United States GDP by State per Capita: 2005p: California data was reported at 46,029.000 USD in 2012. This records an increase from the previous number of 44,898.000 USD for 2011. United States GDP by State per Capita: 2005p: California data is updated yearly, averaging 44,845.500 USD from Dec 1997 (Median) to 2012, with 16 observations. The data reached an all-time high of 48,646.000 USD in 2007 and a record low of 36,636.000 USD in 1997. United States GDP by State per Capita: 2005p: California data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A248: NIPA 2009: GDP by State: Far West Region: Chain Linked 2005 Price.

This table contains 52 series, with data for years 1947 - 2009 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: United States ...) Components (15 items: Total gross national product; Total gross domestic product; Gross domestic product; business; Gross domestic product; business; non-farm ...) Prices (4 items: Current prices; 1992 constant prices; Chained (2000) dollars; Chained (1996) dollars ...).

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

This table contains 909 series, with data for years 1981 - 2012 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Seasonal adjustment (2 items: Seasonally adjusted at annual rates; Trading-day adjusted ...) Prices (2 items: Chained (2002) dollars; 2002 constant prices ...) North American Industry Classification System (NAICS) (303 items: All industries; Business sector industries; Business sector; goods; Business sector; services ...).

About this dataset

Context

The dataset presents the median household income across different racial categories in Chino Hills. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Chino Hills population by race & ethnicity, the population is predominantly Asian. This particular racial category constitutes the majority, accounting for 40.21% of the total residents in Chino Hills. Notably, the median household income for Asian households is $117,115. Interestingly, despite the Asian population being the most populous, it is worth noting that American Indian and Alaska Native households actually reports the highest median household income, with a median income of $134,063. This reveals that, while Asians may be the most numerous in Chino Hills, American Indian and Alaska Native households experience greater economic prosperity in terms of median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Chino Hills.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Chino Hills median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Chino Hills. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Chino Hills, the median household income for the households where the householder is White increased by $3,612(2.84%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $127,315 in 2013 and $130,927 in 2023.
• Black or African American: In Chino Hills, the median household income for the households where the householder is Black or African American decreased by $5,856(4.61%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $127,070 in 2013 and $121,214 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Chino Hills.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Chino Hills median household income by race. You can refer the same here

United States GDPS: 2017p: California data was reported at 3,392.762 USD bn in Dec 2024. This records an increase from the previous number of 3,380.751 USD bn for Sep 2024. United States GDPS: 2017p: California data is updated quarterly, averaging 2,490.285 USD bn from Mar 2005 (Median) to Dec 2024, with 80 observations. The data reached an all-time high of 3,392.762 USD bn in Dec 2024 and a record low of 2,011.958 USD bn in Mar 2005. United States GDPS: 2017p: California data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A071: NIPA 2023: GDP by State: Far West Region: Chain Linked 2017 Price: saar.

This table contains 5976 series, with data for years 1984 - 2011 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Newfoundland and Labrador; New Brunswick; Nova Scotia; Prince Edward Island ...), Value (4 items: Current dollars; 1997 constant dollars; Chained (2002) dollars; Chained (1997) dollars ...), North American Industry Classification System (NAICS) (115 items: All industries; Forestry and logging; Agriculture; forestry; fishing and hunting; Crop and animal production ...).

