According to exit polling in the 2020 Presidential Election in the United States, ** percent of surveyed voters making less than 50,000 U.S. dollars reported voting for former Vice President Joe Biden. In the race to become the next president of the United States, ** percent of voters with an income of 100,000 U.S. dollars or more reported voting for incumbent President Donald Trump.

This graph shows the percentage of votes of the 2016 presidential elections in the United States on November 9, 2016, by income. According to the exit polls, about 53 percent of voters with an income of under 30,000 U.S. dollars voted for Hillary Clinton.

Description:

This dataset combines data from three sources to provide a comprehensive overview of county-level socioeconomic indicators, educational attainment, and voting outcomes in the United States. The dataset includes variables such as unemployment rates, median household income, urban influence codes, education levels, and voting percentages for the 2020 U.S. presidential election. By integrating this data, the dataset enables analysis of how factors like income, education, and unemployment correlate with political preferences, offering insights into regional voting behaviors across the country.

References:

The following reference datasets were used to construct this dataset.

[1] Harvard Dataverse, Voting Data Set by County. Available: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: 10.7910/DVN/VOQCHQ

[2] USDA Economic Research Service, Educational Attainment and Un- employment Data. Available: https://www.ers.usda.gov/data-products/ county-level-data-sets/county-level-data-sets-download-data/

This graph shows the percentage of votes of the 2012 presidential elections in the United States on November 6, 2012, by income. According to the exit polls, about 63 percent of voters with an annual income of less than 30,000 U.S. dollars nationwide have voted for Barack Obama.

Affluent Americans used to vote for Republican politicians. Now they vote for Democrats. In this paper, I show detailed evidence for this decades-in-the-making trend and argue that it has important consequences for the U.S. politics of economic inequality and redistribution. Beginning in the 1990s, the Democratic Party has won increasing shares of rich, upper-middle income, high-income occupation, and stock-owning voters. This appears true across voters of all races and ethnicities, is concentrated among (but not exclusive to) college-educated voters, and is only true among voters living in larger metropolitan areas. In the 2010s, Democratic candidates' electoral appeal among affluent voters reached above-majority levels. I echo other scholars in maintaining that this trend is partially driven by increasingly “culturally liberal” views of educated voters and party elite polarization on those issues, but I additionally argue that the evolution and stasis of the parties' respective economic policy agendas has also been a necessary condition for the changing behavior of affluent voters. This reversal of an American politics truism means that the Democratic Party's attempts to cohere around an economically redistributive policy agenda in an era of rising inequality face real barriers.

According to a September 2024 survey of adults in the United States, ** percent of those with a household income of over ****** U.S. dollars said that they were definitely voting in the 2024 presidential election. In comparison, ** percent of those making less than ****** U.S. dollars were definitely planning to vote in November.

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.

This project examines wage and poverty trends across U.S. states using SQL queries and custom spreadsheets. The goal is to uncover patterns, correlations, and disparities in income distribution, education, political affiliation, and federal policy impact. All data needed for the analysis is included in the custom spreadsheets, making the project full reproducible.

Key Questions Explored: 1. How do Other/ No Vote patterns relate to median wages and poverty rates? 2. What is the relationship between 2024 state voter turnout and party affiliation, median wages, and poverty rates? 3. What are the estimated state wage gaps in 2025? 4. How do state wage gaps relate to education, poverty, and political affiliation? 5. What is the correlation between state poverty rates and federal minimum wage overrides in 2025?

Analysis Includes: -SQL queries to summarize and explore the data -Visualizations highlighting wage gaps, poverty trends, and voter patterns Skills and Tools: SQL, Excel/Spreadsheets, Data Analysis, Data Visualization, Tableau

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

The Voting Habits dataset is a collection of data that provides insights into the voting behaviors of people based on their gender, race, education, and income. The dataset offers a valuable resource for researchers, social scientists, and policymakers who want to understand the factors that influence voting habits and preferences.

