Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

The Cumulative Report includes complete official election results for the 2020 Presidential General Election as of November 29, 2020. Results are released in three separate reports: The Vote By Mail 1 report contains complete results for ballots received by the Board of Elections on or before October 21, 2020, that could be accepted and opened before Election Day. The Vote By Mail 2 Canvass report contains complete results for all remaining Vote By Mail ballots that were received in a drop box or in person at the Board of Elections by 8:00pm on November 3, or were postmarked by November 3 and received timely by the Board of Elections by 10:00am on Friday, November 13. The Vote By Mail 2 Canvass begins on Thursday, November 5. The Provisional Canvass contains complete results for all provisional ballots issued to voters at Early Voting or on Election Day. For more information on this process, please visit the 2020 Presidential General Election Ballot Canvass webpage at https://www.montgomerycountymd.gov/Elections/2020GeneralElection/general-ballot-canvass.html. For turnout information, please visit the Maryland State Board of Elections Press Room webpage at https://elections.maryland.gov/press_room/index.html.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

EPILOGUE: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2Fa5eefdb31428bd5ce99cdf76fa484a63%2Fmap.jpg?generation=1733007717460285&alt=media" alt="">

FINAL UPDATE: It's election night, and the results are coming in. The final update includes the latest poll data from 538, which is from two days ago. Thanks all for following the development of this dataset.

OCTOBER UPDATE: The past month has been typical of the final weeks before the election - rallies, interviews, and advertising. This update includes a transcript of the VP debate between Walz and Vance, and the latest poll summaries.

SEPTEMBER UPDATE: Trump and Harris had their first debate. This update includes the transcript and recent poll results. Also, there was a second attempt to kill former President Trump! No shots fired though on this one. You'll see aerial diagrams of both attempts in the dataset.

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LATE AUGUST UPDATE: The Democratic Party replaced President Biden with his VP, Kamala Harris. It's now Trump v Harris along with one nominee from each of the smaller factions.

https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fmedia.cnn.com%2Fapi%2Fv1%2Fimages%2Fstellar%2Fprod%2F240122181719-trump-kamala-vpx-split-2.jpg%3Fc%3D16x9%26q%3Dw_850%2Cc_fill&f=1&nofb=1&ipt=984b6cf55cf55e1539003ca1c1beaa359625f6e5b08b511b3b018c9d2c959ae5&ipo=imagesg">

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AUGUST UPDATE: This election season just gets crazier and crazier. You'll see new data related to the assassination attempt on former President Trump. There are transcripts of Secret Service hearings and an annotated image of the rally area.

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JULY UPDATE: Added the transcript of the debate between Trump and Biden.

MAY UPDATE: Added some new polls and also a meta-poll assessing the quality of select pollsters.

APRIL UPDATE : The dataset now contains approval ratings for sitting presidents, which includes Biden and Trump.

MARCH UPDATE: As of last week, the presumptive nominees are Joe Biden(D) and Donald Trump(R). They also ran against each other in 2020. Robert F Kennedy Jr is running as an independent.

Presidential elections occur quadrennially in years evenly divisible by 4, on the first Tuesday after November 1. Presidential candidates from the major political parties usually declare their intentions to run as early as the spring of the previous calendar year before the election. The two major parties each nominate one candidate through a process of primary elections and nominating conventions during the election year. (source: Wikipedia)

This dataset contains data on candidates, primary/caucus results, polls, and debate transcripts. Updates and additional data will be added as the landscape develops.

Note: Version 3 of this dataset contains previous coverage of the 2022 Congressional Mid-term Elections.

According to exit polling in ten key states of the 2024 presidential election in the United States, 46 percent of voters with a 2023 household income of 30,000 U.S. dollars or less reported voting for Donald Trump. In comparison, 51 percent of voters with a total family income of 100,000 to 199,999 U.S. dollars reported voting for Kamala Harris.

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

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 statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

The module was administered as a post-election interview. The resulting data are provided along with voting, demographic, district and macro variables in a single dataset.

CSES Variable List The list of variables is being provided on the CSES Website to help in understanding what content is available from CSES, and to compare the content available in each module.

