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.

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 practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

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.

Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%. Margins of error are estimated at the 90% confidence level.

Data Source: Current Population Survey (CPS) Voting Supplement, 2020

Why This Matters

Voting is one of the primary ways residents can have their voices heard by the government. By voting for elected officials and on ballot initiatives, residents help decide the future of their community.

For much of our nation’s history, non-white residents were explicitly prohibited from voting or discriminated against in the voting process. It was not until the Voting Rights Act of 1965 that the Federal Government enacted voting rights protections for Black voters and voters of color.

Nationally, BIPOC citizens and especially Hispanic and Asian citizens have consistently lower voter turnout rates and voter registration rates. While local DC efforts have been taken to remove these barriers, restrictive voter ID requirements and the disenfranchisement of incarcerated and returning residents act as institutionally racist barriers to voting in many jurisdictions.

The District's Response

The DC Board of Elections has lowered the barriers to participate in local elections through online voter registration, same day registration, voting by mail, and non-ID proof of residence.

Unlike in many states, incarcerated and returning residents in D.C. never lose the right to vote. Since 2024, DC has also extended the right to vote in local elections to residents of the District who are not citizens of the U.S.

Although DC residents pay federal taxes and can vote in the presidential election, the District does not have full representation in Congress. Efforts to advocate for DC statehood aim to remedy this.

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.

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).

In U.S. presidential elections since 1964, voter turnout among male and female voters has changed gradually but significantly, with women consistently voting at a higher rate than men since the 1980 election. 67 percent of eligible female voters took part in the 1964 election, compared to 72 percent of male voters. This difference has been reversed in recent elections, where the share of women who voted has been larger than the share of men by around four percent since 2004.

Find your election polling place in Macon-Bibb County. Elections in the county are managed by the Macon-Bibb County Board of Elections.Voters who cast their votes in person must show one of six forms of photo identification. If the voter votes BY MAIL, they DO NOT need a photo ID. Photo ID rules ONLY APPLY to IN-PERSON voting by absentee, advance voting or at the polling place on Election Day.· A current or expired Georgia driver’s license (or Department of Driver Services identification card);· A valid United States military photo identification card;· A valid photo identification card issued by any branch, department agency, or entity of the United States, Georgia, or any other state authorized by law to issue personal identification, including a FREE Georgia Voter Identification Card;· A valid employee photo identification card issued by any branch, department, agency, or entity of the United States, Georgia, or any county, municipality, board, authority of other entity of Georgia;· A valid United States passport; or· A valid tribal photo identification card.Any elector who registered for the first time in Georgia by mail, and did not provide identification at the time of registering may provide one of the six (6) items of photo identification listed above, or for the electors first time voting, may provide one of the following forms of identification: copy of a current utility bill, bank statement, government check, paycheck, or other government document that shows the name and address of elector.IF ELECTOR CANNOT PROVIDE ANY OF THE ABOVE LISTED ID’S THEY MAY VOTE A PROVISIONAL BALLOT IN ACCORDANCE WITH O.C.G.A. 21-2-220 and 21-2-417.If the voter does not have a Georgia driver’s license, or other qualified ID, they can obtain either a FREE Georgia Identification Card from the Department of Driver Services or a FREE Georgia Voter Identification Card at their county registrar’s office. Just contact the Macon-Bibb County Board of Elections located at 2525 Pio Nono Ave., Ste 1200, Macon, GA 31206. Office hours are from 8:30 a.m. to 5:30 p.m. Monday thru Friday. For more information call 478-621-6622 or go to www.gaphotoid.com.In order to get a FREE Georgia Voter Identification Card, a voter will need to provide the following:A photo identity document, or a non-photo identity document (must include voter’s full legal name and date of birth); and· Documentation showing the voter’s date of birth; and· Evidence that voter is registered to vote in Georgia; and· Documentation showing the voter’s name and address of principal residence.The voter may use the same document to satisfy more than one of the above requirements. For additional information, please visit the Secretary of State’s web page at www.sos.state.ga.us.

This dataset represents the results of the 4-digit match performed using the Social Security - Help America Vote Verification (HAVV) system.

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.

