
    Dataset updated
    Jan 18, 2019
    Description

    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.

  2. Most populated cities in the U.S. - median household income 2022

    • statista.com
    Updated Aug 30, 2024
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    Statista (2024). Most populated cities in the U.S. - median household income 2022 [Dataset]. https://www.statista.com/statistics/205609/median-household-income-in-the-top-20-most-populated-cities-in-the-us/
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    Dataset updated
    Aug 30, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

    Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

    Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

  3. a

    SB 1000 Populations

    • cecgis-caenergy.opendata.arcgis.com
    • data.cnra.ca.gov
    • +4more
    Updated Jan 17, 2025
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    California Energy Commission (2025). SB 1000 Populations [Dataset]. https://cecgis-caenergy.opendata.arcgis.com/datasets/sb-1000-populations-
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    Dataset updated
    Jan 17, 2025
    Dataset authored and provided by
    California Energy Commission
    License

    https://www.energy.ca.gov/conditions-of-usehttps://www.energy.ca.gov/conditions-of-use

    Area covered
    Description

    Definitions:Urban: Contiguous urban census tracts with a population of 50,000 or greater. Urban census tracts are tracts where at least 10 percent of the tract's land areas is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Rural Center: Contiguous urban census tracts with a population of less than 50,000. Urban census tracts are tracts where at least 10 percent of the tract's land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Rural: Census tracts where less than 10 percent of the tract's land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Disadvantaged Community (DAC): Census tracts that score within the top 25th percentile of the Office of Environmental Health Hazards Assessment’s California Communities Environmental Health Screening Tool (CalEnviroScreen) 4.0 scores, as well as areas of high pollution and low population, such as ports.Low-income Community (LIC): Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted pursuant to Section 50093 of the California Health and Safety Code.Middle-income Community (MIC): Census tracts with median household incomes between 80 to 120 percent of the statewide median income, or with median household incomes between the threshold designated as low- and moderate-income by the Department of Housing and Community Development’s list of state income limits adopted pursuant to section 50093 of the California Health and Safety Code. High-income Community (HIC): Census tracts with median household income at or above 120 percent of the statewide median income or with median household incomes at or above the threshold designated as moderate-income by the Department of Housing and Community Development’s list of state income limits adopted pursuant to section 50093 of the California Health and Safety Code.Data Dictionary:ObjectID1_: Unique IDShape: Geometric form of the featureSTATEFP: State FIPS CodeCOUNTYFP: County FIPS CodeCOUNTY: County NameTract: Census Tract IDPopulation_2019_5YR: Population from the American Community Survey 2019 5-Year EstimatesPop_dens: Census tract designation as Urban, Rural Center, or RuralDAC: Census tract designation as Disadvantaged or not (DAC or Not DAC)Income_Group: Census tract designation as Low-, Middle-, or High-income Community (LIC, MIC, or HIC)Priority_pop: Census tract designation as Low-income and/or Disadvantaged or not (LIC and/or DAC, or Not LIC and/or DAC)Shape_Length: Census tract shape area (square meters)Shape_Area: Census tract shape length (square meters)Data sources:Urban, rural center, and rural designations are from the 2025 Senate Bill (SB) 1000 AssessmentDisadvantaged community designations are from the California Environmental Protection Agency (CalEPA) under Senate Bill (SB) 535Low-income community designations are from the California Air Resources Board under Assembly Bill (AB) 1550. Middle- and high-income designations are from the SB 1000 Assessments.

  4. c

    20 Richest Cities in California

    • california-demographics.com
    Updated Jun 20, 2024
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    Kristen Carney (2024). 20 Richest Cities in California [Dataset]. https://www.california-demographics.com/richest_cities
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    Dataset updated
    Jun 20, 2024
    Dataset provided by
    Cubit Planning, Inc.
    Authors
    Kristen Carney
    License

    https://www.california-demographics.com/terms_and_conditionshttps://www.california-demographics.com/terms_and_conditions

    Area covered
    California
    Description

    A dataset listing the 20 richest cities in California for 2024, including information on rank, city, county, population, average income, and median income.

  5. High income tax filers in Canada

    • www150.statcan.gc.ca
    • open.canada.ca
    • +1more
    Updated Oct 28, 2024
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    Government of Canada, Statistics Canada (2024). High income tax filers in Canada [Dataset]. http://doi.org/10.25318/1110005501-eng
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    Dataset updated
    Oct 28, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

  6. Income of individuals by age group, sex and income source, Canada, provinces...

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated May 1, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas [Dataset]. http://doi.org/10.25318/1110023901-eng
    Explore at:
    Dataset updated
    May 1, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

  7. B

    Disparities depression guideline development and implementation in...

    • borealisdata.ca
    • search.dataone.org
    Updated Jul 9, 2020
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    Yena Lee (2020). Disparities depression guideline development and implementation in low/middle- vs. high-income countries - Supplementary Materials [Dataset]. http://doi.org/10.5683/SP2/UWKTRN
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 9, 2020
    Dataset provided by
    Borealis
    Authors
    Yena Lee
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Supplementary Materials for manuscript titled, "Disparities in the development and implementation of guidelines for the management of depression in low- and middle- vs. high-income countries: a systematic review."