Water scarcity is a challenge in arid regions across the world (Dolan et al., 2021), and is managed by a wide range of governance and institutional approaches (Olmstead, 2014; Berbel and Esteban, 2019). As climate change and competition for water between uses continues to add pressure to already water-stressed regions (Garrick et al., 2019; Caretta et a., 2022), managers, policy makers, and scientists are seeking alternative management strategies to the insufficient policies currently in place (e.g., Berbel and Esteban). One such region is the western U.S., where water stress has increased due to several factors including long-term drought (Williams et al. 2022), increasing competition between agricultural and urban water users (Garrick et al., 2019), and new valuation of in-stream flows (Lane and Rosenberg, 2019).The arid western U.S. began regulating water allocations during the gold rush period of the mid 1800's (Irwin v. Phillips, California 1855). During this time, water was essential for mining, and so the Prior Appropriation Doctrine for water allocation – which is largely still in use today – grew out of gold mining's system of prioritizing resource allocation based on the date when an individual or organization first laid a claim. This is known as "first in time, first in right", and establishes a system of seniority for water users. Following this 1855 ruling in California, all other western U.S. states (except Alaska) established their own forms of water regulation based in part or in whole on the Prior Appropriation Doctrine. We refer readers to Gopalakrishnan (1973) for a thorough history of the Prior Appropriation Doctrine in the U.S. West.Here we present a new database of western U.S. water rights records. We produced the water rights database presented here in 4 main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1) - (3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (in review). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration. Steps (2) and (3) required us to make assumptions and interpretations, and to remove records from the raw data collection. We describe each of these assumptions and interpretations, as well as go further in depth in methodological details in Lisk et al. (in review).This meta-record for the HarDWR database links to the original meta-record, which then links to the four distinct datasets that comprise the whole database: Harmonize Database of Western U.S.Water Rights (HarDWR). The four dataset that can be accessed are:HarDWR - Raw Water Rights Records: The collection of raw downloaded water right records, sourced from each state; step (1) above.HarDWR - Harmonized Water Rights Records: The harmonized water right records, by state; step (2) above.HarDWR - Cumulative Water Rights Curves: The calculated cumulative water rights curves, by state and by WMA; step (4) above.HarDWR - Water Management Area (WMA) Shapefiles: The spatial boundaries which are the administration unit of water rights for each state.CitationsAnderson, M. T. & Woosley, L. H. Water availability for the western United States: key scientific challenges. (U.S. Dept. of the Interior, U.S. Geological Survey ; For sale by U.S. Geological Survey, Information Services, 2005). https://pubs.usgs.gov/circ/2005/circ1261/pdf/C1261.pdfBerbel, J. & Esteban, E. Droughts as a catalyst for water policy change. Analysis of Spain, Australia (MDB), and California. Glob. Environ. Change 58, 101969 (2019). https://doi.org/10.1016/j.gloenvcha.2019.101969Caretta, M. A. et al. Water. in Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the IPCC Cambridge University Press. (2022). https://doi.org/10.1017/9781009325844.006Carney, C. P., Endter‐Wada, J. & Welsh, L. W. The Accumulating Interest in Water Banks: Assessing Their Role in Mitigating Water Insecurities. JAWRA J. Am. Water Resour. Assoc. 57, 552–571 (2021). https://doi.org/10.1111/1752-1688.12940Dolan, F. et al. Evaluating the economic impact of water scarcity in a changing world. Nat. Commun. 12, 1915 (2021). https://doi.org/10.1038/s41467-021-22194-0Garrick, D. et al. Rural water for thirsty cities: a systematic review of water reallocation from rural to urban regions. Environ. Res. Lett. 14, 043003 (2019). https://doi.org/10.1088/1748-9326/ab0db7Gopalakrishnan, C. The Doctrine of Prior Appropriation and Its Impact on Water Development.: A Critical Survey. Am. J. Econ. Sociol. 32, 61–72 (1973). https://doi.org/10.1111/j.1536-7150.1973.tb02180.xGrogan, D. S. et al. Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality. Geosci. Model Dev. 15, 7287–7323 (2022). https://doi.org/10.5194/gmd-15-7287-2022Grogan, D. et al. Bringing hydrologic realism to water markets. (in review)Irwin v. Phillips. Cal. vol. 140 (1855). https://casetext.com/case/irwin-v-phillipsLane, B. A. & Rosenberg, D. E. Promoting In-Stream Flows in the Changing Western US. J. Water Resour. Plan. Manag. 146, 02519003 (2020). https://doi.org/10.1061/(ASCE)WR.1943-5452.0001145Lisk, M. et al. Harmonized Database of Western U.S Water Rights (HarDWR) v.1. (in review, paper for this database).Null, S. E. & Prudencio, L. Climate change effects on water allocations with season dependent water rights. Sci. Total Environ.571, 943–954 (2016). https://doi.org/10.1016/j.scitotenv.2016.07.081Olmstead, S. M. Climate change adaptation and water resource management: A review of the literature. Energy Econ. 46, 500–509 (2014). https://doi.org/10.1016/j.eneco.2013.09.005Tidwell, V. C. et al. Mapping water availability, projected use and cost in the western United States. Environ. Res. Lett. 9, 064009 (2014). https://doi.org/10.1088/1748-9326/9/6/064009Williams, A. P., Cook, B. I. & Smerdon, J. E. Rapid intensification of the emerging southwestern North American megadrought in 2020–2021. Nat. Clim. Change 12, 232–234 (2022). https://doi.org/10.1038/s41558-022-01290-z

Annual Provincial and Territorial Gross Domestic Product (GDP) at basic prices, by North American Industry Classification aggregates, in percentage share, in current dollars.

This table contains 624 series, with data for years 1981 - 2006 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Prices (2 items: 1997 constant dollars; Chained 1997 dollars ...) North American Industry Classification System (NAICS) (312 items: All industries; Agriculture; forestry; fishing and hunting; Crop production; Animal production ...).

This table contains 12 series, with data for years 1961 - 2008 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Industry, special aggregations (12 items: Business sector industries; Total economy; Business sector goods; Business sector services ...).

