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) 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, 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).

Analysis of ‘2020 US General Election Turnout Rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imoore/2020-us-general-election-turnout-rates on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

Voter turn-out is a measure of civic engagement. Consistent civic engagement can have a significant, positive impact on health outcomes and community health.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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 2020 Italian regional elections were held on 26 January. The dataset contains data on turnout, list votes, votes to the presidential candidate.

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

Description to be added

This repository contains the replication materials for the forthcoming article in Election Law Journal: “The Impact of Vote-By-Mail Policy on Turnout and Vote Share in the 2020 Election." It includes the data and R code necessary to replicate the tables and figures in the article.

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.

This dataset was used to conduct the NYC Campaign Finance Board's voter participation research, published in the 2019-2020 Voter Analysis Report. Each row contains information about an active voter in 2018 and their voting history dating back to 2008, along with geographical information from their place of residence for each year they were registered voters. Because this dataset contains only active voters in the year 2018, this dataset cannot be used to calculate election turnout.

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.

The dataset contains data on voter turnout at 19.00 for the Regional Elections and the 2020 Constitutional Referendum, in the Municipality of Florence.

This repo contains the data introduced in

Immer, A.*, Kristof, V.*, Grossglauser, M., Thiran, P., Sub-Matrix Factorization for Real-Time Vote Prediction, KDD 2020

These data have been collected from OpenData.Swiss every two minutes on two different referendum vote days: May 19, 2019, and February 9, 2020. We use these data to make real-time predictions of the referenda outcome on www.predikon.ch. We publish here the raw data, as retrieved in JSON format from the API. We also provide a python script to help scraping the JSON files.

After unzipping the datasets, you can scrape the data by referendum vote day by doing:

from scraper import scrape_referenda

Scrape the data from February 2, 2020.

data_dir = 'path/to/2020-02-09' data = scrape_referenda(data_dir)

The data variable will be a list of datum dictionaries of the following structure:

{ "vote": 6290, "municipality": 1, "timestamp": "2020-02-09T15:23:10", "num_yes": 222, "num_no": 482, "num_valid": 704, "num_total": 709, "num_eligible": 1407, "yes_percent": 0.3153409090909091, "turnout": 0.503909026297086 }

The datum is as follows:

vote: vote ID as defined by OpenData.Swiss

municipality: municipality ID as defined by OpenData.Swiss

timestamp: date and time at which the JSON files has been published on OpenData.Swiss

num_yes: number of "yes" in the municipality

num_no: number of "no" in the municipality

num_valid: number of valid ballots (the ones counting for the results)

numb_total: total number of ballots (including invalid ones)

num_eligible: number of registered voters

yes_percent: percentage of "yes" (computed as num_yes / num_valid)

turnout: turnout to the vote (computed as num_total / num_eligible)

Don't hesitate to reach out to us if you have any questions!

To cite this dataset:

@inproceedings{immer2020submatrix, author = {Immer, Alexander and Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick}, title = {Sub-Matrix Factorization for Real-Time Vote Prediction}, year = {2020}, booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining}, }

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

Voter turnout for the 2020 Second Primary Election

This is partial replication data for a paper analyzing voting irregularities in Bolivia's 2019 election. This dataset includes municipal-level data for the following presidential elections: 2002, 2005, 2009, 2014, 2019, and 2020. The data includes voter turnout, MAS vote share, the share of the largest opposition party, effective number of parties, and select socioeconomic and demographic indicators.

constitutional referendum 20/21 September 2020 - voter turnout data

We present the Global Dataset on Turnout, GD-Turnout. The files included are the original database, the codebook and the article appeared in Electoral Studies

This dataset lists the total population 18 years and older by census block in Connecticut before and after population adjustments were made pursuant to Public Act 21-13. PA 21-13 creates a process to adjust the U.S. Census Bureau population data to allow for most individuals who are incarcerated to be counted at their address before incarceration. Prior to enactment of the act, these inmates were counted at their correctional facility address.

The act requires the CT Office of Policy and Management (OPM) to prepare and publish the adjusted and unadjusted data by July 1 in the year after the U.S. census is taken or 30 days after the U.S. Census Bureau’s publication of the state’s data.

A report documenting the population adjustment process was prepared by a team at OPM composed of the Criminal Justice Policy and Planning Division (OPM CJPPD) and the Data and Policy Analytics (DAPA) unit. The report is available here: https://portal.ct.gov/-/media/OPM/CJPPD/CjAbout/SAC-Documents-from-2021-2022/PA21-13_OPM_Summary_Report_20210921.pdf

Note: On September 21, 2021, following the initial publication of the report, OPM and DOC revised the count of juveniles, reallocating 65 eighteen-year-old individuals who were incorrectly designated as being under age 18. After the DOC released the updated data to OPM, the report and this dataset were updated to reflect the revision.

constitutional referendum 20/21 September 2020 - data on voter turnout per seat - survey Sunday at 23:00