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.

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

This data package includes the underlying data to replicate the charts and calculations presented in The International Economic Implications of a Second Trump Presidency, PIIE Working Paper 24-20.

If you use the data, please cite as:

McKibbin, Warwick, Megan Hogan, and Marcus Noland. 2024. The International Economic Implications of a Second Trump Presidency. PIIE Working Paper 24-20. Washington: Peterson Institute for International Economics.

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

Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of a network of interacting macroeconomic agents. In this regard, from the perspective of collective network dynamics, here we explore how the topology of the global macroeconomic network affects the patterns of spreading of economic crises. Using a simple toy model of crisis spreading, we demonstrate that an individual country's role in crisis spreading is not only dependent on its gross macroeconomic capacities, but also on its local and global connectivity profile in the context of the world economic network. We find that on one hand clustering of weak links at the regional scale can significantly aggravate the spread of crises, but on the other hand the current network structure at the global scale harbors higher tolerance of extreme crises compared to more “globalized” random networks. These results suggest that there can be a potential hidden cost in the ongoing globalization movement towards establishing less-constrained, trans-regional economic links between countries, by increasing vulnerability of the global economic system to extreme crises.

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.

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

Quarterly Journal of Economics Abstract & Indexing - ResearchHelpDesk - The Quarterly Journal of Economics is a peer-reviewed academic journal published by the Oxford University Press for the Harvard University Department of Economics. Its current editors-in-chief are Robert J. Barro, Lawrence F. Katz, Nathan Nunn, Andrei Shleifer, and Stefanie Stantcheva (Harvard University). It is the oldest professional journal of economics in the English language and covers all aspects of the field—from the journal's traditional emphasis on micro theory to both empirical and theoretical macroeconomics. According to the Journal Citation Reports, the journal has a 2015 impact factor of 6.662, ranking it first out of 347 journals in the category "Economics". It is generally regarded as one of the top 5 journals in economics, together with the American Economic Review, Econometrica, the Journal of Political Economy, and the Review of Economic Studies. The Quarterly Journal of Economics is the oldest professional journal of economics in the English language. Edited at Harvard University's Department of Economics, it covers all aspects of the field. QJE is invaluable to professional and academic economists and students around the world. Scope of the Journal The Quarterly Journal of Economics is the oldest professional journal of economics in the English language. Edited at Harvard University's Department of Economics, it covers all aspects of the field-from the journal's traditional emphasis on micro theory, to both empirical and theoretical macroeconomics. QJE is invaluable to professional and academic economists and students around the world. Impact Factor and Ranking Year Impact Factor Ssi: Economics 2020 15.563 1 out of 377 2019 11.375 1 out of 371 2018 11.775 1 out of 363 2017 7.863 1 out of 353 2016 6.662 1 out of 347 2015 5.538 2 out of 344 2014 6.654 1 out of 333 2013 5.966 3 out of 332 2012 5.278 2 out of 332 2011 5.920 2 out of 320 2010 5.940 2 out of 304 2009 5.647 2 out of 245 This information is taken from the Journal Citation Reports™ (Clarivate, 2021). Abstracting & Indexing Services The Quarterly Journal of Economics is covered by the following abstracting and indexing services: ABI-INFORM Book Review Digest Plus CAB Abstracts Coal Abstracts Criminal Justice Abstracts Current Contents: Social & Behavioral Sciences Current Index to Statistics Dietrich's Index Philosophicus Documentation in Public Administration EconLit Emerald Management Reviews Environmental RouteNet Environmental Sciences & Pollution Management Database Expanded Academic ASAP Family Index Historical Abstracts Human Resources Abstracts IBZ: International Bibliography of Periodical Literature Index of Economic Articles in Journals & Collected Volumes Index to Periodical Articles Related to Law International Bibliography of Humanities & Sociological Literature Leisure, Recreation, and Tourism Abstracts Leisure Tourism Database LexisNexis Operations Research - Management Science Peace Research Abstracts ProQuest Central Public Administration Abstracts Quality Control & Applied Statistics RePec Risk Abstracts SCOPUS Social Science Source Social Sciences Citation Index/Social SciSearch Social Sciences Index Social Work Abstracts Wilson Business Abstracts World Agricultural Economics & Rural Sociology Abstracts Zentralblatt MATH

