In 2025, it was estimated that over 163 million Americans were in some form of employment, while 4.16 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Unemployment Rate - Black or African American (LNS14000006) from Jan 1972 to Sep 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

Graph and download economic data for Unemployment Rate - Women (LNS14000002) from Jan 1948 to Sep 2025 about females, 16 years +, household survey, unemployment, rate, and USA.

The unemployment rate in the Republic of Ireland was 4.7 percent in August 2025, compared with 4.8 percent in the previous month. Between 2000 and 2007, Ireland's unemployment rate was broadly stable, fluctuating between 3.9 and 5.4 percent. Following the global financial crisis, however, Ireland's unemployment rate increased dramatically, eventually peaking at 16.1 percent in early 2012. For the next eight years, unemployment gradually fell, eventually reaching pre-crisis levels in the late 2010s. This was, however, followed by an uptick in unemployment due to the COVID-19 pandemic, which peaked at 7.6 percent in March 2021, before falling to pre-pandemic levels by February 2022. Risk and rewards of the Irish economic model After being quite hard hit by the global financial crisis of 2008, Ireland staged a strong recovery in the mid-2010s, and was frequently the EU's fastest growing economy between 2014 and 2022. This growth, was however, fueled in part by multinational companies, such as Apple, basing their European operations in the country. As of 2022, an adjusted measure of gross national income valued Ireland's economy at around 273 billion Euros, rather than the 506 billion Euros GDP figure. Ireland's close economic relationship with American tech companies also leaves it vulnerable to the political weather in the United States. It is currently unclear, for example, what the recent return to power of Donald Trump as President in early 2025 could mean for the Irish economy going forward. Ireland's labor market As of the third quarter of 2024, there were approximately 2.79 million people employed in the Republic of Ireland. Of these workers, 379,200 people worked in Ireland's human health and social work sector, the most of any industry at that time. Other sectors with high employment levels include wholesale and retail trade, at 323,500 people, and education, at 228,200 people. While unemployment still remains quite low, some indicators suggest a moderate loosening of the labor market. Job vacancies, are slightly down from their peak of 35,300 in Q2 2022, amounting to 28,900 in Q3 2024, while youth unemployment has begun to tick upwards, and was 11.9 percent in January 2025.

As of 2022, former President Bill Clinton was the president who created the most jobs in the United States, at **** million jobs created during his eight year term in office. Former President Ronald Reagan created the second most jobs during his term, at **** million.

Graph and download economic data for Unemployment Rate - Hispanic or Latino (LNS14000009) from Mar 1973 to Sep 2025 about latino, hispanic, 16 years +, household survey, unemployment, rate, and USA.

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

Unemployment Rate in Canada decreased to 6.90 percent in October from 7.10 percent in September of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Labor Force Participation Rate in the United States increased to 62.40 percent in September from 62.30 percent in August of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Content

Hate Crimes

This folder contains data behind the story Higher Rates Of Hate Crimes Are Tied To Income Inequality.

Sources: Kaiser Family Foundation Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation United States Elections Project Southern Poverty Law Center FBI

Correction

Please see the following commit: https://github.com/fivethirtyeight/data/commit/fbc884a5c8d45a0636e1d6b000021632a0861986

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!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Discover the potential job losses in South Africa due to Trump's tariffs, impacting agriculture and automotive sectors.

The outcome of the 2016 election made it abundantly clear that victory in US presidential contests depends on the Electoral College much more than on direct universal suffrage. This fact points to the importance of using state-level models to arrive at adequate predictions of winners and losers in US presidential elections. In fact, the use of a model disaggregated to the state level and focusing on three types of measures—namely, changes in the unemployment rate, presidential popularity, and indicators of long-term patterns in the regional strength of the Democratic and Republican parties—has in the past enabled us to produce fairly accurate forecasts of the number of Electoral College votes for the presidential candidates of the two major American parties. In this article, we bring various modifications to this model to improve its overall accuracy. With Joe Biden out of the race, this revised model predicts that Donald Trump will succeed in winning back the presidency with 341 electoral votes against 197 for Kamala Harris.

