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.

About this dataset

Context

The dataset tabulates the Republican City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Republican City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Republican City was 137, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Republican City population was 137, an increase of 2.24% compared to a population of 134 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Republican City decreased by 71. In this period, the peak population was 208 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Republican City is shown in this column.
• Year on Year Change: This column displays the change in Republican City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Republican City Population by Year. You can refer the same here

Context

This data includes information about the 50 United State legislatures, specifically the Senate chambers.

Content

The information contained within this data set include: 1-the state, 2-the number of Senate seats that Democrats hold in that state, 3-the number of Senate seats that Republicans hold in that state, 4-the number of Senate other parties or nonpartisan seats hold in that state.

I acquired this data through the National Conference of State Legislatures website. The reports data is up to 12-15-2017.

Acknowledgements

I would like to thank the National Conference of State Legislatures for providing this data. I would also like to thank Kaggle for allowing this platform to open source data.

Inspiration

What are the states that are most vulnerable to switch from Republican to Democrat control or from Democrat to Republican control?

Legislative super majorities are defined as being 66% greater than or equal to the number of seats held by the Republican party or the Democratic party. What are the states that are close to being a Republican or Democratic super majority?

Also, what is the least costly state to turn from Republican to Democrat or from Democrat to Republican?

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

Content

FiveThirtyEight's Pollster Ratings

This directory contains the data behind FiveThirtyEight's pollster ratings.

See also:

• FiveThirtyEight's Pollster Ratings
• The Polls Are All Right
• The State Of The Polls, 2016
• How FiveThirtyEight Calculates Pollster Ratings

Past data:

• 2014
• 2016

pollster-stats-full.xlsx contains a spreadsheet with all of the summary data and calculations involved in determining the pollster ratings as well as descriptions for each column.

pollster-ratings.csv has ratings and calculations for each pollster. A copy of this data and descriptions for each column can also be found in pollster-stats-full.xlsx.

raw-polls.csv contains all of the polls analyzed to give each pollster a grade. Descriptions for each column are in the table below.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight [organization page](https://www.kaggle.com/five...

The Politbarometer has been conducted since 1977 on an almost monthly basis by the Forschungsgruppe Wahlen on behalf of the Second German Television (ZDF). Since 1990, this database has also been available for the new German states. The survey focuses on the opinions and attitudes of the voting-age population in the Federal Republic on current political issues, parties, politicians, and voting behavior. From 1990 to 1995 and from 1999 onward, the Politbarometer surveys were conducted separately both in the newly formed eastern and in the western German states (Politbarometer East and Politbarometer West). The separate monthly surveys of a year are integrated into a cumulative data set that includes all surveys of a year and all variables of the respective year. Starting in 2003, the Politbarometer short surveys, collected with varying frequency throughout the year, are integrated into the annual cumulation.
Most important problems in the Federal Republic; satisfaction with democracy; the right people in leading positions; behavior at the polls in the last Federal Parliament election and party preference (Sunday question and rank order procedure); party inclination and party identification; expected winner in the Federal Parliament election 1990; coalition preferences; preference for federal chancellor; preferred top candidate of the SPD; desired secretary-general of the CDU; sympathy scale for selected top politicians in the Federal Republic and in the GDR; most important politicians; attitude to re-election of von Weizsaecker; judgement on health care reform and participation in the costs of medications; judgement on the effects of the petroleum tax increase; attitude to a reduction in low-altitude flights in the Federal Republic; assessment of the security of peace in Europe and feeling of threat from the Warsaw Pact; attitude to modernization of the Western nuclear weapons or securing of peace by doing without nuclear weapons; preferred action of the West in disarmament negotiations and judgement on the disarmament measures of the Soviet Union; attitude to modernization of short-range nuclear missiles and to disarmament negotiations; preference for West German or American attitudes in important political questions; attitude to NATO and American troop withdrawal; judgement on German-American relations and their change since assumption of office by George Bush; judgement on the relation to the Soviet Union and understanding for the concerns of the western alliance partners regarding the reliability of the Federal Republic; attitude to applicants for political asylum and ethnic Germans from Eastern Europe; judgement on the right to asylum and attitude to a limitation of the number of asylum-seekers; attitude to the right to vote for foreigners and to easier acquisition of German citizenship; contacts with foreigners at work; expected election winner in Berlin; interest in the European Election; expected conduct of the Republicans in the EC parliament; classification of the Republicans as right-wing radical and knowledge of Republican voters; attitude to election success by the Republicans as a warning for the established parties; assessment of the Republicans as new Nazis; EC membership as advantage for the Federal Republic; assessment of the significance of parliament decisions at municipal, state, federal and European level; judgement on the monitoring of food quality in the Federal Republic and assumed changes of the quality of controls after European standardization; attitude to radioactive radiation treatment of food to increase shelf-life and feared harm to health; attitude to reduction in working hours to a 35-hour work week; job worries; assessment of the loyalty to the chancellor of the CDU, loyalty to the coalition of the CSU and of the FDP; attitude to a nation-wide CSU; danger of communism; attitude to admission of Eastern European nations into the EC and countries preferred for this; attitude to credits for Poland and to the Oder-Neisse Line; attitude to unification of the two German nations and to the 10-point plan of the government; judgement on the reform demands for the GDR as West German intervention in the matters of the GDR; judgement on the number of GDR refugees and relief for emigrants; understanding for emmigration; feared disadvantages for jobs and housing from emigrants; recognition of citizens of the GDR as Germans; judgement on the reunification of the two German nations; attitude to financial aid for the GDR and tying this aid to more extensive reforms; judgement on media reporting on the GDR; interest in a visit to the GDR; relatives in the GDR; visit to the GDR dependent on lifting of the visa requirement and forced currency exchange; attitude to abortion and to the abortion law ruling; judgement on CDU policies regarding questionsof ethnic Germans from Eastern Europe, asylum-seekers, abortion and reunification; self-classification on a left-right continuum; judgement on...