  8. U.S. inflation rate versus wage growth 2020-2025

    • statista.com
    Updated May 8, 2025
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    Statista (2025). U.S. inflation rate versus wage growth 2020-2025 [Dataset]. https://www.statista.com/statistics/1351276/wage-growth-vs-inflation-us/
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    Dataset updated
    May 8, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2020 - Mar 2025
    Area covered
    United States
    Description

    In March 2025, inflation amounted to 2.4 percent, while wages grew by 4.3 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.

  9. G

    Median Income of Individuals (1995$)

    • open.canada.ca
    • ouvert.canada.ca
    jp2, zip
    Updated Mar 14, 2022
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    Natural Resources Canada (2022). Median Income of Individuals (1995$) [Dataset]. https://open.canada.ca/data/en/dataset/ce81114f-8893-11e0-b048-6cf049291510
    Explore at:
    zip, jp2Available download formats
    Dataset updated
    Mar 14, 2022
    Dataset provided by
    Natural Resources Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    This map shows median income of individuals, for the population 15 years of age and over reporting income in 1995 (not including institutional residents). The highest median incomes were observed in the urban areas of Vancouver, Calgary, Toronto, Ottawa-Hull, Montreal, and Quebec. According to the 1996 Census, close to 21 million individuals reported income for 1995. The total income from all sources reflected a 6% decrease when compared to 1990. The national median income for Canada was $18 891. Incomes of individuals and families living in rural Canada tended to be lower than for urban areas. Differences in income distributions were also significant within major urban areas. Nearly 80% of the total population lived in census metropolitan areas.

  10. U.S. poverty rate in the United States 2023, by race and ethnicity

    • statista.com
    Updated Jun 25, 2025
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    Statista (2024). U.S. poverty rate in the United States 2023, by race and ethnicity [Dataset]. https://www.statista.com/statistics/200476/us-poverty-rate-by-ethnic-group/
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    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

  11. Single-earner and dual-earner census families by number of children

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated Jun 27, 2024
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    Government of Canada, Statistics Canada (2024). Single-earner and dual-earner census families by number of children [Dataset]. http://doi.org/10.25318/1110002801-eng
    Explore at:
    Dataset updated
    Jun 27, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Families of tax filers; Single-earner and dual-earner census families by number of children (final T1 Family File; T1FF).

  12. Employment Income Statistics (7), Highest Certificate, Diploma or Degree...

    • open.canada.ca
    • data.urbandatacentre.ca
    • +1more
    html, xml
    Updated Feb 23, 2022
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    Statistics Canada (2022). Employment Income Statistics (7), Highest Certificate, Diploma or Degree (11), Location of Study (5), Immigrant Status and Period of Immigration (11C), Age at Immigration (8), Class of Worker (3) and Sex and Age (24) for the Population Aged 15 Years and Over Who Worked Full-Time Full-Year in 2015 Excluding CIP six-digit categories 22.0101 'Law' and 51.2001 'Pharmacy' in Private Households of Canada, Provinces and Territories and Census Metropolitan Areas, 2016 Census - 25% Sample Data [Dataset]. https://open.canada.ca/data/en/dataset/d89c8b36-869b-44b0-b03e-718178a2f653
    Explore at:
    html, xmlAvailable download formats
    Dataset updated
    Feb 23, 2022
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Time period covered
    May 10, 2016
    Area covered
    Canada
    Description

    This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

  13. Assets and debts by net worth quintile, Canada, provinces and selected...

    • www150.statcan.gc.ca
    • open.canada.ca
    • +2more
    Updated Oct 29, 2024
    + more versions
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    Government of Canada, Statistics Canada (2024). Assets and debts by net worth quintile, Canada, provinces and selected census metropolitan areas, Survey of Financial Security (x 1,000,000) [Dataset]. http://doi.org/10.25318/1110004901-eng
    Explore at:
    Dataset updated
    Oct 29, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table contains 58320 series, with data for years 1999 - 2016 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (20 items: Canada; Atlantic; Newfoundland and Labrador; Prince Edward Island; ...); Assets and debts (27 items: Total assets; Private pension assets; Registered Retirement Savings Plans (RRSPs), Registered Retirement Income Funds (RRIFs), Locked-in Retirement Accounts (LIRAs) and other; Employer-sponsored Registered Pension Plans (EPPs); ...); Net worth quintiles (6 items: Total, all net worth quintiles; Lowest net worth quintile; Second net worth quintile; Middle net worth quintile; ...); Statistics (6 items: Total values; Percentage of total assets or total debts; Number holding asset or debt; Percentage holding asset or debt; ...); Confidence intervals (3 items: Estimate; Lower bound of a 95% confidence interval; Upper bound of a 95% confidence interval).

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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(2019). Vital Signs: Jobs by Wage Level - Region [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Jobs-by-Wage-Level-Region/dzb5-6m5a

Vital Signs: Jobs by Wage Level - Region

Explore at:
json, csv, application/rdfxml, application/rssxml, tsv, xmlAvailable download formats
Dataset updated
Jan 18, 2019
Description

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.

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