The Great Depression of the early twentieth century is widely considered the most devastating economic downturn that the developed world has ever seen. Industrial output was severely affected across Europe, and in Germany alone, it fell to just 58 percent of its pre-Depression level by 1932. Other Central European countries, such as Austria and Czechoslovakia, also saw their output fall to just sixty percent of their pre-Depression levels, while output in Western and Northern Europe declined by much less. By 1937/8, almost a decade after the Wall Street Crash, most of these countries saw their industrial output increase above its pre-Depression level. Germany saw its output increase to 132 percent of its 1928 output, as it emerged as Europe's strongest economy shortly before the beginning of the Second World War.

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

We study banking panics in the Great Depression using a newly digitized dataset on panic intensity, regional monetary aggregates and economic activity.

The estimated number of banks and thrifts in the United States fell from around 31,000 in 1920 to 26,000 in 1929, when the onset of the Great Depression would then see it fall further, below 15,000 in 1933. This marks a cumulative decline of over 16,000 banks and thrifts, which is equal to a drop of more than 52 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 1920s 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 March 6 until March 13. This break allowed Congress to pass the Emergency Banking Act on March 9, 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 March 15.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/." This table displays the proportion of adults who were ever told they had a depressive disorder in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This indicator is based on the question: "“Has a doctor, nurse or other health professional EVER told you that you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?” NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Between the Wall Street Crash of 1929 and the end of the Great Depression in the late 1930s, the Soviet Union saw the largest growth in its gross domestic product, growing by more than 70 percent between 1929 and 1937/8. The Great Depression began in 1929 in the United States, following the stock market crash in late October. The inter-connectedness of the global economy, particularly between North America and Europe, then came to the fore as the collapse of the U.S. economy exposed the instabilities of other industrialized countries. In contrast, the economic isolation of the Soviet Union and its detachment from the capitalist system meant that it was relatively shielded from these events. 1929-1932 The Soviet Union was one of just three countries listed that experienced GDP growth during the first three years of the Great Depression, with Bulgaria and Denmark being the other two. Bulgaria experienced the largest GDP growth over these three years, increasing by 27 percent, although it was also the only country to experience a decline in growth over the second period. The majority of other European countries saw their GDP growth fall in the depression's early years. However, none experienced the same level of decline as the United States, which dropped by 28 percent. 1932-1938 In the remaining years before the Second World War, all of the listed countries saw their GDP grow significantly, particularly Germany, the Soviet Union, and the United States. Coincidentally, these were the three most powerful nations during the Second World War. This recovery was primarily driven by industrialization, and, again, the U.S., USSR, and Germany all experienced the highest level of industrial growth between 1932 and 1938.

Data supporting the chapter "A Second Look at the U.S. Great Depression from a Neoclassical Perspective."

This contains the dataset of the 1936 household consumption survey and 1930 census data used in "Fiscal Policy and Economic Recovery: The Case of the 1936 Veterans' Bonus." The underlying household survey data come from ICPSR study 08908. The Census data come from the IPUMS 5% sample from the 1930 Census. The primary data file is urban_lprob.dta. urban_nodups.dta contains a subset of these data for programming convenience. For further documentation, see the paper, and the data and program files posted on the American Economic Review's website.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,

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.

On October 29, 1929, the U.S. experienced the most devastating stock market crash in it's history. The Wall Street Crash of 1929 set in motion the Great Depression, which lasted for twelve years and affected virtually all industrialized countries. In the United States, GDP fell to it's lowest recorded level of just 57 billion U.S dollars in 1933, before rising again shortly before the Second World War. After the war, GDP fluctuated, but it increased gradually until the Great Recession in 2008. Real GDP Real GDP allows us to compare GDP over time, by adjusting all figures for inflation. In this case, all numbers have been adjusted to the value of the US dollar in FY2012. While GDP rose every year between 1946 and 2008, when this is adjusted for inflation it can see that the real GDP dropped at least once in every decade except the 1960s and 2010s. The Great Recession Apart from the Great Depression, and immediately after WWII, there have been two times where both GDP and real GDP dropped together. The first was during the Great Recession, which lasted from December 2007 until June 2009 in the US, although its impact was felt for years after this. After the collapse of the financial sector in the US, the government famously bailed out some of the country's largest banking and lending institutions. Since recovery began in late 2009, US GDP has grown year-on-year, and reached 21.4 trillion dollars in 2019. The coronavirus pandemic and the associated lockdowns then saw GDP fall again, for the first time in a decade. As economic recovery from the pandemic has been compounded by supply chain issues, inflation, and rising global geopolitical instability, it remains to be seen what the future holds for the U.S. economy.

This dataset is about book subjects and is filtered where the books includes Understanding economic recovery in the 1930s : endogenous propagation in the Great Depression. It has 10 columns such as book subject, earliest publication date, latest publication date, average publication date, and number of authors. The data is ordered by earliest publication date (descending).