Boundaries of Orleans Parish voting precincts as defined by the New Orleans City Charter. New Orleans voting precincts are drawn according to the New Orleans Home Rule Charter as required by the State of Louisiana. A precinct is defined in the state of Louisiana's election code as the smallest political unit of a ward having defined geographical boundaries. Precinct boundaries were updated September 25, 2015, in order to satisfy population changes discovered by the Orleans Registrar of Voters Office. The changes have been made by the City of New Orleans and verified by the Louisiana Secretary of State's Office. Information about voter registation can be found here: https://www.sos.la.gov/ElectionsAndVoting/Pages/RegistrationStatisticsParish.aspx https://www.municode.com/library/la/new_orleans/codes/code_of_ordinances?nodeId=PTIICO_CH58EL_ARTIIELPRState LawRS 18:532. Establishment of precinctsA. Subject to the provisions of R.S. 18:532.1 and 1903, the governing authority of each parish shall establish precincts, define the territorial limits for which each precinct is established, prescribe their boundaries, and designate the precincts. The governing authority of each parish shall by ordinance adopt the establishment and boundaries of each precinct in accordance with the timetable as set forth herein and in accordance with R.S. 18:532.1.B.(1)(a) Each precinct shall be a contiguous, compact area having clearly defined and clearly observable boundaries coinciding with visible features readily distinguishable on the ground and approved extensions of such features, such as designated highways, roads, streets, rivers, or canals, and depicted on United States Bureau of the Census base maps for the next federal decennial census, except where the precinct boundary is coterminous with the boundary of a parish or an incorporated place when the boundaries of a single precinct contain the entire geographic area of the incorporated place. Except as otherwise provided in this Paragraph, on and after July 1, 1997, any precinct boundary which does not coincide with a visible feature shall be changed by the parish governing authority to coincide with a visible feature in accordance with R.S. 18:532.1.(b) For the purposes of this Paragraph, the term "approved extension" shall mean an extension of one visible feature to another visible feature which has been approved by the secretary of the Senate and the clerk of the House of Representatives or their designees and which is or which will be a census tabulation boundary.(2) No precinct shall be wholly contained within the territorial boundaries of another precinct, except that a precinct which contains the entire geographical area of an incorporated place and in which the total number of registered voters at the last general election was less than three hundred may be so contained.(3) No precinct shall contain more than two thousand two hundred registered voters within its geographic boundaries. Within thirty days after the completion of each canvass, the registrar of voters of each parish shall notify the parish governing authority of every precinct in the parish which contains more than two thousand two hundred registered voters within its geographic boundaries. Within sixty days of such notification, the parish governing authority shall divide such precincts by a visible feature in accordance with R.S. 18:532.1.(4)(a) No precinct shall contain less than three hundred registered voters within its geographical boundaries, except:(i) When necessary to make it more convenient for voters in a geographically isolated and unincorporated area to vote. A voter in a geographically isolated and unincorporated area shall mean a voter whose residen

Do minority voters respond to co-racial or co-ethnic candidates? That is does the increased chance of substantive representation translate into increased participation? Here, we focus on this question among African American voters. While much of the empirical literature on this question has produced conflicting answers, recent studies suggest that minority candidates can significantly increase minority turnout. We argue that past work on this topic does not adequately account for the fact that minority voters in places with minority candidates may systematically differ in their level of participation than minority voters in places without minority candidates. In this study we address the weakness of previous research designs and offer a new design that exploits the redistricting process to gain additional leverage over this question. The redistricting process allows us to correctly model the selection process and ensure that voters who were moved to districts with African American candidates through the redistricting process are comparable to voters that remained in existing districts with white candidates. We find little evidence that African American voter turnout increases when voters are moved to African America candidates. We find some evidence that white voters, however, tend to vote at lower rates when they are represented by African American candidates.

