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.

This dataset contains counts of voter registration and voter turnout for all counties in the United States for the years 2004-2022. It also contains measures of each county's Democratic and Republican partisanship, including six-year longitudinal partisan indices for 2006-2022.

This dataset was designed to provide information on the personal and political backgrounds, political attitudes, and relevant behavior of party leaders. The data pertain to Democratic and Republican party elites holding office during the election year of 1984 and include County and State Chairs, members of the Democratic and Republican National Committees, and delegates to the 1984 National Conventions. These data focus on the "representativeness" of the party elites on a variety of dimensions and also permit a comparison of party leaders from the local, state, and national organizational levels. Special emphasis is placed on the presidential election, the presidential nominations system, public policy issues current in the 1984 campaign, and the future of the political parties. In addition, special note was taken of the views of women and minorities and the problem of providing them with representation in the parties. The question of whether their policy views and ideologies differed from other political party elites was also explored. Specific variables include characterization of respondent's political beliefs on the liberal-conservative scale, length of time the respondent had been active in the party, and the respondent's opinions on minorities in the party, party unity, national- and local-level party strength, and party loyalty. Respondents were also queried on attitudes toward important national problems, defense spending, and inflation. In addition, their opinions were elicited on controversial provisions instituted by their parties and on the directions their parties should take in the future. Demographic characteristics are supplied as well.

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

Dataset

This dataset was created by TonyTriolo

Contents

Has the U.S. Supreme Court become more conservative than the public? We introduce results of three surveys conducted over the course of a decade that ask respondents about their opinions on the policy issues before the Court. Using these novel data, we show for the first time that the gap between the Court and the public has grown since 2020, with the Court moving from being quite close to the average American to a position that is more conservative than the majority of Americans. Second, in contrast to findings showing consistency in the public's approval of or deference to the Court, we find that the public's expectations of the Court vary significantly over time and in tandem with changes in the Court's composition and recent rulings. Even so, many members of the public currently underestimate the Court's conservative leaning. Third, we find that respondents' perceptions of the Court's ideology relative to their own are associated with support for institutional changes, but with important differences between Democrats and Republicans. The fact that so many people currently underestimate how conservative the Court is implies that support for proposed changes to the Court may be weaker than it would be if people knew with greater accuracy the Court's conservative nature.

Previous research has shown that political leanings correlate with various psychological factors. While surveys and experiments provide a rich source of information for political psychology, data from social networks can offer more naturalistic and robust material for analysis. This research investigates psychological differences between individuals of different political orientations on a social networking platform, Twitter. Based on previous findings, we hypothesized that the language used by liberals emphasizes their perception of uniqueness, contains more swear words, more anxiety-related words and more feeling-related words than conservatives’ language. Conversely, we predicted that the language of conservatives emphasizes group membership and contains more references to achievement and religion than liberals’ language. We analysed Twitter timelines of 5,373 followers of three Twitter accounts of the American Democratic and 5,386 followers of three accounts of the Republican parties’ Congressional Organizations. The results support most of the predictions and previous findings, confirming that Twitter behaviour offers valid insights to offline behaviour.

Are issue attitudes of rural residents aligned with those of Republicans in the U.S.? Previous research demonstrates an urban-rural divide in issue attitudes whereby rural residents tend to adopt more conservative policy positions and urban residents more liberal ones. In this paper, we investigate whether this notion holds true, or if rural residents are indeed their own unique constituency that carries interests different from what is traditionally “Republican”. We examine 22 canonical issues that are widely discussed in the political discourse and leverage data from the 2020 ANES to compare responses between rural and urban residents, Democrats and Republicans, and the interaction between these factors. In doing so, we find that urban-rural issue differences reflect partisan issue differences - e.g., rural Democrats resemble their urban counterparts and urban Republicans resemble their rural counterparts - rather than rural areas specifically being more Republican. However, we identify certain issues relating to immigration, transgender individuals, and income inequality where rural Democrats are more conservative than urban Democrats. These results point to the role of partisan nationalization in issue stances across the urban-rural spectrum, with some important exceptions. We also highlight the idea that rural America is not always reflective of conservatism and Republicanism.

It is widely reported that partisanship in the United States Congress is at an historic high. Given that individuals are persuaded to follow party lines while having the opportunity and incentives to collaborate with members of the opposite party, our goal is to measure the extent to which legislators tend to form ideological relationships with members of the opposite party. We quantify the level of cooperation, or lack thereof, between Democrat and Republican Party members in the U.S. House of Representatives from 1949–2012. We define a network of over 5 million pairs of representatives, and compare the mutual agreement rates on legislative decisions between two distinct types of pairs: those from the same party and those formed of members from different parties. We find that despite short-term fluctuations, partisanship or non-cooperation in the U.S. Congress has been increasing exponentially for over 60 years with no sign of abating or reversing. Yet, a group of representatives continue to cooperate across party lines despite growing partisanship.

