Since 1789, 45 different men have served as President of the United States, and the average age of these men when taking office for the first time was approximately 57 years. Two men, Grover Cleveland and Donald Trump, were elected to two non-consecutive terms, and Donald Trump's victory in 2024 made him the oldest man ever elected as president, where he will be 78 years and seven months old when taking office again. Record holders The oldest president to take office for the first time was Joe Biden in 2021, at 78 years and two months - around five months younger than Donald Trump when he assumes office in 2025. The youngest presidents to take office were Theodore Roosevelt in 1901 (42 years and 322 days), who assumed office following the assassination of William McKinley, and the youngest elected president was John F Kennedy in 1961 (43 years and 236 days). Historically, there seems to be little correlation between age and electability, and the past five presidents have included the two oldest to ever take office, and two of the youngest. Requirements to become president The United States Constitution states that both the President and Vice President must be at least 35 years old when taking office, and must have lived in the United States for at least 14 years of their life. Such restrictions are also in place for members of the U.S. Congress, although the age and residency barriers are lower. Additionally, for the roles of President and Vice President, there is a "natural-born-citizen" clause that was traditionally interpreted to mean candidates must have been born in the U.S. (or were citizens when the Constitution was adopted). However, the clause's ambiguity has led to something of a reinterpretation in the past decades, with most now interpreting it as also applying to those eligible for birthright citizenship, as some recent candidates were born overseas.

The average height of the 45 men who have served as the President of the United States is approximately 180cm (5'11"); this is roughly five centimeters (two inches) taller than the average U.S. male in 2020. Abraham Lincoln has the distinction of being the tallest U.S. president in history, at 193cm (6'4"), while James Madison was the shortest (and lightest) U.S. president at 163cm (5'4"). US presidents are getting taller Of the ten most recent presidents, only Jimmy Carter has been shorter than the presidential average, while none of the presidents who have served since the beginning of the twentieth century have been shorter than the national average. Since Ronald Reagan became president in 1981, George W. Bush and Joe Biden are the only U.S. president to have been shorter than six feet tall; by just half an inch. Trump height controversy Former President Donald Trump made headlines in 2018, when his official height increased from 6'2" (the height from all previously-existing records, including his 2012 drivers license) to 6'3"*. Many in the media speculated that this was to prevent him from being classified as obese according to his body mass index. A number of photos also circulated on social media showing Trump next to (and visibly shorter than) a number of athletes who are officially 6'3", while photos of him standing next to Barack Obama were used to show that he may be closer to his predecessor's height, at 6'1". Nonetheless, Trump's medical report from June 3. 2020, shows that his official height remained at 6'3".

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in President township. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in President township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in President township, the median household income stands at $91,250 for householders within the 45 to 64 years age group, followed by $51,250 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $44,250.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 President township median household income by age. You can refer the same here

The President’s Malaria Initiative (PMI) is a U.S. Government initiative designed to reduce malaria deaths and illnesses in target countries in sub-Saharan Africa with a long-term vision of a world without malaria. This asset contains two data files that hold budget code information for projects with the associated FY18 budget and activity descriptions. USAID has made these data publicly available since 2006 as part of the Country Malaria Operating Plans. The data are updated annually.

