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.

The EM-DAT Public Table is a flat representation of EM-DAT data in a single downloadable table. Most impact variables are part of the public table (see Impact Variables). The public table provides a flat view of the general structure in which each record (row) corresponds to a disaster impacting a country.

I used pivot tables in combination with a heat map to quickly show the severity (by deaths) of each type of disaster, by region as a drop down, each year. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F21036995%2F9a85181a2764627469832038bdbac6da%2FEM_Data.png?generation=1723575827178988&alt=media" alt="">

Data Dictionary https://doc.emdat.be/docs/data-structure-and-content/emdat-public-table/

License information: UCLouvain 2023

This Database License Agreement (the Agreement) is made between yourself (the Licensee) and Université catholique de Louvain (UCLouvain), a Belgian University with its registered office located at Place de l’Université, 1, B-1348 Louvain-la-Neuve, Belgium, acting through its Research Group “Center for Research on the Epidemiology of Disasters” or CRED (the Licensor).

WHEREAS the Licensor has developed the EM-DAT database (hereinafter the Database) made available on the internet subject to its conditions of use;

WHEREAS the Database aims at providing an objective basis for impact and vulnerability assessment and rational decision-making in disaster situations by collecting, organizing, and giving access to validated data on the human impact of disasters (such as the number of people killed, injured, or affected), and the disaster-related economic damage estimates;

The Licensor wishes to lay down the conditions enabling the Licensee to use the Database for Commercial Purposes.

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.

Dataset

This dataset was created by Soumik Nayak00

Released under CC0: Public Domain

Contents

This dataset arises from a cross-sectional survey conducted to explore how Generation Z's cognition of the post-COVID-19 economic recession influences their Pro-Environmental Behaviors (PEBs) across distinct spheres. The data was collected from respondents aged 18–26, residing in six major cities across Pakistan. The survey instrument measured variables related to emergency relevance, emergency coping, positive and negative environmental affective reactions, and self-reported PEBs in each sphere. The dataset serves as the empirical foundation for testing an integrative model informed by Affective Events Theory, which examines the interplay of emergency cognition and affective reactions in driving environmental behavior during economic crises. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis, offering insights into differential behavioral patterns across the identified spheres and advancing understanding of the challenges and opportunities for environmental action during economic downturns.This resource is valuable for researchers and policymakers interested in behavioral responses to intersecting economic and environmental challenges.

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.

The earthquake and subsequent tsunami in Japan in 2011 was the costliest natural disaster since 1900, with losses reaching 235 billion U.S. dollars. The tsunami hit the nuclear plant at Fukushima, causing a nuclear disaster in the area. Hurricane Katrina, which hit the Gulf Coast of the United States in 2005, and Hurricane Harvey, which hit the North American country in 2017, tied with the second-largest economic losses in the period, each with 125 billion U.S. dollars.

Weather Disaster Costs and Deaths

Costs and Deaths of Billion Dollar Weather Disasters in the US

By Throwback Thursday [source]

About this dataset

The Billion Dollar Weather Disasters in the US dataset is a valuable resource containing comprehensive historical data on weather events in the United States that have caused billions of dollars in damages and resulted in loss of lives. It provides insights into various types and categories of weather disasters, such as hurricanes, tornadoes, floods, wildfires, and more.

The dataset includes essential information about each weather disaster event, starting with its name or title referred to as Disaster. A brief summary or description of each event is provided under the column Description, giving readers an understanding of its impact and extent. Furthermore, the dataset categorizes each disaster based on its type under the column Disaster Type. This classification helps researchers and analysts to identify patterns or common characteristics among similar types of weather disasters.

One crucial aspect covered by this dataset is the economic impact of these severe weather events. The total cost incurred due to each catastrophic occurrence has been meticulously recorded in millions of dollars. To ensure accuracy across different time periods, these costs are adjusted for inflation using the Consumer Price Index (CPI), providing a standardized measure that enables meaningful comparisons between different events.

A significant measure reflecting the severity of these weather disasters is the number of deaths they have caused. This dataset presents this valuable statistic under the column Deaths, allowing researchers to assess not only economic implications but also human impacts associated with each disaster event.

