As of 2019, Argentina was the country that spent the most time in economic recession in the Americas since 1950. Up to one third of the time since 1950, the Argentine economy was in contraction. In Venezuela, the percentage of time in recession amounted to 28 percent in the same period, whereas in the U.S. it represented around ten percent.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
In 2019, ** percent of American respondents said that President Trump would be the most responsible if the United States were to enter into a recession. This is compared to **** percent of respondents, who said that former President Barack Obama would be the most responsible.
In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the ** largest metro areas in the United States. The Californian metro area received an overall score of **** percent, which was compiled after factors such as home price volatility and average home loan-to-value ratio were examined.
Sample data for HESS-2019-205 submission
Description: This file contains the event magnitudes and spacing for Cases 1 & 3 presented in the submitted manuscript to HESS titled "Recession analysis 42 years later - work yet to be done".
CVS File: This file is an ordered set of the normalized event magnitude [-] and the start date fo the event (Time/Timescale [-])
Matlab File: The file is presented is in a .mat file extension created in Matlab. The data is divided into 3 columns: mag, value, and start_locs. The column of "Mag" defines the event magnitudes, which are log-normally distributed with a mean 1 of a standard deviation of 1. The column of "value" defines the event duration which has a mean of 2.5 and a standard deviation of 1. The "start_locs" column as the cumulative event durations that identify the start time of each event. Below is the associated Matlab code used to create the file: %% Matlab Code %% mag= lognrnd(1,1[number_of_events,1]); %create log-normally distributed dataset of event magnitudes for a defined number of events mag(mag<0)=1; %remove any negative magnitudes value=round(lognrnd(2.5,1,[number_of_events,1])); %create log-normally distributed dataset of event durations for a defined number of events value(value<=0)=1; %remove any negative durations start_locs=[2;cumsum(value)]; %create cumulative event start time-series
Between ************ and *********, global recession fear went through periods of sharp increases three times. First, in the summer of 2019, due to an escalation in U.S.-China relations and a recession signal being flashed by the bond market. The second peak of worldwide recession fear took place in **********, as a result of the alarming jump in the rate of COVID-19 cases. The fear of recession started to increase sharply again in *************, as the conflict between Russia and Ukraine escalated.
This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation, PIIE Policy Brief 19-16. If you use the data, please cite as: Reifschneider, David, and David Wilcox. (2019). Average Inflation Targeting Would Be a Weak Tool for the Fed to Deal with Recession and Chronic Low Inflation. PIIE Policy Brief 19-16. Peterson Institute for International Economics.
In 2019, 52 percent of PERE (private equity real estate) investors or managers globally said they were planning to increase their technology investments in the event of an economic slowdown. However, 28 percent said they would decrease their overall technology investments.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
We estimate across-county spending flows between firms and consumers for every county in the United States, providing a new consumption link that has not been studied previously. We highlight the importance of this link by estimating the effect of changes in local housing wealth on consumption and employment from 2001 to 2019. We generally find that the effect from changes in housing wealth crosses borders to affect consumption and employment in a pattern consistent with our spending flows. However, we find potential consumers who reside outside the local commuting zone disproportionately affect local spending and employment during the Great Recession.
This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Are Central Banks Out of Ammunition to Fight a Recession? Not Quite, PIIE Policy Brief 19-18.
If you use the data, please cite as: Gagnon, Joseph E., and Christopher G. Collins. (2019). Are Central Banks Out of Ammunition to Fight a Recession? Not Quite. PIIE Policy Brief 19-18. Peterson Institute for International Economics.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Over 44.7 million Americans carry student loan debt, with the total amount valued at approximately $1.31 trillion (Quarterly Report, 2019). Ergo, consumer spending, a factor of GDP, is stifled and negatively impacts the economy (Frizell, 2014, p. 22). This study examined the relationship between student loan debt and the probability of a recession in the near future, as well as the effects of proposed student loan forgiveness policies through the use of a created model. The Federal Reserve Bank of St. Louis’s website (FRED) was used to extract data regarding total GDP per quarter and student loan debt per quarter ("Federal Reserve Economic Data," 2019). Through the combination of the student loan debt per quarter and total GDP per quarter datasets, the percentage of total GDP composed of student loan debt per quarter was calculated and fitted to a logistic curve. Future quarterly values for total GDP and the percentage of total GDP composed by student loan debt per quarter were found through Long Short Term Models and Euler’s Method, respectively. Through the creation of a probability of recession index, the probability of recession per quarter was compared to the percentage of total GDP composed by student loan debt per quarter to construct an exponential regression model. Utilizing a primarily quantitative method of analysis, the percentage of total GDP composed by student loan debt per quarter was found to be strongly associated[p < 1.26696* 10-8]with the probability of recession per quarter(p(R)), with the p(R) tending to peak as the percentage of total GDP composed of student loan debt per quarter strayed away from the carrying capacity of the logistic curve. Inputting the student loan debt forgiveness policies of potential congressional bills proposed by lawmakers found that eliminating 49.7 % and 36.7% of student loan debt would reduce the recession probabilities to be 1.73545*10-29% and 9.74474*10-25%, respectively.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Inspired by:
