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.

Analysis of ‘US non-voters poll data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-non-voters-poll-datae on 28 January 2022.

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

About this dataset

This dataset contains the data behind Why Many Americans Don't Vote.

Data presented here comes from polling done by Ipsos for FiveThirtyEight, using Ipsos’s KnowledgePanel, a probability-based online panel that is recruited to be representative of the U.S. population. The poll was conducted from Sept. 15 to Sept. 25 among a sample of U.S. citizens that oversampled young, Black and Hispanic respondents, with 8,327 respondents, and was weighted according to general population benchmarks for U.S. citizens from the U.S. Census Bureau’s Current Population Survey March 2019 Supplement. The voter file company Aristotle then matched respondents to a voter file to more accurately understand their voting history using the panelist’s first name, last name, zip code, and eight characters of their address, using the National Change of Address program if applicable. Sixty-four percent of the sample (5,355 respondents) matched, although we also included respondents who did not match the voter file but described themselves as voting “rarely” or “never” in our survey, so as to avoid underrepresenting nonvoters, who are less likely to be included in the voter file to begin with. We dropped respondents who were only eligible to vote in three elections or fewer. We defined those who almost always vote as those who voted in all (or all but one) of the national elections (presidential and midterm) they were eligible to vote in since 2000; those who vote sometimes as those who voted in at least two elections, but fewer than all the elections they were eligible to vote in (or all but one); and those who rarely or never vote as those who voted in no elections, or just one.

The data included here is the final sample we used: 5,239 respondents who matched to the voter file and whose verified vote history we have, and 597 respondents who did not match to the voter file and described themselves as voting "rarely" or "never," all of whom have been eligible for at least 4 elections.

If you find this information useful, please let us know.

License: Creative Commons Attribution 4.0 International License

Source: https://github.com/fivethirtyeight/data/tree/master/non-voters

This dataset was created by data.world's Admin and contains around 6000 samples along with Race, Q27 6, technical information and other features such as: - Q4 6 - Q8 3 - and more.

How to use this dataset

• Analyze Q10 3 in relation to Q8 6
• Study the influence of Q6 on Q10 4
• More datasets

Acknowledgements

If you use this dataset in your research, please credit data.world's Admin

Start A New Notebook!

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

In the social sciences, randomized experimentation is the optimal research design for establishing causation. However, for a number of practical reasons, researchers are sometimes unable to conduct experiments and must rely on observational data. In an effort to develop estimators that can approximate experimental results using observational data, scholars have given increasing attention to matching. In this article, we test the performance of matching by gauging the success with which matching approximates experimental results. The voter mobilization experiment presented here comprises a large number of observations (60,000 randomly assigned to the treatment group and nearly two million assigned to the control group) and a rich set of covariates. This study is analyzed in two ways. The first method, instrumental variables estimation, takes advantage of random assignment in order to produce consistent estimates. The second method, matching estimation, ignores random assignment and analyzes the data as though they were nonexperimental. Matching is found to produce biased results in this application because even a rich set of covariates is insufficient to control for preexisting differences between the treatment and control group. Matching, in fact, produces estimates that are no more accurate than those generated by ordinary least squares regression. The experimental findings show that brief paid get-out-the-vote phone calls do not increase turnout, while matching and regression show a large and significant effect.

In many democracies, voter turnout is higher among the rich than the poor. But do changes in income lead to changes in electoral participation? We address this question with unique administrative data matching a decade of individual tax records with voter rolls in a large municipality in northern Italy. We document several important findings. First, levels of income and turnout both dropped disproportionately among relatively poor citizens following the Great Recession. Second, we show that within-individual changes in income have an effect on participation, which is modest on average due to diminishing returns, but can be consequential among the poor. Third, we find that declining turnout of voters facing economic insecurity has exacerbated the income skew in participation, suggesting that income inequality and turnout inequality may reinforce each other. We discuss the theoretical implications of these results, set in a context with strong civic traditions and low barriers to voting.

