7 datasets found
  1. F

    Median Household Income in the United States

    • fred.stlouisfed.org
    json
    Updated Sep 11, 2024
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    (2024). Median Household Income in the United States [Dataset]. https://fred.stlouisfed.org/series/MEHOINUSA646N
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    jsonAvailable download formats
    Dataset updated
    Sep 11, 2024
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2023 about households, median, income, and USA.

  2. i

    Richest Zip Codes in Missouri

    • incomebyzipcode.com
    Updated Dec 18, 2024
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    Cubit Planning, Inc. (2024). Richest Zip Codes in Missouri [Dataset]. https://www.incomebyzipcode.com/missouri
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    Dataset updated
    Dec 18, 2024
    Dataset authored and provided by
    Cubit Planning, Inc.
    License

    https://www.incomebyzipcode.com/terms#TERMShttps://www.incomebyzipcode.com/terms#TERMS

    Area covered
    Missouri
    Description

    A dataset listing the richest zip codes in Missouri per the most current US Census data, including information on rank and average income.

  3. 2023 American Community Survey: B19037E | Age of Householder by Household...

    • data.census.gov
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    ACS, 2023 American Community Survey: B19037E | Age of Householder by Household Income in the Past 12 Months (in 2023 Inflation-Adjusted Dollars) (Native Hawaiian and Other Pacific Islander Alone Householder) (ACS 1-Year Estimates Detailed Tables) [Dataset]. https://data.census.gov/table/ACSDT1Y2023.B19037E?q=california+table+of+income+by+age+and+zip+codes
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

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

    Time period covered
    2023
    Description

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

  4. i

    Richest Zip Codes in New Jersey

    • incomebyzipcode.com
    Updated Dec 18, 2024
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    Cubit Planning, Inc. (2024). Richest Zip Codes in New Jersey [Dataset]. https://www.incomebyzipcode.com/newjersey
    Explore at:
    Dataset updated
    Dec 18, 2024
    Dataset authored and provided by
    Cubit Planning, Inc.
    License

    https://www.incomebyzipcode.com/terms#TERMShttps://www.incomebyzipcode.com/terms#TERMS

    Area covered
    New Jersey
    Description

    A dataset listing the richest zip codes in New Jersey per the most current US Census data, including information on rank and average income.

  5. i

    Richest Zip Codes in West Virginia

    • incomebyzipcode.com
    Updated Dec 18, 2024
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    Cubit Planning, Inc. (2024). Richest Zip Codes in West Virginia [Dataset]. https://www.incomebyzipcode.com/westvirginia
    Explore at:
    Dataset updated
    Dec 18, 2024
    Dataset authored and provided by
    Cubit Planning, Inc.
    License

    https://www.incomebyzipcode.com/terms#TERMShttps://www.incomebyzipcode.com/terms#TERMS

    Area covered
    West Virginia
    Description

    A dataset listing the richest zip codes in West Virginia per the most current US Census data, including information on rank and average income.

  6. Gallup US Daily

    • redivis.com
    application/jsonl +7
    Updated Jan 26, 2022
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    Stanford University Libraries (2022). Gallup US Daily [Dataset]. http://doi.org/10.57761/t2fr-tw89
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    arrow, application/jsonl, sas, spss, avro, stata, parquet, csvAvailable download formats
    Dataset updated
    Jan 26, 2022
    Dataset provided by
    Redivis Inc.
    Authors
    Stanford University Libraries
    Area covered
    United States
    Description

    Abstract

    From 2008-2017, Gallup conducted a daily telephone survey asking U.S. adults about various political, economic, and well-being topics. Gallup interviewed approximately 1,000 U.S. adults each day and 350,000 U.S. adults annually. The survey’s large sample sizes allow researchers to conduct more detailed analyses.

    In 2018, Gallup transitioned to a weekly Politics and Economy survey conducted by telephone, and a monthly mail survey on Well-Being. 1,500 weekly telephone interviews of U.S. adults were conducted for the Politics and Economy survey.

    In 2019, the Politics and Economy survey was discontinued. The U.S. Well-Being survey track for 2019 only includes data from January-August 2019 and was discontinued in 2020.

    For more details about the U.S. Daily Poll and how it has evolved, see Methodology and Supporting Files, below.

    Methodology

    The methodology for the Gallup US Daily poll has changed over time, and is summarized below. For greater detail, see the Supporting Files.

    2008-2017

    Gallup conducted a daily survey asking 1,000 U.S. adults about various political, economic, and well-being topics*. *On any given evening, approximately 200 Gallup interviewers conduct computer-assisted telephone interviews with randomly sampled respondents, aged 18 and older, including cellphone users and Spanish-speaking respondents from all 50 U.S. states and the District of Columbia. The survey includes many standard demographics such as race, income, education, employment status, and occupation. Location data, such as ZIP codes, allow researchers to map the responses to particular parts of the country and accumulate data for local-level comparison and interpretation.

    Between Jan. 2, 2008-Dec. 31, 2012, all 1,000 daily interviews were from one survey.

    Between Jan. 3, 2013-2017, 500 interviews were conducted using the Well-Being survey, and 500 interviews were conducted using the Politics and Economy survey. Certain items appear on both survey tracks.

    2018

    In 2018, Gallup transitioned to a weekly Politics-Economy survey conducted by telephone, and a monthly mail survey on Well-Being. 1,500 weekly telephone interviews of U.S. adults were conducted for the Politics-Economy survey.