The Paycheck Protection Program (PPP) is a $953-billion business loan program established by the United States federal government, led by the Donald Trump administration in 2020 through the Coronavirus Aid, Relief, and Economic Security Act (CARES Act) to help certain businesses, self-employed workers, sole proprietors, certain non-profit organizations, and tribal businesses continue paying their workers.

The Paycheck Protection Program allows entities to apply for low-interest private loans to pay for their payroll and certain other costs. The amount of a PPP loan is approximately equal to 2.5 times the applicant's average monthly payroll costs. In some cases, an applicant may receive a second draw typically equal to the first. The loan proceeds may be used to cover payroll costs, rent, interest, and utilities. The loan may be partially or fully forgiven if the business keeps its employee counts and employee wages stable. The program is implemented by the U.S. Small Business Administration. The deadline to apply for a PPP loan was March 31, 2021.

Some economists have found that the PPP did not save as many jobs as purported and aided too many businesses that were not at risk of going under. They noted that other programs, such as unemployment insurance, food assistance, and aid to state and local governments, would have been more efficient at strengthening the economy. Opponents to this view note that the PPP functioned well to prevent business closures and cannot be measured on the number of jobs saved alone.

According to a 2022 study, the PPP: cumulatively preserved between 2 and 3 million job-years of employment over 14 months at a cost of $169K to $258K per job-year retained. These numbers imply that only 23 to 34 percent of PPP dollars went directly to workers who would otherwise have lost jobs; the balance flowed to business owners and shareholders, including creditors and suppliers of PPP-receiving firms. Program incidence was ultimately highly regressive, with about three-quarters of PPP funds accruing to the top quintile of households. PPP's breakneck scale-up, its high cost per job saved, and its regressive incidence have a common origin: PPP was essentially untargeted because the United States lacked the administrative infrastructure to do otherwise. Harnessing modern administrative systems, other high-income countries were able to better target pandemic business aid to firms in financial distress. Building similar capacity in the U.S. would enable improved targeting when the next pandemic or other large-scale economic emergency inevitably arises.

Additional Information Field: Value Created: April 5, 2022 Format: CSV License: Other (Public Domain) Size: 428.6 MiB

The Gross Domestic Product (GDP) in the United States expanded 3.80 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' mortgage refinancing, past prices, health care, political affiliation and opinion about the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education. Other topics in this series typically include economic attitudes, home price expectations, savings and financial investments, political affiliation, and opinion on the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Professional organizations in the United States have weathered considerable change over the past year, navigating heightened economic uncertainty, shifting demographics and evolving regulatory landscapes. While the national economy has grown, its pace has slowed, with almost a third of states either in recession or at high risk, which directly impacts organizations that depend on membership dues and event revenue. Rising unemployment and persistent inflation have prompted professionals to tighten their budgets, resulting in curbed discretionary spending and turbulent renewal rates in membership organizations. Despite these challenges, new digital and innovative services, such as career support, online resource libraries and strategic partnerships, have helped associations diversify their revenue streams, boost engagement and maintain a constant presence during volatile economic cycles. As a result, industry revenue has risen at a CAGR of 0.5% over the past five years, including a 0.8% increase in this year, reaching $26.7 billion in 2025. Over the past five years, professional organizations have undergone a sweeping digital transformation, achieving steady financial growth even amid economic volatility. Profit has grown at a CAGR of 4.5% during this period, driven by the adoption of digital technologies and diversified revenue models. Nearly half of all organizational programming is now delivered online, a strategic move that responds to the preferences of younger professionals for flexible, remote and mobile-first engagement. These virtual offerings have not only cut costs associated with physical events but have also generated new income streams through digital certification, subscription-based learning and hybrid conferences. As baby boomers retire and Gen Z professionals enter the workforce, the shift toward cost-effective, scalable solutions has reinforced steady profit expansion by enabling organizations to serve broader and more dynamic audiences. Looking ahead, the pressure for innovation and adaptation will only intensify. Shrinking federal support, especially under recent Trump administration policies, has prompted organizations to diversify funding, rely more on private grants and overhaul practices to comply with revised education and DEI standards. Success in this new environment will require ongoing investment in technology, a robust social media presence and a balanced approach to serving both retirees and Gen Z entrants. Those that harness data-driven strategies, foster multigenerational engagement and continuously update their services to reflect workforce trends will not only mitigate risks but also position themselves as indispensable resources for professionals seeking career growth. In turn, industry revenue is forecast to climb at a CAGR of 0.7% to $27.6 billion from 2025 to 2030.