The dataset contains information collected from surveys and polls conducted in different countries. It includes data on the demographics of voters, such as their age, gender, race, education, and income, as well as their voting patterns in past elections.

The dataset is particularly useful for understanding the impact of social and economic factors on voting behavior. For example, researchers can use the dataset to explore how income and education levels influence political preferences, or how gender and race affect voting behavior.

Moreover, policymakers can use the insights gained from the dataset to develop strategies to encourage more people to participate in elections, improve voter turnout, and ensure that voting is more inclusive and representative.

Overall, the Voting Habits dataset is an essential resource for anyone interested in understanding the complex dynamics of voting behavior and developing effective policies to enhance democratic participation.

1) Data Introduction • The Voters and Non-Voters Dataset is based on surveys and polls conducted in various countries that collect voter and non-voters' voting behaviors and patterns based on demographic factors such as gender, race, education, and income.

2) Data Utilization (1) Voters and Non-Voters Dataset has characteristics that: • The dataset contains demographic information such as age, gender, race, education level, income, and variables related to voting behavior such as voting status and voting propensity in past elections. • It is designed to analyze the impact of socio-economic factors on voting participation and political preferences. (2) Voters and Non-Voters Dataset can be used to: • Analyzing Voting Behavior Influencing Factors: It can be used for statistical and machine learning analysis of how various factors such as income, education, gender, race, etc. affect voting participation rates and political choices. • Policy Development and Voter Engagement Strategy: It can be used as a basis for raising turnout and establishing inclusive election policies, which can be applied to developing strategies to increase voting participation of specific groups.

What difference does it make if the state makes people vote? The question is central to normative debates about the rights and duties of citizens in a democracy, and to contemporary policy debates in a number of Latin American countries over what actions states should take to encourage electoral participation. Focusing on a rare case of abolishing compulsory voting in Venezuela, this article shows that not forcing people to vote yielded a more unequal distribution of income. The evidence supports Arend Lijphart’s claim, advanced in his 1996 presidential address to the American Political Science Association, that compulsory voting can offset class bias in turnout and, in turn, contribute to the equality of influence.

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.

This statistic shows the results of a survey on the distribution of populism amongst eligible voters in Germany in 2020, by income. In accordance with the definition provided by the study, around ** percent of respondents with an income of less than ***** euros were classified as populist. Of those with an income of more than ***** euros, **** percent were classified as populist.

Do electorates hold governments accountable for the distribution of economic welfare? Building on the finding of “class-biased economic voting” in the United States, we examine how OECD electorates respond to alternative distributions of income gains and losses. Drawing on individual-level electoral data and aggregate election results across 15 advanced democracies, we examine whether lower- and middle-income voters defend their distributive interests by punishing governments for concentrating income gains among the rich. We find no indication that non-rich voters punish rising inequality, and substantial evidence that electorates positively reward the concentration of aggregate income growth at the top. Our results suggest that governments commonly face political incentives systematically skewed in favor of inegalitarian economic outcomes. At the same time, we find that the electorate’s tolerance of rising inequality has its limits: class biases in economic voting diminish as the income shares of the rich grow in magnitude.

Description

These data come from the 2016 CCES and allow interested students to model the individual correlates of the Trump vote in 2016.

