Graph and download economic data for Unemployment Rate for United States (M0892AUSM156SNBR) from Apr 1929 to Jun 1942 about unemployment, rate, and USA.

From the late 19th century until the 1980s, the United States' unemployment rate was generally somewhere between three and ten percent of the total workforce. The periods when it peaked were in times of recession or depression - the Panic of 1893, which lasted until 1897, saw unemployment peak at over 18 percent, whereas the post-WWI recession saw unemployment spike to almost 12 percent in 1921.

However, the longest and most-severe period of mass unemployment in U.S. history came during the Great Depression - unemployment rose from just 3.2 percent in 1929 to one quarter of the total workforce in 1933, and it was not until the Second World War until it fell below five percent once more. Since this time, unemployment has never exceeded 10 percent, although it did come close during the recessions of the 1970s and 1980s.

More recent unemployment statistics for the U.S. can be found here.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 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.

ABSTRACT OBJECTIVE Quantify, for both genders, the correlation between the depression incidence rate and the unemployment rate in Portugal between 1995 and 2013. METHODS An ecological study was developed to correlate the evolution of the depression incidence rates estimated by the General Practitioner Sentinel Network and the annual unemployment rates provided by the National Statistical Institute in official publications. RESULTS There was a positive correlation between the depression incidence rate and the unemployment rate in Portugal, which was significant only for males (R2 = 0.83, p = 0.04). For this gender, an increase of 37 new cases of depression per 100,000 inhabitants was estimated for each 1% increase in the unemployment rate between 1995 and 2013. CONCLUSIONS Although the study design does not allow the establishment of a causal association between unemployment and depression, the results suggest that the evolution of unemployment in Portugal may have had a significant impact on the level of mental health of the Portuguese, especially among men.

ObjectiveTo examine the relationship between young adults' labor force participation and depression in the context of the COVID-19 pandemic.Design, setting, participantsData come from the nationally-representative EPICOV cohort study set up in France, and were collected in 2020 and 2021 (3 waves of online or telephone interviews: 02/05/2020–12/06/2020; 26/10/2020–14/12/2020; 24/06/2021–09/08/2021) among 2,217 participants aged 18–30 years. Participants with prior mental health disorder (n = 50) were excluded from the statistical analyses.ResultsUsing Generalized Estimating Equation (GEE) models controlled for participants' socio-demographic and health characteristics and weighted to be nationally-representative, we found that compared to young adults who were employed, those who were studying or unemployed were significantly more likely to experience depression assessed using the PHQ-9 (multivariable ORs, respectively: OR: 1.29, 95% CI 1.05–1.60 and OR: 1.50, 1.13–1.99). Stratifying the analyses by age, we observed that unemployment was more strongly associated with depression among participants 25–30 years than among those who were 18–24 years (multivariable ORs, respectively, 1.78, 95% CI 1.17–2.71 and 1.41, 95% CI 0.96–2.09). Being out of the labor force was, to the contrary, more significantly associated with depression among participants 18–24 years (multivariable OR: 1.71, 95% CI 1.04–2.82, vs. 1.00, 95% CI 0.53–1.87 among participants 25–30 years). Stratifying the analyses by sex, we found no significant differences in the relationships between labor market characteristics and depression (compared to participants who were employed, multivariable ORs associated with being a student: men: 1.33, 95% CI 1.01–1.76; women: 1.19, 95% CI 0.85–1.67, multivariable ORs associated with being unemployed: men: 1.60, 95% CI 1.04–2.45; women: 1.47, 95% CI 1.01–2.15).Conclusions and relevanceOur study shows that in addition to students, young adults who are unemployed also experience elevated levels of depression in the context of the COVID-19 pandemic. These two groups should be the focus of specific attention in terms of prevention and mental health treatment. Supporting employment could also be a propitious way of reducing the burden of the COVID-19 pandemic on the mental health of young adults.

Analysis of ‘Unemployment and mental illness survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/michaelacorley/unemployment-and-mental-illness-survey on 28 January 2022.

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

Context

This is a paid research survey to explore the linkage between mental illness and unemployment. NAMI has conducted multiple surveys verifying the high unemployment rate among those with mental illness, but this is the only survey to date which targets causation (why they are unemployed). Statistical significance of the variance has long since been proven by previous, larger samples.

