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 Feb 2025 about peak, trough, recession indicators, and USA.

Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q3 2024 about recession indicators, GDP, and USA.

Graph and download economic data for OECD based Recession Indicators for OECD and Non-member Economies from the Peak through the Trough (OECDNMERECDM) from 1960-02-01 to 2022-02-28 about OECD and Non-OECD, peak, trough, and recession indicators.

By November 2025, it is projected that there is a probability of 33.56 percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

Graph and download economic data for OECD based Recession Indicators for Euro Area from the Period following the Peak through the Trough (EUROREC) from Mar 1960 to Aug 2022 about peak, trough, recession indicators, Euro Area, and Europe.

Stagflation (stagnation and inflation in one word) depicts a time period when an economy is not only suffering from a recession (declining GDP), but high unemployment and inflation rates as well. Usually unemployment and inflation are inversely related, which makes stagflation a rare occurrence. It first happened in the 1970s, when OPEC put an oil embargo on the United States, resulting in oil prices skyrocketing to three times the standard value at that time. As of September 2023, the price of oil fell by 20 percent in comparison to last year after having increased by 76 perent as a result of Russian invasion of Ukraine. The has been signs of stagflation in some countries through 2022 and 2023, but falling inflation rates indicate that the worst has been avoided.

The base flow recession time constant (tau) is a hydrologic index that characterizes the ability of a ground-water system to supply flow to a stream draining from that system. Tau and other correlated hydrologic indices have been used as explanatory variables to greatly improve the predictive power of low-flow regression equations. Tau can also be used as an indicator of streamflow dependence on groundwater inflow to the channel. Tau values were calculated for 10 streamgages in the Niobrara National Scenic River study area. The calculated tau values were then used to create a kriged map. 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, with differing potential to demonstrate the effects of management changes 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 2020 recession did not follow the trend of previous recessions in the United States because only six months elapsed between the yield curve inversion and the 2020 recession. Over the last five decades, 12 months, on average, has elapsed between the initial yield curve inversion and the beginning of a recession in the United States. For instance, the yield curve inverted initially in January 2006, which was 22 months before the start of the 2008 recession. A yield curve inversion refers to the event where short-term Treasury bonds, such as one or three month bonds, have higher yields than longer term bonds, such as three or five year bonds. This is unusual, because long-term investments typically have higher yields than short-term ones in order to reward investors for taking on the extra risk of longer term investments. Monthly updates on the Treasury yield curve can be seen here.

Leading Economic Index the United States increased to 101.10 in February of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Coincident Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In January 2025, prices had increased by three percent compared to January 2024 according to the 12-month percentage change in the consumer price index — the monthly inflation rate for goods and services in the United States. The data represents U.S. city averages. In economics, the inflation rate is a measure of the change in price level over time. The rate of decrease in the purchasing power of money is approximately equal. A projection of the annual U.S. inflation rate can be accessed here and the actual annual inflation rate since 1990 can be accessed here. InflationOne of the most important economic indicators is the development of the Consumer Price Index in a country. The change in this price level of goods and services is defined as the rate of inflation. The inflationary situation in the United States had been relatively severe in 2022 due to global events relating to COVID-19, supply chain restrains, and the Russian invasion of Ukraine. More information on U.S. inflation may be found on our dedicated topic page. The annual inflation rate in the United States has increased from 3.2 percent in 2011 to 8.3 percent in 2022. This means that the purchasing power of the U.S. dollar has weakened in recent years. The purchasing power is the extent to which a person has available funds to make purchases. According to the data published by the International Monetary Fund, the U.S. Consumer Price Index (CPI) was about 258.84 in 2020 and is forecasted to grow up to 325.6 by 2027, compared to the base period from 1982 to 1984. The monthly percentage change in the Consumer Price Index (CPI) for urban consumers in the United States was 0.1 percent in March 2023 compared to the previous month. In 2022, countries all around the world are experienced high levels of inflation. Although Brazil already had an inflation rate of 8.3 percent in 2021, compared to the previous year, while the inflation rate in China stood at 0.85 percent.