Themes: MICRO-LEVEL DATA:

Identification and study administration variables: weighting factors;election type; date of election 1st and 2nd round; study timing (post election study, pre-election and post-election study, between rounds of majoritarian election); mode of interview; gender of interviewer; date questionnaire administered; primary electoral district of respondent; number of days the interview was conducted after the election

Demography: age; gender; education; marital status; union membership; union membership of others in household; current employment status; main occupation; employment type - public or private; industrial sector; occupation of chief wage earner and of spouse; household income; number of persons in household; number of children in household under the age of 18; attendance at religious services; religiosity; religious denomination; language usually spoken at home; race; ethnicity; region of residence; rural or urban residence

Survey variables: respondent cast a ballot at the current and the previous election; respondent cast candidate preference vote at the previous election; satisfaction with the democratic process in the country; last election was conducted fairly; form of questionnaire (long or short); party identification; intensity of party identification; political parties care what people think; political parties are necessary; recall of candidates from the last election (name, gender and party); number of candidates correctly named; sympathy scale for selected parties and political leaders; assessment of the state of the economy in the country; assessment of economic development in the country; degree of improvement or deterioration of economy; politicians know what people think; contact with a member of parliament or congress during the past twelve months; attitude towards selected statements: it makes a difference who is in power and who people vote for; people express their political opinion; self-assessment on a left-right-scale; assessment of parties and political leaders on a left-right-scale; political information items

DISTRICT-LEVEL DATA:

number of seats contested in electoral district; number of candidates; number of party lists; percent vote of different parties; official voter turnout in electoral district

MACRO-LEVEL DATA:

founding year of parties; ideological families of parties; international organization the parties belong to; left-right position of parties assigned by experts; election outcomes by parties in current (lower house/upper house) legislative election; percent of seats in lower house received by parties in current lower house/upper house election; percent of seats in upper house received by parties in current lower house/upper house election; percent of votes received by presidential candidate of parties in current elections; electoral turnout; electoral alliances permitted during the election campaign; existing electoral alliances; most salient factors in the election; head of state (regime type); if multiple rounds: selection of head of state; direct election of head of state and process of direct election; threshold for first-round victory; procedure for candidate selection at final round; simple majority or absolute majority for 2nd round victory; year of presidential election (before or after this legislative election); process if indirect election of head of state; head of government (president or prime minister); selection of prime minister; number of elected legislative chambers; for lower and upper houses was coded: number of electoral segments; number of primary districts; number of seats; district magnitude (number of members elected from each district); number of secondary and tertiary electoral districts; compulsory voting; votes cast; voting procedure; electoral formula; party threshold; parties can run joint lists; requirements for joint party lists; possibility of apparentement; types of apparentement agreements; multi-party endorsements; multi-party endorsements on ballot; ally party support; constitu...

Terms of Access: By downloading the data, you agree to use the data only for academic research, agree not to share the data with outside parties, and agree not to attempt to re-identify individuals in the data set. We require this in order to protect the privacy of individuals in the data set and to comply with agreements made with TargetSmart. Abstract: We present the results of a large, $8.9 million campaign-wide field experiment, conducted among 2 million moderate and low-information “persuadable” voters in five battleground states during the 2020 US Presidential election. Treatment group subjects were exposed to an eight-month-long advertising program delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden. We found no evidence the program increased or decreased turnout on average. We find evidence of differential turnout effects by modeled level of Trump support: the campaign increased voting among Biden leaners by 0.4 percentage points (SE: 0.2pp) and decreased voting among Trump leaners by 0.3 percentage points (SE: 0.3pp), for a difference-in-CATES of 0.7 points that is just distinguishable from zero (t(1035571) = −2.09, p = 0.036, DIC = 0.7 points, 95% CI = [−0.014, −0.00]). An important but exploratory finding is that the strongest differential effects appear in early voting data, which may inform future work on early campaigning in a post-COVID electoral environment. Our results indicate that differential mobilization effects of even large digital advertising campaigns in presidential elections are likely to be modest.

