VITAL SIGNS INDICATOR
Income (EC4)

FULL MEASURE NAME
Household income by place of residence

LAST UPDATED
January 2023

DESCRIPTION
Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 4Pb (1970)
Form STF3 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B19001 (2005-2021; household income by place of residence)
Form B19013 (2005-2021; median household income by place of residence)
Form B08521 (2005-2021; median worker earnings by place of employment)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Income derived from the decennial Census data reflects the income earned in the prior calendar year, whereas income derived from the American Community Survey (ACS) data reflects the prior 12 month period; note that this inconsistency has a minor effect on historical comparisons (see Income and Earnings Data section of the ACS General Handbook - https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch09.pdf). ACS 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

Quintile income for 1970-2000 is imputed from decennial Census data using methodology from the California Department of Finance. Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index (CPI) for 2021 specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data uses national CPI for 1970. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in San Francisco County: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 11,467(3.17%) households where the householder is under 25 years old, 149,304(41.25%) households with a householder aged between 25 and 44 years, 114,032(31.51%) households with a householder aged between 45 and 64 years, and 87,109(24.07%) households where the householder is over 65 years old.
• In San Francisco County, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Francisco County median household income by age. You can refer the same here

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.

VITAL SIGNS INDICATOR Income (EC4)

FULL MEASURE NAME Household income by place of residence

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B19013 (2006-2017; place of residence) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

VITAL SIGNS INDICATOR Income (EC5)

FULL MEASURE NAME Worker income by workplace (earnings)

LAST UPDATED October 2016

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B08521 (2006-2015; place of employment) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2015; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

The Department of Housing (DOH) collects data on the number of households that have remained sheltered or stable and/or have received support with their basic needs through federal and state funded programs. HUD categorizes these programs as: Housing Development and Minor Home Repair Public Facilities, Micro-Enterprise and Economic Development, General Public Services, Fair Housing, Core Service Agencies and Shelters. The State categorizes these programs generally as serving persons experiencing or at risk of experiencing homelessness. Subgrantees that administer these programs use the online service platform, City Data Services, to report quarterly updates including the number and general demographic of clients served by each program. At the end of the year, the data is aggregated into an annual report that is submitted to HUD in the form of a Consolidated Annual Performance and Evaluation Report (CAPER) through their online portal, IDIS, and through other reporting tools for the State. These annual reports allow DOH to gain a full scope of clients served, identify specific program outcomes and highlights other work engaged in during a specific program year. Additionally, the data in this report is then disaggregated to show 2) The demographics (race/ethnicity) of those benefitting from the program, which can be compared to the overall County demographics. The granularity of the data makes the racial, spatial, income, and gender disparities that too often negatively impact households’ housing status apparent. Since these data show services provision at the individual level, publication of these measures will inform the community of the scale, cost, and trade-offs of addressing housing related challenges in the Bay area.

In addition to the distribution of need, these data show how DOH and its subgrantees have directed a variety of resources to support multiple individuals across San Mateo County in 2024. In recent years, DOH has seen a continued decrease in federal funding that is made available to local jurisdictions and governments. To stabilize this decline, DOH has leveraged state Permanent Local Housing Allocation (PLHA) funds to meet community needs and is being reported in this data. The data reflects that approximately 40% of the individuals that were served in 2024 will be at or below 30% of the Area Median Income (AMI). Further highlighting the need for services among low and extremely low-income individuals in the County. Additionally, the Hispanic population in County accounts for 25% of the total population, but the 2023 data shows that over 50% of individuals served identified as Hispanic. This data is representative of individuals who identify with one Race, as well as also identifing as Hispanic. Providing specific counts will enable San Mateo County community members to better understand the decision-making process and equity impacts of DOH, stimulating equity, accountability, and engagement. In addition to the distribution of need, these data show how DOH and its subgrantees have directed a variety of resources to support multiple individuals across San Mateo County in 2024. In recent years, DOH has seen a continued decrease in federal funding that is made available to local jurisdictions and governments. To stabilize this decline, DOH has leveraged state Permanent Local Housing Allocation (PLHA) funds to meet community needs and is being reported in this data. The data reflects that over half of the individuals that were served in 2024 will be at or below 30% of the Area Median Income (AMI). Further highlighting the need for services among low and extremely low-income individuals in the County. Additionally, the Hispanic population in County accounts for 25% of the total population, but the 2023 data shows that over 50% of individuals served identified as Hispanic. This data is representative of individuals who identify with one Race, as well as also identifing as Hispanic. Providing specific counts will enable San Mateo County community members to better understand the decision-making process and equity impacts of DOH, stimulating equity, accountability, and engagement.