You are free to visualize and publish results, please just credit me by name.

Collection methodology

I received several messages about methodology of collection because various people would like to use this data for papers.

• I paid respondents on Survey Monkey in a general population sampling. I did not target any specific demographic as not to get skewed results. Survey Monkey stratifies the sample according to certain characteristics like income and location.

• I know that the general population sampling went well because the number of people self identifying as having a mental illness is consistent with larger samples.

• Although we disqualified people without a mental illness, they were still given the complete survey. That means that the data contains sampling of people with and without mental illness and a yes/no indicator.

Potential area's to investigate

• Linkage between unemployment and education level
• The effect of a gap on your resume on income level
• Symptom/side effects impact on employment
• The effectiveness of social welfare programs
• The linkage between gaps in your resume and hospitalizations due to mental illness

Content

***Sample size:** n = 334; 80 w/ mental illness - this proportion is approximately equal to estimates of the general population diagnosed with mental illness (typically estimated at 20-25% according to various studies).*

Questions:

I identify as having a mental illness Response
Education  Response
I have my own computer separate from a smart phone Response
I have been hospitalized before for my mental illness  Response
How many days were you hospitalized for your mental illness Open-Ended Response
I am currently employed at least part-time Response
I am legally disabled  Response
I have my regular access to the internet  Response
I live with my parents Response
I have a gap in my resume  Response
Total length of any gaps in my resume in months.  Open-Ended Response
Annual income (including any social welfare programs) in USD  Open-Ended Response
I am unemployed Response
I read outside of work and school  Response
Annual income from social welfare programs Open-Ended Response
I receive food stamps  Response
I am on section 8 housing  Response
How many times were you hospitalized for your mental illness  Open-Ended Response

I have one of the following issues in addition to my illness:
  Lack of concentration
  Anxiety
  Depression
  Obsessive thinking
  Mood swings
  Panic attacks
  Compulsive behavior
  Tiredness

Age Response
Gender Response
Household Income  Response
Region Response
Device Type Response

Important data transformation note

When comparing the actual rate to government statistics, it is important to take into account the labor force participation rate (the % of people who are legally considered to be in the workforce). People not included in the unemployment statistic, like discouraged workers (for example the mentally ill) will be "not participating" in the workforce.

Other studies

1. Nami: https://www.nami.org/Press-Media/Press-Releases/2014/Mental-Illness-NAMI-Report-Deplores-80-Percent-Une

2. NIH: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182106/

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

Objectives. This study investigated the association between unemployment and depressive symptoms and major depression disorder worldwide using a systematic review and meta-analysis. Methods. Search time was limited to all articles published in English until December 2020. In the association between unemployment and depression, first, the results of qualified studies were extracted and, then, the results of each study were pooled with each other using the random effects method. Results. The prevalence of depression in the unemployed is 21%, 95% confidence interval (CI) [18, 24%]. This prevalence for depression symptoms is 24%, 95% CI [20, 28%] and for major depressive disorder is 16%, 95% CI [9–24%]. The association between unemployment and depressive symptoms was odds ratio (OR) 2.06, 95% CI [1.85, 2.30] and the association for major depressive disorder was OR 1.88, 95% CI [1.57, 2.25]. The association between unemployment and depression in men was OR 2.27, 95% CI [1.76, 2.93] and in women was OR 1.62, 95% CI [1.40, 1.87]. Conclusions. What is clear from the present study is that unemployment can lead to a higher prevalence of depressive symptoms and major depressive disorder, thereby undermining the mental health of the unemployed.

Are people under economic stress more or less likely to vote, and why? With large observational datasets and a survey experiment involving unemployed Americans, we show that unemployment depresses participation. But it does so more powerfully when the unemployment rate is low, less powerfully when it is high. Whereas earlier studies have explained lower turnout among the unemployed by stressing the especially high opportunity costs these would-be voters face, our evidence points to the psychological effects of unemployment and of campaign messages about it. When unemployment is high, challengers have an incentive to blame the incumbent, thus eliciting anger among the unemployed. Psychologists have shown anger to be an approach or mobilizing emotion. When joblessness is low, campaigns tend to ignore it. The jobless thus remain in states of depression and self-blame, which are demobilizing emotions.

