As of November 2020, 66.8 percent of the eligible voting population in the United States voted in the 2020 presidential election. Voter turnout was highest in New Jersey and Minnesota.

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.

According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed 18 to 29 year old voters reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, ** percent of voters aged 65 and older reported voting for incumbent President Donald Trump.

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

According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed females reported voting for former Vice President Joe Biden. In the race to become the next President of the United States, ** percent of men reported voting for incumbent President Donald Trump.

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.

Description to be added

As of *****************, former Vice President Joe Biden had won *** Electoral College votes in the race to become the next president of the United States, securing him the presidency. Candidates need *** votes to become the next president of the United States.

This information is a snapshot in time, subject to change, and is intended to provide an unofficial indication of returns. Ballots, once returned, proceed toward counting only after the voter's signature has been verified (as required by law). You can download a text file of the Voter IDs of all ballots returned.

According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed Black voters reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, ** percent of white voters reported voting for incumbent President Donald Trump.

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.

This study contains files of Presidential election votes by State, County, and Town for each U.S. Presidential election year from 1964-2020. From Dave Leip, Atlas of U.S. Presidential Elections. Note: MIT posted similar publicly available data beginning with 1976 at https://doi.org/10.7910/DVN/42MVDX

Information available in each dataset

If you want to know what each Presidential Election dataset contains before downloading it, for easy reference, the CCSS Data Services team prepared a spreadsheet summarizing the contents of each dataset. You can view them in this Summary of contents and codebooks spreadsheet.

The summary spreadsheet contains the following: 1. A matrix table summarizing the information available in each Presidential election dataset 2. Codebook describing the variables in the Presidential Election vote data at the State level 3. Codebook describing the variables in the Presidential Election vote data at the County level 4. Codebook describing the variables in the Presidential Election vote data at the Town level 5. A matrix table listing the statistics and graphs included in each Presidential election dataset

Labels of the variables in the State, County, and Town data, as well as a description of each tab in the dataset, are also available here: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_national.html

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional years of data available going back to 1912 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 Discovery and Replication Services. These files were last checked for updates in June 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.

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

Description to be added

As of November 23, 2020, over ***** million voters in the 2020 Presidential Election in the United States cast their votes before Election Day. Of these early votes, **** million were returned via mail-in ballots.

Description to be added

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

US election season is here, which means it's time to analyze some polling data! Less than four years ago, Donald Trump was elected president of the United States, and on November 2nd, 2020, he will run for re-election against a Democratic challenger. Kernels may answer a variety of questions, including: "How accurate were the polls in 2016?", "Which Democratic challenger will fare best against Trump in 2020 according to the polls?", "Which states are anybody's game?", and of course the ultimate question, "Will Trump win again in 2020?" Then, after November 2nd, we can ask ourselves what happened in 2020!

The two datasets included here include:

• US 2016 General Election Results by State and County (from Data World)
• US 2020 General Election Polling Data (from FiveThirtyEight)

This data should be used in conjunction with the 2016 General Election Polling Data from this link: https://www.kaggle.com/fivethirtyeight/2016-election-polls

The 2020 polling data will be updated regularly until the election on November 2nd, 2020. Then, I will upload a dataset of 2020 general election final results. As time goes on and we get closer to the election, we will acquire higher quantities of data and data that is more representative of what might happen on November 2nd. Have fun!

Description to be added

This county-level map shows. Voter Turnout for the 2020 U.S. Presidential election Data from County Health Rankings.Voter turnout is the percentage of citizen population aged 18 or older who voted in the 2020 U.S. Presidential election.Areas in dark blue indicate a lower voter turnout, while areas in light blue indicate a higher voter turnout. Data comes from County Health Rankings, a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation.Voting collectively influences the health of our communities and healthier communities are more likely to vote. Studies show that communities with higher voter turnout tend to also have better self-reported general health, fewer chronic health conditions, a lower overall mortality rate, and less depression. Learn more about voter turnout from County Health Rankings & Roadmaps.A number of different policies can affect voter turnout, such as voter id laws, early voting, and mail-in ballots. Learn more about voter turnout strategies and initiatives.