Author:
Source: Unknown -
Please cite:

1. Title: 1984 United States Congressional Voting Records Database

   1. Source Information: (a) Source: Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc. Washington, D.C., 1985. (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) (c) Date: 27 April 1987

   2. Past Usage

   3. Publications

      1. Schlimmer, J. C. (1987). Concept acquisition through representational adjustment. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine, CA. -- Results: about 90%-95% accuracy appears to be STAGGER's asymptote
      2. Predicted attribute: party affiliation (2 classes)

   4. Relevant Information: This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition).

   5. Number of Instances: 435 (267 democrats, 168 republicans)

   6. Number of Attributes: 16 + class name = 17 (all Boolean valued)

   7. Attribute Information:

   8. Class Name: 2 (democrat, republican)

   9. handicapped-infants: 2 (y,n)

   10. water-project-cost-sharing: 2 (y,n)

   11. adoption-of-the-budget-resolution: 2 (y,n)

   12. physician-fee-freeze: 2 (y,n)

   13. el-salvador-aid: 2 (y,n)

   14. religious-groups-in-schools: 2 (y,n)

   15. anti-satellite-test-ban: 2 (y,n)

   16. aid-to-nicaraguan-contras: 2 (y,n)

   17. mx-missile: 2 (y,n)

   18. immigration: 2 (y,n)

   19. synfuels-corporation-cutback: 2 (y,n)

   20. education-spending: 2 (y,n)

   21. superfund-right-to-sue: 2 (y,n)

   22. crime: 2 (y,n)

   23. duty-free-exports: 2 (y,n)

   24. export-administration-act-south-africa: 2 (y,n)

   25. Missing Attribute Values: Denoted by "?"

   NOTE: It is important to recognize that "?" in this database does not mean that the value of the attribute is unknown. It means simply, that the value is not "yea" or "nay" (see "Relevant Information" section above).

   Attribute: #Missing Values: 1: 0 2: 0 3: 12 4: 48 5: 11 6: 11 7: 15 8: 11 9: 14 10: 15 11: 22 12: 7 13: 21 14: 31 15: 25 16: 17 17: 28

