This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

Voting precincts are the most granular spatial units for reporting election outcomes, whereas census geographies, such as block groups, census tracts, and ZIP Code Tabulation Areas (ZCTAs), are commonly used for publishing demographic, economic, health, and environmental data. This dataset bridges the two by reallocating precinct-level votes to standard census geographies through a systematic and replicable framework. The reallocation assumes that votes within each precinct are distributed proportionally to the household population. Household population counts from census block groups—the smallest census unit with regularly updated population estimates—are used to allocate votes to fractions created by the intersection of precinct and census boundaries. This process is implemented using three allocation strategies: areal weighting, impervious surface weighting, and Regionalized Land Cover Regression (RLCR). Results from all three methods are provided. Among these, the RLCR method demonstrates the highest accuracy based on validation against voter-level ground truth data and is recommended as the primary version for analysis. The alternative methods may serve as robustness checks or sensitivity tests. The dataset currently includes the 2016 and 2020 U.S. general elections and is designed for seamless integration with other datasets, such as the American Community Survey (ACS), CDC PLACES, or IRS Statistics of Income (SOI), via the GEOID field.

The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020, 2024) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (24 November 2025).; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

This study contains files of Presidential election votes by Congressional District for each U.S. Presidential election year from 2016-2020. From Dave Leip, Atlas of U.S. Presidential Elections.

Dave Leip's website

At the Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Services. These files were last checked for updates on 19 February 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

For additional information on file layout, etc. see: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_national.html

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.

State-by-state ESRI shapefiles of 2020 precinct-level general election results.

Voters use salient issues to inform their vote choice. Using 2020 Cooperative Election Study (CES) data, we analyze how short, medium, and long term issues informed the vote for president in the 2020 election, which witnessed record-setting participation. To explain the dynamics of presidential vote choice, we employ a voter typology advanced by Key (1966). Specifically, compared to standpatters, who in 2020 registered the same major party vote as in 2016, we find that new voters in 2020 and voters switching their preferences from 2016 cast their ballots in favor of Democrat Joe Biden. In the end, President Donald Trump was denied reelection by new voters and vote switchers principally because certain issues had a notable effect in moving their presidential preferences in the Democratic direction.

This study contains files of Presidential election votes in the state of New York by Congressional District, Legislative District, County, Town, and Precinct for each U.S. Presidential election year from 2016-2020. From Dave Leip, Atlas of U.S. Presidential Elections.

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional states and years of data available going back to 1992 but at a fee.

Sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Services.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

This update adds 2016 and 2020 votes along with some updates in previous years to align with county data sets.

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview:July 2020 municipal wards were collected by LTSB through the WISE-Decade system. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2011 wards are carried over to the 2020 dataset for existing wards. New wards created since 2011 due to annexations, detachments, and incorporation are allocated population from Census 2010 collection blocks. LTSB has topologically integrated the data, but there may still be errors.Election Data Overview:The 2012-2020 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data:Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward. The data then is distributed down to the block level, again based on total population. When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file. Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note…We use a static, official ward layer (in this case created in 2020) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if Cityville has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward 5 was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards 1 and 4 according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018Elections 2020: Wards 2020Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (occurred with spring 2020) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2018Blocks 2011 [with all election data] -> Wards 2020 (then MCD 2020, and County 2020) All election data (including later elections) is aggregated to the Wards 2020 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2020.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2020. This is due to boundary corrections in the data from 2011 to 2020, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

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 ...

The dataset includes the number of eligible voters and voter turnout as a percentage of the large-scale elections in Thurgau 2008, 2012, 2016 and 2020 by political municipalities. (Note: New District Regulations from 2010)Note to the year 2020: Data as published in Official Journal No. 12/2020 of 20 March 2020 (Districts of Arbon, Kreuzlingen, Münchwilen and Weinfelden) and in Official Journal No 27/2020 of 3 July 2020 (Frauenfeld District)

This is a dataset of the Dáil Constituencies in 2016, the number of seats, candidates, quota, total electorate and poll.

This is a dataset of the Dail Constituencies in 2016, the number of seats, candidates, quota, total electorate and poll.

Our codes provide a tool for researchers using any part of the integrated datasets of the European Social Survey (European Social Survey Cumulative File, ESS 1-9, 2020) project to easily differentiate between respondents based on their political affiliation, dividing them into pro-government and pro-opposition groups. Individuals are coded as “government supporters”, “opposition supporters” and “non-identifiers” according to their survey response, while we excluded refusals. The database includes data for 422 985 respondents from eight data rounds between 2002 and 2020 from 33 European countries, organized all in all in 215 country-years.

There are two data files attached.

1. The “European Government-Opposition Voters (EGOV) Data Set” is a comma-separated values table (.csv format file) that includes three variables.

a. The variable “votedforwinner” differentiates between government voters (1), opposition voters (0) and non-voters (missing values); thus it defines the government-opposition status of European voters based on their last vote on the previous election.

b. The variable “closetowinner” differentiates between government partisans (1), opposition partisans (0) and non-partisans (missing values); thus it defines the government-opposition status of European party identifiers based on their partisan attachment.

c. The variable “cseqno” is a unique identification number for European Social Survey (ESS) respondents included in the integrated data sets of the ESS project.