Content

Primary Candidates 2018

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:

dem_candidates.csv includes:

We examine state legislator behavior at the passage stage of voting on restrictive voter identification (ID) bills from 2005 to 2013. Partisan polarization of state lawmakers on voter ID laws is well known but we know very little with respect to other determinants driving this political division. With rare exceptions, a major shortcoming of extant research evaluating the passage of voter ID bills stems from using the state legislature as the unit of analysis. We depart from existing scholarship by using the state legislator as our primary unit of analysis and we cover the entirety of the period when restrictive voter ID laws became a frequent agenda item in state legislatures, from the first passage of a strict photo ID bill in 2005 to the latest measures passed in 2013. Beyond the obviously significant effect of party affiliation, we find that there exists a notable relationship between the racial composition a member’s district, region, and electoral competition, and the likelihood that a state lawmaker supports a voter ID bill. Democratic lawmakers representing substantial black district populations are more opposed to restrictive voter ID laws, whereas Republican legislators with substantial black district populations are more supportive. Controlling for party, we find southern lawmakers (particularly Democrats) to be more opposed to restrictive voter ID legislation. Further, all else equal, we find black legislators in the South to be the least supportive of restrictive voter ID bills, which is likely tied to the historical context associated with state laws restricting electoral participation. Finally, in those state legislatures where electoral competition is not intense, partisan polarization over voter ID laws is less stark, which likely reflects the expectation that the reform will have little bearing on the outcome of state legislative contests.

Under what circumstances do White Americans receive better representation than racial and ethnic minorities? To answer this fundamental question, we examine how well national policy outcomes match the preferences of 520,000 Black, Latino, Asian American, and White citizens from 2006 to 2022. Average racial gaps in responsiveness are small regardless of issue area. However, White voters are significantly advantaged when Republicans control government. Respondents’ class, age, and ideology do not explain these patterns. Respondents’ partisanship explains some, but not all, of them. To further investigate these disparities, we analyze roll-call votes in Congress, focusing on the Senate—the pivotal lawmaking institution. Similar patterns emerge: Republican Senators better represent White (versus Black or Latino) constituents. Moreover, Black-White disparities are larger in states where Black Americans comprise more of the population, suggesting that party-based racial disparities might reflect White racial attitudes. Indeed, we find that state-level White resentment predicts Black-White representational disparities. For replication notes and instructions, see: README.txt For codebook for primary analysis datasets, see: cooebook.txt Dataverse includes code and raw datafiles use to create final datasets.