International Data & Economic Analysis (IDEA) is USAID's comprehensive source of economic and social data and analysis. IDEA brings together over 12,000 data series from over 125 sources into one location for easy access by USAID and its partners through the USAID public website. The data are broken down by countries, years and the following sectors: Economy, Country Ratings and Rankings, Trade, Development Assistance, Education, Health, Population, and Natural Resources. IDEA regularly updates the database as new data become available. Examples of IDEA sources include the Demographic and Health Surveys, STATcompiler; UN Food and Agriculture Organization, Food Price Index; IMF, Direction of Trade Statistics; Millennium Challenge Corporation; and World Bank, World Development Indicators. The database can be queried by navigating to the site displayed in the Home Page field below.

The statistic shows the unemployment rate in selected world regions between 2015 and 2023. In 2023, the unemployment rate in the Arab World was estimated to have been at 9.88 percent. Unemployment around the globe Following the global financial crisis in 2008, unemployment saw considerable downturns around the globe, most notably in 2009. Unemployment rates, despite experiencing dramatic improvements over the years following the crisis, still have not reached pre-2009 levels for the large majority of countries. The same trend is followed with unemployment among the youth between the ages of 15 and 24, around the world. Many youth experienced layoffs after 2008, mainly because their skills were interchangeable and easily replaceable and as a result, youth unemployment increased, although the situation has improved slightly. The unemployment rate in selected world regions remained relatively stagnant year-over-year from 2012 to 2013, however is expected to improve over the long run based on current employment trends. Economic improvement around the world is primarily evident from growth of real gross domestic product , which has been relatively positive in most countries with the exception of those in the euro area. Growth of real gross domestic product points to economic growth as well as a higher productivity within each country. On the other hand, other indicators of economic health, such as inflation, point to further economic distraught, as inflation is expected to increase globally, most prominently in non-developed countries.

The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries.

This statistic shows the share of economic sectors in the global gross domestic product (GDP) from 2013 to 2023. In 2022, agriculture contributed 4.25 percent, industry contributed approximately 27.22 percent and services contributed about 61.76 percent to the global gross domestic product. See global GDP for comparison.

Description: This dataset contains historical economic data spanning from 1871 to 2024, used in Jaouad Karfali’s research on Economic Cycle Analysis with Numerical Time Cycles. The study aims to improve economic forecasting accuracy through the 9-year cycle model, which demonstrates superior predictive capabilities compared to traditional economic indicators.

Dataset Contents: The dataset includes a comprehensive range of economic indicators used in the research, such as:

USGDP_1871-2024.csv – U.S. Gross Domestic Product (GDP) data. USCPI_cleaned.csv – U.S. Consumer Price Index (CPI), cleaned and processed. USWAGE_1871-2024.csv – U.S. average wages data. EXCHANGEGLOBAL_cleaned.csv – Global exchange rates for the U.S. dollar. EXCHANGEPOUND_cleaned.csv – U.S. dollar to British pound exchange rates. INTERESTRATE_1871-2024.csv – U.S. interest rate data. UNRATE.csv – U.S. unemployment rate statistics. POPTOTUSA647NWDB.csv – U.S. total population data. Significance of the Data: This dataset serves as a foundation for a robust economic analysis of the U.S. economy over multiple decades. It was instrumental in testing the 9-year economic cycle model, which demonstrated an 85% accuracy rate in economic forecasting when compared to traditional models such as ARIMA and VAR.

Applications:

Economic Forecasting: Predicts a 1.5% decline in GDP in 2025, followed by a gradual recovery between 2026-2034. Economic Stability Analysis: Used for comparing forecasts with estimates from institutions like the IMF and World Bank. Academic and Institutional Research: Supports studies in economic cycles and long-term forecasting. Source & Further Information: For more details on the methodology and research findings, refer to the full paper published on SSRN:

https://ssrn.com/author=7429208 https://orcid.org/0009-0002-9626-7289

• Jaouad Karfali

The GDP per capita for countries is shown in this dataset for the different years. This economic metric shows the economic output per person and determines the country’s situation based on its economic growth. This dataset can be used to analyze the prosperity of a country based on its economic growth. Countries with higher GDP per countries are determined to be developed whereas countries with low GDP per capita are determined to be developing countries. This dataset can be used to analyze a country’s wealth and prosperity.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.