From the onset of the Global Financial Crisis in the Summer of 2007, the world economy experienced an almost unprecedented period of turmoil in which millions of people were made unemployed, businesses declared bankruptcy en masse, and structurally critical financial institutions failed. The crisis was triggered by the collapse of the U.S. housing market and subsequent losses by investment banks such as Bear Stearns, Lehman Brothers, and Merrill Lynch. These institutions, which had become over-leveraged with complex financial securities known as derivatives, were tied to each other through a web of financial contracts, meaning that the collapse of one investment bank could trigger the collapse of several others. As Lehman Brothers failed on September 15. 2008, becoming the largest bankruptcy in U.S. history, shockwaves were felt throughout the global financial system. The sudden stop of flows of credit worldwide caused a financial panic and sent most of the world's largest economies into a deep recession, later known as the Great Recession. The World Economy in recession
More than any other period in history, the world economy had become highly interconnected and interdependent over the period from the 1970s to 2007. As governments liberalized financial flows, banks and other financial institutions could take money in one country and invest it in another part of the globe. Financial institutions and other non-financial companies became multinational, meaning that they had subsidiaries and partners in many regions. All this meant that when Wall Street, the center of global finance in New York City, was shaken by bankruptcies and credit freezes in late 2007, other advanced economies did not need to wait long to feel the tremors. All of the G7 countries, the seven most economically advanced western-aligned countries, entered recession in 2008, before experiencing an even deeper trough in 2009. While all returned to growth by 2010, this was less stable in the countries of the Eurozone (Germany, France, Italy) over the following years due to the Eurozone crisis, as well as in Japan, which has had issues with low growth since the mid-1990s.

In 2023, the U.S. GDP increased from the previous year to about 27.36 trillion U.S. dollars. This increase in GDP can be attributed to a continued rebound from the impact of the coronavirus pandemic. Gross domestic product (GDP) refers to the market value of all goods and services produced within a country. In 2023, the United States has the largest economy in the world. See, for example, the Russian GDP for comparison.

What is GDP? Gross domestic product is one of the most important indicators used to analyze the health of an economy. GDP is defined by the BEA as the market value of goods and services produced by labor and property in the United States, regardless of nationality. It is the primary measure of U.S. production. The OECD defines GDP as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs).

GDP and national debt

Although the United States had the highest Gross Domestic Product (GDP) in the world in 2022, this does not tell us much about the quality of life in any given country. GDP per capita at purchasing power parity (PPP) is an economic measurement that is thought to be a better method for comparing living standards across countries because it accounts for domestic inflation and variations in the cost of living.

While the United States might have the largest economy, the country that ranked highest in terms of GDP at PPP was Luxembourg, amounting to around 141,333 international dollars per capita. Singapore, Ireland, and Qatar also ranked highly on the GDP PPP list, and the United States ranked 9th in 2022.

Graph and download economic data for Coincident Economic Activity Index for the United States (USPHCI) from Jan 1979 to Dec 2024 about coincident economic activity, indexes, and USA.

Approximately one quarter of the UK population have a migration background (first- or second-generation immigrants). Some ethnic minority groups are more likely to be in atypical or flexible employment than the White British majority. In particular during a time of health and economic crisis, such as the COVID–19 pandemic, those ethnic groups were expected to be economically more vulnerable than other groups. This study shows the increased vulnerability of some ethnic minority groups during COVID–19 by looking at their labour market outcomes compared to White British. Specifically, we ask whether it was their disproportionate presence in flexible employment or in shut-down occupations that made some ethnic minority groups vulnerable to adverse labour market outcomes during the COVID–19 recession? Using the COVID–19 recession in the UK as a case study, we employ weighted linear probability models with 2021 data from the Evidence for Equality National Survey (EVENS) to look at changes in economic indicators across ethnic groups and gender. We report heterogeneity in flexible employment rates within the non-White group and between the non-White and the White British group. By using a conditional decomposition method, we aim to show that those ethnic minority groups who were disproportionately on flexible contracts experienced worse economic effects than the White British group. The collection consists of the Stata Do-File which can be used to reproduce the study.Was it their disproportionate presence in flexible employment or in shut-down occupations that made some ethnic minority groups vulnerable to adverse labour market outcomes during the COVID–19 recession? Using the COVID–19 recession in the UK as a case study, we employ weighted linear probability models with 2021 data from the Evidence for Equality National Survey (EVENS) to look at changes in economic indicators across ethnic groups and gender. We report heterogeneity in flexible employment rates within the non-White group and between the non-White and the White British group. By using a conditional decomposition method, we conclude that those ethnic minority groups who were disproportionately on flexible contracts experienced worse economic effects than the White British group. EVENS used web-based interviews and computer-assisted (CATI) telephone interviews. EVENS aimed to better represent ethnic minorities compared to existing data sets regarding the range of represented minority population groups. To cite from the online Abstract of EVENS: "....EVENS survey used an 'open' survey approach, which requires participants to opt-in to the survey instead of probability-based approaches that invite individuals to participate following their identification within a pre-defined sampling frame. This 'open' approach sought to overcome some of the limitations of probability-based methods in order to reach a large number and diverse mix of people from religious and ethnic minorities." (UK Data Service: SN-9116)