   1. Class Distribution: (2 classes)
   2. 45.2 percent are democrat
   3. 54.8 percent are republican

   Class predictiveness and predictability: Pr(C|A=V) and Pr(A=V|C) Attribute 1: (A = handicapped-infants) 0.91; 1.21 (C=democrat; V=y) 0.09; 0.10 (C=republican; V=y) 0.43; 0.38 (C=democrat; V=n) 0.57; 0.41 (C=republican; V=n) 0.75; 0.03 (C=democrat; V=?) 0.25; 0.01 (C=republican; V=?) Attribute 2: (A = water-project-cost-sharing) 0.62; 0.45 (C=democrat; V=y) 0.38; 0.23 (C=republican; V=y) 0.62; 0.45 (C=democrat; V=n) 0.38; 0.23 (C=republican; V=n) 0.58; 0.10 (C=democrat; V=?) 0.42; 0.06 (C=republican; V=?) Attribute 3: (A = adoption-of-the-budget-resolution) 0.91; 0.87 (C=democrat; V=y) 0.09; 0.07 (C=republican; V=y) 0.17; 0.11 (C=democrat; V=n) 0.83; 0.44 (C=republican; V=n) 0.64; 0.03 (C=democrat; V=?) 0.36; 0.01 (C=republican; V=?) Attribute 4: (A = physician-fee-freeze) 0.08; 0.05 (C=democrat; V=y) 0.92; 0.50 (C=republican; V=y) 0.99; 0.92 (C=democrat; V=n) 0.01; 0.01 (C=republican; V=n) 0.73; 0.03 (C=democrat; V=?) 0.27; 0.01 (C=republican; V=?) Attribute 5: (A = el-salvador-aid) 0.26; 0.21 (C=democrat; V=y) 0.74; 0.48 (C=republican; V=y) 0.96; 0.75 (C=democrat; V=n) 0.04; 0.02 (C=republican; V=n) 0.80; 0.04 (C=democrat; V=?) 0.20; 0.01 (C=republican; V=?) Attribute 6: (A = religious-groups-in-schools) 0.45; 0.46 (C=democrat; V=y) 0.55; 0.46 (C=republican; V=y) 0.89; 0.51 (C=democrat; V=n) 0.11; 0.05 (C=republican; V=n) 0.82; 0.03 (C=democrat; V=?) 0.18; 0.01 (C=republican; V=?) Attribute 7: (A = anti-satellite-test-ban) 0.84; 0.75 (C=democrat; V=y) 0.16; 0.12 (C=republican; V=y) 0.32; 0.22 (C=democrat; V=n) 0.68; 0.38 (C=republican; V=n) 0.57; 0.03 (C=democrat; V=?) 0.43; 0.02 (C=republican; V=?) Attribute 8: (A = aid-to-nicaraguan-contras) 0.90; 0.82 (C=democrat; V=y) 0.10; 0.07 (C=republican; V=y) 0.25; 0.17 (C=democrat; V=n) 0.75; 0.41 (C=republican; V=n) 0.27; 0.01 (C=democrat; V=?) 0.73; 0.03 (C=republican; V=?) Attribute 9: (A = mx-missile) 0.91; 0.70 (C=democrat; V=y) 0.09; 0.06 (C=republican; V=y) 0.29; 0.22 (C=democrat; V=n) 0.71; 0.45 (C=republican; V=n) 0.86; 0.07 (C=democrat; V=?) 0.14; 0.01 (C=republican; V=?) Attribute 10: (A = immigration) 0.57; 0.46 (C=democrat; V=y) 0.43; 0.28 (C=republican; V=y) 0.66; 0.52 (C=democrat; V=n) 0.34; 0.23 (C=republican; V=n) 0.57; 0.01 (C=democrat; V=?) 0.43; 0.01 (C=republican; V=?) Attribute 11: (A = synfuels-corporation-cutback) 0.86; 0.48 (C=democrat; V=y) 0.14; 0.06 (C=republican; V=y) 0.48; 0.47 (C=democrat; V=n) 0.52; 0.43 (C=republican; V=n) 0.57; 0.04 (C=democrat; V=?) 0.43; 0.03 (C=republican; V=?) Attribute 12: (A = education-spending) 0.21; 0.13 (C=democrat; V=y) 0.79; 0.42 (C=republican; V=y) 0.91; 0.80 (C=democrat; V=n) 0.09; 0.06 (C=republican; V=n) 0.58; 0.07 (C=democrat; V=?) 0.42; 0.04 (C=republican; V=?) Attribute 13: (A = superfund-right-to-sue) 0.35; 0.27 (C=democrat; V=y) 0.65; 0.42 (C=republican; V=y) 0.89; 0.67 (C=democrat; V=n) 0.11; 0.07 (C=republican; V=n) 0.60; 0.06 (C=democrat; V=?) 0.40; 0.03 (C=republican; V=?) Attribute 14: (A = crime) 0.36; 0.34 (C=democrat; V=y) 0.64; 0.49 (C=republican; V=y) 0.98; 0.63 (C=democrat; V=n) 0.02; 0.01 (C=republican; V=n) 0.59; 0.04 (C=democrat; V=?) 0.41; 0.02 (C=republican; V=?) Attribute 15: (A = duty-free-exports) 0.92; 0.60 (C=democrat; V=y) 0.08; 0.04 (C=republican; V=y) 0.39; 0.34 (C=democrat; V=n) 0.61; 0.44 (C=republican; V=n) 0.57; 0.06 (C=democrat; V=?) 0.43; 0.04 (C=republican; V=?) Attribute 16: (A = export-administration-act-south-africa) 0.64; 0.65 (C=democrat; V=y) 0.36; 0.30 (C=republican; V=y) 0.19; 0.04 (C=democrat; V=n) 0.81; 0.15 (C=republican; V=n) 0.79; 0.31 (C=democrat; V=?) 0.21; 0.07 (C=republican; V=?)

