Unemployment Rate in the United States increased to 4.30 percent in August from 4.20 percent in July of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains year-wise macroeconomic indicators for over 200 countries from 2010 to 2025, extracted programmatically using the World Bank Open Data API.

It includes key indicators critical for policy makers, economists, data scientists, and financial analysts. The data has been cleaned, structured, and exported as a CSV — making it ready for analysis, dashboards, and forecasting models.

📦 Included Indicators

• Inflation (Consumer Price Index %)
• GDP (Current USD)
• GDP per Capita
• GDP Growth (% Annual)
• Unemployment Rate
• Real Interest Rate
• Public Debt (% of GDP)
• Government Expense and Revenue
• Current Account Balance
• Gross National Income
• Tax Revenue

📊 Columns Overview

📈 Use Cases

• Economic trend visualization
• Country comparison dashboards
• Machine learning forecasting models
• Macroeconomic policy analysis

📡 Data Source

• API: World Bank Open Data
• Extracted using: Python, requests, pandas, SQLAlchemy

🧠 Ideal For

• Data scientists
• Policy researchers
• Students in economics or finance
• Kaggle forecasting competitions

✅ Format

• CSV format (UTF-8)
• 200+ countries × 15 years × 13 indicators = ~40,000+ rows

Data was collected from the FRED website.

Contains economic indicators often associated with recessions along with recession status data. Data collected on smallest time unit and earliest time date available for each indicator which results in many nulls but increased flexibility for the users of this dataset.

• recession: "1" recessionary period, "0" non-recessionary period (Monthly)
• cpi: CPI (1982-1984=INDEX 100) (Monthly)
• gdp: Real GDP Billions of Chained 2017 Dollars (Quarterly)
• unemployment: Unemployment Rate (Monthly)
• m2: M2 Billions of Dollars (Monthly)
• fed_funds: Federal Funds Rate (Monthly)
• ten_two: 10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity (Monthly)
• residential: Real Residential Property Price Rate (Quarterly)

Comprehensive description of each variable can be found at https://fred.stlouisfed.org/

Unemployment Rate in Brazil decreased to 5.60 percent in July from 5.80 percent in June of 2025. This dataset provides the latest reported value for - Brazil Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset is about countries in Africa. It has 54 rows. It features 3 columns: GDP, and unemployment.

This dataset is about countries in Northern Africa. It has 6 rows. It features 3 columns: GDP, and unemployment.

Global Data: GDP, Life Expectancy & More

This dataset comprises 204 entries and 38 attributes, providing a comprehensive analysis of key economic and social indicators across various countries. It includes a diverse range of metrics, allowing for in-depth exploration of global trends related to GDP, education, health, and environmental factors.

Key Features:

• GDP: Gross Domestic Product (in current US dollars), representing the total economic output of a country.
• Sex Ratio: The ratio of males to females in the population, highlighting demographic trends.
• Life Expectancy: Average lifespan for males and females, an essential indicator of healthcare quality.
• Education Enrollment Rates: Data on primary, secondary, and post-secondary education enrollment for males and females, reflecting educational attainment.
• Unemployment Rate: Percentage of the labor force that is unemployed, indicating economic health.
• Homicide Rate: Number of homicides per 100,000 population, providing insight into safety and crime levels.
• Urban Population Growth: Rate of growth in urban populations, illustrating migration trends.
• CO2 Emissions: Carbon dioxide emissions per capita, an important measure of environmental impact.
• Forested Area: Percentage of land covered by forests, indicating biodiversity and environmental health.
• Tourist Numbers: Total number of international visitors, which can reflect a country's tourism potential.

Applications and Uses:

1. Research and Analysis: Ideal for researchers studying the correlation between economic performance and social indicators. This dataset can help identify trends and patterns relevant to global development.

2. Policy Development: Policymakers can utilize this data to inform decisions on education, healthcare, and environmental policies, aiming to improve national outcomes.

3. Machine Learning and Data Science: Data scientists can apply machine learning techniques to predict economic trends, analyze social impacts, or classify countries based on various indicators.