The 2016 U.S. presidential election was contested by Donald J. Trump of the Republican Party, and Hillary Rodham Clinton of the Democratic Party. Clinton had been viewed by many as the most likely to succeed President Obama in the years leading up to the election, after losing the Democratic nomination to him in 2008, and entered the primaries as the firm favorite. Independent Senator Bernie Sanders soon emerged as Clinton's closest rival, and the popularity margins decreased going into the primaries. A few other candidates had put their name forward for the Democratic nomination, however all except Clinton and Sanders had dropped out by the New Hampshire primary. Following a hotly contested race, Clinton arrived at the Democratic National Convention with 54 percent of pledged delegates, while Sanders had 46 percent. Controversy emerged when it was revealed that Clinton received the support of 78 percent of Democratic superdelegates, while Sanders received just seven percent. With her victory, Hillary Clinton became the first female candidate nominated by a major party for the presidency. With seventeen potential presidential nominees, the Republican primary field was the largest in US history. Similarly to the Democratic race however, the number of candidates thinned out by the time of the New Hampshire primary, with Donald Trump and Ted Cruz as the frontrunners. As the primaries progressed, Trump pulled ahead while the remainder of the candidates withdrew from the race, and he was named as the Republican candidate in May 2016. Much of Trump's success has been attributed to the free media attention he received due to his outspoken and controversial behavior, with a 2018 study claiming that Trump received approximately two billion dollars worth of free coverage during the primaries alone. Campaign The 2016 presidential election was preceded by, arguably, the most internationally covered and scandal-driven campaign in U.S. history. Clinton campaigned on the improvement and expansion of President Obama's more popular policies, while Trump's campaign was based on his personality and charisma, and took a different direction than the traditional conservative, Republican approach. In the months before the election, Trump came to represent a change in how the U.S. government worked, using catchy slogans such as "drain the swamp" to show how he would fix what many viewed to be a broken establishment; painting Clinton as the embodiment of this establishment, due to her experience as First Lady, Senator and Secretary of State. The candidates also had fraught relationships with the press, although the Trump campaign was seen to have benefitted more from this publicity than Clinton's. Controversies Trump's off the cuff and controversial remarks gained him many followers throughout the campaign, however, just one month before the election, a 2005 video emerged of Trump making derogatory comments about grabbing women "by the pussy". The media and public's reaction caused many high-profile Republicans to condemn the comments (for which he apologized), with many calling for his withdrawal from the race. This controversy was soon overshadowed when it emerged that the FBI was investigating Hillary Clinton for using a private email server while handling classified information, furthering Trump's narrative that the Washington establishment was corrupt. Two days before the election, the FBI concluded that Clinton had not done anything wrong; however the investigation had already damaged the public's perception of Clinton's trustworthiness, and deflected many undecided voters towards Trump. Results Against the majority of predictions, Donald Trump won the 2016 election, and became the 45th President of the United States. Clinton won almost three million more votes than her opponent, however Trump's strong performance in swing states gave him a 57 percent share of the electoral votes, while Clinton took just 42 percent. The unpopularity of both candidates also contributed to much voter abstention, and almost six percent of the popular vote went to third party candidates (despite their poor approval ratings). An unprecedented number of faithless electors also refused to give their electoral votes to the two main candidates, instead giving them to five non-candidates. In December, it emerged that the Russian government may have interfered in this election, and the 2019 Mueller Report concluded that Russian interference in the U.S. election contributed to Clinton's defeat and the victory of Donald Trump. In total, 26 Russian citizens and three Russian organizations were indicted, and the investigation led to the indictment and conviction of many top-level officials in the Trump campaign; however Trump and the Russian government both strenuously deny these claims, and Trump's attempts to frame the Ukrainian government for Russia's involvement contributed to his impeachment in 2019.

About this dataset

Context

The dataset tabulates the President township population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of President township. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 279 (60.52% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the President township population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in President township is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the President township is shown in the following column.

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 President township Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in President township: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 2(0.87%) households where the householder is under 25 years old, 31(13.54%) households with a householder aged between 25 and 44 years, 117(51.09%) households with a householder aged between 45 and 64 years, and 79(34.50%) households where the householder is over 65 years old.
• In President township, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 President township median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of President township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for President township. The dataset can be utilized to understand the population distribution of President township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in President township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for President township.

Key observations

Largest age group (population): Male # 55-59 years (49) | Female # 50-54 years (35). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the President township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the President township is shown in the following column.
• Population (Female): The female population in the President township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in President township for each age group.

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 President township Population by Gender. You can refer the same here

This dataset is about books and is filtered where the book is Abraham Lincoln : the United States president who abolished the curse of slavery in his country, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Analysis of ‘2020 US General Election Turnout Rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imoore/2020-us-general-election-turnout-rates on 28 January 2022.

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

Intro

Voter turnout is the percentage of eligible voters who cast a ballot in an election. Eligibility varies by country, and the voting-eligible population should not be confused with the total adult population. Age and citizenship status are often among the criteria used to determine eligibility, but some countries further restrict eligibility based on sex, race, or religion.

Context

The historical trends in voter turnout in the United States presidential elections have been determined by the gradual expansion of voting rights from the initial restriction to white male property owners aged 21 or older in the early years of the country's independence, to all citizens aged 18 or older in the mid-20th century. Voter turnout in United States presidential elections has historically been higher than the turnout for midterm elections. https://upload.wikimedia.org/wikipedia/commons/a/a7/U.S._Vote_for_President_as_Population_Share.png" alt="f">

Content

Turnout rates by demographic breakdown from the Census Bureau's Current Population Survey, November Voting and Registration Supplement (or CPS for short). This table are corrected for vote overreporting bias. For uncorrected weights see the source link.