This dataset covers ballots 255-60, and 262-63, spanning January, March, May, July, September-October, and December 1957. The dataset contains the data resulting from these polls in ASCII. The ballots are as follows: 255 - January This Gallup poll seeks the opinions of Canadians mainly on current events and news issues. Some of this poll's question were also intended to ascertain respondents' political opinions. Respondents were asked questions so that they could be grouped according to geographic, demographic and social variables as well. Topics of interest include: alcohol consumption; beer sales in grocery stores; beverage consumption; Canadian Arts Council; car ownership; federal election; government funding for art; immigration policy; interesting things done by people; New Years resolutions; the most important world event; preferred political parties; predictions for 1957; prohibition of alcohol; railway workers strike; public utilities strike; television ownership; temperament; union membership; voting behaviour; and winter vacations. Basic demographics variables are also included. 256 - March This Gallup poll seeks to obtain the views of Canadians on current issues of national importance. Included are questions on labour unions, religion, and activities people do and feel should be allowed on Sundays. Respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: belief in the New Testament; car ownership; the federal election; the ideal number of children; labour union criticisms; whether newspapers should be allowed on Sunday; old age pension amounts; whether organized sports should be allowed on Sunday; preferred political parties; physical exam requirements to be able to drive a vehicle; religious influence; Sunday activities; whether theatres should be allowed to be open on Sunday; union membership; the influence of the United Nations, and voting behaviour. Basic demographics variables are also included. 257 - May This Gallup poll seeks the opinions of Canadians on issues of importance to the government and to the country. Included are questions regarding voting patterns and elections, America's influence over Canada, and travelling habits of Canadians. The respondents were also asked questions so that they could be grouped according to geographic, demographic and social variables. Topics of interest include: the 35 hour work week; America's influence over Canada; the church's refusal to wed divorcees; the cost of taking a trip; the federal election; foreign policy; preferred political parties; the purpose of taking a trip; tax cuts; union membership; transportation used to take a trip; and voting behaviour. Basic demographics variables are also included. 258 - May This Gallup poll aims to collect the opinions of Canadians on issues of importance to the country and to the government. This survey focuses on mostly political topics, such as elections and voting, and the influence of the United States over Canada. Respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: American investment in Canada, the American lifestyle; Canada's dependence on the United States, the federal election; financial dependence on the United States; government policy; how hard people work; religious services; Sunday school; union membership; and voting behaviour. Basic demographics variables are also included. 259 - July This Gallup poll seeks to collect the opinions of Canadians. The majority of questions either deal directly with politics or the Federal election that was held in the month before this poll. Questions also inquire about voting patterns and issues that affect how respondents vote. Respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: whether respondents have been in a small boat recently; car ownership; Dr. Salk's polio vaccination; government priorities; John Diefenbaker; Louis St. Laurent; preferred political party; predictions and opinions for the next federal election; Progressive Conservative party; the Queen's visit to Ottawa; reactions to the federal election results; smoking habits and quitting; swimming ability; union membership; voting behaviour; and why the Conservatives won the federal election. Basic demographics variables are also included. 260 - September: first sample with 1223 respondents This Gallup poll is interested in collecting Canadians' opinions. The predominant subject of the survey questions is politics, including everything from the Queen to nuclear weapons testing and fallout. There were also questions asked to help group the respondents according to geographic, demographic, and social variables. Topics of interest include: A-bomb testing; American television programs; awareness of cabinet ministers; the British Commonwealth as a trading partner; Canadian television programmes; car ownership; federal elections; Governor General preference; H-bomb testing; inflation and high prices; job-type preference; John Diefenbaker's performance as Prime Minister; Louis St. Laurent's successor; nuclear weapons and fallout; performance of the advisors to the Queen; Russia's foreign policy objectives; speeches given by the Queen; television ownership; union membership; the United States as a trading partner; and voting behaviours. Basic demographics variables are also included. 260-c2 - September: same as above; second sample with 952 respondents 262 - October This Gallup poll seeks to collect the opinions of Canadians on important political issues, both in Canada and abroad. The major political issues discussed within Canada include prices, defence and unemployment, although lighter issues such as advertising and how spare time is spent are also discussed. Respondents were also asked questions so that they could be classified according to geographic, demographic and social variables. Basic demographics variables are also included. 263 - December This Gallup poll seeks to collect the opinions of Canadians on important political issues, both in Canada and abroad. The major political issues discussed within Canada include prices, defence and unemployment, although lighter issues such as advertising and how spare time is spent are also discussed. Respondents were also asked questions so that they could be classified according to geographic, demographic and social variables. The topics of interest include: whether advertisements are believable or not; the Arab Israeli conflict in Palestine; car ownership; the Conservative party; defense policy; the federal election; government control of schools; how spare time is spent; John Diefenbaker's performance as Prime Minister; the number of jobs held by respondents; preferred political parties; price trends; Unemployment rates; union membership; and voting behaviour. Basic demographics variables are also included. The codebook for this dataset is available through the UBC Library catalogue, with call number HN110.Z9 P84.

