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.

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.

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

The Global Financial Crisis (2007-2008), which began due to the collapse of the U.S. housing market, had a negative effect in many regions across the globe. The global recession which followed the crisis in 2008 and 2009 showed how interdependent and synchronized many of the world's economies had become, with the largest advanced economies showing very similar patterns of negative GDP growth during the crisis. Among the largest emerging economies (commonly referred to as the 'E7'), however, a different pattern emerged, with some countries avoiding a recession altogether. Some commentators have particularly pointed to 2008-2009 as the moment in which China emerged on the world stage as an economic superpower and a key driver of global economic growth. The Great Recession in the developing world While some countries, such as Russia, Mexico, and Turkey, experienced severe recessions due to their connections to the United States and Europe, others such as China, India, and Indonesia managed to record significant economic growth during the period. This can be partly explained by the decoupling from western financial systems which these countries undertook following the Asian financial crises of 1997, making many Asian nations more wary of opening their countries to 'hot money' from other countries. Other likely explanations of this trend are that these countries have large domestic economies which are not entirely reliant on the advanced economies, that their export sectors produce goods which are inelastic (meaning they are still bought during recessions), and that the Chinese economic stimulus worth almost 600 billion U.S. dollars in 2008/2009 increased growth in the region.

This thesis explores the devastating economic consequences that a hypothetical World War III could have on the global economy. Unlike the previous world wars, this conflict would unfold in a highly globalized, digitally interconnected world—meaning the economic damage would be even more widespread and severe.Drawing from history, the paper analyzes past wars like World War I and II, highlighting how those events caused GDP contractions, hyperinflation, destruction of infrastructure, and long-term debt. It uses these precedents to build realistic scenarios for what could happen if WWIII breaks out today. The study models short-term disruptions like stock market crashes, currency collapse, and trade blockades; medium-term issues like mass unemployment and inflation; and long-term impacts such as technological regression and widespread economic stagnation.The thesis provides regional assessments as well—evaluating how countries like the U.S., China, and nations in Europe and the Global South would fare in different war scenarios, from limited conflicts to full-scale nuclear exchanges. It also discusses secondary effects like energy and food shortages, famine, and the collapse of consumer demand in non-essential sectors.Importantly, the paper doesn’t stop at doom and gloom. It outlines strategic policy responses such as emergency fiscal controls, global debt restructuring, a possible new Bretton Woods system, and a modern-day Marshall Plan to help rebuild economies post-war.In conclusion, the research emphasizes that preventing World War III is not just a matter of global peace, but an absolute economic necessity. Even the strongest economies could collapse, and recovery could take decades—if at all. The thesis serves as both a warning and a call for proactive international diplomacy, economic safeguards, and collective accountability.

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 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.

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.

A growing body of literature scrutinizes the harmful consequences of capital flight to offshore financial destinations. While financial integration is a well-known facilitator of capital flight, we shed light on an under-appreciated determinant--the availability of an IMF bailout. Expanding on previous literature analyzing moral hazard in the context of IMF programs, we introduce a socially even more destructive mechanism that we label the `crash for cash' effect. We argue that by drawing on the IMF, elites can benefit from accumulating excessive debt to extract rents and hide these safely in offshore financial destinations while steering their countries into financial disaster. To test this mechanism, we show that elite wealth in offshore bank accounts has a first-order impact on a captured government's willingness to draw on a lender of last resort. From a policy perspective, our analysis underscores the importance of closing financial loopholes to mitigate the devastating socio-economic effects of sophisticated financial engineering in a financially integrated global economy.

This dataset is about book subjects. It has 3 rows and is filtered where the books is The impoverishment of nations : the issues facing the post meltdown global economy. It features 2 columns including publication dates.

This survey focuses on a variety of topics including US and the global economy, collapse of Drexel Burnham, AIDS, homeless, health care, education, substance abuse, women in the workplace, and the advantage of a MBA.

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 Great Recession of 2008-2009 caused a dramatic drop in the volume of world trade, after two decades of nearly unbroken growth in export growth across the globe. The Global Financial Crisis, which began in the United States in the Summer of 2007, but quickly spread to other regions, caused international flows of money to freeze. This lack of international financing in the global economy led to a drop in aggregate demand, as well as causing many goods exporters to be unable to finance short-term expenditures on credit. World merchandise exports collapsed in 2009, falling by around one-fifth. This fall was made up quickly in the recovery, however, as exports already surpassed their 2008 levels by 2011.