This paper studies the impact of immigration to the United States on the vote share for the Republican Party using county-level data from 1990 to 2016. Our main contribution is to show that an increase in high-skilled immigrants decreases the share of Republican votes, while an inflow of low-skilled immigrants increases it. These effects are mainly due to the indirect impact on existing citizens' votes and this is independent of the origin country and race of immigrants. We find that the political effect of immigration is heterogeneous across counties and depends on their skill level, public spending and non-economic characteristics.

Data and code for peer-reviewed article published in American Economic Journal: Applied Economics. When citing this dataset, please also cite the associated article. A sample Publication Citation is provided below.

More than 39 million people and 14.2 million households span more than 163,000 square miles of Californian’s urban, suburban and rural communities. California has the fifth largest economy in the world and is the most populous state in the nation, with nation-leading diversity in race, ethnicity, language and socioeconomic conditions. These characteristics make California amazingly unique amongst all 50 states, but also present significant challenges to counting every person and every household, no matter the census year. A complete and accurate count of a state’s population in a decennial census is essential. The results of the 2020 Census will inform decisions about allocating hundreds of billions of dollars in federal funding to communities across the country for hospitals, fire departments, school lunch programs and other critical programs and services. The data collected by the United States Census Bureau (referred hereafter as U.S. Census Bureau) also determines the number of seats each state has in the U.S. House of Representatives and will be used to redraw State Assembly and Senate boundaries. California launched a comprehensive Complete Count Census 2020 Campaign (referred to hereafter as the Campaign) to support an accurate and complete count of Californians in the 2020 Census. Due to the state’s unique diversity and with insights from past censuses, the Campaign placed special emphasis on the hardest-tocount Californians and those least likely to participate in the census. The California Complete Count – Census 2020 Office (referred to hereafter as the Census Office) coordinated the State’s operations to complement work done nationally by the U.S. Census Bureau to reach those households most likely to be missed because of barriers, operational or motivational, preventing people from filling out the census. The Campaign, which began in 2017, included key phases, titled Educate, Motivate and Activate. Each of these phases were designed to make sure all Californians knew about the census, how to respond, their information was safe and their participation would help their communities for the next 10 years.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

This table contains 89 series, with data for years 1947 - 2009 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: United States ...) Components (32 items: Gross national product; Total personal consumption expenditures; durable goods; Total personal consumption expenditures; Gross domestic product ...) Prices (4 items: Current prices; Chained (1996) dollars; Chained (2000) dollars; 1992 constant prices ...).

United States GDPS: 2012p: saar: California data was reported at 2,652.488 USD bn in Jun 2018. This records an increase from the previous number of 2,628.729 USD bn for Mar 2018. United States GDPS: 2012p: saar: California data is updated quarterly, averaging 2,133.809 USD bn from Mar 2005 (Median) to Jun 2018, with 54 observations. The data reached an all-time high of 2,652.488 USD bn in Jun 2018 and a record low of 1,959.066 USD bn in Mar 2005. United States GDPS: 2012p: saar: California data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A111: NIPA 2018: GDP by State: Far West Region: Chain Linked 2012 Price: saar.

United States GDPS: 2012p: Per Capita: California data was reported at 65,160.000 USD in 2017. This records an increase from the previous number of 63,635.000 USD for 2016. United States GDPS: 2012p: Per Capita: California data is updated yearly, averaging 55,520.000 USD from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 65,160.000 USD in 2017 and a record low of 42,438.000 USD in 1997. United States GDPS: 2012p: Per Capita: California data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A111: NIPA 2018: GDP by State: Far West Region: Chain Linked 2012 Price: saar.

United States GDPS: saar: California data was reported at 2,935.332 USD bn in Jun 2018. This records an increase from the previous number of 2,888.220 USD bn for Mar 2018. United States GDPS: saar: California data is updated quarterly, averaging 2,060.922 USD bn from Mar 2005 (Median) to Jun 2018, with 54 observations. The data reached an all-time high of 2,935.332 USD bn in Jun 2018 and a record low of 1,711.573 USD bn in Mar 2005. United States GDPS: saar: California data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A110: NIPA 2018: GDP by State: Far West Region: Current Price: saar.

United States GDPS: 2017p: CA: Government data was reported at 353.142 USD bn in Dec 2024. This records an increase from the previous number of 349.977 USD bn for Sep 2024. United States GDPS: 2017p: CA: Government data is updated quarterly, averaging 315.570 USD bn from Mar 2005 (Median) to Dec 2024, with 80 observations. The data reached an all-time high of 353.142 USD bn in Dec 2024 and a record low of 294.593 USD bn in Sep 2012. United States GDPS: 2017p: CA: Government data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A071: NIPA 2023: GDP by State: Far West Region: Chain Linked 2017 Price: saar.