    2019

    The Politics-Economy survey was discontinued in 2019. GPSS is a suggested source of some of these poll questions. (Note that GPSS is not a daily survey, and does not have as rich a sample size). The U.S. Well-Being survey track for 2019 only includes data from January-August 2019 and was discontinued in 2020.

    Usage

    See supporting files:

    • Gallup_Data_Use_Agreement.pdf

    %3C!-- --%3E

    Bulk Data Access

    Metadata access is required to view this section.

  7. d

    Glassdoor Data US Salary, Executive Pay & Company Insights • Matchable...

    • datarade.ai
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    Canaria Inc., Glassdoor Data US Salary, Executive Pay & Company Insights • Matchable Glassdoor Data with Google Maps for HR Analytics, Payroll & Financial Strategy [Dataset]. https://datarade.ai/data-products/canaria-s-glassdoor-salary-data-detailed-usa-company-sp-canaria-inc
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset authored and provided by
    Canaria Inc.
    Area covered
    United States
    Description

    📊 Glassdoor Data for U.S. Salary Intelligence, Executive Compensation & HR Strategy Glassdoor Data is one of the most actionable and trusted sources of alternative data for understanding salary benchmarks, executive compensation, and company-level payroll dynamics. At Canaria Inc., we've enhanced and structured raw Glassdoor Data into a matchable, high-quality dataset that supports advanced compensation modeling, HR analytics, financial strategy, and company analysis.

    Our enriched Glassdoor Data provides detailed salary estimates, executive pay signals, and employee ratings across thousands of U.S. companies. Each company record is normalized and includes verified identifiers, industry tags, and public metadata. To further increase precision and usability, we match each company with Google Maps data — enabling geographic insights such as office location, branch metadata, and review context.

    This salary and payroll data product is designed for compensation strategists, HR teams, market analysts, and financial professionals looking to model workforce costs, track pay trends, and benchmark companies across industries and regions.

    🧠 Use Cases: What Problems This Glassdoor Data Solves Whether you're adjusting salary bands, modeling pay trends, benchmarking executive compensation, or integrating compensation insights into a portfolio strategy, this dataset helps teams replace guesswork with evidence-backed decision-making.

    💰 Compensation Benchmarking & Strategy • Benchmark base salary, executive compensation, and payroll trends by industry • Understand compensation differences by company size, structure, and market segment • Compare companies based on leadership pay, employee ratings, and public sentiment • Improve transparency and equity across internal salary bands with external data • Support DEI and gender pay equity initiatives with data-backed validations

    📈 Financial Intelligence & Valuation Modeling • Integrate salary and payroll estimates into DCF or profitability models • Use leadership compensation data to evaluate fiscal responsibility or growth maturity • Assess pay-to-revenue ratios for private companies or startup valuation proxies • Track cost structures across competitors and industries using public salary trends • Correlate high executive pay or high salary growth with hiring trends and expansion risk

    📊 HR Analytics & Payroll Planning • Use Glassdoor Data to calibrate compensation plans, bonuses, and incentive structures • Align headcount forecasting with real-world salary benchmarks • Benchmark benefits, perks, and compensation packages across employers • Monitor hiring sentiment and satisfaction through employee reviews and scores • Analyze which companies are retaining employees via positive review trends

    🔍 Company Analysis & Leadership Trends • Monitor leadership hiring and compensation levels across mid-size and enterprise firms • Use CEO and executive pay benchmarks to compare strategic leadership investment • Connect compensation signals with business growth stage and industry maturity • Validate company credibility and financial practices using Glassdoor transparency signals • Detect early warning signs in company health through review count declines or rating shifts

    🌐 Matchable Glassdoor Data with Google Maps & Company Profiles Our Glassdoor Data product is enhanced with matchability to company profiles and location intelligence — turning salary insights into full company intelligence.

    📍 Match with Google Maps • Each record includes location-aware metadata such as ZIP code, coordinates, and physical address • Connect salary insights with Google Maps business categories and branch distribution • Identify executive pay variations across headquarters vs. regional offices • Power ABM (account-based marketing) and location-specific compensation modeling

    🔗 Match with Company Profiles • Linked with LinkedIn company URLs, size ranges, and industry classifications • Fully structured data that joins seamlessly with job postings, revenue, or valuation datasets • Company keys allow you to analyze salary vs. hiring, sentiment vs. headcount, and more • Extend Glassdoor salary data into broader firmographic or market research projects

    🔗 Data Quality, Delivery & Enrichment Canaria’s Glassdoor Data is built for seamless delivery and fast integration into enterprise systems.

    • Clean, deduplicated, and normalized data • Filterable by company size, industry, review count, or compensation level • Scalable to match with job postings, HRIS, CRM, or BI tools • Updated monthly to track compensation shifts and company ratings in near real-time

    🎯 Who Uses Canaria’s Glassdoor Data? • HR & People Analytics Teams modeling compensation benchmarks • Finance Teams & Controllers tracking labor costs and comp-to-revenue ratios • Recruiters & Talent Teams refining offers based on market pay expectations • Private Equity & VCs modeling operating costs and salary risks • Compensation Consultants building sala...

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(2024). Median Household Income in the United States [Dataset]. https://fred.stlouisfed.org/series/MEHOINUSA646N

Median Household Income in the United States

MEHOINUSA646N

Explore at:
19 scholarly articles cite this dataset (View in Google Scholar)
jsonAvailable download formats
Dataset updated
Sep 11, 2024
License

https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

Area covered
United States
Description

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2023 about households, median, income, and USA.

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