Job training and career counseling providers are critical in workforce development, particularly for underserved and economically disadvantaged populations. Ongoing structural challenges in the labor market, including long-term unemployment, skills mismatches and barriers to employment for youth, veterans and individuals with limited education, support the need for providers regardless of economic conditions. In recent years, shifting industry requirements, infusions of federal funding and increasing interest in affordable alternatives to four-year degrees have supported underlying demand. Unprecedented swings in labor market dynamics, ranging from COVID-19 to rising interest in vocational skills, have highlighted the importance of job training and career counseling providers and expanded their revenue opportunities. Collectively, these trends have supported the industry’s expansion, with revenue rising at a CAGR of 1.1% to an estimated $17.1 billion over the past five years. Shifting federal priorities in 2025 could reverse the industry’s momentum. Between 2022 and 2024, increases in federal funding by the Biden Administration lifted revenue growth as more people were able to access programs, while providers were able to make investments in expanding enrollment and investing in technologies. In 2025, potential cuts in federal spending could impact the industry’s viability, as both public and private providers depend on this funding. For example, in May 2025, the Trump administration cut the Job Corps program, the largest federally funded initiative providing vocational training and education to low-income youth, citing concerns over cost, program effectiveness and facility safety. While legal challenges have temporarily paused the closure, the event underscores how sudden changes in federal priorities can impact social service-based employment programs. Moving forward, the industry faces opportunities and risks. While ongoing labor market disruptions, demographic shifts and the need for lifelong learning are expected to sustain interest in job training and career counseling services, headwinds are mounting. Potential reductions in federal funding, uncertainty around program continuity and the challenge of aligning training with local labor market needs present significant obstacles. Shifts in federal priorities pose the largest threat, with the industry’s ability to adapt to these forces shaping its performance over the next five years. These operating pressures will slow the industry’s growth, leading revenue to rise at a CAGR of 0.2% to an estimated $17.3 billion.

The study charted Finnish people's values and attitudes. The themes of the Autumn 2020 survey included the coronavirus (COVID-19) pandemic, financing the welfare state, happiness, equality, birth rate, and social problems. First, the respondents were presented with a variety of attitudinal statements concerning, among other topics, the Government's actions to combat COVID-19, politics, employment, reliability of information, and alcohol use. Next, the survey examined the respondents' attitudes towards rebalancing public finance after the COVID-19 pandemic. Opinions on financing the welfare state were also charted with various questions. For instance, it has been said that financing the welfare state requires that Finns must work more/longer in the future than they do at present, one way or other. Relating to this statement, the respondents were asked to evaluate whether several ways of achieving the goal of making Finns work more/longer were good or bad (e.g. increasing the number of weekly working hours or making it more difficult to take early retirement or get disability pension). Everyday well-being and happiness were also surveyed. The respondents were asked how happy they were at present and how satisfied they were with various matters, such as their income level, relationship status, and opportunities to influence in society. Questions also focused on what the respondents thought contributed to a happy life, for instance whether they thought that good relationships, health, social respect, interesting hobbies, or spirituality were prerequisites for happiness. Several questions charted views on equality and inequality among Finns (e.g. the presence of gender, generational, regional and occupational equality/inequality in Finland). Views on the reasons behind the low birth rate in Finland were examined next (e.g. whether the respondents thought unemployment or general uncertainty contributed to the low birth rate). The respondents were also asked which policy means they thought might be effective in increasing the birth rate. Social problems were examined with questions on whether the respondents had personally experienced or otherwise closely witnessed problems such as anxiety or depression, bullying, substance addiction, problem use of alcohol, or gambling problems, during the past few years. Finally, the respondents' views were surveyed regarding the impact of Donald Trump and his administration on, for instance, the global status of and respect for the United States. Opinions on Finland's EU membership and the euro as Finland's currency were also examined. Background variables included gender, age group, size of the respondent's municipality of residence, region of residence, employer type, employment status, type of employment contract, occupational group, employment sector, trade union membership, political party preference (which party the respondent would vote for), self-perceived social class, and annual income of the respondent's household.