Modeling and predicting U.S. recessions using machine learning techniques
As variáveis do FRED-MD como preditivas e a USREC como alvo (período de 1979-2019)
Diversos Modelos: probit, logit, LDA, árvores Naive-Bayes Algumas variáveis tiveram que ser transformadas em mensais (interpolação cúbica)
128 varibles. Grupos: Output and Income Labor Market Consumption and Orders Orders and Inventories Money and Credit Interest Rates and Exchange Rates Prices Stock Market
This map shows which areas have concentrations of high risk businesses in the event of an economic downturn. Areas in red have a higher concentration of one or more of the five categories (by NAICS code): Clothing/Accessory stores, General Merchandise stores, Arts/Entertainment/Recreation, Accommodation, and Food Service/Drinking Places. The popup breaks down count of businesses per category and percent of businesses for the area. Data is 2019 vintage and available by county, tract, and block group. Overall, in the US, these 5 categories make up 11.8% of total businesses.Esri's Business Summary Data: Esri's Business Locations data is extracted from a comprehensive list of businesses licensed from Infogroup. It summarizes the comprehensive list of businesses from Infogroup for select NAICS and SIC summary categories by geography and includes total number of businesses, total sales, and total number of employees for a trade area.Esri's U.S. 2019 Data: Population, age, income, race, home value, spending, business, and market potential are among the topics included in the data suite. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. To browse, all data variables available within Esri's demographics explore the Data Browser. Additional Esri Resources:Get StartedEsri DemographicsU.S. 2019 Esri Updated DemographicsBusiness Summary DataMethodologies
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
We use the yield curve to predict future GDP growth and recession probabilities. The spread between short- and long-term rates typically correlates with economic growth. Predications are calculated using a model developed by the Federal Reserve Bank of Cleveland. Released monthly.
According to a survey from 2019, the majority of Italian companies operating in Turkey believed that the Turkish economy was in stage of recession. On the other hand, 21 percent of enterprises declared that the economy in Turkey was stable, whereas 28 percent of Italian companies stated it was in a stage of growth.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Stata dataset and do file M. Giuliani (2019). Benchmarking or spillovers: The economic vote before and during the Great Recession, in "Quaderni di Scienza Politica", 26(3): 383-408
Spreadsheet used to calculated hydrograph recession statistical parameters (Minimum, Most Probable Value, and Maximum) for the Stochastic Empirical Loading Dilution Model (SELDM) . The spreadsheet was used in conjunction with the SELDM simulations used in the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053, and after using the Hydrograph.xlsx spreadsheet.
In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.
The value of foreign direct investment (FDI) inflows to the Gulf Cooperation Council (GCC) region in the period between January to July dropped from **** billion U.S. dollars in the 2019 to about **** billion U.S. dollars in 2020 post the COVID-19 pandemic. The global oil demand dropped significantly in April 2020 which worsened the economic situation of the region.
This map shows which areas have concentrations of high risk businesses in the event of an economic downturn. Areas in red have a higher concentration of one or more of the five categories (by NAICS code): Clothing/Accessory stores, General Merchandise stores, Arts/Entertainment/Recreation, Accommodation, and Food Service/Drinking Places. The popup breaks down count of businesses per category and percent of businesses for the area. Data is 2019 vintage and available by county, tract, and block group. Overall, in the US, these 5 categories make up 11.8% of total businesses.Esri's Business Summary Data: Esri's Business Locations data is extracted from a comprehensive list of businesses licensed from Infogroup. It summarizes the comprehensive list of businesses from Infogroup for select NAICS and SIC summary categories by geography and includes total number of businesses, total sales, and total number of employees for a trade area.Esri's U.S. 2019 Data: Population, age, income, race, home value, spending, business, and market potential are among the topics included in the data suite. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. To browse, all data variables available within Esri's demographics explore the Data Browser. Additional Esri Resources:Get StartedEsri DemographicsU.S. 2019 Esri Updated DemographicsBusiness Summary DataMethodologies
As of 2019, Argentina was the country that spent the most time in economic recession in the Americas since 1950. Up to one third of the time since 1950, the Argentine economy was in contraction. In Venezuela, the percentage of time in recession amounted to 28 percent in the same period, whereas in the U.S. it represented around ten percent.