This article examines the federal response to mortgage distress during the Great Depression. It documents features of the housing cycle of the 1920s and early 1930s, focusing on the growth of mortgage debt and the subsequent sharp increase in mortgage defaults and foreclosures during the Depression. It summarizes the major federal initiatives to reduce foreclosures and reform mortgage market practices, focusing especially on the activities of the Home Owners' Loan Corporation (HOLC), which acquired and refinanced one million delinquent mortgages between 1933 and 1936. Because the conditions under which the HOLC operated were unusual, the author cautions against drawing strong policy lessons from the HOLC's activities. Nonetheless, similarities between the Great Depression and the recent episode suggest that a review of the historical experience can provide insights about alternative policies to relieve mortgage distress.

The Canadian Biomarker Integration Network in Depression (CAN-BIND) is a national program of research and learning. From 2013 to 2017, data were collected from 211 participants with major depressive disorder and 112 healthy individuals. The objective of this data-set is to integrate detailed clinical, imaging, and molecular data to predict outcome for patients experiencing a Major Depressive Episode (MDE) and receiving pharmacotherapy reflective of standard practice. The clinical characterization consists of symptom assessment, behavioural dimensions, and environmental factors. The neuroimaging data consist of structural, resting and task-based functional, and diffusion-weighted MRI images, as well as scalp-recorded EEG data. The molecular data currently consist of DNA methylation, inflammatory markers and urine metabolites. Baseline and Phase 1 (Weeks 2-8) data are now available for request.

The PHQ-9 Student Depression Dataset contains responses from 500 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions.

The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories: Minimal (0-4) Mild (5-9) Moderate (10-14) Moderately Severe (15-19) Severe (20-27)

This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include: Sentiment Analysis: Analyzing emotional tones in text responses to assess depression. Text Classification: Classifying responses into different depression severity levels. Predictive Modeling: Predicting depression severity based on textual responses. Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression. The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources. When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including:

Data Privacy: Anonymizing personal information. Informed Consent: Ensuring participants give consent before data collection. Support Resources: Providing support for individuals who may exhibit serious mental health concerns.

Applications: Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques. AI in Healthcare: It can be used in the development of tools for automated mental health assessment. Education: Training students or professionals in recognizing depression symptoms and analyzing responses.

This essay assesses whether network linkages within the banking system amplified the real effects of bank failures during the Great Contraction. In 1929, nearly all interbank deposits held by Federal Reserve member banks belonged to "shadowy" nonmember banks which were outside the regulatory reach of federal regulators. Regional banking panics in the early 1930s drained these interbank deposits from central reserve city banks. Money-center banks in Chicago and New York responded to volatile and declining interbank deposits by changing their asset composition. They reduced their lending to businesses and individuals, and increased their holdings of cash and government bonds.

In the article we analyze the financial behavior of Dutch households during the Great Depression with household level data on income and expenditure from contemporary budget surveys. The data we use consists of published budget surveys from the Netherlands, Amsterdam, The Hague, and Utrecht (1932-1937). The data are available on paper but we have created Excel files that contain a large part but not all of the data published on paper in the 1930s. We have exported the Excel Files in CSV format to R, which we then used to create the tables in our paper, to execute a regression analysis, and to perform additional analyses for the paper’s appendix.

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.

Genome-wide summary statistics of major depression from the Psychiatric Genomics Consortium (2025)Meta-analysed summary statistics for:Multi-ancestries, excluding 23andMe [DIV]European-ancestries, excluding 23andMe [EUR]European ancestries, excluding 23andMe and UK Biobank [EUR]European ancestries, top 10k SNPs [EUR]Clinical/interview phenotypes [EUR]Electronic health record / register phenotypes [EUR]Questionnaire symptom phenotypes [EUR]Questionnaire symptom phenotypes, excluding UK Biobank [EUR]Files:*.tsv.gz: summary statistics with rich header information*.txt: meta information for cohorts included in each meta-analysis (tab-separated)daner/daner_*.gz: summary statistics in daner formatssf/*_formatted.tsv.gz: summary statistics in GWAS-SSF

Over the course of their first terms in office, no U.S. president in the past 100 years saw as much of a decline in stock prices as Herbert Hoover, and none saw as much of an increase as Franklin D. Roosevelt (FDR) - these were the two presidents in office during the Great Depression. While Hoover is not generally considered to have caused the Wall Street Crash in 1929, less than a year into his term in office, he is viewed as having contributed to its fall, and exacerbating the economic collapse that followed. In contrast, Roosevelt is viewed as overseeing the economic recovery and restoring faith in the stock market played an important role in this.

By the end of Hoover's time in office, stock prices were 82 percent lower than when he entered the White House, whereas prices had risen by 237 percent by the end of Roosevelt's first term. While this is the largest price gain of any president within just one term, it is important to note that stock prices were valued at 317 on the Dow Jones index when Hoover took office, but just 51 when FDR took office four years later - stock prices had peaked in August 1929 at 380 on the Dow Jones index, but the highest they ever reached under FDR was 187, and it was not until late 1954 that they reached pre-Crash levels once more.