Obtained from NOAA National Centers for Environmental Information (NCEI) U.S., this data serves as a reliable source for understanding past weather calamities within US borders. Its wide range includes devastating storms, destructive wildfires, deadly heatwaves, crippling droughts; all contributing to one overarching objective – better preparedness for future climate-related challenges.

By analyzing this comprehensive dataset, researchers can gain insights into trends over time while identifying regions most vulnerable to specific types of extreme weather events. These findings allow policymakers and emergency response planners to make informed decisions regarding resource allocation, risk mitigation strategies, and community resilience-building initiatives

How to use the dataset

1. Understanding the Columns

The dataset contains several columns that provide important information about each weather disaster event. Let's understand what each column represents:

• Disaster: The name or title of the weather disaster event.
• Disaster Type: The type or category of the weather disaster event.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of the weather disaster event in millions of dollars, adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by the weather disaster event.
• Description: A brief description or summary of the weather disaster event.

2. Exploring Total Cost and Deaths

One key aspect to explore is how much damage was caused by each weather disaster event, as well as its human impact in terms of fatalities. By analyzing these factors, you can gain insights into which types of disasters are more costly and have a higher mortality rate.

You can start by visualizing the Total CPI-Adjusted Cost (Millions of Dollars) column to identify which disasters have been more financially devastating over time. Additionally, you can analyze the Deaths column to gauge which types of disasters have had a greater impact on human lives.

3. Comparing Disasters

Another interesting analysis would involve comparing different disasters based on their characteristics such as type, cost, and fatalities. You can group similar types together and compare their costs or death tolls across different time periods.

For example, you could examine whether hurricanes tend to cause higher financial losses compared to floods or wildfires. Or, you could analyze if certain types of disasters have been more deadly than others.

4. Analyzing Descriptions

The Description column provides a brief summary of each weather disaster event. Analyzing the descriptions can give you valuable insights into the specific circumstances surrounding each event. By understanding the context and conditions, you can get a better understanding of why some events resulted i...

This project will explore the impact of the economic recession on cities and households through a systematic comparison of the experiences of two English cities, Bristol and Liverpool.The research will use both quantitative and qualitative approaches. Interviews will be held in both cities with stakeholders from across the public, private and voluntary and community sectors. A social survey of 1000 households will also be conducted in the two cities covering 10 specific household types. A series of in-depth qualitative interviews will then be held with households drawn from the survey and chosen to illustrate the spectrum of experience.In the context of globalisation and the rescaling of cities and states, the research aims to develop our understanding of the relationship between economic crisis, global connectivity and the transnational processes shaping cities and the everyday lives of residents. It will explore the 'capillary-like' impact of the crisis and austerity measures on local economic development, and local labour and housing markets, as well as highlight the intersecting realities of everyday life for households across the life course.The research will document the responses and coping strategies developed across different household types and evaluate the impact and effectiveness of 'anti-recession' strategies and policies.

This dataset provides the economic disaster risk assessment results of Tajikistan under different return periods, systematically reflecting the economic loss risk under various setting conditions including 10-year, 20-year, 50 year, 100 year return period, and expected scenarios under extreme precipitation background. Among them, "once every 10 years" indicates that extreme events of this intensity occur on average once every 10 years, with an annual probability of 10%, and so on. "Once every 100 years" indicates an annual probability of 1%. The expected scenario refers to the most acceptable risk state that the regional economic system can achieve under specific intervention measures. The data is presented in GeoTIFF raster format with a resolution of 1km, providing risk maps for two types of extreme precipitation indicators, namely R95PTOT (referring to the total precipitation with daily precipitation greater than the 95th percentile of the reference period) and RX5day (referring to the sum of the maximum continuous 5-day precipitation within the year). The field naming convention is as follows: "R95PTOT_10rpuer. tif" represents the economic risk map for the 10-year scenario under the R95PTOT indicator. This dataset integrates 2019 global high-resolution per capita GDP raster data, urbanization level classification data based on the 2020 GHSL (Global Human Settlement Layer) human settlement layer (7 levels in total), and landslide susceptibility probability maps constructed through multi-source environmental variables and random forest models. Among them, urbanization levels are divided according to human settlement density, and after reverse assignment and normalization, they are used to describe vulnerability indicators to reflect the sensitivity of regional economy to natural disasters; The landslide point data mainly comes from the surface landslide data provided by the World Bank, which is converted into central points for spatial modeling. The data covers a wide area but does not include specific occurrence times; After screening and cleaning, 2847 landslide points were shared for model training, and an equal proportion of non landslide points were generated for training validation; The modeling adopts the construction of landslide susceptibility layers through multi-source environmental variables and random forest models; Exposure is characterized by per capita GDP in 2019, with all factors aligned with a 1km pixel standard to ensure spatial accuracy and data consistency. The overall quality of the data is high, the modeling process is rigorous, the risk assessment system structure is reasonable, and it has strong logic and operability. This dataset can be widely applied to economic resilience analysis, disaster risk management, emergency resource allocation, insurance product design and pricing at the national or regional level, and is particularly suitable for supporting disaster reduction investment decisions and the implementation of sustainable development strategies.