A debt-based economy cannot survive without economic growth. However, if private debt consistently grows faster than GDP, the consequences are financial crises and the current unprecedented level of global debt. This policy dilemma is aggravated by the lack of analyses factoring the impact of debt-growth cycles on the environment. What is really the relationship between debt and natural resource sustainability, and what is the role of debt in decoupling economic growth from natural resource availability? Here we present a conceptual Agent-Based Model (ABM) that integrates an environmental system into an ABM representation of Steve Keen’s debt-based economic models. Our model explores the extent to which debt-driven processes, within debt-based economies, enhance the decoupling between economic growth and the availability of natural resources. Interestingly, environmental and economic collapse in our model are not caused by debt growth, or the debt-based nature of the economic system itself (i.e. the ‘what’), but rather, these are due to the inappropriate use of debt by private actors (i.e. the ‘how’). Firms inappropriately use bank credits for speculative goals–rather than production-oriented ones–and for exponentially increasing rates of technological development. This context creates temporal mismatches between natural resource growth and firms’ resource extraction rates, as well as between economic growth and the capacity of the government to effectively implement natural resource conservation policies. This paper discusses the extent to which economic growth and the availability of natural resources can be re-coupled through a more sustainable use of debt, for instance by shifting mainstream banking forces to partially support environmental conservation as well as economic growth.

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.

Sweden Consumer Survey: KI: Prices: Next 12 Months: Fall data was reported at 2.100 % in Jul 2018. This records an increase from the previous number of 1.500 % for Jun 2018. Sweden Consumer Survey: KI: Prices: Next 12 Months: Fall data is updated monthly, averaging 3.500 % from Jan 1996 (Median) to Jul 2018, with 271 observations. The data reached an all-time high of 21.800 % in Mar 2005 and a record low of 0.800 % in Aug 2017. Sweden Consumer Survey: KI: Prices: Next 12 Months: Fall data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.H009: Consumer Survey: National Institute of Economic Research.

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.

Economic Capital Modeling Market Outlook

According to our latest research, the Global Economic Capital Modeling market size was valued at $3.2 billion in 2024 and is projected to reach $8.9 billion by 2033, expanding at a robust CAGR of 12.1% during 2024–2033. The primary driver for this impressive growth is the escalating demand for advanced risk management frameworks across financial institutions worldwide, as they strive to comply with evolving regulatory standards and enhance decision-making capabilities. As organizations grapple with increasing market volatility and complex regulatory environments, the adoption of sophisticated economic capital modeling solutions is becoming indispensable for ensuring financial stability and optimizing capital allocation. This market is further propelled by the integration of artificial intelligence and machine learning technologies, which enable more accurate risk quantification and scenario analysis, thus empowering stakeholders to make data-driven decisions in an ever-changing economic landscape.

Regional Outlook

North America currently commands the largest share of the Economic Capital Modeling market, accounting for over 38% of total global revenue in 2024. This dominance is attributed to the region’s mature financial services sector, early adoption of cutting-edge financial technologies, and the presence of stringent regulatory frameworks such as Dodd-Frank and Basel III. Major financial institutions and insurance companies in the United States and Canada have heavily invested in economic capital modeling solutions to strengthen their risk management capabilities and ensure regulatory compliance. Additionally, North America’s well-established IT infrastructure and the presence of leading software vendors have accelerated the deployment of both on-premises and cloud-based modeling solutions, further consolidating the region’s leadership position in the global market.

Asia Pacific is emerging as the fastest-growing region in the Economic Capital Modeling market, projected to register an impressive CAGR of 15.6% from 2024 to 2033. This rapid expansion is driven by the increasing digitization of banking and insurance sectors, coupled with rising awareness about the importance of robust risk management practices in emerging economies such as China, India, and Southeast Asia. Governments and regulatory bodies across the region are implementing more stringent capital adequacy norms, compelling financial institutions to adopt advanced economic capital modeling solutions. Furthermore, the influx of foreign direct investments, the proliferation of fintech startups, and the region’s large unbanked population are fueling demand for innovative risk management tools, positioning Asia Pacific as a key growth engine for the global market.

In contrast, regions such as Latin America and the Middle East & Africa are witnessing gradual but steady adoption of economic capital modeling solutions. These emerging economies face unique challenges, including limited access to skilled professionals, underdeveloped IT infrastructure, and regulatory ambiguity. However, localized demand is rising as financial institutions in these regions seek to modernize their risk management frameworks and align with international regulatory standards. Policy reforms, government incentives, and collaborations with global technology providers are gradually overcoming adoption barriers, paving the way for incremental growth. Nonetheless, market penetration remains lower compared to developed regions, reflecting the need for targeted capacity-building initiatives and tailored solutions that address regional nuances.

Report Scope

The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.

This survey was undertaken to assess consumer sentiment and buying plans, respondents' satisfaction with the appliances owned, and their opinions about the Cold War between the former Soviet Union and the West and its perceived effect on taxes and the economy, as well as their assessment of the possibility of an outbreak of a major world war in the near future. Open-ended questions were asked concerning evaluations and expectations about price changes, employment, tax reduction, recession, and the national business situation. Additional variables probe respondents' buying intentions for a house, automobiles, appliances, and other consumer durables, as well as their appraisals of present market conditions for purchasing these items. Other variables probe respondents' satisfaction with their location, neighborhood, and living space, and their assessment of their financial status relative to the previous year. Information is also provided on savings. Demographic variables provide information on age, sex, race, marital status, education, occupation, and family income.