The New York Times and CBS News were equal partners in a series of election surveys covering the 1980 election year. The content of this data collection generally concerns the presidential preference of respondents, their reasons for choosing a particular candidate, and their reactions to political and social issues of the campaign. There are 28 datasets in the collection, which fall into three categories: national monthly surveys, primary day surveys, and the election day survey. Parts 1-12 contain national monthly surveys that were conducted by telephone, with approximately 1,500 randomly selected adults in each. Surveys were conducted in January, February, March, April, June, August, September, and October. Two telephone surveys were conducted in September, a pre-debate survey and a post-debate survey. Also, two surveys were conducted in October. A post-election survey was conducted in the days following the election. For the post-election survey, the respondents in Part 11, October Pre-Election National Interviews, were reinterviewed. The post-election survey is released as a panel file and incorporates Part 11 responses as well. Parts 13-27 contain primary day surveys that were conducted in 11 states on the day of the primary at the polling place among a random sample of people who had just voted in either the Democratic or Republican presidential primaries. The questionnaires were self-administered. Surveys were conducted in the following states: New Hampshire, Massachusetts, Florida, Illinois, New York, Wisconsin, Pennsylvania, California, New Jersey, and Ohio. There are separate data files for the Democratic and Republican primaries in New Hampshire, Massachusetts, Illinois, Wisconsin, and Pennsylvania. Demographic information including age, sex, income, race, ethnicity, and occupation is provided for all respondents in Parts 1-27. Part 28 contains a survey conducted on the day of the presidential election. A national sample of voters was administered a questionnaire similar to those given on primary day. Selected voters were asked for whom they had voted and why. Information on time of voting and respondent's sex and race was filled out by the interviewer.

The module was administered as a post-election interview. The resulting data are provided along with voting, demographic, district and macro variables in a single dataset. CSES Variable List The list of variables is being provided on the CSES Website to help in understanding what content is available from CSES, and to compare the content available in each module. Themes: MICRO-LEVEL DATA: Identification and study administration variables: weighting factors; election type; date of election 1st and 2nd round; study timing (post-election study, pre-election and post-election study, between rounds of majoritarian election); mode of interview; gender of interviewer; date questionnaire administered; primary electoral district of respondent; number of days the interview was conducted after the election; language of questionnaire. Demography: year and month of birth; gender; education; marital status; union membership; union membership of others in household; business association membership, farmers´ association membership; professional association membership; current employment status; main occupation; socio economic status; employment type - public or private; industrial sector; current employment status, occupation, socio economic status, employment type - public or private, and industrial sector of spouse; household income; number of persons in household; number of children in household under the age of 18; number of children in household under the age of 6; attendance at religious services; religiosity; religious denomination; language usually spoken at home; region of residence; race; ethnicity; rural or urban residence; primary electoral district; country of birth; year arrived in current country. Survey variables: perception of public expenditure on health, education, unemployment benefits, defense, old-age pensions, business and industry, police and law enforcement, welfare benefits; perception of improving individual standard of living, state of economy, government’s action on income inequality; respondent cast a ballot at the current and the previous election; vote choice (presidential, lower house and upper house elections) at the current and the previous election; respondent cast candidate preference vote at the current and the previous election; difference who is in power and who people vote for; sympathy scale for selected parties and political leaders; assessment of parties on the left-right-scale and/or an alternative scale; self-assessment on a left-right-scale and an optional scale; satisfaction with democracy; party identification; intensity of party identification, institutional and personal contact in the electoral campaigning, in person, by mail, phone, text message, email or social networks, institutional contact by whom; political information questions; expected development of household income in the next twelve month; ownership of residence, business or property or farm or livestock, stocks or bonds, savings; likelihood to find another job within the next twelve month; spouse likelihood to find another job within the next twelve month. DISTRICT-LEVEL DATA: number of seats contested in electoral district; number of candidates; number of party lists; percent vote of different parties; official voter turnout in electoral district. MACRO-LEVEL DATA: election outcomes by parties in current (lower house/upper house) legislative election; percent of seats in lower house received by parties in current lower house/upper house election; percent of seats in upper house received by parties in current lower house/upper house election; percent of votes received by presidential candidate of parties in current elections; electoral turnout; party of the president and the prime minister before and after the election; number of portfolios held by each party in cabinet, prior to and after the most recent election; size of the cabinet after the most recent election; number of parties participating in election; ideological families of parties; left-right position of parties assigned by experts and alternative dimensions; most salient factors in the election; fairness of the election; formal complaints against national level results; election irregularities reported; scheduled and held date of election; irregularities of election date; extent of election violence and post-election violence; geographic concentration of violence; post-election protest; electoral alliances permitted during the election campaign; existing electoral alliances; requirements for joint party lists; possibility of apparentement and types of apparentement agreements; multi-party endorsements on ballot; votes cast; voting procedure; voting rounds; party lists close, open, or flexible; transferable votes; cumulated votes if more than one can be cast; compulsory voting; party threshold; unit for the threshold; freedom house rating; democracy-autocracy polity IV rating; age of the current regime; regime: type of executive; number of months since last lower house and last presidential election; electoral formula for presidential elections; electoral formula in all electoral tiers (majoritarian, proportional or mixed); for lower and upper houses was coded: number of electoral segments; linked electoral segments; dependent formulae in mixed systems; subtypes of mixed electoral systems; district magnitude (number of members elected from each district); number of secondary and tertiary electoral districts; fused vote; size of the lower house; GDP growth (annual percent); GDP per capita; inflation, GDP Deflator (annual percent); Human development index; total population; total unemployment; TI corruption perception index; international migrant stock and net migration rate; general government final consumption expenditure; public spending on education; health expenditure; military expenditure; central government debt; Gini index; internet users per 100 inhabitants; mobile phone subscriptions per 100 inhabitants; fixed telephone lines per 100 inhabitants; daily newspapers; constitutional federal structure; number of legislative chambers; electoral results data available; effective number of electoral and parliamentary parties. Individual level: Modes of data collection differ across countries. A standardized questionnaire was administered in face-to-face interviews, telephone interviews or as fixed form self-administered questionnaire. District level: Aggregation of official electoral statistics. Country level: Expert survey using fixed form self-administered questionnaire. The universe differs across countries. In most countries it includes eligible voters or residents aged 18 or older. Sampling procedures differ across countries. In most cases multistage stratified cluster sampling or stratified systematic random sampling was used. Detailed information on sampling for most countries is available in the codebook.