Original source: https://data.world/government/vep-turnout

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

This contains data relevant for the 2016 US Presidential Election, including up-to-date primary results.

Exploration Ideas

• What candidates within the Republican party have results that are the most anti-correlated?
• Which Republican candidate is Hillary Clinton most correlated with based on county voting patterns? What about Bernie Sanders?
• What insights can you discover by mapping this data?

Do you have answers or other exploration ideas? Add your ideas to this forum post and share your insights through Kaggle Scripts!

Do you think that we should augment this dataset with more data sources? Let us know here!

Data Description

The 2016 US Election dataset contains several main files and folders at the moment. You may download the entire archive via the "Download Data" link at the top of the page, or interact with the data in Kaggle Scripts through the ../input directory.

• PrimaryResults.csv: main primary results file
  • State: state where the primary or caucus was held
  • StateAbbreviation: two letter state abbreviation
  • County: county where the results come from
  • Party: Democrat or Republican
  • Candidate: name of the candidate
  • Votes: number of votes the candidate received in the corresponding state and county (may be missing)
  • FractionVotes: fraction of votes the president received in the corresponding state, county, and primary
• database.sqlite: SQLite database containing the PrimaryResults table with identical data and schema
• county_shapefiles: directory containing county shapefiles at three different resolutions for mapping

Original Data Sources

• Iowa County-Level Results
• New Hampshire County-Level Results
• County Shapefiles

Election Facebook’s Ad Metrics 2024: Trump vs. Harris

A key event of 2024 is the U.S. presidential election. This project focuses on analyzing how Donald Trump and Kamala Harris use advertising to win votes, exploring their strategies, actions, and effectiveness.

Here is the Dataset i have used in the analytic:

File name: trump.zip and harris.zip (Original Data)

The files were downloaded from the Facebook Ad Library. The data focuses on two primary accounts: Trump and Harris, which had the highest number of advertisements and the largest ad spend. These accounts promoted two types of campaigns: presidential campaigns and victory funds. However, I will concentrate solely on the presidential campaigns. Date Range: Based on my research, presidential campaigns typically begin about a year before the election. Therefore, I collected data starting from February 25, 2023, the date Harris announced her candidacy to compete with Trump, up to the current date, December 7, 2024.

File name: Trump-Harris add-id.csv (Processed Data)

This is the main data of the "Election Facebook’s Ad Metrics 2024: Trump vs. Harris"

File name: AD-Tech-Analytic-Project-DashBoard.pbix

Power BI chart imported data from Trump-Harris add-id.csv (Processed Data) and some others

File name: 6state trump data.csv, datamichigan.csv, data nevada.csv

Data that filters from Trump-Harris add-id.csv (Processed Data) have been used in AD-Tech-Analytic-Project-DashBoard.pbix

This dataset is about books and is filtered where the book subjects includes Presidents-United States-Election-History-Anecdotes, featuring 9 columns including author, BNB id, book, book publisher, and book subjects. The preview is ordered by publication date (descending).

Disclaimer: This dataset is distributed by Daniel Gayo-Avello, an associate professor at the Department of Computer Science in the University of Oviedo, for the sole purpose of non-commercial research and it just includes tweet ids.

The dataset contains tweet IDs for all the published tweets (in any language) bettween March 21, 2006 and July 31, 2009 thus comprising the first whole three years of Twitter from its creation, that is, about 1.5 billion tweets (see file Twitter-historical-20060321-20090731.zip).

It covers several defining issues in Twitter, such as the invention of hashtags, retweets and trending topics, and it includes tweets related to the 2008 US Presidential Elections, the first Obama’s inauguration speech or the 2009 Iran Election protests (one of the so-called Twitter Revolutions).

Finally, it does contain tweets in many major languages (mainly English, Portuguese, Japanese, Spanish, German and French) so it should be possible–at least in theory–to analyze international events from different cultural perspectives.

The dataset was completed in November 2016 and, therefore, the tweet IDs it contains were publicly available at that moment. This means that there could be tweets public during that period that do not appear in the dataset and also that a substantial part of tweets in the dataset has been deleted (or locked) since 2016.