Abstract

The 1993/94 Namibia Household Income and Expenditure Survey (HIES) is the first module of the National Household Survey Programme endorsed by the Government in 1993, a follow-up of the 1991 Population and Housing Census and represents one more step in providing useful statistics for charting and assessing the socio-economic development of the Namibian society. This programme is an integrated part of A Five-Year Development Plan of Statistics in Namibia. The purpose of the study is to highlight the living conditions of the Namibian people with the emphasis on the distribution of the economic resources among the Namibian households. The study provides a basic description of the living conditions in Namibia concerning economic activity, housing and infrastructure, possession of capital goods and property, economic standard as well as consumption and expenditure patterns.

Geographic coverage

National coverage

Analysis unit

• Individuals
• Households

Universe

The 1993/94 Namibia Household Income and Expenditure Survey covered all private households in Namibia. Institutional households (like hospitals, hostels, barracks and prisons) are not included in the HIES.

Kind of data

Sample survey data [ssd]

Sampling procedure

There are essentially two sampling procedures that were followed in the HIES 1993/94. One for Walvis Bay and one for the rest of Namibia. These will be addressed in turn. The HIES 1993/94 for most of Namibia follows a two stage sample design, taking random draws of geographical areas before randomly selecting households within that area. To do this, the Central Statistics Office (CSO), under the National Planning Commission (NPC), had to first develop a master sample frame. To develop this master sample frame, a set of geographical areas, Primary Sampling Units (PSUs), was created and contained, on average, between 80 and 200 households. These areas were built from the Enumeration Areas (EAs) prepared for the 1991 Population and Housing Census. Small EAs were combined with adjacent EAs to form PSUs of sufficient size. The rule applied was that the number of households in a PSU according to the 1991 Population and Housing Census should be at least 80 households. About 1300 of the 1695 PSUs are made up of single EAs from the 1991 Population and Housing Census while about 400 PSUs are formed by joining two or more EAs. The 1695 PSUs, covering the whole of Namibia (except the Walvis Bay area), were separated into strata of PSUs by region and by rural, small urban and urban areas. The stratification into rural, small urban and urban areas was based on a classification of enumeration areas conducted during the preparations of the 1991 Population and Housing Census. (Note: A different definition of rural and urban areas is used in the statistical reporting from the 1991 Population and Housing Census and the HIES.) The urban areas in the Khomas region and some urban areas in the Otjozondjupa region were further stratified into high income and middle/low income areas. In this way 32 strata were created for the sampling of PSUs As a result of the way the PSUs were created, the number of PSUs of the master sample frame in each region and in each stratum is roughly proportional to the number of households in the region and in the stratum respectively as estimated in the 1991 Population and Housing Census. Having developed the master sample frame, the HIES 1993/94 was sampled according to a Probability Proporitional to Size (PPS) of PSUs method in the first stage and a fixed size equal probability sample of households in each selected PSU in the seond. 192 PSUs were selected in the first stage as the master sample. Initially the master sample was proportionally allocated over the strata in the master sample frame according to the number of households in the 1991 Population and Housing Census. However, some modifications of the allocation were made based on the following: - The variation between households in income level seems to be generally larger in the urban areas than in the rural areas. - The survey costs are considerably lower in the urban areas. - There should be at least 10 PSUs sampled from each region to allow for reasonably good statistics from each region.

It was deemed necessary to have a slight oversampling in urban areas and in one region (Omaheke). A proportional allocation of the 192 PSUs over urban/rural areas gave 66 urban and 126 rural PSUs. But, given the above specifications, the selection of the master sample resulted into 81 urban and 111 rural PSUs. In the second stage, households were selected from the chosen PSUs. A list of households for each PSU was prepared during a separate listing exercise. The listing was carried out as closely as possible to the start of the data collection in a certain PSU i.e. the month before the HIES survey month of the PSU. The list of households in the PSU was used as the sampling frame for the selection of households and a systematic equal probability random sample of 24 households from each PSU was drawn. As initially mentioned, sampling was done slightly differently in Walvis Bay. This is because the region was not integrated in Namibia until 1 March 1994 and could therefore not be included in the HIES before that date. For planning and logistic reasons Walvis Bay was included in the survey somewhat later - from May 1994. This means that Walvis Bay was included in the HIES during the last six months of the survey year. The sampling procedure was different from the rest of Namibia. In Walvis Bay the municipality authorities have for administrative purposes created computerized registers of the households in all the three main town areas - Central Walvis Bay (incl. Langstrand), Kuisebmund and Narraville. Most of the households in Walvis Bay are covered by these registers. There are some areas, however, which are not covered by the administrative registers. These areas are the hostel areas of Walvis Bay and the area along the Kuiseb river where the Topnaar population lives. To cover also these population groups, CSO conducted listing of the households in these areas. For security reasons all the hostel areas could not be listed but some areas had to be excluded from the HIES. The number of listed households in the hostel areas was 99 and probably about the same number of households was not listed. The number of Topnaar households listed was 73.