4. Educational Purposes: Suitable for students and educators in fields like economics, sociology, and environmental science for practical data analysis exercises.

5. Visualization Projects: Perfect for creating compelling visualizations that illustrate relationships between different metrics, aiding in public understanding and engagement.

By leveraging this dataset, users can uncover insights into how different factors influence a country's development, making it a valuable resource for diverse applications across various fields.

Unemployment Rate in Germany remained unchanged at 6.30 percent in August. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains non-seasonally adjusted California Unemployment Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows; 16-19 ; 20 - 24; 25 - 34; 35 - 44; 45 - 54; 55 -64; 65+. This data is based on a 12-month moving average.

This dataset is invaluable for data science applications due to its granularity and the historical depth it offers. With detailed monthly data on unemployment rates by age groups, data scientists can perform a myriad of analyses:

• Time Series Analysis: Analyzing trends over time to forecast future unemployment rates or identify cyclic patterns.
• Demographic Analysis: Understanding which age groups are most affected by unemployment and how this changes through economic conditions.
• Geographic Analysis: Although the dataset currently focuses on California, if similar data is available for other regions, comparisons can be made to understand regional economic differences.

The dataset can also be merged with other socioeconomic indicators like GDP, education levels, and industry growth metrics to examine broader economic narratives or policy impacts.

Dataset in excel of main macroeconomic indicators growth from 2017 to 2021 for near 200 countries and according to IMF data. It allows us to quickly assess the impact of the COVID19 in the global economic

It includes: real GDP growth, GDP per capita, inflation, unemployment rate, general government net lending /borrowing.

This dataset is about countries in Eastern Asia. It has 5 rows. It features 3 columns: GDP, and unemployment.

The PWSD is a dataset that can be used to answer questions about various public workforce system programs and how these programs fit in with the overall public workforce system and the economy. It was designed primarily to be used as a tool to understand what has been occurring in the Wagner-Peyser program and contains data from quarter 1 of 1995 through quarter 4 of 2008. Also, it was designed to understand the relationship and flow of participants as they go through the public workforce system. The PWSD can be used to analyze these programs both individually and in combination. The PWSD contains economic variables, Unemployment Insurance System data, and data on programs funded by the Workforce Investment Act and Employment Service. Economic variables included are labor force, employment, unemployment, unemployment rate, and gross domestic product data.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

The dataset contains 41265 observations and 21 variables. Each row represents a specific observation or data point. The variables in the dataset include: hpi_type: Type of housing price index data (e.g., traditional, developmental, distress-free, non-metro). hpi_flavor: Flavor of the housing price index data (e.g., purchase-only, all-transactions, expanded-data). frequency: Frequency of the data (e.g., monthly, quarterly). level: Level of geography (e.g., USA or Census Division, State, MSA, Puerto Rico). place_name: Name of the place (e.g., region, state, metropolitan area). place_id: Identifier for the place (e.g., abbreviation, CBSA code). yr: Year of the data. period: Period of the data (e.g., month, quarter). index_nsa: Index, non seasonally adjusted. index_sa: Index, seasonally adjusted. Gross domestic product, constant prices: GDP at constant prices in national currency. Gross domestic product per capita, constant prices: GDP per capita at constant prices. Gross domestic product per capita, current prices: GDP per capita at current prices. Gross domestic product based on purchasing-power-parity (PPP) share of world total: GDP based on PPP as a share of world total GDP. Inflation, average consumer prices: Average consumer price inflation index. Volume of imports of goods and services: Volume change in imports of goods and services. Volume of exports of goods and services: Volume change in exports of goods and services. Unemployment rate: Percentage of total labor force unemployed. Current account balance: Balance of payments current account balance. Date: Date of the data. GSPC.Close: Closing price of the S&P 500 index.