Overview

The Global Disaster & Emergency Response Dataset (2018–2024) offers a comprehensive synthetic simulation of worldwide natural disasters and corresponding humanitarian responses. It spans 7 years and includes 50,000 unique records across 20 countries, covering multiple disaster types such as earthquakes, floods, wildfires, droughts, hurricanes, and volcanic eruptions.

Each record represents a real-world–like event, containing details on intensity, human impact, economic damage, aid efforts, and recovery efficiency. This dataset was generated using probabilistic modeling and random distributions to ensure realistic variability while maintaining a balanced, clean structure for analysis.

Dataset Highlights

-Years covered: 2018–2024

-Regions: 20 countries across all continents

-Records: 50,000 simulated disaster events

-Attributes: 12 descriptive and numeric variables

-Goal: Support ML, visualization, and risk-prediction research

Columns & Descriptions

Possible Use Cases

This dataset is ideal for:

• Disaster prediction and classification models

• Risk assessment and economic loss estimation

• Geospatial visualization and mapping

• Response time forecasting and optimization

• Humanitarian aid efficiency analysis

• Benchmarking deep learning models on synthetic time-series data

-- Data Generation

All records are synthetically generated using the Faker, NumPy, and random sampling libraries. Country-based parameters were adjusted to reflect realistic differences in disaster frequency, average losses, and aid efficiency.

No real-world confidential data is used; this dataset serves as a clean, consistent, and simulation-ready foundation for experimentation.

SBA Coronavirus (COVID-19) Relief Options: Economic Injury Disaster Loan (EIDL) Advances Report and Data

.xlsx file for the replication of the Paper The Complex Crises Database: 70 years of Macroeconomic Crises. It contains the term frequencies of 20 crises sentiment indexes computed from the IMF country report for the period 1956-2016 for 181 countries. (2021-07-02)

Africa Insight, June

The Ministry of Economic Affairs' Water Resources Agency's Disaster Emergency Response Team, utilizing long-term disaster response experience, further combines real-time data such as rainfall, water levels, and reservoir levels, through computer technology to provide water level alerts to the public and relevant units. This helps people understand the risk of home flooding, prepare early, and reduce the occurrence of disasters. This dataset is linked to a Keyhole Markup Language (KML) file list, which is a markup language based on the eXtensible Markup Language (XML) syntax standard, developed and maintained by Google's Keyhole company for expressing geospatial annotations. Documents written in the KML language are referred to as KML files and are used in Google Earth-related software (Google Earth, Google Map, Google Maps for mobile, etc.) for displaying geospatial data. Many GIS-related systems now also use this format for geospatial data exchange, and the KML of this data uses UTF-8 encoding.