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

This dataset (in .csv format), accompanying codebook and replication code serve as supplement to a study titled: “Does the mode of administration impact on quality of data? Comparing a traditional survey versus an online survey via a Voting Advice Application” submitted for publication to the journal: “Survey Research Methods”). The study involved comparisons of responses to two near-identical questionnaires administered via a traditional survey and through a Voting Advice Application (VAA) both designed for and administered during the pre-electoral period of the Cypriot Presidential Elections of 2013. The offline dataset consisted of questionnaires collected from 818 individuals whose participation was elicited through door-to-door stratified random sampling with replacement of individuals who could not be contacted. The strata were designed to take into account the regional population density, gender, age and whether the area was urban or rural. Offline participants completed a pen-and-paper questionnaire version of the VAA in a self-completing capacity, although the person administering the questionnaire remained present throughout. The online dataset involved responses from 10,241 VAA users who completed the Choose4Cyprus VAA. Voting Advice Applications are online platforms that provide voting recommendations to users based on their closeness to political parties after they declare their agreement or disagreement on a number of policy statements. VAA users freely visited the VAA website and completed the relevant questionnaire in a self-completing capacity. The two modes of administration (online and offline) involved respondents completing a series of supplementary questions (demographics, ideological affinity & political orientation [e.g. vote in the previous election]) prior to the main questionnaire consisting of 35 and 30 policy-related Likert-type items for the offline and online mode respectively. The dataset includes all 30 policy items that were common between the two modes, although only the first 19 (q1:q19) appeared in the same order and in the same position in the two questionnaires; as such, all analyses reported in the article were conducted using these 19 items only. The phrasing of the questions was identical for the two modes and is described per variable in the attached codebook.

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

This data release is composed of tables from a database of United States Congressional statistics spanning the time period 1789 through 1989. The sources of the data were studies in the ICPSR collection and other historical texts and studies. There are eleven data files in total, including two additional tables that have been added since the first release. Some files contain records for additional Congresses. The rows in the various files describe different entities. For example, in the Votes Table file, each row contains a record of a vote by a particular member on a particular roll call vote. The Member Table file contains a record for each member of Congress, while the Serves Table file contains a record for each member for every Congress in which he or she served. See the descriptions of each file in the codebook for details about its contents. The data from the various files can be combined by matching the fields that they have in common. Cross-file searches should be conducted using the Member_ID field. However, not every file has the Member_ID field. In those cases, an alternative common field should be used.