The module was administered as a post-election interview. The resulting data are provided along with voting, demographic, district and macro variables in a single dataset. CSES Variable Table The list of variables is being provided on the CSES Website to help in understanding what content is available from CSES, and to compare the content available in each module. Themes: MICRO-LEVEL DATA: Identification and study administration variables: weighting factors; election type; date of election 1st and 2nd round; study timing (post-election study, pre-election and post-election study, between rounds of majoritarian election); mode of interview; gender of interviewer; date questionnaire administered; primary electoral district of respondent; number of days the interview was conducted after the election; language of questionnaire. Demography: year and month of birth; gender; education; marital status; union membership; union membership of others in household; business association membership, farmers´ association membership; professional association membership; current employment status; main occupation; socio economic status; employment type - public or private; industrial sector; current employment status, occupation, socio economic status, employment type - public or private, and industrial sector of spouse; household income; number of persons in household; number of children in household under the age of 18; number of children in household under the age of 6; attendance at religious services; religiosity; religious denomination; language usually spoken at home; region of residence; race; ethnicity; rural or urban residence; primary electoral district; country of birth; year arrived in current country. Survey variables: perception of public expenditure on health, education, unemployment benefits, defense, old-age pensions, business and industry, police and law enforcement, welfare benefits; perception of improving individual standard of living, state of economy, government’s action on income inequality; respondent cast a ballot at the current and the previous election; vote choice (presidential, lower house and upper house elections) at the current and the previous election; respondent cast candidate preference vote at the current and the previous election; difference who is in power and who people vote for; sympathy scale for selected parties and political leaders; assessment of parties on the left-right-scale and/or an alternative scale; self-assessment on a left-right-scale and an optional scale; satisfaction with democracy; party identification; intensity of party identification, institutional and personal contact in the electoral campaigning, in person, by mail, phone, text message, email or social networks, institutional contact by whom; political information questions; expected development of household income in the next twelve month; ownership of residence, business or property or farm or livestock, stocks or bonds, savings; likelihood to find another job within the next twelve month; spouse likelihood to find another job within the next twelve month. DISTRICT-LEVEL DATA: number of seats contested in electoral district; number of candidates; number of party lists; percent vote of different parties; official voter turnout in electoral district. MACRO-LEVEL DATA: election outcomes by parties in current (lower house/upper house) legislative election; percent of seats in lower house received by parties in current lower house/upper house election; percent of seats in upper house received by parties in current lower house/upper house election; percent of votes received by presidential candidate of parties in current elections; electoral turnout; party of the president and the prime minister before and after the election; number of portfolios held by each party in cabinet, prior to and after the most recent election; size of the cabinet after the most recent election; number of parties participating in election; ideological families of parties; left-right position of parties assigned by experts and alternative dimensions; most salient factors in the election; fairness of the election; formal complaints against national level results; election irregularities reported; scheduled and held date of election; irregularities of election date; extent of election violence and post-election violence; geographic concentration of violence; post-election protest; electoral alliances permitted during the election campaign; existing electoral alliances; requirements for joint party lists; possibility of apparentement and types of apparentement agreements; multi-party endorsements on ballot; votes cast; voting procedure; voting rounds; party lists close, open, or flexible; transferable votes; cumulated votes if more than one can be cast; compulsory voting; party threshold; unit for the threshold; freedom house rating; democracy-autocracy polity IV rating; age of the current regime; regime: type of executive; number of months since last lower house and last presidential election; electoral formula for presidential elections; electoral formula in all electoral tiers (majoritarian, proportional or mixed); for lower and upper houses was coded: number of electoral segments; linked electoral segments; dependent formulae in mixed systems; subtypes of mixed electoral systems; district magnitude (number of members elected from each district); number of secondary and tertiary electoral districts; fused vote; size of the lower house; GDP growth (annual percent); GDP per capita; inflation, GDP Deflator (annual percent); Human development index; total population; total unemployment; TI corruption perception index; international migrant stock and net migration rate; general government final consumption expenditure; public spending on education; health expenditure; military expenditure; central government debt; Gini index; internet users per 100 inhabitants; mobile phone subscriptions per 100 inhabitants; fixed telephone lines per 100 inhabitants; daily newspapers; constitutional federal structure; number of legislative chambers; electoral results data available; effective number of electoral and parliamentary parties. Individual level: Modes of data collection differ across countries. A standardized questionnaire was administered in face-to-face interviews, telephone interviews or as fixed form self-administered questionnaire. District level: Aggregation of official electoral statistics. Country level: Expert survey using fixed form self-administered questionnaire. The universe differs across countries. In most countries it includes eligible voters or residents aged 18 or older. Sampling Procedure Comment: Sampling procedures differ across countries. In most cases multistage stratified cluster sampling or stratified systematic random sampling was used. Detailed information on sampling for most countries is available in the codebook.

