According to exit polling in ten key states of the 2024 presidential election in the United States, Donald Trump received the most support from men between the ages of ** and **. In comparison, ** percent of women between the ages of ** and ** reported voting for Kamala Harris.

According to exit polling in ten key states of the 2024 presidential election in the United States, Donald Trump received the most support from white voters between the ages of ** and **. In comparison, ** percent of Black voters between the ages of ** and ** reported voting for Kamala Harris.

According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of surveyed white voters reported voting for Donald Trump. In contrast, ** percent of Black voters reported voting for Kamala Harris.

According to exit polling in *** key states of the 2024 presidential election in the United States, almost ********** of voters who had never attended college reported voting for Donald Trump. In comparison, a similar share of voters with ******** degrees reported voting for Kamala Harris.

According to results on November 6, 2024, former President Donald Trump had received *** Electoral College votes in the race to become the next President of the United States, securing him the presidency. With all states counted, Trump received a total of *** electoral votes. Candidates need *** votes to become the next President of the United States.

This research note employs aggregate State level data to analyze the results of the 2024 US presidential election, and to explore the role played by gender and race in Kamala Harris’s defeat. The Democratic party failed to capitalize on the characteristics of its presidential candidate and, compared to Biden’s election, to expand its support in states with larger female and African American electorates, and faced systematic losses in those with more Hispanic voters. This note contributes to a cumulative macro-micro approach to the analysis of election results, and thus to overcoming both ecological and micrological fallacies.

Abstract: This repository contains the full dataset and model implementation for the analysis of voting patterns in Romania's 2024 presidential elections, focusing on the relationship between territorial economic structures and electoral preferences. The models estimate vote dominance at LAU level using sectoral, demographic, and regional predictors, including spatial autoregression. Particular attention is given to the overrepresentation of Bucharest in national-level FDI statistics, which is corrected through a GDP-based imputation model. For reproducibility, the repository includes: Cleaned and structured input data (LAU, NUTS3), all modelling scripts in R, Tableau maps for visual analysis and public presentation.File DescriptionsLAU.csvThis dataset contains the local-level electoral and socio-economic data for all Romanian LAU2 units used in the spatial and statistical analyses. The file is used as the base for all models and includes identifiers for merging with the shapefile or spatial weights. It includes:- Electoral results by presidential candidate (2024, simulated),- Dominant vote type per locality,- Sectoral employment categories,- Demographic variables (ethnicity, education, age),- Regional and metropolitan classifications,- Weights for modelling.NUTS3.csvThis dataset provides county-level economic indicators (GDP and FDI) over the period 2011–2022. The file supports the construction of regional indicators such as FDI-to-GDP ratios and export structure. Notably, the file includes both original and corrected values of FDI for Bucharest, following the imputation procedure described in the model script.model.RThis R script contains the full modelling pipeline. The script includes both a model variant with Bucharest excluded and an alternative version using corrected FDI values, confirming the robustness of coefficients across specifications. It includes:- Pre-processing of LAU and NUTS3 data,- Imputation of Bucharest FDI using a linear model on GDP,- Survey-weighted logistic regression models for vote dominance per candidate,- Multinomial and hierarchical logistic models,- Seemingly Unrelated Regressions (SUR),- Spatial error models (SEM),- Principal Component Analysis on SEM residuals,- Latent dominance prediction using softmax transformation,- Export of predicted latent vote maps.Maps.twbxThis Tableau workbook contains all final cartographic representations.The workbook uses a consistent colour palette based on candidate-typified economic structures (industry, services, agriculture, shrinking).- Choropleth maps of dominant vote by candidate,- Gradients reflecting latent probabilities from spatial models,- Maps of residuals and ideological factor scores (PCA-derived),- Sectoral economic geographies per county and per locality,- Overlay of dominant vote and sectoral transformation types.

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

This dataset provides detailed information for the 2024 US Presidential Election, offering a valuable resource for political analysis and research. It includes a variety of data types, such as profiles of candidates, primary/caucus results, poll data, and debate transcripts. Key updates have been integrated throughout the election season, including the latest poll figures, transcripts from the Vice-Presidential debate between Walz and Vance, and the debate between Trump and Harris.

Significant events covered within the dataset include an annotated image and transcripts related to an assassination attempt on former President Trump. The political landscape evolved with the Democratic Party replacing President Biden with Kamala Harris in late August, setting up a contest between Trump and Harris, alongside nominees from smaller factions. The dataset also features approval ratings for sitting presidents, including Biden and Trump, and details on candidates like Robert F Kennedy Jr, who is running as an independent. This collection is regularly updated to reflect developments as the election cycle progresses, making it a current and dynamic source for understanding the 2024 US Presidential Election.

Columns

The dataset contains information on candidates with formal bids for the presidency, including the following columns:

• party: The political party affiliation of the candidate (e.g., republican, democrat, other).
• name: The full name of the candidate.
• date_announced: The date on which the candidate formally announced their intention to run for president. This column contains 21 unique values, ranging from 16 November 2022 to 1 July 2024.
• date_withdrawn: The date on which the candidate withdrew their bid for the presidency.