Economic damage from natural hazards can sometimes be prevented and always mitigated. However, private individuals tend to underinvest in such measures due to problems of collective action, information asymmetry and myopic behavior. Governments, which can in principle correct these market failures, themselves face incentives to underinvest in costly disaster prevention policies and damage mitigation regulations. Yet, disaster damage varies greatly across countries. We argue that rational actors will invest more in trying to prevent and mitigate damage the larger a country’s propensity to experience frequent and strong natural hazards. Accordingly, economic loss from an actually occurring disaster will be smaller the larger a country’s disaster propensity – holding everything else equal, such as hazard magnitude, the country’s total wealth and per capita income. At the same time, damage is not entirely preventable and smaller losses tend to be random. Disaster propensity will therefore have a larger marginal effect on larger predicted damages than on smaller ones. We employ quantile regression analysis in a global sample to test these predictions, focusing on the three disaster types causing the vast majority of damage worldwide: earthquakes, floods and tropical cyclones.

This dataset captures multi-market financial indicators that can be used to study financial crises, market stress, and economic stability. It integrates simulated data from stock, bond, and foreign exchange (forex) markets, along with volatility metrics and a binary crisis label.

The dataset provides a comprehensive view of cross-market behavior and is suitable for tasks such as crisis detection, financial risk analysis, and market interdependence studies.

Key Features Time Series Coverage:

Daily data over ~1,000 days for multiple countries

Stock Market Indicators:

Stock_Index → Simulated stock market index values

Stock_Return → Daily percentage change in stock index

Stock_Volatility → 5-day rolling standard deviation of stock returns

Bond Market Indicators:

Bond_Yield → Simulated 10-year government bond yield

Bond_Yield_Spread → Difference between long-term and short-term yields

Bond_Volatility → Simulated volatility in bond yields

Forex Market Indicators:

FX_Rate → Simulated currency exchange rate

FX_Return → Daily percentage change in exchange rate

FX_Volatility → 5-day rolling standard deviation of forex returns

Global Market Stress Indicator:

VIX → Simulated volatility index representing market stress

Target Variable:

Crisis_Label → Binary flag indicating market condition (0 = Normal, 1 = Crisis)

File Information Format: CSV

Rows: ~3,000 (1,000 days × 3 countries)

Columns: 13 (including target label)

Use Cases:

Financial crisis detection

Market stress and contagion analysis

Cross-market economic studies

In 2024, the economic losses due to natural disasters worldwide amounted to about *** billion U.S. dollars. Natural disasters occur as a result of natural processes on Earth. Many different types of natural disasters can occur, including floods, hurricanes, earthquakes, and tsunamis. Natural disasters in 2024 Tropical cyclones generated the highest amount of economic losses in 2024 with *** billion U.S. dollars worldwide. Hurricanes Helene and Milton were the most destructive events worldwide that year with over 100 billion U.S. dollars in economic losses. Flooding events ranked second in the costliest events in 2024, with flooding in Valencia, Spain, and South and Central China being the worst examples. Asia hardest hit by natural disasters A highly destructive force, Asia is one of the most susceptible regions to natural disasters. The repercussions of natural disasters are not only physical, but also economic. Costs may be high – depending on the severity – as areas affected by natural disasters might need to be rebuilt. Lower income countries are more likely to be affected by natural disasters for a multitude of reasons, including a lack of developed infrastructure, inadequate housing, and lack of back-resources.

The global financial crisis, triggered by the 2007 subprime mortgage crisis in the United States, has severely affected financial systems and real economies worldwide, leading to the most serious economic recession since the Great Depression of the 1930s. Behind these two economic recessions, despite different historical contexts and approaches to problem-solving, there are common characteristics associated with the mutual impact of financial crises: the essence of a financial crisis lies in financial instability, reflecting the fluctuations in asset prices. In addition to these two severe financial crises, financial crises of varying scales have occurred intermittently internationally. Considering the past and present, people need to think deeper about how to prevent such crises from happening again, especially mainstream macroeconomic thinking that has far-reaching effects should be reassessed.

Economic sanctions research suggests that sanctioned countries’ overall economic costs tend to be low. We argue that, despite this, sanction costs can force governments of these countries to reallocate budget resources from low-priority spending categories to others in an effort to minimize governments’ political costs. One such low-priority category is disaster preparedness and mitigation. We show that economic sanctions lead to reduced disaster preparedness spending and, as a result, increase the scale of economic and human losses generated by natural disasters in sanctioned countries.