The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

Graph and download economic data for Real-time Sahm Rule Recession Indicator (SAHMREALTIME) from Dec 1959 to Sep 2025 about recession indicators, academic data, and USA.

By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

A recession is due in the U.S. in 2023, according to a majority of macroeconomists in a June 2022 survey. Opinions varied, however, on when in 2023 this new recession could start exactly. Most respondents - ** percent - believed the economic downturn most likely start in the first half of 2023. Meanwhile, ** percent said that it would begin in the latter half of that year. Most Americans thought differently on this topic, believing that the country was already experiencing an economic recession in June 2022. The macroeconomists cited both geopolitical tensions and the increasing costs of energy as the main reasons why pressure would remain on U.S. inflation.

This dataset captures multi-market financial indicators that can be used to study financial crises, market stress, and economic stability. It integrates simulated data from stock, bond, and foreign exchange (forex) markets, along with volatility metrics and a binary crisis label.

The dataset provides a comprehensive view of cross-market behavior and is suitable for tasks such as crisis detection, financial risk analysis, and market interdependence studies.

Key Features Time Series Coverage:

Daily data over ~1,000 days for multiple countries

Stock Market Indicators:

Stock_Index → Simulated stock market index values

Stock_Return → Daily percentage change in stock index

Stock_Volatility → 5-day rolling standard deviation of stock returns

Bond Market Indicators:

Bond_Yield → Simulated 10-year government bond yield

Bond_Yield_Spread → Difference between long-term and short-term yields

Bond_Volatility → Simulated volatility in bond yields

Forex Market Indicators:

FX_Rate → Simulated currency exchange rate

FX_Return → Daily percentage change in exchange rate

FX_Volatility → 5-day rolling standard deviation of forex returns

Global Market Stress Indicator:

VIX → Simulated volatility index representing market stress

Target Variable:

Crisis_Label → Binary flag indicating market condition (0 = Normal, 1 = Crisis)

File Information Format: CSV

Rows: ~3,000 (1,000 days × 3 countries)

Columns: 13 (including target label)

Use Cases:

Financial crisis detection

Market stress and contagion analysis

Cross-market economic studies

Graph and download economic data for NBER based Recession Indicators for the United States from the Period following the Peak through the Trough (USREC) from Dec 1854 to Nov 2025 about peak, trough, recession indicators, and USA.

View monthly updates and historical trends for US Recession Probability. from United States. Source: Federal Reserve Bank of New York. Track economic data…

United States Recession Probability data was reported at 14.120 % in Oct 2019. This records a decrease from the previous number of 14.505 % for Sep 2019. United States Recession Probability data is updated monthly, averaging 7.668 % from Jan 1960 (Median) to Oct 2019, with 718 observations. The data reached an all-time high of 95.405 % in Dec 1981 and a record low of 0.080 % in Sep 1983. United States Recession Probability data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.S021: Recession Probability.

This collection, A Longitudinal Study of Public Response, was conducted to understand the trajectory of risk perception amidst an ongoing economic crisis. A nation-wide panel responded to eight surveys beginning in late September 2008 at the peak of the crisis and concluded in August 2011. At least 600 respondents participated in each survey, with 325 completing all eight surveys. The online survey focused on perceptions of risk (savings, investments, retirement, job), negative emotions toward the financial crisis (sadness, anxiety, fear, anger, worry, stress), confidence in national leaders to manage the crisis (President Obama, Congress, Treasury Secretary, business leaders), and belief in one's ability to realize personal objectives despite the crisis. Latent growth curve modeling was conducted to analyze change in risk perception throughout the crisis. Demographic information includes ethnic origin, sex, age, marital status, income, political affiliation and education.

In 2019, ** percent of American respondents between the ages of ** and ** said they expected to be more impacted in the next recession when compared to the ********* recession. This is in comparison to ** percent of respondents aged 65 and older who said the same.

.xlsx file for the replication of the Paper The Complex Crises Database: 70 years of Macroeconomic Crises. It contains the term frequencies of 20 crises sentiment indexes computed from the IMF country report for the period 1956-2016 for 181 countries. (2021-07-02)

Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q1 2025 about recession indicators, GDP, and USA.

The unemployment rate in the United States falls slowly in expansions, and it may not reach its previous low point before the next recession begins. Based on this feature, I document that the frequent recessions prior to 1983 are associated with an upward trend in the unemployment rate. In contrast, the long expansions beginning in 1983 are associated with a downward trend. I then estimate a two-variable vector autoregression (VAR) that includes the unemployment rate and a recession indicator. Long-horizon forecasts from this VAR conditioned on no future recessions project that the unemployment rate will go to 3.6 percent after a long period with no recessions.

The average for 2024 based on 175 countries was 5.54 index points. The highest value was in Syria: 9.9 index points and the lowest value was in Denmark: 0.7 index points. The indicator is available from 2007 to 2024. Below is a chart for all countries where data are available.

This project will explore the impact of the economic recession on cities and households through a systematic comparison of the experiences of two English cities, Bristol and Liverpool.The research will use both quantitative and qualitative approaches. Interviews will be held in both cities with stakeholders from across the public, private and voluntary and community sectors. A social survey of 1000 households will also be conducted in the two cities covering 10 specific household types. A series of in-depth qualitative interviews will then be held with households drawn from the survey and chosen to illustrate the spectrum of experience.In the context of globalisation and the rescaling of cities and states, the research aims to develop our understanding of the relationship between economic crisis, global connectivity and the transnational processes shaping cities and the everyday lives of residents. It will explore the 'capillary-like' impact of the crisis and austerity measures on local economic development, and local labour and housing markets, as well as highlight the intersecting realities of everyday life for households across the life course.The research will document the responses and coping strategies developed across different household types and evaluate the impact and effectiveness of 'anti-recession' strategies and policies.