This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels.
Data for years 2020 to 2026 are stored in the corresponding repositories:

• covid: 10.5281/zenodo.5713825
• counterfactual: 10.5281/zenodo.5713839

Tables are generated using the SPIN method, based on the RMRIO tables for the year 2015, GDP, imports and exports data from the International Financial Statistics (IFS) and the World Economic Outlooks (WEO) of October 2019 and April 2021.

From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist.

The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels.
The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts:

• SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data.
• SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset.
• figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.

All tables are labelled in 2015 US$ and valued in basic prices.

This panel dataset presents information on the impact of democracy and political stability on economic growth in 15 MENA countries for the period 1983-2022. The data are collected from five different sources; the World Bank Development Indicators (WDI), the World Bank Governance Indicators (WGI), the Penn World Table (PWT), Polity5 from the Integrated Network for Societal Conflict Research (INSCR), and the Varieties of Democracy (V-Dem). The dataset includes ten variables related to economic growth, democracy, and political stability. Data analysis was performed using statistical methods such as R in order to ensure data reliability through imputing missing data; hence, enabling future researchers to explore the impact of political factors on growth in various contexts. The data are presented in two sheets, before and after the imputation for missing values. The potential reuse of this dataset lies in the ability to examine the impact of different political factors on economic growth in the region.

Russia MED Forecast: Export Products: Non Oil and Gas: Conservative Scenario data was reported at 233.546 USD bn in 2026. This records an increase from the previous number of 228.535 USD bn for 2025. Russia MED Forecast: Export Products: Non Oil and Gas: Conservative Scenario data is updated yearly, averaging 224.135 USD bn from Dec 2020 (Median) to 2026, with 7 observations. The data reached an all-time high of 246.712 USD bn in 2022 and a record low of 183.018 USD bn in 2020. Russia MED Forecast: Export Products: Non Oil and Gas: Conservative Scenario data remains active status in CEIC and is reported by Ministry of Economic Development of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Forecast of The Social and Economic Development of The Russian Federation. Data release delayed due to the Ukraine-Russia conflict. No estimation on next release date can be made.

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 International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

The global economic analysis services market is a dynamic sector experiencing robust growth, driven by increasing government regulations, the need for data-driven decision-making across industries, and the rising complexity of global economic landscapes. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key trends, including the growing adoption of advanced analytical techniques like econometrics and machine learning, the increasing demand for specialized services in areas such as environmental economics and regulatory compliance, and the rise of digital platforms providing access to economic data and analysis. Major players such as EY, Deloitte, and ICF are actively investing in expanding their capabilities and geographic reach, further fueling market growth. However, economic downturns and fluctuations in government spending can act as restraints, impacting the market's trajectory in the short term. The market is segmented by service type (e.g., macroeconomic analysis, microeconomic analysis, industry-specific analysis), client industry (e.g., finance, government, energy), and geography. The competitive landscape is characterized by a mix of large multinational consulting firms and specialized boutique firms. The large firms leverage their established brand recognition and extensive global networks, while smaller firms often focus on niche expertise and agility. This competitive dynamic ensures a diverse range of service offerings and pricing strategies, catering to various client needs and budgets. Future growth will be shaped by technological advancements, the evolving regulatory environment, and the continued need for businesses and governments to understand and manage economic risks and opportunities effectively. The increasing use of big data and advanced analytics is expected to drive the demand for more sophisticated and data-intensive economic analysis services, presenting significant opportunities for firms that can adapt and innovate in this evolving landscape.

Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, and required safety net financing. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

We provide free TelluBase data to select public data sources.These are small, but important, subsets of the full product.To start with, we offer all Latin American countries (except Venezuela) with GDP per city and subdivision in 2023 in these PDFs.If you represent an academic institution or a reputable media outlet and think you may benefit from TelluBase data, we may be able to provide it for free. Contact me with your query at scanback@tellusant.comTelluBase covers 218 countries, 2600 cities, and 2500 subdivisions, 2000-2050. It gives a completely exhaustive view of the world economy.