1. The “EGOV – do file” is a do file that can be used to reproduce the content of the above table. These codes are annotated, that is, unusual changes in government composition and overlaps of elections and fieldwork periods are indicated.

The European Government-Opposition Voters Data Set has been produced by using the following pieces of information coming from the (European Social Survey Cumulative File, ESS 1-9, 2020), Comparative Political Data Sets (Armingeon, Isler, Knöpfel, Weisstanner, et al., 2016) and ParlGov (Döring and Manow, 2019) data sets.

•    partisan
  

  preferences, that is, respondents’ vote on the last general election (164 variables, ESS) and respondents’ partisan identity (167 variables, ESS)

•    date of
  

  the interview (year, month, day, ESS)

•    date of
  

  national elections and investitures in each country-case (CPDS and ParlGov)

•    cabinet
  

  composition (CPDS and ParlGov)

•    official
  

  sites on information on national elections for clarification, if necessary

Dataset contains information for primary elections in 2016, 2018 and 2020.

Abstract The 2016 and 2020 municipal elections in São Paulo’s city presented results that deviated from previous ones in the spatial voting patterns and in candidate performance. While the PT in 2016 did not win in any district, with part of these votes "stolen" by Marta Suplicy (PMDB), in 2020 the party, for the first time since 1985, was not among the first place contenders, a position occupied by the PSOL. Through mapping and factor analysis, the present article provides evidence for this deviation and seeks possible explanations by analyzing, through ecological inference, the transfer of votes from one election to another among candidates. The article defends the hypothesis that this transfer is due more to a strategic vote than to electoral realignments. The results show that there was no change in "voter alignment" and the deviations found attributed to the competition strategies adopted by the parties.

The "https://electionstudies.org/data-center/2020-time-series-study/" Target="_blank">American National Election Studies (ANES) 2020 Time Series Study is a continuation of the series of election studies conducted since 1948 to support analysis of public opinion and voting behavior in U.S. presidential elections. This year's study features re-interviews with "https://electionstudies.org/data-center/2016-time-series-study/" Target="_blank">2016 ANES respondents, a freshly drawn cross-sectional sample, and post-election surveys with respondents from the "https://gss.norc.org/" Target="_blank">General Social Survey (GSS). All respondents were assigned to interview by one of three mode groups - by web, video or telephone. The study has a total of 8,280 pre-election interviews and 7,449 post-election re-interviews.

New content for the 2020 pre-election survey includes variables on sexual harassment and misconduct, health insurance, identity politics, immigration, media trust and misinformation, institutional legitimacy, campaigns, party images, trade tariffs and tax policy.

New content for the 2020 post-election survey includes voting experiences, attitudes toward public health officials and organizations, anti-elitism, faith in experts/science, climate change, gun control, opioids, rural-urban identity, international trade, sexual harassment and #MeToo, transgender military service, perception of foreign countries, group empathy, social media usage, misinformation and personal experiences.

(American National Election Studies. 2021. ANES 2020 Time Series Study Full Release [dataset and documentation]. July 19, 2021 version. "https://electionstudies.org/" Target="_blank">https://electionstudies.org/)

David Leip provides election returns from presidential, senatorial, gubernatorial and House races at state, county and precinct level. Data includes names of candidates, parties, popular and electoral vote totals, voter turnout, and more. While some data is available for free on David Leip’s website, MIT researchers have access to more granular data from following elections and years: Presidential Primaries (county level): 2000, 2004, 2008, 2012, 2016, 2020, 2024 Presidential General Elections Results by: State: 1824-2024 County: 1980, 2016, 2020, 2024 Precincts: 1992, 1996, 2016, 2020 Congressional districts: 2016, 2020 Gubernatorial General Election : 2022 House of Representatives (General Election, state, county, congressional districts level): 1992 – 2024 U.S. Senate (General Election, state,county, town level): 2020, 2022, 2024 Registration and Turnout (General Election , state, county level): 1992-2024 DATA AVAILABLE FOR YEARS: 1824-2024 (some coverage gaps)

Theory has long suggested that swing voting is a response to cross-pressures arising from a mix of individual attributes and contextual factors. Unfortunately, existing regression-based approaches are ill-suited to explore the complex combinations of demographic, policy, and political factors that produce swing voters in American elections. This gap between theory and practice motivates our use of an ensemble of supervised machine learning methods to predict swing voters in the 2012, 2016, and 2020 US presidential elections. The results from the learning ensemble substantiate the existence of swing voters in contemporary American elections. Specifically, we demonstrate that the learning ensemble produces well-calibrated and externally valid predictions of swing voter propensity in later elections and for related behaviors such as split-ticket voting. Though interpreting black-box models is more challenging, they can nonetheless provide meaningful substantive insights meriting further exploration. Here, we use flexible model-agnostic tools to perturb the ensemble and demonstrate that cross-pressures (particularly those involving ideological and policy-related considerations) are essential to accurately predict swing voters. This Dataverse entry provides the code necessary to run the ensemble function and replicate the results from the paper. See README.txt for further instructions.