This feature layer represents the boundaries of Connecticut's Senate districts based on the latest redistricting process following the 2020 Census. More information about the 2021 Redistricting Project can be found here.The dataset includes the 36 Senate districts with fields identifying the current CT Senate members and their associated political party for each district. The geometry is derived from the published data from the Connecticut General Assembly.More information about the CT Senate members can be found here.Collection of CT Legislative District published feature layers:Congressional districtsSenate districtsHouse districtsAttributesDistrictSenate district number (text/string)DistrictNSenate district number (number/integer)PartyMember’s political party (Democratic or Republican)Full NameMember’s full name<td st

Context

Let's analyze & data-science the s**t out of campaign finance & contributions (individuals & PACs) for all federal elections over the last 2 decades & a half! My goal is for us to collectively get new insights in this domain.

Content

This includes campaign finance data for all US federal elections (including the every-2-year congressional & every-4-year presidential) from 1990 to 2016. It includes candidate data, PAC data, individual contributions data, PAC to PAC contribution data & PAC to candidates contribution data along with political party, industry, sector & geographical information for the contributions.

Acknowledgements

The source of this data is the Bulk data at https://www.opensecrets.org/

Documentation : https://www.opensecrets.org/open-data/bulk-data-documentation

Please follow the Terms Of Service for using this data : https://www.opensecrets.org/open-data/terms-of-service

Also, I used the Postgres database dump created by Soloman at his github repo here based on the OpenSecrets bulk dataset

Inspiration

Some interesting questions that we should be able to answer with this dataset :

1. How do individual & PAC contributions evolve over time?
2. How are contributions affected by the presidential election year vs midterms?
3. How do contributions differ between different political parties or industries?
4. How do contributions differ across strong Democratic/Republican states? Which states are more/less active in these elections?

While groups have been central to thinking about partisan identity and choices, there has been surprisingly little attention paid to the role of perceptions of the group composition of the parties. We explore this critical linking information in the context of religious groups, some of the chief pivots around which the parties have been sorting. Using three national samples, we show that perceptions of the religious group composition of the parties are often biased – evangelicals overestimate the presence of evangelicals within the Republican Party and the irreligious within the Democratic Party. The key finding is that individuals are far more likely to identify with the party in which they believe their group is well represented – a finding which clarifies the role of party image shifts in constructing partisanship, the limits of the culture war motif, and the importance of social perception in shaping beliefs about party representation.

This scale attempts to measure the strength of general (not political) conservative belief in individuals and groups.The 12 agree -- disagree items of the first form of the scale were selected from an initial pool of 43 items drown from the works of conservative writers such as Edmund Burke. In a succeeding study, the items were reduced to nine, and the population was divided into scoring quartiles as follows: Extreme Conservatives, 7-9, Moderate Conservatives, 5-6, Moderate Liberals, 3-4, Liberals, 0-2.Agreement with each item indicates conservatism.The original measure proved to be subject to a severe problem of agreement response set, which is itself correlated with social status in the same direction as conservatism, although relations still held with controls for response set applied. Those scoring as strongly conservative proved, by comparison with the low scorers, to hold extremely conventional social attitudes, to be more responsive to naturalistic symbo ls, and to place greater emphasis upon duty, conformity and discipline, with controls instituted for education, occupation, socioeconomic status, and possible agreement response set bias. High scorers were most frequently the uninformed, poorly educated, and significantly lower scorers in the author's measures of 'awareness' and 'intellectuality.' It was found that the correlations between conservatism and party affiliation and public issues tended to be low, a finding which was corroborated on a cross-national sample by Campbell et al. (1960). It was also found that the scale did discriminate significantly between Republican and Democratic party elites and the 'spectator' elites -- governalistic and academic. However, for the rank-and file citizen, there is little or no connection between a person's stand on specific political and social issues, his party identification, his political behavior and his more general attitude toward change -- his conservative or liberal ideology. However this scale did a fairly good job of discriminating members of the public who had changed party identification from Democrat to Republican or from Republican to Democrat. Photiadis and Biggar found the conservatism scale to be related to religious orthodoxy and extrinsic religious belief, as well as confirming many of the earlier correlates found by McClosky.In a more recent study, Matthews and Prothro (1966) administered five McClosky items to a large sample of southern Negroes and whites. It was found that 33% of the Negroes and only 19% of the whites sampled scored at the resistant end of the scale. Both Negroes and whites who favored change scor ed higher on the author's Political Participation Scale although the difference was greater for Negroes than for whites. The relationship persisted even when the effects of political interest and information were statistically controlled. It is interesting to note that while a sample of southern Negro college students were found to be much more favorable toward change than the adults sampled, over two thirds of the students scored on the 'conservative' end of Campbell et al.'s Domestic Attitude Scale. Scoring information: High scores indicate greater conservatism. Two summary scores are created, CONSERV1, which reflects the original agree/disagree scoring, and CONSERV2 which reflects the 4-point agree/disagree scoring used in this administration.See codebook for additional information