The data indicates that 62% of the candidate entries are Republican, 19% are Democrat, and 19% represent other parties.

Distribution

The data file is typically provided in a CSV format. A sample file will be made available separately on the platform. The dataset is listed as Version 1.0 and has a quality rating of 5 out of 5. While specific row or record counts are not currently available, the dataset is structured to facilitate analysis of various aspects of the 2024 US Presidential Election. It is available globally and offered as a free dataset. The data types included are tabular and text.

Usage

This dataset is an ideal resource for a multitude of applications and use cases, including:

• Political analysis and research into election trends.
• Government and civic record analysis.
• Natural Language Processing (NLP) tasks, utilising the debate transcripts for sentiment analysis, topic modelling, or text generation.
• Artificial Intelligence (AI) and Machine Learning (ML) model development for election outcome prediction, voter behaviour analysis, or candidate performance assessment.
• Studying the historical context and evolution of presidential campaigns.
• Developing visualisations and dashboards for public understanding of the election.

Coverage

The dataset focuses on the 2024 US Presidential Election and its related events, primarily covering the United States. The time range for data updates spans from March through to the final election night update, with candidate announcement dates beginning as early as November 2022 and extending into July 2024. This includes critical periods such as primary elections, nominating conventions, and general election campaigning. While primarily focused on the 2024 cycle, Version 3 of this dataset previously included coverage of the 2022 Congressional Mid-term Elections. The dataset provides insights into various demographic aspects through its focus on candidates from different political parties (Republican, Democrat, and other factions) and covers key figures like Joe Biden, Donald Trump, Kamala Harris, Robert F Kennedy Jr, Walz, and Vance.

License

CC By 4.0

Who Can Use It

This dataset is suitable for a wide range of users, including:

• Data analysts and scientists seeking to model election outcomes or understand political dynamics.
• Researchers and academics in political science, sociology, and media studies.
• Journalists and media organisations for reporting, investigative journalism, and creating data-driven stories about the election.
• Students undertaking projects or coursework related to American politics and elections.
• Developers and engineers creating applications or tools for political engagement or data visualisation.
• Government agencies or civic organisations interested in public opinion and electoral processes.

Dataset Name Suggestions

• 2024 US Presidential Election Data
• US Presidential Campaign 2024 Analytics
• American Election Data: 2024 Cycle
• US Presidential Candidate & Poll Data

Attributes

Original Data Source: [

A website provided by the Secretary of the State that provides real time data on election results and voter turnout across the State.

This data set consists of all Fulton County Election results from April 2012 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.

According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of Protestant Christian voters reported voting for Donald Trump. In comparison, only ** percent of Jewish voters reported voting for Trump.

According to an October 2024 survey, young Americans were much more likely to vote for Kamala Harris in the November 2024 presidential elections. Of those between the ages of 18 and 29, 60 percent said they were planning on voting for Harris, compared to 33 percent who said they planned on voting for Trump. In contrast, Trump was much more popular among those between 45 and 64 years old.

Electoral registrations for parliamentary and local government elections as recorded in electoral registers for England, Wales, Scotland and Northern Ireland.

2024 Pennsylvania: Trump vs. Harris | RealClearPolling

2024 Ohio Senate - Brown vs. Moreno | RealClearPolling

The European Parliament’s 2024 post-electoral Eurobarometer looks at citizens' voting behaviour in the recent European elections, at the main issues driving citizens’ voting decision and at their attitudes towards the EU and the European Parliament.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
• Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

The Parliament’s Spring 2024 Eurobarometer reveals strong interest among citizens in the upcoming European elections (6-9 June) and awareness of their significance in the current geopolitical context. The survey sheds light on Europeans’ voting behaviour, their attitudes towards campaign topics as well as on preferences for the priority values for the next term of the European Parliament. It focuses also on citizens’ perception of the EP and EU, on their perspective on life in the EU, as well as on their opinions about the EU within the current global context.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
• Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

According to exit polls for the 2024 Iowa Republican caucuses, former President Donald Trump led the way among voters between the ages 45 and older. The vote was split more evenly among young voters, with 22 percent voting for Trump, 30 percent for DeSantis, 25 percent for Haley, and 21 percent for Ramaswamy. DeSantis, Ramaswamy, and Hutchinson dropped out of the presidential race shortly after the Iowa caucuses

Does skin color significantly influence electoral outcomes in Mexico? Departing from traditional theoretical determinants of electoral success (i.e., partisanship and candidate’s quality), recent research suggests that skin color discrimination –pervasive in virtually all spheres of life in Mexico– translates over into electoral politics, hindering the probabilities dark-skinned candidates have of winning. Building on these trailblazing studies, this article examines the results of the 2024 Mexican General Election to replicate the analysis with completely transparent methods, fully available data and incorporating most, if not all, relevant control variables (including machine- and human-coded skin tone measures, three different experience variables, along with sex, age, education, party and state). Our results suggest that quality, rather than complexion, is the best predictor for electoral success. We conclude that, rather than disproving its existence, our findings reveal that electoral contests are not the best site to test the political salience of skin tone.