The global financial crisis, triggered by the 2007 subprime mortgage crisis in the United States, has severely affected financial systems and real economies worldwide, leading to the most serious economic recession since the Great Depression of the 1930s. Behind these two economic recessions, despite different historical contexts and approaches to problem-solving, there are common characteristics associated with the mutual impact of financial crises: the essence of a financial crisis lies in financial instability, reflecting the fluctuations in asset prices. In addition to these two severe financial crises, financial crises of varying scales have occurred intermittently internationally. Considering the past and present, people need to think deeper about how to prevent such crises from happening again, especially mainstream macroeconomic thinking that has far-reaching effects should be reassessed.

The Eurobarometer series is a unique cross-national and cross-temporal survey program conducted on behalf of the European Commission. These surveys regularly monitor public opinion in the European Union (EU) member countries and consist of standard modules and special topic modules. The standard modules address attitudes towards European unification, institutions and policies, measurements for general socio-political orientations, as well as respondent and household demographics. The special topic modules address such topics as agriculture, education, natural environment and resources, public health, public safety and crime, and science and technology. This round of Eurobarometer surveys includes the standard modules and covers the following special topics: (1) the financial and economic crisis, (2) the future of the European Union, (3) globalization, and (4) European citizenship. Questions pertain to household financial situation, opinions on performance of the EU economy, reformation of the financial system, national currency and the Euro, public debt, the EU exiting present crisis and preparing for the next decade, and attitudes towards globalization. Other questions address country identification, opinions of European citizenship, the EU achievements for citizens, representation and democracy, the European Citizens' Initiative, and participation of citizens in society. Demographic and other background information collected includes age, gender, nationality, origin of birth (personal and parental), marital status, age when stopped full-time education, occupation, left-right political self-placement, household composition, ownership of a fixed or mobile telephone, difficulties in paying bills, level in society, and Internet use. In addition, country-specific data includes type and size of locality, region of residence, and language of interview (select countries).

Abstract

This research was conducted in Turkey in June-July 2010 as part of the third round of The Financial Crisis Survey. Data from 364 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Turkey.

Researchers revisited establishments interviewed in Turkey Enterprise Survey 2008. Efforts were made to contact all respondents of the baseline survey to determine which of the companies were still operating and which were not. From the information collected during telephone interviews, indicators were computed to measure the effects of the financial crisis on key elements of the private economy: sales, employment, finances, and expectations of the future.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study was the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The manufacturing and services sectors were the primary business sectors of interest. This corresponded to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies were targeted for interviews. Services firms included construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government ownership were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

1152 establishments that participated in Turkey Enterprise Survey 2008 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 650 establishments.

Stratified random sampling was used in Turkey Enterprise Survey 2008. Three levels of stratification were implemented: industry, establishment size, and oblast (region).

For industry stratification, the universe was divided into 5 manufacturing industries, 1 services industry -retail -, and two residual sectors. Each manufacturing industry had a target of 160 interviews. The services industry and the two residual sectors had a target of 120 interviews. For the manufacturing industries sample sizes were inflated by about 33% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in 5 regions. These regions are Marmara, Aegean, South, Central Anatolia and Black Sea-Eastern.

The Turkey sample contains panel data. The wave 1 panel "Investment Climate Private Enterprise Survey implemented in Turkey" consisted of 1325 establishments interviewed in 2005. A total of 425 establishments have been re-interviewed.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

The source of the sample frame was twofold. Universe estimates were taken from the TOBB database which contains a full list of establishments in manufacturing sectors. TOBB refers to the Union of Chambers and Commodity Exchanges of Turkey. Universe estimates for service sectors were taken from the Statistical Institute of Statistics (SIS) with additional information based on SIC code from the Turkish Studies Institute (TSI). Comparisons were made between estimates in TOBB and SIS to establish that the two sources are comparable and hence can be used side by side.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 43% (2811 out of 6458 establishments).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following survey instrument is available: - Financial Crisis Survey Questionnaire

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks and callbacks.

Abstract

This research was conducted in Hungary in February-March 2010 as part of the second round of The Financial Crisis Survey. Data from 152 establishments from private nonagricultural formal sector was analyzed to quantify the effect of the 2008 global financial crisis on companies in Hungary.

Researchers revisited establishments interviewed in Hungary Enterprise Survey 2009. Efforts were made to contact all respondents of the baseline survey to determine which of the companies were still operating and which were not. From the information collected during telephone interviews, indicators were computed to measure the effects of the financial crisis on key elements of the private economy: sales, employment, finances, and expectations of the future.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study was the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The manufacturing and services sectors were the primary business sectors of interest. This corresponded to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies were targeted for interviews. Services firms included construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government ownership were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

291 establishments that participated in Hungary Enterprise Survey 2009 were contacted for The Financial Crisis Survey. The implementing contractor received directions that the final achieved sample should include at least 150 establishments.

For Hungary Enterprise Survey 2009, the sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 23 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. Each sector had a target of 90 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in three regions. These regions are Central Hungary, West Hungary and East Hungary.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

For most countries covered in 2008-2009 BEEPS, two sample frames were used. The first was supplied by the World Bank and consisted of enterprises interviewed in BEEPS 2005. The World Bank required that attempts should be made to re-interview establishments responding to the BEEPS 2005 survey where they were within the selected geographical regions and met eligibility criteria. That sample is referred to as the Panel. The second frame for Hungary was the Dun & Bradstreet database, which was considered the most reliable for the country. That frame was sent to the TNS statistical team in London to select the establishments for interviews.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 4.6% (29 out of 630 establishments).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following survey instrument is available: - Financial Crisis Survey Questionnaire

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks and callbacks.