This data is used for article of macroeconomic of some Asian countries in long period which explained about four Asian countries, such as Indonesia, Malaysia, Singapore, and South Korea. This data has taken from World Bank Development Indicators (WDI) database and is formed by Vector Auto Regression (VAR) model, then empirical result is executed by Granger causality model on E-views 11 program to gauge the relationship between gross domestic product, exchange rate, inflation rate, foreign direct investment, net export, government expenditures, unemployment rate, and savings. The results showed that most of gross domestic product of sample and other macro-economy variables have not causality relationship.

The Gross Domestic Product (GDP) in the United States expanded 3.80 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Since the oil price shock in 1974 unemployment increased significantly and also did not really decline in periods of economic upswings in Europe. This is especially the case for the countries of the European Union; therefore we face a special need for explanation. Looking at the member states on finds considerable differences. Since 1977 the unemployment rate within the EU is higher than the average unemployment rate of all OECD countries. The economic upswing in the second half of the 80s relaxed the labor market but nevertheless the unemployment rate remained on a high level. This study deals with the development of unemployment between 1974 and 1993 in four different G7 countries: Germany, France, Great Britain and Italy. Besides the common trend of an increasing unemployment rate, there are significantly different developments within the four countries. The analysis is divided in two parts: the first part looks at the reasons for the increase in unemployment in the considered countries; the second part aims to explain the difference between the developments of unemployment during economic cycles in the different countries. After the description of similarities and differences of labor markets in the four countries it follows a long term analysis based on annual data as well as a short and medium term analysis on quarterly data. This is due to the fact that short and medium term developments are mainly influenced by cyclical economic developments but long term developments are mainly influenced by other factors like demographical and structural changes. A concrete question within this framework is if an increase in production potential can contribute to a decrease in unemployment. For the long term analysis among others the Hysteresis-hypothesis (Hysteresis = Greek: to remain; denotes the remaining effect; in this context: remaining of unemployment) used for the explanation of the persistence of a high unemployment rate. According to this approach consisting unemployment is barely decreased after economic recovery despite full utilization of capacity. According to the Hysteresis-hypothesis there are two reasons for this. The first reason is that for long term unemployed the abilities to work and the qualification level decreased, their human capital is partly devalued. The second reason is that employees give up wage restraint, because they do not fear unemployment anymore and therefore enforce higher real wages. Besides economic recovery companies are not willing to hire long term unemployed with a lower expected productivity for the higher established tariff wages. In the context of the empirical investigation a multiple explanatory approach is chosen which takes supply side and demand side factors into consideration. The short and medium term analysis refers to Okun´s law (=an increase in the unemployment rate is connected with a decrease of the GDP; if the unemployment rate stays unchanged, the GDP grows with 3% p.a.) and aims to analyze more detailed the reactions of unemployment to economic cycles. A geometrical lag-model is compared with a lag-model ager Almon. This should ensure a precise as possible analysis of the Okun´s relations and coefficients. Register of tables in HISTAT: A.: Unemployment in the European G7 countries B.: Analysis of unemployment in the Federal Republic of Germany C.: Basic numbers: International comparison A.: Unemployment in the European G7 countries A.1. Determinates of unemployment in the EU, Germany (1974-1993) A.2. Determinates of unemployment in the EU, France (1974-1993) A.3. Determinates of unemployment in the EU, Great Britain (1974-1993) A.4. Determinates of unemployment in the EU, Italy (1974-1993) B: Analysis of unemployment in the Federal Republic of Germany B.1. Growth of unemployment in the Federal Republic of Germany (1984-1991) B.2. Output and unemployment in the Federal Republic of Germany (1961-1990) C: Basic numbers: International comparison C.1. Unemployment in EU countries, the USA, Japan and Switzerland (1960-1996) C.2. Gainful employments in EU countries, the USA, Japan and Switzerland (after inland and residency concept) (1960-1996) C.3. Employees in EU countries, the USA and Japan (1960-1996) C.4. Population in EU countries, the USA and Japan (1960-1996)

This dataset is about countries per year in Burundi. It has 64 rows. It features 4 columns: country, GDP, and unemployment.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

Unemployment Rate in Japan decreased to 2.30 percent in July from 2.50 percent in June of 2025. This dataset provides the latest reported value for - Japan Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.