The impact of institutions on the economic vote stands as a well-established proposition for the advanced democracies of Europe. We know less, however, regarding the institutional effects on the economic vote in the developing democracies of Latin America. Carrying out an analysis of presidential elections in 18 Latin American countries, we offer evidence that the usual Euro-centric conceptualization of the clarity of responsibility is not ideal for understanding the economic vote in this region. There does exist a powerful effect of institutions on the economic vote within Latin American democracies, but one uniquely associated with its presidential regimes and dynamic party systems. Rules for these elections – such as concurrence, term limits, and second-round voting – suggest that we should reconceptualize the notion of the clarity of responsibility in Latin America, focusing more on individuals in power and their constraints, and less on the political parties from which they hail.

This data is a recent survey data we collected by using Survey Monkey.

We asked how much people will vote Pete Buttigieg as President of the US, if he is nominee, and asked many reasons by scalar-bar questions which is created by us based on the initial open question survey.

This survey is completely original, not related with his campaign.

Insight Survey of Pete Buttigieg https://www.surveymonkey.com/r/L3H3CKD

We are looking for a data scientist or a causal analyst who has great ability to extract the insights from this type of data format. The winner of the best result will be honored by a spinning out company who will focus on commercial delivery of this analysis. Marketing Research has been struggling this type of open and close questions why people like a brand and products.

Find WHYs.

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.

Analysis of ‘🗳 Primary Candidates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/primary-candidatese on 28 January 2022.

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

About this dataset

This folder contains the data behind the stories:

• We Researched Hundreds Of Races. Here’s Who Democrats Are Nominating.
• How’s The Progressive Wing Doing In Democratic Primaries So Far?
• We Looked At Hundreds Of Endorsements. Here’s Who Republicans Are Listening To.

This project looks at patterns in open Democratic and Republican primary elections for the U.S. Senate, U.S. House and governor in 2018.

dem_candidates.csv contains information about the 811 candidates who have appeared on the ballot this year in Democratic primaries for Senate, House and governor, not counting races featuring a Democratic incumbent, as of August 7, 2018.

rep_candidates.csv contains information about the 774 candidates who have appeared on the ballot this year in Republican primaries for Senate, House and governor, not counting races featuring a Republican incumbent, through September 13, 2018.

Here is a description and source for each column in the accompanying datasets.

dem_candidates.csv and rep_candidates.csv include:

Column	Description
Candidate	All candidates who received votes in 2018’s Democratic primary elections for U.S. Senate, U.S. House and governor in which no incumbent ran. Supplied by Ballotpedia.
State	The state in which the candidate ran. Supplied by Ballotpedia.
District	The office and, if applicable, congressional district number for which the candidate ran. Supplied by Ballotpedia.
Office Type	The office for which the candidate ran. Supplied by Ballotpedia.
Race Type	Whether it was a “regular” or “special” election. Supplied by Ballotpedia.
Race Primary Election Date	The date on which the primary was held. Supplied by Ballotpedia.
Primary Status	Whether the candidate lost (“Lost”) the primary or won/advanced to a runoff (“Advanced”). Supplied by Ballotpedia.
Primary Runoff Status	“None” if there was no runoff; “On the Ballot” if the candidate advanced to a runoff but it hasn’t been held yet; “Advanced” if the candidate won the runoff; “Lost” if the candidate lost the runoff. Supplied by Ballotpedia.
General Status	“On the Ballot” if the candidate won the primary or runoff and has advanced to November; otherwise, “None.” Supplied by Ballotpedia.
Primary %	The percentage of the vote received by the candidate in his or her primary. In states that hold runoff elections, we looked only at the first round (the regular primary). In states that hold all-party primaries (e.g., California), a candidate’s primary percentage is the percentage of the total Democratic vote they received. Unopposed candidates and candidates nominated by convention (not primary) are given a primary percentage of 100 but were excluded from our analysis involving vote share. Numbers come from official results posted by the secretary of state or local elections authority; if those were unavailable, we used unofficial election results from the New York Times.
Won Primary	“Yes” if the candidate won his or her primary and has advanced to November; “No” if he or she lost.