To make easier to understand the decay of tweet IDs in the dataset a number of representative samples (99% confidence level and 0.5 confidence interval) are provided.

In general terms, 85.5% ±0.5 of the historical tweets are available as of May 19, 2020 (see file Twitter-historical-20060321-20090731-sample.txt). However, since the amount of tweets vary greatly throughout the period of three years covered in the dataset, additional representative samples are provided for 90-day intervals (see the file 90-day-samples.zip).

In that regard, the ratio of publicly available tweets (as of May 19, 2020) is as follows:

• March 21, 2006 to June 18, 2006: 88.4% ±0.5 (from 5,512 tweets).
• June 18, 2006 to September 16, 2006: 82.7% ±0.5 (from 14,820 tweets).
• September 16, 2006 to December 15, 2006: 85.7% ±0.5 (from 107,975 tweets).
• December 15, 2006 to March 15, 2007: 88.2% ±0.5 (from 852,463 tweets).
• March 15, 2007 to June 13, 2007: 89.6% ±0.5 (from 6,341,665 tweets).
• June 13, 2007 to September 11, 2007: 88.6% ±0.5 (from 11,171,090 tweets).
• September 11, 2007 to December 10, 2007: 87.9% ±0.5 (from 15,545,532 tweets).
• December 10, 2007 to March 9, 2008: 89.0% ±0.5 (from 23,164,663 tweets).
• March 9, 2008 to June 7, 2008: 66.5% ±0.5 (from 56,416,772 tweets; see below for more details on this).
• June 7, 2008 to September 5, 2008: 78.3% ±0.5 (from 62,868,189 tweets; see below for more details on this).
• September 5, 2008 to December 4, 2008: 87.3% ±0.5 (from 89,947,498 tweets).
• December 4, 2008 to March 4, 2009: 86.9% ±0.5 (from 169,762,425 tweets).
• March 4, 2009 to June 2, 2009: 86.4% ±0.5 (from 474,581,170 tweets).
• June 2, 2009 to July 31, 2009: 85.7% ±0.5 (from 589,116,341 tweets).

The apparent drop in available tweets from March 9, 2008 to September 5, 2008 has an easy, although embarrassing, explanation.

At the moment of cleaning all the data to publish this dataset there seemed to be a gap between April 1, 2008 to July 7, 2008 (actually, the data was not missing but in a different backup). Since tweet IDs are easy to regenerate for that Twitter era (source code is provided in generate-ids.m) I simply produced all those that were created between those two dates. All those tweets actually existed but a number of them were obviously private and not crawlable. For those regenerated IDs the actual ratio of public tweets (as of May 19, 2020) is 62.3% ±0.5.

In other words, what you see in that period (April to July, 2008) is not actually a huge number of tweets having been deleted but the combination of deleted *and* non-public tweets (whose IDs should not be in the dataset for performance purposes when rehydrating the dataset).

Additionally, given that not everybody will need the whole period of time the earliest tweet ID for each date is provided in the file date-tweet-id.tsv.

For additional details regarding this dataset please see: Gayo-Avello, Daniel. "How I Stopped Worrying about the Twitter Archive at the Library of Congress and Learned to Build a Little One for Myself." arXiv preprint arXiv:1611.08144 (2016).

If you use this dataset in any way please cite that preprint (in addition to the dataset itself).

If you need to contact me you can find me as @PFCdgayo in Twitter.

This dataset is about books and is filtered where the book subjects includes Presidents' spouses-United States-History, featuring 9 columns including author, BNB id, book, book publisher, and book subjects. The preview is ordered by publication date (descending).

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in President township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In President township, the median income for all workers aged 15 years and older, regardless of work hours, was $33,250 for males and $23,676 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in President township. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetownship of President township.

- Full-time workers, aged 15 years and older: In President township, among full-time, year-round workers aged 15 years and older, males earned a median income of $61,250, while females earned $53,750, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of President township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in President township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 President township median household income by race. You can refer the same here

Context

• These datasets were produced as part of a little research project I undertook for a blog post on sentiment analysis, which you can access here: https://bit.ly/32PmWdf

• I uploaded a more extensive dataset of presidential speeches on Kaggle here: https://bit.ly/2E7Fmvw

Content

The datasets were created to compare sentiment scores across two text types (tweets versus political speeches) and three sentiment models (Pattern, Vader and the polarity model incorporated in Stanford CoreNLP).