Abstract (en): These data are being released as a preliminary version to facilitate early access to the study for research purposes. This collection has not been fully processed by ICPSR at this time, and data are released in the format provided by the principal investigators. As the study is processed and given enhanced features by ICPSR in the future, users will be able to download the updated versions of the study. Please report any data errors or problems to user support, and we will work with you to resolve any data-related issues. The American National Election Study (ANES): 2016 Pilot Study sought to test new instrumentation under consideration for potential inclusion in the ANES 2016 Time Series Study, as well as future ANES studies. Much of the content is based on proposals from the ANES user community submitted through the Online Commons page, found on the ANES home page. The survey included questions about preferences in the presidential primary, stereotyping, the economy, discrimination, race and racial consciousness, police use of force, and numerous policy issues, such as immigration law, health insurance, and federal spending. It was conducted on the Internet using the YouGov panel, an international market research firm that administers polls that collect information about politics, public affairs, products, brands, as well as other topics of general interest. The purpose of this study was to test questions for inclusion on the ANES 2016 Time Series, as well as other future ANES studies. Respondents were selected from the YouGov panel survey administered on the Internet. Response to these surveys are on a volunteer basis. The data are not weighted. This collection contains two weight variables, WEIGHT and WEIGHT_SPSS. The variable WEIGHT is the weight for analysis that is intended to generalize to the population. The variable WEIGHT_SPSS is the weight recommended to be used by SPSS users not using the Complex Samples procedures and will account for the smaller effective sample size. For more information on weights, please see the ANES 2016 Pilot Study Codebook and User Guide found within the zip package, as well as visit the ANES Data Center Web site. United States citizens age 18 or older. Smallest Geographic Unit: state The study was conducted on the Internet using the YouGov panel. The YouGov panel consists of a large and diverse set of over a million respondents who have volunteered to complete surveys online and who regularly receive invitations to do so. They receive points usually worth about 21 to 50 cents for each survey they complete. The points are redeemable for various gift cards, a YouGov t-shirt, or UNICEF a donation. A respondent has to complete about 40 surveys to be eligible for any reward. Respondents were selected from the YouGov panel by sample matching. Matching is intended to make the individuals who complete the survey represent the population on the variables used for matching. Respondents were matched to United States citizens in the 2010 American Community Survey (ACS) sample by gender, age, race, and education, and to the November 2010 Current Population Survey (CPS) for voter registration and turnout status, and to the 2007 Pew Religious Life Survey on interest in politics and party identification. 1,200 individuals from the YouGov panel were selected for the ANES Pilot Study to match the target population defined by the ACS, CPS, and Pew surveys. After data collection the sample was weighted by YouGov using propensity scores using a logistic regression with age, gender, race/ethnicity, years of education, region, and party identification included in the model. For more information on sampling, please see the ANES 2016 Pilot Study Codebook and User Guide found within the zip package, as well as visit the ANES Data Center Web site. web-based surveyThis collection has not been fully processed by ICPSR. All of the files are available in one zipped package. This collection will be fully curated at a later date. For more information on the ANES 2016 Pilot Study, please refer to the ANES Data Center Web site.

The project Hellenic National Election Studies (ELNES) facilitates research on the causes and consequences of voting behaviour and on the way democracy works in a country under financial crisis. ELNES produces hundreds of euro worth of data on electoral behaviour in Greece at a fractional cost. By effectively taking advantage of new technology tools, ELNES provides a unique resource for scholars interested in deep and consequential questions on Greek politics.
HelpMeVote 2015 is a Greek Voting Advice Application that was completed more than 570000 times in the period from its official beginning (07/01/2015) until the Election Day (25/01/2015). Following the cleaning techniques provided by Andreadis (2012, 2014) the cleaned dataset includes 543870 cases. The number of citizens who have participated in the Greek Parliamentary Elections of January 2015 is 6330786. Thus, if we suppose that all HelpMeVote users have used it only once and that almost all of them have participated in the elections, then we can estimate that HelpMeVote users are circa 8.6% of those who participated in the Greek Parliamentary Elections of January 2015.
Andreadis, I. (2012) To Clean or not to Clean? Improving the Quality of VAA Data XXII World Congress of Political Science (IPSA), Madrid
Andreadis, I. (2014) Data Quality and Data Cleaning in Garzia, D. Marschall, S. (eds) Matching Voters with Parties and Candidates. Voting Advice Applications in Comparative Perspective, ECPR Press

In recent years, powerful new forms of influence have been discovered that the internet has made possible. In the present paper, we introduce another new form of influence which we call the “opinion matching effect” (OME). Many websites now promise to help people form opinions about products, political candidates, and political parties by first administering a short quiz and then informing people how closely their answers match product characteristics or the views of a candidate or party. But what if the matching algorithm is biased? We first present data from real opinion matching websites, showing that responding at random to their online quizzes can produce significantly higher proportions of recommendations for one political party or ideology than one would expect by chance. We then describe a randomized, controlled, counterbalanced, double-blind experiment that measured the possible impact of this type of matching on the voting preferences of real, undecided voters. With data obtained from a politically diverse sample of 773 eligible US voters, we observed substantial shifts in voting preferences toward our quiz’s favored candidate–between 51% and 95% of the number of people who had supported that candidate before we administered and scored the quiz. These shifts occurred without any participants showing any awareness of having been manipulated. In summary, in the present study we show not only that OME is a large effect; we also show that biased online questionnaires exist that might be shifting people’s opinions without their knowledge.