This study aimed to identify the factors influencing South Korean voters’ attitudes towards increasing public expenditure on health and to identify whether the issue of healthcare expenditure influenced candidate choice in the 2012 Korean presidential election. The study used the data from a survey conducted by the Institute of Korean Politics at Seoul National University immediately following the 2012 presidential election. The survey was completed by a nationwide sample of 1,200 people aged 19 or over using a face-to-face interview method and proportional quota sampling based on sex, age, and region. About 44.3% of respondents had a positive attitude toward increasing public health expenditure. There was no significant difference by the candidate they supported (conservative Park Geun-hye or liberal Moon Jae-in). In particular, even 44.9% of conservative supporters agreed with more spending. Politically neutral respondents (OR = 1.76, 90% CI 1.22–2.54) and strong conservative party supporters (OR = 1.53, 90% CI 1.05–2.25) were more likely to support public health expenditure increase compared to strong liberal party supporters. Also, respondents who believed that the economic gap in the country was widening were 1.91 times more likely to support an increase in public health expenditures. However, the issue of health expenditure had no influence on voters’ choice of presidential candidates, and in particular no negative effect of choice of the ruling (conservative) party’s candidate. Our results should be interpreted with care; one possible reason for this lack of effect might be that constituents voted along partisan lines regardless of their attitude to the welfare issue; another possible explanation might be the success of the “left click strategy” of the conservative party. That is, the conservatives did not reject economic democratization or social welfare expansion. Further research should be done to explain why attitudes to health spending did not directly affect choice of candidate.

