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.

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.

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

Data underlying the report of a study that assesses and quantifes the impacts of the financial crisis and subsequent global economic recession on the growth and performance of UK SME employers. Analyses existing data from two previous survey sources on SME employers in the pre-recession and recessionary periods. Covers how the problems in the banking sector have affected the supply of finance to the SME sector, and whether this has depressed business performance and investment. Looks at the impact of the recession has been more serious for particular types of entrepreneurs and businesses.

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 data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

This project will assess the equality impact of the recent recession on the labour utilisation and position of women and men in the labour market, and on the employment of lone parents. These impacts will be considered against the backcloth of longer term demographic and policy developments leading up to and during the recent economic downturn. An important question to be considered is whether as a result of surplus labour, increased labour market competition, and intensified business conditions, recession acts to heighten the employment penalties experienced by women. This could occur through increased sex discrimination, or fewer efforts by employers to apply equality and diversity policy as a means of recruiting and retaining staff. To explore this question we will use recent innovations in statistical matching techniques to form comparison groups of men matched to women to explore whether women and men who are comparable in terms of their individual characteristics differ in their labour market outcomes. Secondary analysis. Analysis of trends in unemployment, economic activity and time related underemployment by NUTS 2 geographical level, comparing trends in Northern England counties against National and regional trends. The data was used to produce data tables for part of an appraisal of current modelling strategies used by local governments for labour market projections, which require re-evaluation in the context of the recent economic crisis.

The Gross Domestic Product (GDP) in Germany contracted 0.30 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - Germany GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) in Italy expanded 0.40 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - Italy GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

The US Social Security Disability Insurance (SSDI) program is designed to provide income support to workers who become unable to work because of a severe, long-lasting disability. In this study, we use administrative data to estimate the effect of labor market conditions, as measured by the unemployment rate, on the number of SSDI applications, the number and composition of initial allowances and denials, and the timing of applications relative to disability onset. We analyze the period of the Great Recession, and compare this period with business cycle effects over the past two decades, from 1992 through 2012.

The Gross Domestic Product (GDP) in Argentina contracted 0.10 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Argentina GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interview transcripts with a sample of advanced manufacturing firms(aerospace, electrical, pharmaceutical and automotive sectors), and related policy and business organisations, in the East Midlands, North West and Central Belt of Scotland. The results of a firm questionnaire survey with advanced manufacturing firms (aerospace, electrical, pharmaceutical and automotive sectors) in British manufacturing areas.The recession from 2008, and the persistent sectoral and spatial imbalances in the recovery, have provoked political calls to 'rebalance' the economy. According to Government representatives, Britain needs to 'reindustrialise', to rediscover its talent for manufacturing. Strengthening manufacturing in the Midlands and North will aid economic stability, raise productivity, and promote a more even distribution of growth. It has been argued that traditional industrial regions should develop new types of high-technology, 'advanced' manufacturing activities. Such calls for rebalancing have triggered a major debate on whether the British economy can in any way 're-industrialise'. Optimists point to resurgent clusters of manufacturing industries. Others are sceptical and argue that British manufacturing has been undermined by the 2008 recession, long-term weaknesses and an unsupportive institutional context. In this view, supply chains in British manufacturing are now too thin, fragmented and sparse to support industrial renewal on the scale required. There is evidence to show uneven regional trends in manufacturing, especially between the North and South of Britain and, according to some, advanced manufacturing is growing at a much faster rate in Southern England due to its research intensity and proximity to high-technology institutions. There is a pressing need to know how, and how far, industrial regions in Britain are developing advanced manufacturing. Relatively little is known about any potential regional manufacturing renaissance and the significance of location. There are several hypotheses. Some argue that advanced manufacturing develops best in specialised clusters and in local 'ecosystems' in which firms benefit from shared capabilities, resources, spill-overs and intermediaries. Others emphasise broader-scale external economies across sectors, so that location in cities and regions with a wide range of growing industries is more important to manufacturing performance. There is also debate about the degree to which location in traditional industrial regions aids or hinders advanced manufacturing. In a 'phoenix industry' view, manufacturing can be revived in traditional industrial regions by networks of small firms and by the diversification and branching of new sectors. This project tackles these questions. It places the performance of advanced manufacturing firms in the context of changes in supply chains and examines whether there is increasing specialisation of regions and locations in particular tasks, roles and functions rather than in entire industries. This project will examine the geographical, organisational and economic dynamics of four key manufacturing industries: electrical, computing and optical equipment; aerospace; pharmaceuticals; and motor vehicles. The project would proceed in three connected stages. The first stage would be to use and combine existing micro-data sources to examine the central issues on the relationships between manufacturing performance and location and investigate the key determinants of firm growth, performance and innovation in these industries. The project will use and combine several data-sets to provide a detailed analysis of change since the early 1970s. The second stage of the project will carry out a postal and online survey of firms in the four industries. This will explore the relationships between location and firm performance in more depth. For each industry, the survey aims to compare a set of firms within traditional industrial regions (in the North, Wales, Scotland or Midlands) with a similar group of firms in Southern regions. The final stage of the project will focus on manufacturers in these industries in four Midlands/Northern regions (selecting one region where each industry is well represented). In these areas, it will use firm interviews and focus groups to discuss findings, and identify and sound out key policy lessons and implications

The Gross Domestic Product (GDP) in Hong Kong expanded 0.40 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Hong Kong GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Austria expanded 0.30 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Austria GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Canada contracted 0.40 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Canada GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Colombia expanded 0.50 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Colombia GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Seychelles expanded 4.60 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides - Seychelles GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.