In a global context in which authoritarian regimes often hold elections, defeating dictators at the polls can play a key role in transitions to democracy. When the opposition is allowed to campaign for votes in such elections, there are strong reasons to believe that its efforts will be more persuasive than those of the authoritarian incumbent. This article examines the effect of televised campaign advertising on vote choice in the 1988 plebiscite that inaugurated Chile's transition to democracy. Using matching to analyze postelectoral survey data, it shows that the advertising of the opposition’s no campaign made Chileans more likely to vote against dictator Augusto Pinochet, whereas the advertising of the government’s yes campaign had no discernible effect. These findings suggest that the no campaign played an important causal role in the change of political regime.

There is a long tradition of imputation studies looking at how abstainers would vote if they had to. This is crucial for democracies because when abstainers and voters have different preferences, the electoral outcome ceases to reflect the will of the people. In this paper, we apply a non-parametric method to revisit old evidence. We impute the vote of abstainers in 15 European countries using Coarsened Exact Matching (CEM). While traditional imputation methods rely on the choice of voters that are on average like abstainers, and simulate full turnout, CEM only imputes the vote of the abstainers that are similar to voters, and allows to simulate an electoral outcome under varying levels of turnout, including levels that credibly simulate compulsory voting. We find that higher turnout would benefit social democratic parties while imposing substantial losses to extreme left and green parties.

This article explains variation in the quality of representation in the context of European Parliament elections. Specifically, it clarifies how voters relate to political parties on the issue of European integration and whether they are represented, misrepresented or indifferent to this issue. The analysis shows that perceived benefits of European integration do drive a perfect voter-party match while perceived costs, when high, drive a perfect match between Eurosceptic voters and likeminded parties and make voters less indifferent. The analysis draws attention to the high number of status-quo voters who, in the absence of a party with similar views, could channel their vote towards a party promoting integration, but only if their knowledge about the EU and its benefits increases.

This study examines the impact of residential mobility on electoral participation among the poor by matching data from Moving to Opportunity, a U.S.-based multi-city housing mobility experiment, with nationwide individual voter data. Nearly all participants in the experiment were Black and Hispanic families who originally lived in high-poverty public housing developments. Notably, the study finds that receiving a housing voucher to move to a low-poverty neighborhood decreased adult participants’ voter participation for nearly two decades—a negative impact equal to or outpacing that of the most effective get-out-the-vote campaigns in absolute magnitude. This finding has important implications for understanding residential mobility as a long-run depressant of voter turnout among extremely low-income adults.

This paper estimates the electoral effects of conditional cash transfers (CCTs)-the fastest-growing social policy in the developing world-in three presidential elections in Brazil. It analyzes municipal level electoral results and survey data, and employs matching techniques to reduce causal inference problems typical of observational studies. Results shows that CCTs are associated with increased performance by the incumbent party presidential candidate in all three elections, but that these effects have been reaped by incumbents from different parties. It also shows that CCTs have had no discernible impacts on party identification and the performance of incumbent parties in legislative elections. Together, these findings suggest that CCTs are significant in the short-run, but lack the capacity to induce substantial long-term voter realignments.