This Gallup poll aims to collect the views of Canadians on leading topics of the day. The questions are mostly political, focusing on political parties, policies, and other issues of importance to Canadians and government. The respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: Barry Goldwater as the next American president; Canada joining the United States; Canadian flag design; Conservative party; death penalty for murderers; Diefenbaker's performance as leader of the Conservative party; federal elections; whether influence or merit is more important in today's world; the Liberal party; major family problems; major problems facing government; Pearson's performance as Liberal leader; preferred political parties; Quebec separating from the rest of Canada; reasons people are poor; smoking habits; union membership; and voting behaviour. Basic demographics variables are also included.

Intro

Voter turnout is the percentage of eligible voters who cast a ballot in an election. Eligibility varies by country, and the voting-eligible population should not be confused with the total adult population. Age and citizenship status are often among the criteria used to determine eligibility, but some countries further restrict eligibility based on sex, race, or religion.

Context

The historical trends in voter turnout in the United States presidential elections have been determined by the gradual expansion of voting rights from the initial restriction to white male property owners aged 21 or older in the early years of the country's independence, to all citizens aged 18 or older in the mid-20th century. Voter turnout in United States presidential elections has historically been higher than the turnout for midterm elections. https://upload.wikimedia.org/wikipedia/commons/a/a7/U.S._Vote_for_President_as_Population_Share.png" alt="f">

Content

Turnout rates by demographic breakdown from the Census Bureau's Current Population Survey, November Voting and Registration Supplement (or CPS for short). This table are corrected for vote overreporting bias. For uncorrected weights see the source link.

Original source: https://data.world/government/vep-turnout

Data on how many seats change each election.

If the value is positive, then the party that held the presidency at the time of the election gained seats, and vice versa.

Includes info on whether an election was a midterm or presidential election, as well as if the presidency flipped or was held during presidential elections.

Context

I like history, so figured I would compile a dataset on all US Presidential elections throughout history, and how they went.

Content

Inside are datasets for the winners, candidates, and turnout for each presidential election.

Acknowledgements

Banner Image By Gilbert Stuart https://www.clarkart.edu/artpiece/detail/george-washington, Public Domain, https://commons.wikimedia.org/w/index.php?curid=591229

Data from https://github.com/TheUpshot/presidential-precinct-map-2020 released under MIT license: https://github.com/TheUpshot/presidential-precinct-map-2020/blob/main/LICENSE. For more detail, see https://www.nytimes.com/interactive/2021/upshot/2020-election-map.html.

Presidential precinct data for the 2020 general election

The Upshot scraped and standardized precinct-level election results from around the country, and joined this tabular data to precinct GIS data to create a nationwide election map. This map does not have full coverage for every state: data availability and caveats for each state are listed below, and statistics about data coverage are available here. We are releasing this map's data for attributed re-use under the MIT license in this repository.

The GeoJSON dataset can be downloaded at: https://int.nyt.com/newsgraphics/elections/map-data/2020/national/precincts-with-results.geojson.gz

Properties on each precinct polygon:

• GEOID: unique identifier for the precinct, formed from the five-digit county FIPS code followed by the precinct name/ID (eg, 30003-08 or 39091-WEST MANSFIELD)
• votes_dem: votes received by Joseph Biden
• votes_rep: votes received by Donald Trump
• votes_total: total votes in the precinct, including for third-party candidates and write-ins
• votes_per_sqkm: total votes divided by the area of the precinct, rounded to one decimal place
• pct_dem_lead: (votes_dem - votes_rep) / (votes_dem + votes_rep), rounded to one decimal place (eg, -21.3)

Due to licensing restrictions, we are unable to include the 2016 election results that appear in our interactive map.