***- sentiment_speeches_Kaggle.csv: *** The dataset contains sentiment codings (Pattern,Vader & Stanford model) for al inauguration and state of the union speeches of US presidents since 1917 on sentence level.

-sentiment_tweets°Kaggle.csv:

The dataset contains sentiment codings (Pattern,Vader & Stanford model) for a sample of around 11500 tweets of US politicians (Donald J. Trump (Rep.), Rand Paul (Rep.),Ted Cruz (Rep.), Alexandria Ocasio-Cortez (Dem.), Nancy Pelosi (Dem.) and Bernie Sanders (Dem.)). The sentiment polarity has been computed on tweet-level, not sentence-level. I simply scraped the last 2000-ish tweets of each timeline using the GetOldTweets module for Python. You can read more about the data collection process in the aforementioned blog post (https://bit.ly/32PmWdf).

Distribution of sentiment scores

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2Fac6b4a3ca5a33d654f7fb2c755165634%2Fdistribution%20sentiment%20comparison.png?generation=1595583728484033&alt=media" alt="">

** Some example analyses **

• The fact that the analyses of presidential speeches has been performed on sentence-level makes an analysis of polarity-development within the same text extremely easy. For example, one can easily plot the polarity scores across a single inaugural speech, like I did here for the inaugural addressees since JFK: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2Fb7ff60d3cbc674632f301564bd80636f%2Fsentiment_inauguration.png?generation=1595583860959296&alt=media" alt="">

• One could also compare how these models code a particular tweet or speech and index the amount of (dis)agreement betwene the different models: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2Fb207991f30deb2b14c0ef048f0ebdad8%2Fagreement_vs_disagreement_libraries.png?generation=1595583966382283&alt=media" alt="">

You can read more about the data collection, wrangling and analysis process in the aforementioned blog post.

The 18th presidential election of the United States was contested in 1856 by James Buchanan of the Democratic Party, John C Frémont of the Republican Party, and former President Millard Fillmore of the Native American (Know Nothing) Party. This was the first time that the Republican Party (founded in 1854) fielded a nominee, and, although unsuccessful here, the Republicans would go on to win 13 of the next 15 US presidential elections. Results No candidate won over half of the popular vote, however Buchanan's plurality did give him 59 percent of the electoral votes, making him the fifteenth President of the United States. With this victory, Buchanan became the only President in US history to be elected despite the incumbent president being from the same party and eligible for re-election. Buchanan won 19 out of 31 states (including all of the south), while Frémont took 11 states (all "free states" and in the north), and Fillmore carried just one state; Maryland. The reason for the Democratic Party's dominance in the south was their emphasis on sovereignty, giving states autonomy on the issue of slavery. The Know Nothing Party The ironically titled Native American Party, which began as a secret society, was an anti-Catholic, anti-immigration and xenophobic organization, that became the largest third party in the US in the 1850s. Although they changed their name to the American Party in 1855, they were most commonly known as the "Know Nothing" Party, as when members were asked about specific details regarding the movement they were obliged to reply with "I know nothing". While the party's existence was short-lived, they were the main alternative to the Democratic Party in the south during this time, as the newly-formed Republican Party's anti-slavery stance made them unpopular in the south.

Proportion of subjects who correctly identified presidents as presidents in Experiments 1 and 2, in order of when they served their first term.

United States The Economist YouGov Polls: 2024 Presidential Election: Donald Trump data was reported at 46.000 % in 29 Oct 2024. This stayed constant from the previous number of 46.000 % for 22 Oct 2024. United States The Economist YouGov Polls: 2024 Presidential Election: Donald Trump data is updated weekly, averaging 43.000 % from May 2023 (Median) to 29 Oct 2024, with 61 observations. The data reached an all-time high of 46.000 % in 29 Oct 2024 and a record low of 38.000 % in 31 Oct 2023. United States The Economist YouGov Polls: 2024 Presidential Election: Donald Trump data remains active status in CEIC and is reported by YouGov PLC. The data is categorized under Global Database’s United States – Table US.PR004: The Economist YouGov Polls: 2024 Presidential Election (Discontinued). If an election for president were going to be held now and the Democratic nominee was Joe Biden and the Republican nominee was Donald Trump, would you vote for...