Key information about US Nominal GDP Growth

• United States Nominal GDP Growth was reported at 5.028 % in Dec 2024.
• This records a decrease from the previous number of 5.032 % for Sep 2024.
• US Nominal GDP Growth data is updated quarterly, averaging 6.138 % from Mar 1948 to Dec 2024, with 308 observations.
• The data reached an all-time high of 19.646 % in Mar 1951 and a record low of -6.835 % in Jun 2020.
• US Nominal GDP Growth data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Nominal GDP: Y-o-Y Growth: Quarterly: Seasonally Adjusted.

United States GDP: 2000p: sa: Income Payments to the rest of the world data was reported at 348.000 USD bn in Mar 2009. This records a decrease from the previous number of 450.000 USD bn for Dec 2008. United States GDP: 2000p: sa: Income Payments to the rest of the world data is updated quarterly, averaging 48.200 USD bn from Mar 1947 (Median) to Mar 2009, with 249 observations. The data reached an all-time high of 663.100 USD bn in Jun 2007 and a record low of 3.200 USD bn in Sep 1947. United States GDP: 2000p: sa: Income Payments to the rest of the world data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A175: NIPA 2003: GDP by Expenditure.

The Gross Domestic Product (GDP) in Saudi Arabia was worth 1237.53 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Saudi Arabia represents 1.17 percent of the world economy. This dataset provides - Saudi Arabia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Jordan is a small, middle-income, open economy, with a limited natural resources base and active trade flows. As the integration of Jordan in the World Economy progresses, enhancing Jordan's environmental management can not only improve the wellbeing of Jordanians, but also enable the country to better compete in increasingly environmentally conscious markets. To date there has not yet been a comprehensive assessment of Jordan's environmental agenda, particularly in terms of providing indications on how to integrate long-term environmental concerns into the development process. Striking a balance between breadth and depth of the analysis, this report intends to help fill such a gap and to provide insights that can inform the dialogue between the World Bank and the Government of Jordan on a selected number of areas of particular relevance for continued sustainable economic and social development. The report has been prepared by a World Bank team that has worked in full partnership and cooperation with a Jordanian team, led by the Ministry of Environment, and representing a broad cross-section of Government institutions. To achieve its core objectives of identifying key strategic priorities for improved environmental policy across sectoral boundaries, the Country Environmental Analysis (CEA) analyzes sequentially the country's key environmental concerns and their relative priority; the linkages between development and environmental pressure in selected themes or sectors (water quality, road transport), and the capacity of Jordan's institutions to reconcile development and growth objectives. Most of the data and information used for the report have been collected in the period 2007-2008, although efforts have been made to take into account selected a key developments in relevant policies occurred since then.

Analysis of ‘Maddison Project Dataset 2020 Population by Region’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mathurinache/maddison-project-dataset-2020-population-by-region on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Maddison Project Database provides information on comparative economic growth and income levels over the very long run. The 2020 version of this database covers 169 countries and the period up to 2018. For questions not covered in the documentation, please contact maddison@rug.nl.

Content

We now offer a new 2020 update of the Maddison Project database, which uses a different methodology compared to the 2018 update. The approach of the 2018 update is identical to that of Penn World Tables, and consistent with recent economic and statistical research in this field. However, applying this approach systematically results in historical outcomes that are not consistent with current insights by economic historians, as explained in Bolt and Van Zanden (2020).

The 2020 update has to some extent gone back to the original Maddison approach to remedy for this (see documentation). Both the 2018 and the 2020 datasets incorporate the available recent work by economic historians on long term economic growth, the 2020 is most complete in this respect.

Acknowledgements

Attribution requirement -

All original papers must be cited when:

the data is shown in any graphical form subsets of the full dataset that include less than a dozen (12) countries are used for statistical analysis or any other purposes

A list of original papers can be found in the source sheet of the database. When neither a) or b) apply, then the MPD as a whole should be cited.

Maddison Project Database, version 2020. Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update ”.

Inspiration

You can find some inspiration here : https://ourworldindata.org/global-economic-inequality-introduction

--- Original source retains full ownership of the source dataset ---