Please contact dear.upshot@nytimes.com if you have any questions about data quality or sourcing, beyond the caveats we describe below.

General caveats

• Where possible, we used official precinct boundaries provided by the states or counties, but in most cases these were not available and we generated boundaries ourselves, using L2 voter-file points to guess the precinct for each census block group; this results in generally accurate precinct boundaries, but can be rough in no- or very-low-population places like business parks or uninhabited rural land.
  • Because of this, spatially joining our precinct GeoJSON to other geographic datasets will most likely yield less-than-ideal output.
• Some of the results we gathered are unofficial/uncertified, since the certified tabulations hadn't yet been released at time of gathering.
• A very small portion of the tabular precinct results (roughly 0.01%) could not be joined to the precinct boundaries, and thus these results are not present in the GeoJSON.
• A few areas, such as rural Maine, Vermont and Hawaii, contain no voters, and those polygons are excluded from the GeoJSON.

State-by-state data availability and caveats

Note: One of the most common causes of precinct data being unusable is "countywide" tabulations. This occurs when a county reports, say, all of its absentee ballots together as a single row in its Excel download (instead of precinct-by-precinct); because we can't attribute those ballots to specific precincts, that means that all precincts in the county will be missing an indeterminite number of votes, and therefore can't be reliably mapped. In these cases, we drop the entire county from our GeoJSON.

• AL: ❌ absentee and provisional results are reported countywide
• AK: ❌ absentee, early, and provisional results are reported district-wide
• AZ: ✅
• AR: ⚠️ we could not generate or procure precinct maps for Jefferson County or Phillips County
• CA: ⚠️ only certain counties report results at the precinct level, additional collection is in progress
• CO: ✅
• CT: ⚠️ township-level results rather than precinct-level results
• DE: ✅
• DC: ✅
• FL: ⚠️ precinct results not yet available statewide
• GA: ✅
• HI: ✅
• ID: ⚠️ many counties ...

Context

The goal of this dataset is to provide a tidy way to access to the transcripts of speeches given by various US politicians in the context of the 2020 US Presidential Election. Transcripts have been scraped from rev.com. Some other information, such as location and type of speech, have been manually added to the dataset.

Content

The dataset has the following columns:

speaker: Who gave the speech

title: a title or a description of speech

text: the transcript of the speech

location: the location or the platform where the speech was give

type: type of speech (e.g., campaign speech, interview or debate)

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Description:

This dataset contains comprehensive voting data for the 2024 US elections, focusing on general ballot measures. This information includes voting results from various sources and tracking public opinion about political parties and candidates across states and demographic groups. Each item in the dataset represents a specific poll. Along with detailed information about the dates of the polls. Survey organization, sample size, margin of error, Percentage of respondents supporting each political party or candidates

Key Features:

Poll Date:The date when the poll was conducted.

Polling Organization: The name of the organization that conducted the poll.

Sample Size: The number of respondents in the poll.

Margin of Error: The statistical margin of error for the poll results.

Party/Candidate Support: Percentage of respondents who support each political party or candidate.

State/Demographics: Geographic and demographic breakdowns of the polling data.

Use Cases:

Analyzing trends in public opinion leading up to the 2024 U.S. elections. Comparing support for different political parties and candidates over time. Studying the impact of key events on voter preferences. Informing political strategies and campaign planning.

Description:

This dataset combines data from three sources to provide a comprehensive overview of county-level socioeconomic indicators, educational attainment, and voting outcomes in the United States. The dataset includes variables such as unemployment rates, median household income, urban influence codes, education levels, and voting percentages for the 2020 U.S. presidential election. By integrating this data, the dataset enables analysis of how factors like income, education, and unemployment correlate with political preferences, offering insights into regional voting behaviors across the country.

References:

The following reference datasets were used to construct this dataset.

[1] Harvard Dataverse, Voting Data Set by County. Available: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: 10.7910/DVN/VOQCHQ

[2] USDA Economic Research Service, Educational Attainment and Un- employment Data. Available: https://www.ers.usda.gov/data-products/ county-level-data-sets/county-level-data-sets-download-data/