Altogether 144 households from Walvis Bay were selected by mainly a stratified one-stage sample design to be included in the HIES sample. 24 households were sampled during each of the six months (6 * 24 = 144). Separate strata were defined for Central Walvis Bay (incl. Langstrand), Kuisebmund (excl. the hostel areas), Narraville, the hostel areas and the Topnaar population and altogether 36, 54, 36, 6 and 12 households respectively were sampled from each stratum. (During May-July 1994 10 households were selected each month in Kuisebmund excluding the hostel areas. During August-October 1994 only 8 households were selected in Kuisebmund excluding the hostel areas while 2 households were selected in the hostel areas.)

Sampling deviation

There were numerous issues related to coverage and data collection. These are extensively documented in Section 9 of the Technical and Administrative Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The contents of the HIES set of questionnaires were mainly decided on by the statistical user community. The first user-producer meetings took place during March/April 1992 during the first short-term mission of two Swedish consultants. Users from the following institutions took part in these meetings: the National Planning Commission, the Ministry of Finance, the Ministry of Trade and Industry, the Ministry of Labour, the Ministry of Local Government and Housing, the Ministry of Lands, Resettlement and Rehabilitation , the Bank of Namibia, the Namibian Economic Policy Research Unit (NEPRU) and the Social Sciences Division of the University of Namibia. Based on the pilot survey experiences and other considerations, revisions of the HIES design took place during July - August 1993. Major changes were made in the questionnaires. All of the questionnaires were translated into the major languages of Namibia.

The main forms were: - FORM I: Particulars on Individuals and Households. Filled in at the first interview visit which normally took place the week before the survey month. - FORM II: Daily Record Book. Given to the household at the first visit. The Daily Record Book. The household was urged to record all their transactions on a daily basis in the book. If no literate person was available in the household or its proximity, frequent visits had to be paid by the interviewer. The first interview visit was followed by weekly visits to the household for collecting data on transactions. - FORM III: Cash disbursement and Receipts. Transactions in cash recorded by the household were transferred by the interviewers on a weekly basis. The interviewer also regularly probed the households for cash transactions which they might have forgotten to record in the Daily Record Book. - FORM IV: Transactions in Kind. The interviewer also probed the households for in kind transactions which they might have forgotten to record in the Daily Record Book. - FORM V: Household Opinions. Consists of a module about household opinions concerning how to improve the economic well-being of the households. The Form V interview took place at the last interview visit after the survey month when the data collection concerning all other forms was ready.

Cleaning

U.S. Census Bureau QuickFacts statistics for Granite Bay CDP, California. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