Quarterly Journal of Economics Impact Factor 2024-2025 - ResearchHelpDesk - The Quarterly Journal of Economics is a peer-reviewed academic journal published by the Oxford University Press for the Harvard University Department of Economics. Its current editors-in-chief are Robert J. Barro, Lawrence F. Katz, Nathan Nunn, Andrei Shleifer, and Stefanie Stantcheva (Harvard University). It is the oldest professional journal of economics in the English language and covers all aspects of the field—from the journal's traditional emphasis on micro theory to both empirical and theoretical macroeconomics. According to the Journal Citation Reports, the journal has a 2015 impact factor of 6.662, ranking it first out of 347 journals in the category "Economics". It is generally regarded as one of the top 5 journals in economics, together with the American Economic Review, Econometrica, the Journal of Political Economy, and the Review of Economic Studies. The Quarterly Journal of Economics is the oldest professional journal of economics in the English language. Edited at Harvard University's Department of Economics, it covers all aspects of the field. QJE is invaluable to professional and academic economists and students around the world. Scope of the Journal The Quarterly Journal of Economics is the oldest professional journal of economics in the English language. Edited at Harvard University's Department of Economics, it covers all aspects of the field-from the journal's traditional emphasis on micro theory, to both empirical and theoretical macroeconomics. QJE is invaluable to professional and academic economists and students around the world. Impact Factor and Ranking Year Impact Factor Ssi: Economics 2020 15.563 1 out of 377 2019 11.375 1 out of 371 2018 11.775 1 out of 363 2017 7.863 1 out of 353 2016 6.662 1 out of 347 2015 5.538 2 out of 344 2014 6.654 1 out of 333 2013 5.966 3 out of 332 2012 5.278 2 out of 332 2011 5.920 2 out of 320 2010 5.940 2 out of 304 2009 5.647 2 out of 245 This information is taken from the Journal Citation Reports™ (Clarivate, 2021). Abstracting & Indexing Services The Quarterly Journal of Economics is covered by the following abstracting and indexing services: ABI-INFORM Book Review Digest Plus CAB Abstracts Coal Abstracts Criminal Justice Abstracts Current Contents: Social & Behavioral Sciences Current Index to Statistics Dietrich's Index Philosophicus Documentation in Public Administration EconLit Emerald Management Reviews Environmental RouteNet Environmental Sciences & Pollution Management Database Expanded Academic ASAP Family Index Historical Abstracts Human Resources Abstracts IBZ: International Bibliography of Periodical Literature Index of Economic Articles in Journals & Collected Volumes Index to Periodical Articles Related to Law International Bibliography of Humanities & Sociological Literature Leisure, Recreation, and Tourism Abstracts Leisure Tourism Database LexisNexis Operations Research - Management Science Peace Research Abstracts ProQuest Central Public Administration Abstracts Quality Control & Applied Statistics RePec Risk Abstracts SCOPUS Social Science Source Social Sciences Citation Index/Social SciSearch Social Sciences Index Social Work Abstracts Wilson Business Abstracts World Agricultural Economics & Rural Sociology Abstracts Zentralblatt MATH

Laos LA: Scientific and Technical Journal Articles data was reported at 85.100 Unit in 2016. This records an increase from the previous number of 68.600 Unit for 2015. Laos LA: Scientific and Technical Journal Articles data is updated yearly, averaging 42.600 Unit from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 85.100 Unit in 2016 and a record low of 17.700 Unit in 2003. Laos LA: Scientific and Technical Journal Articles data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Technology. Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.; ; National Science Foundation, Science and Engineering Indicators.; Gap-filled total;

San Marino Scientific and Technical Journal Articles data was reported at 7.500 Unit in 2016. This stayed constant from the previous number of 7.500 Unit for 2015. San Marino Scientific and Technical Journal Articles data is updated yearly, averaging 2.600 Unit from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 7.500 Unit in 2016 and a record low of 0.200 Unit in 2003. San Marino Scientific and Technical Journal Articles data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s San Marino – Table SM.World Bank: Technology. Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.; ; National Science Foundation, Science and Engineering Indicators.; Gap-filled total;

Trinidad and Tobago TT: Scientific and Technical Journal Articles data was reported at 185.200 Unit in 2016. This records an increase from the previous number of 147.200 Unit for 2015. Trinidad and Tobago TT: Scientific and Technical Journal Articles data is updated yearly, averaging 168.300 Unit from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 236.800 Unit in 2011 and a record low of 120.500 Unit in 2003. Trinidad and Tobago TT: Scientific and Technical Journal Articles 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: Technology. Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.; ; National Science Foundation, Science and Engineering Indicators.; Gap-filled total;

United States US: Scientific and Technical Journal Articles data was reported at 408,985.300 Unit in 2016. This records a decrease from the previous number of 429,139.000 Unit for 2015. United States US: Scientific and Technical Journal Articles data is updated yearly, averaging 403,928.200 Unit from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 440,229.700 Unit in 2014 and a record low of 321,765.900 Unit in 2003. United States US: Scientific and Technical Journal Articles 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: Technology. Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.; ; National Science Foundation, Science and Engineering Indicators.; Gap-filled total;

Summary of the most and least competitive countries: WEF-GCI versus CSI.

Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

About the AuthorsProf. Jeffrey SachsDirector, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume LafortuneDirector, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: EmailProf. Christian KrollProf. of Sustainability, IU International University of Applied Sciences; Senior Advisor, SDSN; Scientific Co-Director of the SDG IndexChristian Kroll is Professor of Sustainability. He created the prototype SDG Index as the world’s first measurement tool of the SDGs in the September 2015 publication “Sustainable Development Goals: Are the rich countries ready” with a foreword by Kofi Annan. Christian was honoured as a Young Global Leader by the World Economic Forum in 2018 for his achievements. He authored articles in scientific journals spanning several disciplines. He lectures as full professor on sustainable development, sustainable finance (ESG), circular economy, and CSR at IU International University of Applied Sciences, and previously taught classes at the London School of Economics and Political Science, Hertie School of Governance in Berlin, and held positions at Jacobs University Bremen and Bertelsmann Stiftung. Christian gained a PhD from the London School of Economics and Political Science with a thesis entitled “Towards a Sociology of Happiness”. His research has featured in national and international media such as BBC World News, Harvard Business Review, Washington Post, Le Monde, Die Zeit, ARD, Spiegel Online among others.Contact: Email / Twitter / WebGrayson FullerSenior Analyst, SDG Index, SDSNGrayson Fuller is the Senior Analyst at SDSN. His role consists of managing the data, coding, and statistical analyses for the SDG Index and Dashboards report. He additionally carries out research related to sustainable development. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese, and Russian. He enjoys playing violin and hails from Atlanta, GA.Contact: EmailFinn WoelmCoordinator for Data Science and Research, SDSNFinn Woelm coordinates data science and research projects at the SDSN. He focuses on statistical analyses, data visualization, and web development. Prior to joining the SDSN, Finn co-founded a startup and worked for a number of organizations, including the International Panel on Social Progress. He holds a Bachelor of Arts in Interdisciplinary Studies from Naropa University in Boulder, Colorado, USA. Finn is passionate about open source, collaborative governance, and the environment.About the PublishersSustainable Development Solutions Network (SDSN)The Sustainable Development Solutions Network (SDSN) has been operating since 2012 under the auspices of the UN Secretary-General. SDSN mobilizes global scientific and technological expertise to promote practical solutions for sustainable development, including the implementation of the Sustainable Development Goals (SDGs) and the Paris Climate Agreement.Bertelsmann StiftungThe Bertelsmann Stiftung is one of the largest foundations in Germany. It works to promote social inclusion and is committed to advancing this goal through programs that improve education, shape democracy, advance society, promote health, vitalize culture and strengthen economies. The Bertelsmann Stiftung is a non-partisan, private operating foundation.Cambridge University PressCambridge University Press dates from 1534 and is part of the University of Cambridge. Its mission is to unlock people’s potential with the best learning and research solutions. Its vision is a world of learning and research inspired by Cambridge. Playing a leading role in today’s global market place, Cambridge University Press has over 50 offices around the globe, and distributes products to nearly every country in the world.

This data product provides agricultural output, input and total factor productivity (TFP) growth rates, but not levels, across the countries and regions of the world in a consistent, comparable way, for 1961-2010.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with link to Excel files For complete information, please visit https://data.gov.

GDP Growth Indicator: GDP = (( GDPt- GDP t-1)/ GDP t-1)*10 Control Of Corruption Indicator: Corupption Perception Index Government Effectiveness Indicator: a measure of the quality of public services, the quality of civil services and their degree of independence from political pressure, the quality of policy formulation and implementation, and the credibility of commitments government to this policy. Political Stability and Absence of Violence/Teorism Indicator: a measure of the likelihood of a change in power that could impact not only policy sustainability but also reduce citizen’s ability to choose and replace rulers peacefull. Regulator Quality Indicator: a measure of a government's ability to formulate and implement policies and regulations and its ability to promote private sector development Rule of Law Indicator: an Index Rules of Law

All of the data are measuring in ratio (%) and the source are from the World Bank.

Niger NE: Scientific and Technical Journal Articles data was reported at 56.100 Unit in 2016. This stayed constant from the previous number of 56.100 Unit for 2015. Niger NE: Scientific and Technical Journal Articles data is updated yearly, averaging 31.950 Unit from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 66.100 Unit in 2010 and a record low of 22.100 Unit in 2003. Niger NE: Scientific and Technical Journal Articles data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Niger – Table NE.World Bank: Technology. Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.; ; National Science Foundation, Science and Engineering Indicators.; Gap-filled total;

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization.