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

Trinidad and Tobago TT: GDP: PPP data was reported at 43,233.782 Intl $ mn in 2017. This records a decrease from the previous number of 43,485.445 Intl $ mn for 2016. Trinidad and Tobago TT: GDP: PPP data is updated yearly, averaging 26,469.902 Intl $ mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 45,657.491 Intl $ mn in 2015 and a record low of 9,678.766 Intl $ mn in 1990. Trinidad and Tobago TT: GDP: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

In 2024, the gross government debt of China amounted to an estimated 89 percent of the country's gross domestic product (GDP), compared to 21 percent for Russia. For China, this was an increase over 2001 levels, when the gross government debt amounted to 25 percent of the country's GDP. Russia, on the other hand, has reduced this figure from 2001 levels, when gross government debt was 44 percent of the country's GDP.

Cambodia KH: GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 2,390.000 USD in 2023. This records an increase from the previous number of 2,270.000 USD for 2022. Cambodia KH: GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 310.000 USD from Dec 1977 (Median) to 2023, with 47 observations. The data reached an all-time high of 2,390.000 USD in 2023 and a record low of 100.000 USD in 1977. Cambodia KH: GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cambodia – Table KH.World Bank.WDI: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;

According to a survey conducted by Ipsos on predictions for global issues in 2019, 51 percent of Malaysian respondents somewhat agree with the statement that the global economy would be better in the current year compared to the last. Malaysians were more optimistic this year about the global economy compared to the previous year.

United States US: GDP: Growth: Gross Value Added: Services data was reported at 2.621 % in 2015. This records an increase from the previous number of 2.221 % for 2014. United States US: GDP: Growth: Gross Value Added: Services data is updated yearly, averaging 2.335 % from Dec 1998 (Median) to 2015, with 18 observations. The data reached an all-time high of 4.456 % in 1999 and a record low of -1.772 % in 2009. United States US: GDP: Growth: Gross Value Added: Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Annual Growth Rate. Annual growth rate for value added in services based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Services correspond to ISIC divisions 50-99. They include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

About the Project The project explores alternative methods of measuring economic diversification and investigating its associated impacts on the Saudi Arabian economy and other GCC countries. By utilizing a financial portfolio framework reconciled with economic growth theory, the economy is viewed as a portfolio of economic sectors, each contributing to the overall output growth. Results demonstrated that diversification policies have been effective, as the economy moves towards higher growth with lower instability. Key Points Evidence confirms that there is a positive correlation between the economic growth rate and its volatility/risk in the Gulf Cooperation Council (GCC) region. In other words, there is a trade-off between the benefits of oil and gas activity and the volatility resulting from unpredictable commodity price swings in such resource dependent economies. Our analysis uses a financial portfolio framework approach (and more specifically an efficient frontier analysis), treating economic sectors as individual investments. We calculate a relative risk measure termed the ‘beta coefficient’ and assemble a portfolio of sectors with varying weights to find the efficient frontier. If the beta of the portfolio representing the economy is above global average, the economy will generally grow faster than the global average but with greater volatility – the upturns will be higher and the downturns deeper. We aim to shed light on diversification policy from this novel, if not yet widely accepted, perspective. The GCC economies exhibit ‘high beta,’ particularly Qatar. Saudi Arabia sits in the middle of the group, but above the global average, while Oman has the lowest coefficient of the group. Saudi Arabia’s National Transformation Plan to 2020 and economic Vision 2030 envisage an economy that is still invested in oil and gas activity at 45 percent of total output. While diversification policies in these plans promote economic growth, it still leaves the economy exposed to the volatility of energy markets. In comparison, the optimal mix of economic sectors could increase the growth rate by more than 1 percent annually and nearly halve the expected volatility (to less than 60 percent of growth rate). Saudi Arabia’s historical economic policies were effective in achieving some diversification. However, their benefits could be increased by policies that balance productive efficiency with diversification of economic activity. The difference between policy-optimized portfolio and non-constrained optimization can be used to estimate the size of the fiscal stabilization fund needed to protect the economy from stop/go risks to diversification objectives.

This is the replication package for "China, Europe and the Great Divergence: A Study in Historical national Accounting". As a result of recent advances in historical national accounting, estimates of GDP per capita are now available for a number of European economies back to the medieval period, including Britain, the Netherlands, Italy and Spain. The approach has also been extended to Asian economies, including India and Japan. So far, however, China, which has been at the center of the Great Divergence debate, has been absent from this approach. This paper adds China to the picture and shows that the Great Divergence began earlier than originally suggested by the California School, but later than implied by older Eurocentric writers.

Grenada GD: GDP: PPP data was reported at 1,609.191 Intl $ mn in 2017. This records an increase from the previous number of 1,523.902 Intl $ mn for 2016. Grenada GD: GDP: PPP data is updated yearly, averaging 922.970 Intl $ mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 1,609.191 Intl $ mn in 2017 and a record low of 450.649 Intl $ mn in 1990. Grenada GD: GDP: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Grenada – Table GD.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

Liberia: Economic growth: the rate of change of real GDP: The latest value from is percent, unavailable from percent in . In comparison, the world average is 0.00 percent, based on data from countries. Historically, the average for Liberia from to is percent. The minimum value, percent, was reached in while the maximum of percent was recorded in .