This survey forms part of the project 'Kris och krispolitik i Norden under mellankrigstiden' at the Department of History at Stockholm University. The study deals with the social conditions for unemployed during the depression in the thirties; who became unemployed, the circumstances of the entry into unemployment, the influence of unemployment on living condition, and possibilities to go back to work when the crisis is over. The dataset includes information on the situation of the unemployed at five different occasions during the thirties; 1930, the year of unemployment, 1933, year unemployment came to an end, and 1935.

In July 2024, 3.16 billion U.S. dollars were paid out in unemployment benefits in the United States. This is an increase from June 2024, when 2.62 billion U.S. dollars were paid in unemployment benefits. The large figures seen in 2020 are largely due to the impact of the coronavirus pandemic. Welfare in the U.S. Unemployment benefits first started in 1935 during the Great Depression as a part of President Franklin D. Roosevelt’s New Deal. The Social Security Act of 1935 ensured that Americans would not fall deeper into poverty. The United States was the only developed nation in the world at the time that did not offer any welfare benefits. This program created unemployment benefits, Medicare and Medicaid, and maternal and child welfare. The only major welfare program that the United States currently lacks is a paid maternity leave policy. Currently, the United States only offers 12 unpaid weeks of leave, under certain circumstances. However, the number of people without health insurance in the United States has greatly decreased since 2010. Unemployment benefits Current unemployment benefits in the United States vary from state to state due to unemployment being funded by both the state and the federal government. The average duration of people collecting unemployment benefits in the United States has fluctuated since January 2020, from as little as 4.55 weeks to as many as 50.32 weeks. The unemployment rate varies by ethnicity, gender, and education levels. For example, those aged 16 to 24 have faced the highest unemployment rates since 1990 during the pandemic. In February 2023, the Las Vegas-Henderson-Paradise, NV metropolitan area had the highest unemployment rate in the United States.

BackgroundThis study aims to determine how the unemployment anxiety of university students affects their depression level. It also examines the mediating role of the feeling of personal unaccomplishment between these effects.MethodsThe data was analyzed using the PROCESS method. The research was conducted on 843 students attending different public or foundation (private) universities in Türkiye. Random sampling was used to select the participants.ResultsAs a result of the analysis, it was determined that there is a positive, significant, moderate (R = 0.509, p 

A series of recessions in the 1970s and 1980s meant that unemployment rates in some Western European countries rose to their highest levels since the Great Depression in the 1930s. While countries such as West Germany closed out the period of prosperity (known as the "Golden Age of Capitalism") with unemployment rates below one percent, figures rose gradually in the 1970s, and then furthermore in the 1980s. Throughout the 1960s and 1970s, the highest levels of unemployment in the listed countries were observed in Ireland and the United States; although the highest levels of unemployment in the 1980s were observed in Spain, during its transition to democracy. Of the major economic powers listed here, Japan saw the least amount of fluctuation, with a high of just 2.5 percent in the given periods; almost half of the U.S.' lowest unemployment figure in these periods.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

This study aims to identify the significance of age and employment to individual function during COVID-19. An online survey included 509 Israeli citizens, ages 18–78, who reported individual function, depression, fears related to COVID-19 and demographic characteristics. Structural Equation Modeling (SEM) analysis showed a good fit between our model and the data. Age and employment were negatively associated with depression and economic fears related to COVID-19 that, in turn, were negatively associated with individual function. The effect of age and employment on individual function was fully mediated via depression and economic fears related to COVID-19. The discussion addresses our findings in the context of the victimization paradox. Although COVID-19 related health complications are more frequent among older adults, our results suggest that practitioners responsible for public mental health during viral pandemics should consider young age and unemployment as risk factors for depression and low individual function.

The Volcker Shock was a period of historically high interest rates precipitated by Federal Reserve Chairperson Paul Volcker's decision to raise the central bank's key interest rate, the Fed funds effective rate, during the first three years of his term. Volcker was appointed chairperson of the Fed in August 1979 by President Jimmy Carter, as replacement for William Miller, who Carter had made his treasury secretary. Volcker was one of the most hawkish (supportive of tighter monetary policy to stem inflation) members of the Federal Reserve's committee, and quickly set about changing the course of monetary policy in the U.S. in order to quell inflation. The Volcker Shock is remembered for bringing an end to over a decade of high inflation in the United States, prompting a deep recession and high unemployment, and for spurring on debt defaults among developing countries in Latin America who had borrowed in U.S. dollars.

Monetary tightening and the recessions of the early '80s

Beginning in October 1979, Volcker's Fed tightened monetary policy by raising interest rates. This decision had the effect of depressing demand and slowing down the U.S. economy, as credit became more expensive for households and businesses. The Fed funds rate, the key overnight rate at which banks lend their excess reserves to each other, rose as high as 17.6 percent in early 1980. The rate was allowed to fall back below 10 percent following this first peak, however, due to worries that inflation was not falling fast enough, a second cycle of monetary tightening was embarked upon starting in August of 1980. The rate would reach its all-time peak in June of 1981, at 19.1 percent. The second recession sparked by these hikes was far deeper than the 1980 recession, with unemployment peaking at 10.8 percent in December 1980, the highest level since The Great Depression. This recession would drive inflation to a low point during Volcker's terms of 2.5 percent in August 1983.

The legacy of the Volcker Shock

By the end of Volcker's terms as Fed Chair, inflation was at a manageable rate of around four percent, while unemployment had fallen under six percent, as the economy grew and business confidence returned. While supporters of Volcker's actions point to these numbers as proof of the efficacy of his actions, critics have claimed that there were less harmful ways that inflation could have been brought under control. The recessions of the early 1980s are cited as accelerating deindustrialization in the U.S., as manufacturing jobs lost in 'rust belt' states such as Michigan, Ohio, and Pennsylvania never returned during the years of recovery. The Volcker Shock was also a driving factor behind the Latin American debt crises of the 1980s, as governments in the region defaulted on debts which they had incurred in U.S. dollars. Debates about the validity of using interest rate hikes to get inflation under control have recently re-emerged due to the inflationary pressures facing the U.S. following the Coronavirus pandemic and the Federal Reserve's subsequent decision to embark on a course of monetary tightening.

BackgroundThe unemployed more often suffer from depression than the employed. We examined whether mental health deterioration occurs already before unemployment implicating health selection, or whether it mostly occurs after becoming exposed to the experience rendering causal explanations more likely.MethodsWe used nationally representative Finnish register data to examine changes in depressive morbidity as measured by antidepressant medication in 1995–2009 over four years before and since a new onset of unemployment (N = 28 000) at the age of 30–60 compared to the employed (N = 124 136). We examined separately those who became continuously long-term unemployed, intermittently unemployed and unemployed with eventual re-employment in the second, third or fourth year since the year of onset. Annual repeated measurements were analysed using generalised estimation equations.ResultsAmong the employed antidepressant medication increased slowly but steadily over the study period and it was mainly at a lower level than among the unemployed. In the four years leading to unemployment there was excess increase in medication that was generally stronger among those with longer duration of the eventual unemployment experience. During unemployment medication decreased in all groups except among the intermittently unemployed. By the first year of re-employment antidepressant medication reached a level similar to that among the employed and afterwards followed no consistent trend.ConclusionsThe associations of unemployment and re-employment with depressive morbidity appear to be largely driven by health selection. The question of potential causal associations remains unresolved for intermittent unemployment in particular.

We examine the depreciation of skills among unemployed German workers using a panel of skill measures linked to administrative data. Both the reemployment hazard and reemployment earnings steadily decline with unemployment duration. Indicators of depression and loneliness also rise substantially. However, we find no decline in a wide range of cognitive and non-cognitive skills while workers remain unemployed. We find the same pattern in a panel of American workers. The results imply that skill depreciation in general human capital is unlikely to be a major explanation for observed duration dependence in reemployment outcomes.

