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.

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.

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.

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 NBER: Recorded Recession data was reported at 0.000 Unit in Oct 2018. This stayed constant from the previous number of 0.000 Unit for Sep 2018. United States NBER: Recorded Recession data is updated monthly, averaging 0.000 Unit from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 1.000 Unit in Jun 2009 and a record low of 0.000 Unit in Oct 2018. United States NBER: Recorded Recession 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. An interpretation of US Business Cycle Expansions and Contractions data provided by The National Bureau of Economic Research (NBER). A value of 1 is a recessionary period, while a value of 0 is an expansionary period.

From the Summer of 2007 until the end of 2009 (at least), the world was gripped by a series of economic crises commonly known as the Global Financial Crisis (2007-2008) and the Great Recession (2008-2009). The financial crisis was triggered by the collapse of the U.S. housing market, which caused panic on Wall Street, the center of global finance in New York. Due to the outsized nature of the U.S. economy compared to other countries and particularly the centrality of U.S. finance for the world economy, the crisis spread quickly to other countries, affecting most regions across the globe. By 2009, global GDP growth was in negative territory, with international credit markets frozen, international trade contracting, and tens of millions of workers being made unemployed.

Global similarities, global differences

Since the 1980s, the world economy had entered a period of integration and globalization. This process particularly accelerated after the collapse of the Soviet Union ended the Cold War (1947-1991). This was the period of the 'Washington Consensus', whereby the U.S. and international institutions such as the World Bank and IMF promoted policies of economic liberalization across the globe. This increasing interdependence and openness to the global economy meant that when the crisis hit in 2007, many countries experienced the same issues. This is particularly evident in the synchronization of the recessions in the most advanced economies of the G7. Nevertheless, the aggregate global GDP number masks the important regional differences which occurred during the recession. While the more advanced economies of North America, Western Europe, and Japan were all hit hard, along with countries who are reliant on them for trade or finance, large emerging economies such as India and China bucked this trend. In particular, China's huge fiscal stimulus in 2008-2009 likely did much to prevent the global economy from sliding further into a depression. In 2009, while the United States' GDP sank to -2.6 percent, China's GDP, as reported by national authorities, was almost 10 percent.

Measures of regional economic sentiment, extracted from the Beige Book using natural language processing methods, consistently delivered reliable real-time forecasts of US recessions from the mid-1980s through the COVID-19 pandemic recession. Since then, recession risk probabilities have been choppy, with several false alarms. We attribute this unreliability to a post-2021 disconnect between measures of economic activity and the sentiment of business and community leaders.

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.

During the Great Recession of 2008-2009, the advanced economies of the G7 experienced a period of acute financial crises, downturns in the non-financial economy, and political instability. The governments of these countries in many cases stepped in to backstop their financial sectors and to try to stimulate their economies. The scale of these interventions was large by historical standards, with observers making comparisons to the measures of the New Deal which the U.S. undertook in the 1930s to end the Great Depression.

The bailouts of financial institutions and stimulus packages caused the government debt ratios of the United States, United Kingdom, and Japan in particular to rise sharply. The UK's government debt ratio almost doubled due to the bailouts of Northern Rock and Royal Bank of Scotland. On the other hand, the increases in government debt in the Eurozone were more measured, due to the comparative absence of stimulus spending in these countries. They would later be hit hard during the Eurozone crisis of the 2010s, when bank lending to the periphery of the Eurozone (Portugal, Spain, Ireland and Greece in particular) would trigger a sovereign debt crisis. The Canadian government, led by a Conservative premier, engaged in some fiscal stimulus to support its economy, but these packages were small in comparison to that in most other of the G7 countries.