dem_candidates.csv includes:

Column	Description
Gender	“Male” or “Female.” Supplied by Ballotpedia.
Partisan Lean	The FiveThirtyEight partisan lean of the district or state in which the election was held. Partisan leans are calculated by finding the average difference between how a state or district voted in the past two presidential elections and how the country voted overall, with 2016 results weighted 75 percent and 2012 results weighted 25 percent.
Race	“White” if we identified the candidate as non-Hispanic white; “Nonwhite” if we identified the candidate as Hispanic and/or any nonwhite race; blank if we could not identify the candidate’s race or ethnicity. To determine race and ethnicity, we checked each candidate’s website to see if he or she identified as a certain race. If not, we spent no more than two minutes searching online news reports for references to the candidate’s race.
Veteran?	If the candidate’s website says that he or she served in the armed forces, we put “Yes.” If the website is silent on the subject (or explicitly says he or she didn’t serve), we put “No.” If the field was left blank, no website was available.
LGBTQ?	If the candidate’s website says that he or she is LGBTQ (including indirect references like to a same-sex partner), we put “Yes.” If the website is silent on the subject (or explicitly says he or she is straight), we put “No.” If the field was left blank, no website was available.
Elected Official?	We used Ballotpedia, VoteSmart and news reports to research whether the candidate had ever held elected office before, at any level. We put “Yes” if the candidate has held elected office before and “No” if not.
Self-Funder?	We used Federal Election Committee fundraising data (for federal candidates) and state campaign-finance data (for gubernatorial candidates) to look up how much each candidate had invested in his or her own campaign, through either donations or loans. We put “Yes” if the candidate donated or loaned a cumulative $400,000 or more to his or her own campaign before the primary and “No” for all other candidates.
STEM?	If the candidate identifies on his or her website that he or she has a background in the fields of science, technology, engineering or mathematics, we put “Yes.” If not, we put “No.” If the field was left blank, no website was available.
Obama Alum?	We put “Yes” if the candidate mentions working for the Obama administration or campaign on his or her website, or if the candidate shows up on this list of Obama administration members and campaign hands running for office. If not, we put “No.”
Dem Party Support?	“Yes” if the candidate was placed on the DCCC’s Red to Blue list before the primary, was endorsed by the DSCC before the primary, or if the DSCC/DCCC aired pre-primary ads in support of the candidate. (Note: according to the DGA’s press secretary, the DGA does not get involved in primaries.) “No” if the candidate is running against someone for whom one of the above things is true, or if one of those groups specifically anti-endorsed or spent money to attack the candidate. If those groups simply did not weigh in on the race, we left the cell blank.
Emily Endorsed?	“Yes” if the candidate was endorsed by Emily’s List before the primary. “No” if the candidate is running against an Emily-endorsed candidate or if Emily’s List specifically anti-endorsed or spent money to attack the candidate. If Emily’s List simply did not weigh in on the race, we left the cell blank.
Gun Sense Candidate?	“Yes” if the candidate received the Gun Sense Candidate Distinction from Moms Demand Action/Everytown for Gun Safety before the primary, according to media reports or the candidate’s website. “No” if the candidate is running against an candidate with the distinction. If Moms Demand Action simply did not weigh in on the race, we left the cell blank.
Biden Endorsed?	“Yes” if the candidate was endorsed by Joe Biden before the primary. “No” if the candidate is running against a Biden-endorsed candidate or if Biden specifically anti-endorsed the candidate. If Biden simply did not weigh in on the race, we left the cell blank.
Warren Endorsed?	“Yes” if the candidate was endorsed by Elizabeth Warren before the primary. “No” if the candidate is running against a Warren-endorsed candidate or if Warren specifically anti-endorsed the candidate. If Warren simply did not weigh in on the race, we left the cell blank.
Sanders Endorsed?	“Yes” if the candidate was endorsed by Bernie Sanders before the primary. “No” if the candidate is running against a Sanders-endorsed candidate or if Sanders specifically anti-endorsed the candidate. If Sanders simply did not weigh in on the race, we left the cell