Background: Recent trends suggest that university graduates seeking jobs are more susceptible to common mental disorders, such as depression, anxiety, or stress. However, the mental health issues among unemployed graduates has not been explored in Bangladesh yet.Aims: This study aimed to assess for the first time the prevalence and associated risk factors of depression, anxiety, and stress among Bangladesh Civil Service (BCS) job seekers. Three hundred four graduates residing in Rajshahi, Bangladesh, who were preparing to attend the 40th BCS examination, the most sought-after employment opportunity in the country, were surveyed.Methods: Measures included socio-demographics, field of study, and career-related variables, and the Bangla Depression Anxiety Stress Scale (DASS-21). Chi-square test, Fisher exact test, and binary logistic regression with “depression,” “anxiety,” and “stress” as the dependent variables were carried out to identify the factors associated with these.Results: Overall, the prevalence of moderate to extremely severe depression, anxiety, and stress was 49.3%, 53.6%, and 28.3%, respectively, with no detectable differences between genders. Insecurity related to a BCS job (OR = 0.41; CI = 0.26–0.65, p < 0.001; ref: job insecurity), family and social pressure to obtain a BCS job (OR = 4.58; CI = 1.67–12.56, p < 0.001), and stress (OR = 8.33; CI = 4.47–15.51, p < 0.001) emerged as independent predictors for depression. In addition, having part-time job was associated with anxiety (OR = 2.38; CI = 1.34–4.23, p = 0.003), and security in a BCS job and serving the nation through this job were negatively associated with stress (OR = 0.59; CI = 0.35–0.98, p = 0.042 vs. OR = 0.59; CI = 0.36–1.00, p = 0.05).Conclusion: The relatively high rates of depression, anxiety, and stress among graduate job seekers should prompt implementation of market force initiatives that incorporate interventions related to the major risk factors uncovered herein.

This dataset contains county-level information for U.S. counties from 2020 to 2022, aiming to explore the potential relationship between COVID-19 vaccination coverage and the prevalence of severe depression. It integrates multiple data sources, including public health statistics, socioeconomic indicators, environmental variables, and demographic characteristics. The dataset is structured to support spatial, temporal, and statistical analysis.Key Variables Include:Mental Health: Severe depression rates per 100,000 population for 2021 and 2022COVID-19 Metrics: Case rates per 100,000 (2021, 2022), and vaccination rates (2-dose complete, 5+ population)Socioeconomic Data: Unemployment rates, median household income, percent of adults with bachelor's degree or higherEnvironmental Factors: Average daily sunlight (KJ/m²), cooling degree daysDemographics: Population size, gender distribution, age distribution, urbanization rateHealth Behavior Indicators: Rates of smoking, obesity, physical inactivity, and excessive drinkingLog-transformed versions of several variables are also included to support regression modeling and machine learning tasks.Purpose:The dataset is curated for research that investigates the interplay between COVID-19 vaccination campaigns and mental health outcomes, with potential applications in spatial epidemiology, public health policy, and social determinants of health research.Temporal Coverage: 2020–2022Geographic Scope: U.S. counties (N ≈ 3,000+)Data Format: XlsxSuggested Citation: Wencong Cui, Yuqing Wang, "COVID-19 Vaccination and Depression: U.S. County-Level Dataset (2020–2022)", Figshare, 2025. DOI: 10.6084/m9.figshare.29451644

The estimated number of banks and thrifts in the United States fell from around ****** in 1920 to ****** in 1929, when the onset of the Great Depression would then see it fall further, below ****** in 1933. This marks a cumulative decline of over ****** banks and thrifts, which is equal to a drop of more than ** percent in 13 years. Tumultuous Twenties Despite the economic prosperity associated with the Roarin' 1920s in the U.S., it was a tumultuous decade in financial terms, with more separate recessions than any other decade. However, the ***** was also privy to frivolous lending policies among many banks, which saw the banking sector collapse in the wake of the Wall Street Crash in 1929. Many banks failed as the Great Depression and unemployment spread across the country, and customers or businesses could not afford to repay their loans. It was only after this financial crisis where the federal government began keeping more stringent and accurate records on its banking sector, therefore precise figures and the reasons behind these bank failures are not always clear. Franklin D. Roosevelt Just two days after assuming office in 1933, Franklin D. Roosevelt drastically declared a bank holiday, and all banks in the country were closed from ******* until ********. This break allowed Congress to pass the Emergency Banking Act on *******, which saw the Federal Reserve provide deposit insurance for all reopened banks thereafter. Through his first fireside chat, Roosevelt then encouraged Americans to re-deposit their money in the banks again, which successfully restored much of the public's faith in the banking system - it is estimated that over half of the cash withdrawn during the Great Depression was then returned to the banks by ********.