This data release contains three different datasets that were used in the Scientific Investigations Report: Spatial and Temporal Distribution of Bacterial Indicators and Microbial Source Tracking within Tumacácori National Historical Park and the Upper Santa Cruz River, Arizona, 2015-16. These datasets contain regression model data, estimated discharge data, and calculated flux and yields data. Regression Model Data: This dataset contains data used in a regression model development in the SIR. The period of data ranged from May 25, 1994 to May 19, 2017. Data from 2015 to 2017 were collected by the U.S. Geological Survey. Data prior to 2015 were provided by various agencies. Listed below are the different data contained within this dataset: - Season represented as an indicator variable (Fall, Spring, Summer, and Winter) - Hydrologic Condition represented as an indicator variable (rising limb, recession limb, peak, or unable to classify) - Flood (binary variable indicating if the sample was collected during a flood event or not) - Decimal Date (DT) represented as a continuous variable - Sine of DT represented as a continuous variable for periodic function to describe seasonal variation - Cosine of DT represented as a continuous variable for periodic function to describe seasonal variation Estimated Discharge: This dataset contains estimated discharge at four different sites between 03/02/2015 and 12/14/2016. The discharge was estimated using nearby streamgage relations and methods are described in detail in the SIR . The sites where discharge was estimated are listed below. - NW8; 312551110573901; Nogales Wash at Ruby Road - SC3; 312654110573201; Santa Cruz River abv Nogales Wash - SC10; 313343110024701; Santa Cruz River at Santa Gertrudis Lane - SC14; 09481740; Santa Cruz River at Tubac, AZ Calculated Flux and Yields: This dataset contains calculated flux and yields for E. coli and suspended sediment concentrations. Mean daily flux was calculated when mean daily discharge was available at a corresponding streamgage. Instantaneous flux was calculated when instantaneous discharge (at 15-minute intervals) were available at a corresponding streamgage, or from a measured or estimated discharge value. The yields were calculated using the calculated flux values and the area of the different watersheds. Methods and equations are described in detail in the SIR. Listed below are the data contained within this dataset: - Mean daily E. coli flux, in most probable number per day - Mean daily suspended sediment, in flux, in tons per day - Instantaneous E. coli flux, in most probable number per second - Instantaneous suspended sediment flux, in tons per second - E. coli, in most probable number per square mile - Suspended sediment, in tons per square mile

Using micro-data on six surveys–the Gallup World Poll 2005–2023, the U.S. Behavioral Risk Factor Surveillance System, 1993–2022, Eurobarometer 1991–2022, the UK Covid Social Survey Panel, 2020–2022, the European Social Survey 2002–2020 and the IPSOS Happiness Survey 2018–2023 –we show individuals’ reports of subjective wellbeing in Europe declined in the Great Recession of 2008/9 and during the Covid pandemic of 2020–2021 on most measures. They also declined in four countries bordering Ukraine after the Russian invasion in 2022. However, the movements are not large and are not apparent everywhere. We also used data from the European Commission’s Business and Consumer Surveys on people’s expectations of life in general, their financial situation and the economic and employment situation in the country. All of these dropped markedly in the Great Recession and during Covid, but bounced back quickly, as did firms’ expectations of the economy and the labor market. Neither the annual data from the United Nation’s Human Development Index (HDI) nor data used in the World Happiness Report from the Gallup World Poll shifted much in response to negative shocks. The HDI has been rising in the last decade reflecting overall improvements in economic and social wellbeing, captured in part by real earnings growth, although it fell slightly after 2020 as life expectancy dipped. This secular improvement is mirrored in life satisfaction which has been rising in the last decade. However, so too have negative affect in Europe and despair in the United States.