BackgroundData on the potential influence of macroeconomic recessions on maternal diseases during pregnancy are scarce. We aimed to assess potential change in prevalence of pregnancy-induced hypertensive disorders (preeclampsia and gestational hypertension) during the first years of the major national economic recession in Iceland, which started abruptly in October 2008.Methods and FindingsWomen whose pregnancies resulted in live singleton births in Iceland in 2005–2012 constituted the study population (N = 35,211). Data on pregnancy-induced hypertensive disorders were obtained from the Icelandic Medical Birth Register and use of antihypertensive drugs during pregnancy, including β-blockers and calcium channel blockers, from the Icelandic Medicines Register. With the pre-collapse period as reference, we used logistic regression analysis to assess change in pregnancy-induced hypertensive disorders and use of antihypertensives during the first four years after the economic collapse, adjusting for demographic and pregnancy characteristics, taking aggregate economic indicators into account. Compared with the pre-collapse period, we observed an increased prevalence of gestational hypertension in the first year following the economic collapse (2.4% vs. 3.9%; adjusted odds ratio [aOR] 1.47; 95 percent confidence interval [95%CI] 1.13–1.91) but not in the subsequent years. The association disappeared completely when we adjusted for aggregate unemployment rate (aOR 1.04; 95% CI 0.74–1.47). Similarly, there was an increase in prescription fills of β-blockers in the first year following the collapse (1.9% vs.3.1%; aOR 1.43; 95% CI 1.07–1.90), which disappeared after adjusting for aggregate unemployment rate (aOR 1.05; 95% CI 0.72–1.54). No changes were observed for preeclampsia or use of calcium channel blockers between the pre- and post-collapse periods.ConclusionsOur data suggest a transient increased risk of gestational hypertension and use of β-blockers among pregnant women in Iceland in the first and most severe year of the national economic recession.

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.

This release looks at the increase in unemployment during the recent economic downturn. Increases in unemployment will be compared across regions in the UK, age groups, gender and other characteristics. Claimant count data will also be included.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Unemployment during the economic downturn

This statistic shows the national debt of Greece from 2020 to 2023, with projections until 2030. In 2023, the national debt in Greece was around 420.4 billion U.S. dollars. In a ranking of debt to GDP per country, Greece is currently ranked third. Greece's struggle after the financial crisis Greece is a developed country in the EU and is highly dependent on its service sector as well as its tourism sector in order to gain profits. After going through a large economic boom from the 1950s to the 1970s as well as somewhat high GDP growth in the early to mid 2000s, Greece’s economy took a turn for the worse and struggled intensively, primarily due to the Great Recession, the Euro crisis as well as its own debt crisis. National debt within the country saw significant gains over the past decades, however roughly came to a halt due to financial rescue packages issued from the European Union in order to help Greece maintain and improve their economical situation. The nation’s continuous rise in debt has overwhelmed its estimated GDP over the years, which can be attributed to poor government execution and unnecessary spending. Large sums of financial aid were taken from major European banks to help balance out these government-induced failures and to potentially help refuel the economy to encourage more spending, which in turn would decrease the country’s continuously rising unemployment rate. Investors, consumers and workers alike are struggling to see a bright future in Greece, whose chances of an economic comeback are much lower than that of other struggling countries such as Portugal and Italy. However, Greece's financial situation might improve in the future, as it is estimated that at least its national debt will decrease - slowly, but steadily. Still, since its future participation in the European Union is in limbo as of now, these figures can only be estimates, not predictions.

IntroductionSelf-rated health is demonstrated to vary substantially by both personal socio-economic status and national economic conditions. However, studies investigating the combined influence of individual and country level economic indicators across several countries in the context of recent global recession are limited. This paper furthers our knowledge of the effect of recession on health at both the individual and national level.MethodsUsing the Life in Transition II study, which provides data from 19,759 individuals across 26 European nations, we examine the relationship between self-rated health, personal economic experiences, and macro-economic change. Data analyses include, but are not limited to, the partial proportional odds model which permits the effect of predictors to vary across different levels of our dependent variable.ResultsHousehold experiences with recession, especially a loss of staple good consumption, are associated with lower self-rated health. Most individual-level experiences with recession, such as a job loss, have relatively small negative effects on perceived health; the effect of individual or household economic hardship is strongest in high income nations. Our findings also suggest that macroeconomic growth improves self-rated health in low-income nations but has no effect in high-income nations. Individuals with the greatest probability of “good” self-rated health reside in wealthy countries ($23,910 to $50, 870 GNI per capita).ConclusionBoth individual and national economic variables are predictive of self-rated health. Personal and household experiences are most consequential for self-rated health in high income nations, while macroeconomic growth is most consequential in low-income nations.