These wards were produced by the Legislative Technology Services Bureau for the 2011 Legislative Redistricting Project. Election data from the current decade is included.Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data AttributesWard Data Overview: These municipal wards were created by grouping Census 2010 population collection blocks into municipal wards. This project started with the release of Census 2010 geography and population totals to all 72 Wisconsin counties on March 21, 2011, and were made available via the Legislative Technology Services Bureau (LTSB) GIS website and the WISE-LR web application. The 180 day statutory timeline for local redistricting ended on September 19, 2011. Wisconsin Legislative and Congressional redistricting plans were enacted in 2011 by Wisconsin Act 43 and Act 44. These new districts were created using Census 2010 block geography. Some municipal wards, created before the passing of Act 43 and 44, were required to be split between assembly, senate and congressional district boundaries. 2011 Wisconsin Act 39 allowed communities to divide wards, along census block boundaries, if they were divided by newly enacted boundaries. A number of wards created under Wisconsin Act 39 were named using alpha-numeric labels. An example would be where ward 1 divided by an assembly district would become ward 1A and ward 1B, and in other municipalities the next sequential ward number was used: ward 1 and ward 2. The process of dividing wards under Act 39 ended on April 10, 2012. On April 11, 2012, the United States Eastern District Federal Court ordered Assembly Districts 8 and 9 (both in the City of Milwaukee) be changed to follow the court’s description. On September 19, 2012, LTSB divided the few remaining municipal wards that were split by a 2011 Wisconsin Act 43 or 44 district line.Election Data Overview: Election data that is included in this file was collected by LTSB from the Government Accountability Board (GAB)/Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. The ward data that is collected after each decennial census is made up of collections of whole and split census blocks. (Note: Split census blocks occur during local redistricting when municipalities include recently annexed property in their ward submissions to the legislature).Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the GAB/WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the GAB at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2011) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election Results: The process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from GAB/WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards spring 2017 (Census 2010 totals used for disaggregation)Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (Occurred with spring 2017) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2016Blocks 2011 [with all election data] -> Wards 2011 (then MCD 2011, and County 2011) All election data (including later elections such as 2016) is aggregated to the Wards 2011 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though municipal and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same. Therefore, data totals within a county should be the same between 2011 wards and 2018 wards.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2018. This is due to (a) boundary corrections in the data from 2011 to 2018, and (b) annexations, where a block may have been reassigned.

The aims of this study stem from innovative matching formal theory of voting in the national parliamentary elections with a unique data set on candidates in parliamentarian elections in selected countries of Central and Eastern Europe in order to test specific hypotheses. The formal theory of voting is based on the game theoretical approach from which precise hypotheses are deduced. Theory tells us that by conditioning their ballots on policy outcomes, voters can use elections to control politicians. Presumably, politicians anticipate that they will be sanctioned for poor party performance, and thus have an incentive to implement policies, through their parties and other political units, that correspond to the preferences of the electorate. Does the system of repeated elections function as a mechanism of electoral control, and if it does, what factors influence its effectiveness? We consider this question in a broad context of studies on parliamentary elections. A main empirical contribution of study is the creation of a dataset on all candidates that stood for office in parliamentarian elections in selected countries of Eastern Europe. The dataset allows researchers to address the problem of who wins and who loses parliamentary elections in this region, among other research questions. We call these data, the East European Parliamentarian and Candidate Data (EAST PaC). These data consist of all candidates – not only winners, but also election losers – who stood for elective office in the national legislature in Poland, Hungary and Ukraine in all post-Communist elections. Poland is exceptional, for EAST PaC contains data from two elections during the Communist era, as well.

Abstract: We show that higher-office election incentives affec mayoral corruption levels. Using measures of political corruption compiled by Ferraz and Finan (2011), we replicate and refine their findings on second-term mayoral reelection incentives using coarsened exact matching. We proceed to add data on mayors who subsequently ran for governor, senator, or state or federal legislator and test whether higher office reelection incentives have the same effect on levels of corruption in the candidate's previous lower office term. Using coarsened exact matching, we find that first-term mayors illegally use about 58 percent fewer funds than second-term mayors, and second-term mayors who run for higher office illegally use about 86 percent fewer funds than second-term mayors who never run for higher office. Across various matching estimators, we consistently find that the disincentive for corrupt behavior when a second-term mayor runs for higher office is about twice as strong as the disincentive for corrupt behavior when a first-term mayor runs for a second term.

Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a map that shows the membership of the Parliament resulting from 22 May 1979 election. Map of Canada (with 20 urban insets) shows election results with Federal Electoral Districts coloured by the winning party. Matching table lists members elected and gives voting data. Charts summarize results of 1974 and 1979 elections by province and territory.