The annual salary received by members of the United States Congress in 2025 is ******* U.S. dollars. This has been the case since 2009. The Government Ethics Reform Act of 1989 provides an automatic cost of living adjustment increase in line with the

Context

I wanted to make this finance data about US representatives (very generous of OpenSecrets.org to provide that) available to all for easy data analysis & data science.

Content

This dataset contains the personal finance details of US representatives (Senate, House & the Executive) on the following topics from 2004 to 2016 (varying date ranges for different topics) : * Agreements * Assets * Compensation * Gifts * Honoraria * Income * Liability * Positions * Transactions * Travel

For each of these topics, it has exact amounts or amount ranges, details about the topic like asset type, asset income, industry, sector, etc. & candidate information including candidate name, party, chamber & state & district. There is also information about the members of the 113th, 114th & 115th congress along with congressional committees.

You can find the official Data Dictionary Data Dictionary & the User Guide

Acknowledgements

The source of this data is the Bulk data at https://www.opensecrets.org/

Documentation : https://www.opensecrets.org/open-data/bulk-data-documentation

Please follow the Terms Of Service for using this data : https://www.opensecrets.org/open-data/terms-of-service

Inspiration

I hope that we can analyze this data & understand more about the personal finance of US representatives to help us all going forward. Some questions to be answered : * Which candidates have the highest/lowest net worth? * What kind of investments & in which industry/sector do candidates that you are interested in have? * What are the trends that we in terms of income, investments, etc. for different chambers/parties?

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This graph shows the median wealth of US lawmakers in Congress from 2008 to 2018, for both chambers. In 2018, the median wealth in the Senate amounted to 1.76 million U.S. dollars.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

US Census American Community Survey (ACS) 2014, 5-year estimates of the key economic characteristics of Congressional Districts (114th US Congress) geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2014 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Annual fact sheets providing statistics on the Social Security and Supplemental Security Income programs, including the number of people receiving benefits and the amount of total monthly payments, in each state, territory, and Congressional district. Report for 2006.

Comprehensive demographic dataset for Congress Heights, Washington, DC, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

What is the Pay Gap? The pay gap is a comparison between women’s and men’s typical (median) earnings by dividing women’s median earnings by men’s median earnings. A ratio that is equal to “1.0” indicates that women’s median earnings are equal to men’s median earnings. A ratio of less than “1.0” indicates that women’s earnings are less than men’s earnings; and, a ratio greater than “1.0” indicates that women’s earnings are greater than men’s.This page provides data for the Equal Pay Gap performance measure. The earning"s ratio is calculated by dividing women"s median earnings by the men"s median earnings. The performance measure dashboard is available at 3.25 Equal Pay Ratio 9th Congressional District. Additional Information Source: Contact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: ExcelPreparation Method: Publish Frequency: annuallyPublish Method: manualData Dictionary

To what extent do members of Congress respond unequally to people in different economic situations? How does partisan control of the agenda change the way in which Senators respond to the poor? Using data from the 2004 National Annenberg Election Survey, and multiple roll call votes, I examine Senate responsiveness for the 107th through 111th Congresses. The results show consistent responsiveness toward upper income constituents. Moreover, Republicans are more responsive than Democrats to middle-income constituents in the 109th Congress, and a case study of the 107th Senate reveals that responsiveness toward the wealthy increases once Democrats take control of the chamber.

In 2024, the operating income of the Svenska Mässan Stiftelse (Swedish Exhibition & Congress Centre Group) amounted to roughly **** billion Swedish kronor. This was a decrease compared to the previous year, when the income stood at **** billion kronor.

This poll, conducted February 27, 2001, is part of a continuing series of surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked for their opinions of President George W. Bush and his handling of the presidency, following Bush's first address to Congress earlier in the evening. Views were sought on the proposals outlined by Bush during the address, including income tax cuts, increased education spending, increased funding for Social Security and Medicare, and using the federal budget surplus to reduce the national debt. Respondents were queried as to whether they thought Bush or other individuals were in charge of the administration, whether Democrats would work with the administration, and whether the priorities of the Bush administration were shared by the American people. Respondents were also asked whether they supported Bush's proposed $1.6 trillion tax cut and whether it would be possible to preserve social programs after such tax a cut. Background information on respondents includes age, gender, political party, marital status, education, religion, children in household, race, Hispanic descent, and household income.

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 3.25 Equal Pay Ratio 9th Congressional District. Data Dictionary

This data set matches district demographic information to a member of Congress's legislative actions from 1972 through 2013. The unit of analysis is individual members of Congress. For each member of Congress there is data on: personal characteristics for age, gender, race, ideology, etc. district information for income, education, employment, etc. legislative information for number of bills introduced, number enacted into law, etc. committee information

Comprehensive demographic dataset for South Congress, Austin, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

US Census American Community Survey (ACS) 2019, 5-year estimates of the key economic characteristics of Congressional Districts (116th US Congress) geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2019 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

NOTE: Data based on a sample except in P3, P4, H3, and H4. For.information on confidentiality protection, sampling error,.nonsampling error, definitions, and count corrections see.http://www.census.gov/prod/cen2000/doc/cd110s.pdf

NOTE: Data based on a sample except in P3, P4, H3, and H4. For.information on confidentiality protection, sampling error,.nonsampling error, definitions, and count corrections see.http://www.census.gov/prod/cen2000/doc/cd110s.pdf

This study contains selected electoral and aggregate economic, ecological, and demographic data at the congressional district level for districts of the 87th and 88th Congresses in the period 1961-1965. Data are provided for the number of votes cast for the Democratic and the Republican parties, and the percentage of votes cast for the majority party in the biennial elections for United States Representatives in the period 1952-1962, as well as the total votes cast for the office of president, and the number of votes cast for each party's presidential candidate in the 1952, 1956, and 1960 election. Data are also provided for population and housing characteristics, including total population by household, group quarters, institutions, age group, gender, marital status, race, nationality, and urban and rural residency. Additional demographic variables describe the congressional districts in terms of education, income, employment status and occupation, veteran status, births, deaths, and marriages.