Do party leaflets increase turnout, or does campaigning require canvass visits in order to increase turnout? Get Out The Vote (GOTV) experiments consistently find that campaigning needs to be personal in order to be effective. However, the imbalance between US and European-based studies has led to recent calls for further European GOTV experiments. There are also comparatively few partisan experiments. I report the findings of a UK-based field experiment conducted with the Liberal Democrats in 2017. Results show that party leaflets boost turnout by 4.3 percentage points, while canvassing has a small additional effect (0.6 percentage points). The study also represents the first individual level experiment to compare GOTV effects between postal voters and in-person voters outside the US.

Dataset from our ICWSM 2017 paper. When using this resource, please use the following citation:

Aragón P., Gómez V., Kaltenbrunner A. (2017) To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion, ICWSM-17- 11th International AAAI Conference on Web and Social Media, Montreal, Canada.

@inproceedings {aragon2017ICWSM,
author = {Arag\'on, Pablo and G\'omez, Vicen\c{c} and Kaltenbrunner, Andreas},
title = {To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion},
booktitle = {ICWSM-17 - 11th International AAAI Conference on Web and Social Media},
publisher = {The AAAI Press},
location = {Montreal, Canada},
year = 2017
}

More info about this dataset can also be found at:

Aragón P., Gómez V., Kaltenbrunner A., (2017) Detecting Platform Effects in Online Discussions, Policy & Internet, 9, 2017.

@article{aragon2017PI,
author = {Arag\'on, Pablo and G\'omez, Vicen\c{c} and Kaltenbrunner, Andreas},
title = {Detecting Platform Effects in Online Discussions},
journal = {Policy & Internet},
volume = {9},
number = {4},
pages = {420-443},
doi = {10.1002/poi3.158},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/poi3.158},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/poi3.158},
year = {2017}
}

Crawling process

We built a crawling process that collects all the stories in the front page of Meneame from 2011 to 2015 (both years included). We then performed a second crawling process to collect every comment from the discussion thread of each story. From both crawling processes, we obtained 72,005 stories and 5,385,324 comments.

It is important to highlight two issues taken into account when the crawler was designed. First, the machine-readable robots.txt file on Meneame does not disallow this process. Second, the footnote of Meneame indicates the licenses of the code, graphics and content of the website. The license for content is Attribution 3.0 Spain (CC BY 3.0 ES) which allows us to release this dataset.

Fields

Every discussion thread is stored in a JSON file named with the URL slug of the corresponding story in Meneame, located in a yyyy-mm-dd folder. The JSON file is an array of elements with the following fields:

id (string): ID of the story/comment


sent (timestamp): Date of the story/comment as yyyy-MM-ddThh:mm:ssZ.


message (string): Text of the story/comment


user (string): Username of the authoring story/comment


karma (number): Karma score of the comment when the crawling was performed


comments_count (number): Number of comments in reply to the story/post


votes (number): Number of votes to the story/comment


thread (string): URL of the thread


thread_id (string): Sequential arriving order to the thread (0 if story, >=1 if comment)


depth (string): Depth within the thread (0 if story, >=1 if comment)


url (string): URL of the specific story/comment







title (string): Title, only available for stories.


published (string): Date when published on the front page, only available for stories.


tags (string): Tags, only available for stories.


clics (string): Number of clicks, only available for stories.


users (string): Number of user votes, only available for stories.


anonymous (string): Number of anonymous votes, only available for stories.


negatives (string): Number of negative votes, only available for stories.







in_reply_to_id (string): ID of the parent story/comment, only available for comments.


in_reply_to_user (string): Authoring user of the parent story/comment, only available for comments.


in_reply_to_thread_id (string): Sequential arriving order to the thread of of the parent story/comment, only available for comments.

Acknowledgment

This work is supported by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502).