Format

A data frame with 64600 observations on the following 21 variables. | Column | Description | |-------------|---------------------------------------------------------------------------------------------------------------| | uid | Numeric vector, a unique identifier for the respondent as they first appear in the CCES data. | | state | Character vector for the state in which the respondent resides. | | votetrump | Numeric that equals 1 if the respondent says s/he voted for Trump in 2016. | | age | Numeric vector for age, roughly calculated as 2016 - birthyr from the CCES data. | | female | Numeric that equals 1 if the respondent is a woman. | | collegeed | Numeric vector that equals 1 if the respondent says s/he has a college degree. | | racef | Character vector for the race of the respondent. | | famincr | Numeric vector for the respondent's household income, ranging from 1 (Less than $10,000) to 12 ($150,000 or more).| | ideo | Numeric vector for the respondent's ideology on a liberal-conservative discrete scale. 1 = very liberal, 5 = very conservative.| | pid7na | Numeric vector for the respondent's partisanship on the 1-7 scale. 1 = Strong Democrat, 7 = Strong Republican. Other party supporters are coded as NA.| | bornagain | Numeric vector for whether the respondent self-identifies as a born-again Christian. | | religimp | Numeric vector for the importance of religion to the respondent. 1 = not at all important, 4 = very important. | | churchatd | Numeric vector for the extent of church attendance for the respondent. 1 = never, 6 = more than once a week. | | prayerfreq | Numeric vector for the frequency of prayer for the respondent. 1 = never, 7 = several times a day. | | angryracism | Numeric vector for how angry the respondent is that racism exists. 1 = strongly agree, 5 = strongly disagree. | | whiteadv | Numeric vector for agreement with the statement that white people have advantages over others in the U.S. 1 = strongly agree, 5 = strongly disagree.| | fearraces | Numeric vector for agreement with the statement that the respondent fears other races. 1 = strongly disagree, 5 = strongly agree.| | racerare | Numeric vector for agreement with the statement that racism is rare in the U.S. 1 = strongly disagree, 5 = strongly agree.| | lrelig | Numeric vector serving as a latent estimate for religiosity from bornagain, religimp, churchatd, and prayerfreq variables. Higher values = more religiosity.| | lcograc | Numeric vector serving as a latent estimate for cognitive racism, derived from racerare and whiteadv variables. | | lemprac | Numeric vector serving as a latent estimate for empathetic racism, derived from fearraces and angryracism variables.|

Details The latent estimates for religiosity, cognitive racism, and empathetic racism come from a graded response model estimated in mirt. The concepts of "cognitive racism" and "empathetic racism" come from DeSante and Smith.

This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey on the topic of voting and registration in the United States, which was administered as a supplement to the November 2012 CPS questionnaire. The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. Data from the CPS are provided for the week prior to the survey. The voting and registration supplement data are collected every two years to monitor trends in the voting and nonvoting behavior of United States citizens in terms of their different demographic and economic characteristics. The supplement was designed to be a proxy response supplement, meaning a single respondent could provide answers for all eligible household members. The supplement questions were asked of all persons who were both United States citizens and 18 years of age or older. The CPS instrument determined who was eligible for the voting and registration supplement through the use of check items that referred to basic CPS items, including age and citizenship. Respondents were queried on whether they were registered to vote in the November 6, 2012 election, main reasons for not being registered to vote, main reasons for not voting, whether they voted in person or by mail, and method used to register to vote. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, disability status, educational attainment, occupation, and income.

Scholars have long attributed the income-participation gap-- which is the observation that the rich participate in politics more than the poor-- to income-based differences in the resources, recruitment, mobilization, and psychology underpinning political behavior. I argue that these explanations require a longer time horizon than the empirical evidence permits. Education, for example, typically ends in young adulthood and so cannot logically mediate the effect of income on participation in late adulthood. To resolve this temporal problem, I propose that there are two income-participation gaps: one based on current economic status and another on childhood economic history. I situate this argument in a developmental framework and present evidence for it using six studies. The results, while mixed at times, indicate that there are two gaps, that the size of each gap changes over the life course, and that their joint effect creates a larger income-participation gap than estimated by prior research.

This data collection supplies standard monthly labor force data for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Besides the CPS core questions, this survey gathered additional data on citizenship, voter registration, and voter participation in the 1982 congressional elections. Information on demographic characteristics, such as age, sex, race, marital status, veteran status, household relationship, educational background, Hispanic origin, and number and ages of children in household, is available for each person in the household enumerated.

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county