The statistic shows the gross domestic product (GDP) of the United States from 1987 to 2024, with projections up until 2030. The gross domestic product of the United States in 2024 amounted to around 29.18 trillion U.S. dollars. The United States and the economy The United States’ economy is by far the largest in the world; a status which can be determined by several key factors, one being gross domestic product: A look at the GDP of the main industrialized and emerging countries shows a significant difference between US GDP and the GDP of China, the runner-up in the ranking, as well as the followers Japan, Germany and France. Interestingly, it is assumed that China will have surpassed the States in terms of GDP by 2030, but for now, the United States is among the leading countries in almost all other relevant rankings and statistics, trade and employment for example. See the U.S. GDP growth rate here. Just like in other countries, the American economy suffered a severe setback when the economic crisis occurred in 2008. The American economy entered a recession caused by the collapsing real estate market and increasing unemployment. Despite this, the standard of living is considered quite high; life expectancy in the United States has been continually increasing slightly over the past decade, the unemployment rate in the United States has been steadily recovering and decreasing since the crisis, and the Big Mac Index, which represents the global prices for a Big Mac, a popular indicator for the purchasing power of an economy, shows that the United States’ purchasing power in particular is only slightly lower than that of the euro area.

This dataset provides information on the three macroeconomic sectoral balances, covering 195 countries over 45 years.

Macroeconomic analysis often focuses on the 'twin deficits' - the government deficit and the current account deficit. This is an incomplete view which leaves out the private sector balance.

The three sectoral balances must sum up to zero, by accounting identity (UN System of National Accounts 2008):

(S – I) + (T – G) + (M – X) = 0

Economists using the two-dimensional view have famously missed the global financial crisis, while those using accounting models covering all three sectoral balances were able to predict it (Bezemer 2010, Galbraith 2012). However, data on private sector deficits/surpluses is not readily available. Only the public and current account balances are published regularly by the IMF's World Economic Outlook.

Beyond developed countries, looking at the private sector balance is critical for analyzing and crafting policies in developing countries. The frequently recommended policy of 'fiscal consolidation', i.e. reducing public deficits, is revealed in the sectoral balances to also reduce, ceteris paribus, the private sector surplus (or increase its deficit), slowing down or even reversing development and poverty reduction (Assa and Morgan 2025).

The dataset was calculated based on two publicly available series from the IMF World Economic Outlook (downloaded October 2025): General government net lending/borrowing (coded as GOV) and Current account balance (coded as CAB). From this we calculated the private sector balance as PRV = CAB - GOV. We converted CAB to ROW (ROW = -CAB), the rest of the world balance, and made sure that ROW, GOV and PRV add up to zero as required by the national accounting identity. All years containing IMF forecasts were removed.

References:

Assa, J., & Morgan, M. (2025). The General Relativity of Fiscal Space: Theory and Applications. Review of Political Economy, 1-35.

Bezemer, D. J. (2010). Understanding financial crisis through accounting models. Accounting, organizations and society, 35(7), 676-688.

Galbraith, J. K. (2012). Who are these economists, anyway?. In Contributions in Stock-flow Modeling: Essays in Honor of Wynne Godley (pp. 63-75). London: Palgrave Macmillan UK.

United Nations (2008). System of National Accounts 2008. https://unstats.un.org/unsd/nationalaccount/sna2008.asp

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

United States Recession Prob: Yield Curve: 3 Month Treasury Yield data was reported at 2.250 % in Oct 2018. This records an increase from the previous number of 2.130 % for Sep 2018. United States Recession Prob: Yield Curve: 3 Month Treasury Yield data is updated monthly, averaging 4.620 % from Jan 1959 (Median) to Oct 2018, with 718 observations. The data reached an all-time high of 16.300 % in May 1981 and a record low of 0.010 % in Dec 2011. United States Recession Prob: Yield Curve: 3 Month Treasury Yield 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.

Graph and download economic data for OECD based Recession Indicators for New Zealand from the Peak through the Trough (DISCONTINUED) (NZLRECDM) from 1960-02-01 to 2017-10-31 about peak, trough, New Zealand, and recession indicators.

This dataset contains base-flow recession time constant (tau) contours that are interpreted from tau values calculated at streamgages in the Niobrara National Scenic River study area. The contours were created by interpolating the calculated tau values using geostatistical kriging methods. Kriging is a geostatistical method that can be used to determine optimal weights for measurements at sampled locations (streamgages) for the estimation of values at unsampled locations (ungaged sites). The kriged tau map could be used (1) as the basis for identifying areas with different hydrologic responsiveness, and (2) in the development of regional low-flow regression equations. The Geostatistical Analyst tools in ArcGIS Pro version 2.5.2 (Environmental Systems Research Institute, 2012) were used to create the kriged tau map and perform cross validation to determine the root mean square error (RMSE) of the tau map. The results of the kriging were then exported from ArcGIS to contours.