This is likely a foundational survey for Pew Research Center's renowned Political Typology study, which moves beyond simple "Republican/Democrat/Independent" labels to create a more nuanced classification of the U.S. electorate. This methodology groups Americans into cohesive groups based on their values, political beliefs, and partisan affiliation, not just their voting habits. The core purpose is to analyze and measure political polarization, revealing the deep divisions—as well as the unexpected areas of agreement—that exist within and between parties. It explores the growing ideological consistency (or inconsistency) among the public and the shrinking size of the political "center."

This feature layer represents the boundaries of Connecticut's Senate districts based on the latest redistricting process following the 2020 Census. More information about the 2021 Redistricting Project can be found here.The dataset includes the 36 Senate districts with fields identifying the current CT Senate members and their associated political party for each district. The geometry is derived from the published data from the Connecticut General Assembly.More information about the CT Senate members can be found here.Collection of CT Legislative District published feature layers:Congressional districtsSenate districtsHouse districtsAttributesDistrictSenate district number (text/string)DistrictNSenate district number (number/integer)PartyMember’s political party (Democratic or Republican)Full NameMember’s full nameFull Name + PartyMember’s full name, plus political partyTermThe two-year term during which the member serves in their elected roleAdjacent Color IDAn ID for the purpose of symbolization, so that each polygon receives a different color than the polygon adjacent to it.

Two parallel processes structure American politics in the current moment: partisan polarization and the increasing linkage between racial attitudes and issue preferences of all sorts. We develop a novel theory that roots these two trends in historical changes in party coalitions. Changing racial compositions of the two major parties led to the formation of racialized images about Democrats and Republicans in people’s minds—and these images now structure Americans’ partisan loyalties and policy preferences. We test this theory in three empirical studies. First, using the American National Election Studies we trace the growing racial gap in party coalitions as well as the increasing overlap between racial and partisan affect. Then, in two original survey studies we directly measure race–party schemas and explore their political consequences. We demonstrate that race–party schemas are linked to partisan affect and issue preferences—with clear implications for the recent developments in U.S. politics.

Lists of Twitter acccounts of U.S. political elites from government, news media and political parties. Metadata on number of followers (as of December 2015), date of account registration and Twitter ID. List of news media actors contains major newspapers and national TV stations as well as politically influential media persons and individual journalists. List of politicians contains all members of U.S. congress, governors, presidential candidates and main accounts of the Republican and Democratic parties. Metadata on politicians contains political office and party affiliation. List of government actors contains federal agencies, secretaries, the President and affiliated accounts.

This feature layer represents the boundaries of Connecticut's House of Representative’s districts based on the latest redistricting process following the 2020 Census. More information about the 2021 Redistricting Project can be found here.The dataset includes the 151 house districts with fields identifying the current CT House of Representatives members and their associated political party for each district. The geometry is derived from the published data from the Connecticut General Assembly.More information about the CT House of Representatives members can be found here.Collection of CT Legislative District published feature layers:Congressional districtsSenate districtsHouse districtsAttributesDistrictHouse district number (text/string)DistrictNHouse district number (number/integer)PartyMember’s political party (Democratic or Republican)Full Name<p style='line-height:normal; margin-bottom:0i

This dataset is a subset of the Stanford Congressional Record dataset: Gentzkow, Matthew, Jesse M. Shapiro, and Matt Taddy. Congressional Record for the 43rd-114th Congresses: Parsed Speeches and Phrase Counts. Palo Alto, CA: Stanford Libraries [distributor], 2018-01-16. https://data.stanford.edu/congress_text It contains speeches from the daily subset (covering 1981–2017) made by members of the Democratic and Republican parties that include 20 whitespace-separated tokens or more and did not… See the full description on the dataset page: https://huggingface.co/datasets/macleginn/stanford_congress_record_longer_speeches.

What factors influence agenda setting behavior in state legislatures in the United States? Using the localized effects of climate change, we examine whether notable changes in temperature can raise the salience of the issue, thus encouraging a legislative response. To evaluate the behavior of individual legislators around climate policy, we utilize an original dataset that includes geographic mapping of climate anomalies at the state legislative district level and incorporates individual, chamber, district and state characteristics to predict climate bill sponsorship. Using a multi-level model that estimates climate-change bill sponsorship among 25,000 legislators from 2011-2015, we find a robust relationship between temperature anomalies and bill sponsorship for Democratic members of state legislators while Republicans are unresponsive to such factors. Our data and methodological approach allow us to examine legislative action on climate change beyond final policy passage and offers an opportunity to understand the motivations behind climate innovation in the American states.