This dataset includes Statistics Canada table 46-10-0050-01, titled "Total family income and owner characteristics at the residential property level by income quintiles". The dataset has been split up into three tables: Table A includes the number of properties and average assessment value of properties by the owner's income quintile, the property type (eg. detached house, condominium), and by family type (lone-parent family, couple family, and other census family). Table B includes includes the number of properties and average assessment value of properties by the owner's income quintile, the property type (eg. detached house, condominium), and by pension income categories (eg. whether or not the owner of the property is receiving a pension). Table C includes includes includes the number of properties and average assessment value of properties by the owner's income quintile, the property type (eg. detached house, condominium), and by residency participation types (eg. whether the property is owned by resident owners only or a mix of resident and non-resident owners). The tables have been edited to include only geographies from British Columbia and to have the unique ID numbers added to the Census Subdivisions and Census Metropolitan Areas. The tables are available in CSV and Excel Workbook format. Definitions and notes are included at the bottom of the spreadsheet. This data set was collected as part of the Canadian Housing Statistics Program by Statistics Canada. Geographies: Abbotsford-Mission, census metropolitan area, Abbotsford, Mission, Kelowna, census metropolitan area, Central Okanagan, Central Okanagan J, Kelowna, Lake Country, Peachland, West Kelowna, Vancouver, census metropolitan area, Anmore, Belcarra, Bowen Island, Burnaby, Coquitlam, Delta, Langley, city, Langley, municipal district, Lions Bay, Maple Ridge, Metro Vancouver A, New Westminster, North Vancouver, city, North Vancouver, municipal district, Pitt Meadows, Port Coquitlam, Port Moody, Richmond, Surrey, Vancouver, West Vancouver, White Rock, Victoria, census metropolitan area, Central Saanich, Colwood, Esquimalt, Highlands, Juan de Fuca (Part 1), Langford, Metchosin, North Saanich, Oak Bay, Saanich, Sidney, Sooke, Victoria, View Royal, British Columbia, outside of census metropolitan areas, Alberni-Clayoquot A, Alberni-Clayoquot B, Alberni-Clayoquot C, Alberni-Clayoquot D, Alberni-Clayoquot E, Alberni-Clayoquot F, Alert Bay, Armstrong, Ashcroft, Barriere, Bulkley-Nechako A, Bulkley-Nechako B, Bulkley-Nechako C, Bulkley-Nechako D, Bulkley-Nechako E, Bulkley-Nechako F, Bulkley-Nechako G, Burns Lake, Cache Creek, Campbell River, Canal Flats, Cariboo A, Cariboo B, Cariboo C, Cariboo D, Cariboo E, Cariboo F, Cariboo G, Cariboo H, Cariboo I, Cariboo J, Cariboo K, Cariboo L, Castlegar, Central Coast A, Central Coast C, Central Coast D, Central Coast E, Central Kootenay A, Central Kootenay B, Central Kootenay C, Central Kootenay D, Central Kootenay E, Central Kootenay F, Central Kootenay G, Central Kootenay H, Central Kootenay I, Central Kootenay J, Central Kootenay K, Chase, Chetwynd, Chilliwack, Clearwater, Clinton, Coldstream, Columbia-Shuswap A, Columbia-Shuswap B, Columbia-Shuswap C, Columbia-Shuswap D, Columbia-Shuswap E, Columbia-Shuswap F, Comox, Comox Valley A, Comox Valley B (Lazo North), Comox Valley C (Puntledge - Black Creek), Courtenay, Cowichan Valley A, Cowichan Valley B, Cowichan Valley C, Cowichan Valley D, Cowichan Valley E, Cowichan Valley F, Cowichan Valley G, Cowichan Valley H, Cowichan Valley I, Cranbrook, Creston, Cumberland, Dawson Creek, Duncan, East Kootenay A, East Kootenay B, East Kootenay C, East Kootenay E, East Kootenay F, East Kootenay G, Elkford, Enderby, Fernie, Fort St. James, Fort St. John, Fraser Lake, Fraser Valley A, Fraser Valley B, Fraser Valley C, Fraser Valley D, Fraser Valley E, Fraser Valley F, Fraser Valley G, Fraser Valley H, Fraser-Fort George A, Fraser-Fort George C, Fraser-Fort George D, Fraser-Fort George E, Fraser-Fort George F, Fraser-Fort George G, Fraser-Fort George H, Fruitvale, Gibsons, Gold River, Golden, Grand Forks, Granisle, Greenwood, Harrison Hot Springs, Hazelton, Hope, Houston, Hudson's Hope, Invermere, Juan de Fuca (Part 2), Kamloops, Kaslo, Kent, Keremeos, Kimberley, Kitimat, Kitimat-Stikine A, Kitimat-Stikine B, Kitimat-Stikine C (Part 1), Kitimat-Stikine C (Part 2), Kitimat-Stikine D, Kitimat-Stikine E, Kitimat-Stikine F, Kootenay Boundary A, Kootenay Boundary B / Lower Columbia-Old-Glory, Kootenay Boundary C / Christina Lake, Kootenay Boundary D / Rural Grand Forks, Kootenay Boundary E / West Boundary, Ladysmith, Lake Cowichan, Lantzville, Lillooet, Logan Lake, Lumby, Lytton, Mackenzie, Masset, McBride, Merritt, Midway, Montrose, Mount Waddington A, Mount Waddington B, Mount Waddington C, Mount Waddington D, Nakusp, Nanaimo, Nanaimo A, Nanaimo B, Nanaimo C, Nanaimo E, Nanaimo F, Nanaimo G, Nanaimo H, Nelson, New Denver, New Hazelton, North Coast A, North Coast C,...

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.