Consumer Confidence in the United States decreased to 57.90 points in March from 64.70 points in February of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

After a period of gradual decline, the average annual rate on a 30-year fixed-rate mortgage in the United States rose to 6.81 percent in 2023, up from the record-low 2.96 percent in 2021. The rate for 15-year fixed mortgages and five-year ARM mortgages followed a similar trend. This was a result of the Federal Reserve increasing the bank rate - a measure introduced to tackle the rising inflation. U.S. home prices going through the roof Mortgage rates have a strong impact on the market – the lower the rate, the lower the loan repayment. The rate on a 30-year fixed-rate mortgage decreasing after the Great Recession has stimulated the market and boosted home sales. Another problem consumers face is the fact that house prices are rising at an unaffordable level. The median sales price of a new home sold surged in 2021, while the median weekly earnings of a full-time employee maintained a more moderate increase. What are the differences between 15-year and 30-year mortgages? Two of the most popular loan terms available to homebuyers are the 15-year fixed-rate mortgage and the 30-year fixed-rate mortgage. The 30-year option appeals to more consumers because the repayment is spread out over 30 years, meaning the monthly payments are lower. Consumers choosing the 15-year option will have to pay higher monthly payments but benefit from lower interest rates.

We study the impact of seasonal adjustment on the properties of business cycle expansion and recession regimes using analytical, simulation and empirical methods. Analytically, we show that the X-11 adjustment filter both reduces the magnitude of change at turning points and reduces the depth of recessions, with specific effects depending on the length of the recession. A Monte Carlo analysis using Markov-switching models confirms these properties, with particularly undesirable effects in delaying the recognition of the end of a recession. However, seasonal adjustment can help to clarify the true regime when this is well underway. These results continue to hold when a seasonally non-stationary process with regime-dependent mean is misspecified as one with deterministic seasonal effects. The empirical findings, based on four coincident US business cycle indicators, reinforce the analytical and simulation results by showing that seasonal adjustment leads to the identification of longer and shallower recessions than obtained using unadjusted data.

This data release contains three different datasets that were used in the Scientific Investigations Report: Spatial and Temporal Distribution of Bacterial Indicators and Microbial Source Tracking within Tumacacori National Historical Park and the Upper Santa Cruz River, Arizona, 2015-16. These datasets contain regression model data, estimated discharge data, and calculated flux and yields data. Regression Model Data: This dataset contains data used in a regression model development in the SIR. The period of data ranged from May 25, 1994 to May 19, 2017. Data from 2015 to 2017 were collected by the U.S. Geological Survey. Data prior to 2015 were provided by various agencies. Listed below are the different data contained within this dataset: Season represented as an indicator variable (Fall, Spring, Summer, and Winter) Hydrologic Condition represented as an indicator variable (rising limb, recession limb, peak, or unable to classify) Flood (binary variable indicating if the sample was collected during a flood event or not) Decimal Date (DT) represented as a continuous variable Sine of DT represented as a continuous variable for periodic function to describe seasonal variation Cosine of DT represented as a continuous variable for periodic function to describe seasonal variation Estimated Discharge: This dataset contains estimated discharge at four different sites between March 2, 2015 and December 14, 2016. The discharge was estimated using nearby streamgage relations and methods are described in detail in the SIR. The sites where discharge was estimated are listed below. NW8; 312551110573901; Nogales Wash at Ruby Road SC3; 312654110573201; Santa Cruz River abv Nogales Wash SC10; 313343110024701; Santa Cruz River at Santa Gertrudis Lane SC14; 09481740; Santa Cruz River at Tubac, AZ Calculated flux and Yields: This dataset contains calculated flux and yields for E. coli and suspended sediment concentrations. Mean daily flux was calculated when mean daily discharge was available at a corresponding streamgage. Instantaneous flux was calculated when instantaneous discharge (at 15-minute intervals) were available at a corresponding streamgage, or from a measured or estimated discharge value. The yields were calculated using the calculated flux values and the area of the different watersheds. Methods and equations are described in detail in the SIR. Listed below are the data contained within this dataset: Mean daily E. coli flux, in most probable number per day Mean daily suspended sediment flux, in tons per day Instantaneous E. coli flux, in most probable number per second Instantaneous suspended sediment flux, in tons per second E. coli, in most probable number per square mile Suspended sediment, in tons per square mile