The latest data from show economic growth of 2.6 pour cent, which is an increase from the rate of growth of -0.8 pour cent in the previous quarter and
an increase compared to the growth rate of -2 pour cent in the same quarter last year. The economic growth time series for Islande cover the period...

Chad: Uneven economic development index, 0 (low) - 10 (high): The latest value from 2024 is 8.4 index points, a decline from 8.7 index points in 2023. In comparison, the world average is 5.28 index points, based on data from 176 countries. Historically, the average for Chad from 2007 to 2024 is 8.96 index points. The minimum value, 8.4 index points, was reached in 2024 while the maximum of 9.3 index points was recorded in 2009.

The latest data from show economic growth of 3.9 pour cent, which is a decrease from the rate of growth of 5 pour cent in the previous quarter and
an increase compared to the growth rate of 3.2 pour cent in the same quarter last year. The economic growth time series for Singapour cover the period...

Denton economic data from the American Community Survey (ACS)

Ivory Coast CI: GDP: USD: Gross National Income: Atlas Method data was reported at 37.488 USD bn in 2017. This records an increase from the previous number of 36.048 USD bn for 2016. Ivory Coast CI: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 9.444 USD bn from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 37.488 USD bn in 2017 and a record low of 620.763 USD mn in 1962. Ivory Coast CI: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

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.

Gambia's Exports of goods and services (real) is US$162,084,333 which is the 143rd highest in the world ranking. Transition graphs on Exports of goods and services (real) in Gambia and comparison bar charts (USA vs. Japan vs. Gambia), (Namibia vs. Botswana vs. Gambia) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Netherlands NL: Business-Financed GERD: % of GDP data was reported at 1.282 % in 2021. This records a decrease from the previous number of 1.321 % for 2020. Netherlands NL: Business-Financed GERD: % of GDP data is updated yearly, averaging 0.894 % from Dec 1981 (Median) to 2021, with 35 observations. The data reached an all-time high of 1.321 % in 2020 and a record low of 0.751 % in 1981. Netherlands NL: Business-Financed GERD: % of GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Netherlands – Table NL.OECD.MSTI: Gross Domestic Expenditure on Research and Development: OECD Member: Annual.

In the Netherlands, beginning with the 2013 data, the following methodological improvements led to breaks in series in the business sector (increase), the government sector (decrease), and at the total economy level (increase): better collection and treatment methods for measuring and reporting R&D expenditures related to external R&D personnel (alignment with the 2015 Frascati Manual); reclassification from the government to the business sector of public corporations engaged in market production; and a better follow-up of non-respondents. In 2012, the method for sampling enterprises included in ISIC industries 84 to 99 (community, social, and personal services) as well as the breakdown of personnel data by occupation were modified leading to breaks in series in the business and government sectors. In 2011, the method for producing business enterprise data changed: all observed enterprises are included whereas before 2011, only enterprises with substantial R&D activities (i.e. with a minimum number of R&D personnel) were incorporated. Subsequent changes affected the higher education sector: before 1999, a large number of PhD candidates were formally employed by research institutes (in the government sector) financing their research. From 1999, universities became the formal employer of PhD candidates and their research activities moved from the Government sector to the Higher Education sector. Besides this, the R&D activities of the Universities of Applied Sciences (HBO) were taken into account for the first time. Finally the R&D activities of the Academic hospitals were increasingly underestimated due to the merging of the Academic hospitals and (parts) of the Faculties of Medicine of the universities into so-called University Medical Centers (UMC's). This started in 1998 and meant for instance that staff of the Faculty of Medicine of the university became employees of the UMC. As a result, data on R&D in the field of medical sciences were also revised. As of 2000, newly-recruited researchers on the payroll of the Netherlands Organisation for Scientific Research (NOW), previously included in the Government sector, were included with personnel in the higher education sector. In 1982 and 1990, the methodology of the survey on R&D expenditure changed.

In 2003, Statistics Netherlands revised the panel of the R&D survey for the Government and PNP sectors, resulting in breaks in series for both. Also beginning in 2003, R&D personnel in the PNP sector are grouped with Government sector R&D personnel.

In 1994 and 1996 there were major expansions of the scope of the Business Enterprise sector survey; R&D expenditure and personnel data in the latter sector and in the whole economy are thus not comparable with those for the previous years.

In 1990 and 1999, new methods for calculating GUF are introduced for GBARD series.

Timor-Leste TL: GDP: USD: Gross National Income: Atlas Method data was reported at 2.321 USD bn in 2017. This records a decrease from the previous number of 2.904 USD bn for 2016. Timor-Leste TL: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 2.710 USD bn from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 4.192 USD bn in 2013 and a record low of 621.406 USD mn in 2004. Timor-Leste TL: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Timor-Leste – Table TL.World Bank: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;