economic systems research assignment

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Economics Research Methods

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Introduction to Research Methods

Research methods help you as a researcher answer a specific economic questions The first step in becoming a proficient researcher is to build some skills in finding, evaluating and using academic literature. Research in economics, as in any other academic field,builds on the work of previous researchers. A literature review is a important first step when beginning a research project to get an sense of what research has already been done on the topic.

A literature review is a survey of existing literature that provides context for your research contribution, and demonstrates your subject knowledge. It is also the way to tell the story of how your research extends knowledge in your field.

The first step to writing a successful literature review is knowing how to find and evaluate literature on your topic.This guide is designed to introduce you to tools and give you skills you can use to effectively  do economics research..

Below are some questions to think about as you begin your research:

Questions to ask as you think about your literature review:

What is my research question.

Choosing a valid research question is something you will need to discuss with your academic advisor and/or POS committee. Ideas for your topic may come from your coursework, lab rotations, or work as a research assistant. Having a specific research topic allows you to focus your research on a project that is manageable. Beginning work on your literature review can help narrow your topic.

What kind of literature review is appropriate for my research question?

Depending on your area of research, the type of literature review you do for your thesis will vary. Consult with your advisor about the requirements for your discipline. You can view theses and dissertations from your field in the library's Digital Repository can give you ideas about how your literature review should be structured.

What kind of literature should I use?

The kind of literature you use for your thesis will depend on your discipline. The Library has developed a list of Guides by Subject with discipline-specific resources. For a given subject area, look for the guide titles "[Discipline] Research Guide." You may also consult our liaison librarians for information about the literature available your research area.

How will I make sure that I find all the appropriate information that informs my research?

Consulting multiple sources of information is the best way to insure that you have done a comprehensive search of the literature in your area. The What Literature to Search tab has information about the types of resources you may need to search. You may also consult our liaison librarians for assistance with identifying resources..

How will I evaluate the literature to include trustworthy information and eliminate unnecessary or untrustworthy information?

While you are searching for relevant information about your topic you will need to think about the accuracy of the information, whether the information is from a reputable source, whether it is objective and current. Our guides about Evaluating Scholarly Books and Articles and Evaluating Websites will give you criteria to use when evaluating resources.

How should I organize my literature? What citation management program is best for me?

Citation management software can help you organize your references in folders and/or with tags. You can also annotate and highlight the PDFs within the software and usually the notes are searchable. To choose a good citation management software, you need to consider which one can be streamlined with your literature search and writing process. Here is a guide page comparing EndNote, Mendeley & Zotero. The Library also has guides for three of the major citation management tools:

• EndNote & EndNote Web Guide
• Mendeley Guide
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What steps should I take to ensure academic integrity?

The best way to ensure academic integrity is to familiarize yourself with different types of intentional and unintentional plagiarism and learn about the University's standards for academic integrity. Start with this guide . The Library also has a guide about your rights and responsibilities regarding copyrighted images and figures that you include in your thesis.

Where can I find writing and editing help?

Writing and editing help is available at the Graduate College's Center for Communication Excellence . The CCE offers individual consultations, peer writing groups, workshops and seminars to help you improve your writing.

Where can I find I find formatting standards? Technical support?

The Graduate College has a Dissertation/ Thesis website with extensive examples and videos about formatting theses and dissertations. The site also has templates and formatting instructions for Word and LaTex .

What citation style should I use?

The Graduate College thesis guidelines require that you "use a consistent, current academic style for your discipline." The Library has a Citation Style Guides resource you can use for guidance on specific citation styles. If you are not sure, please consult your advisor or liaison librarians for help.

Adapted from The Literature Review: For Dissertations, by the University of Michigan Library. Available: https://guides.lib.umich.edu/dissertationlitreview

Presentation Slides

Slides from the ECON 594X class presentation" held on October 7, 2020 are below:

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400+ Economic Project Topics: How to Choose and Excel in Research

Economic project topics play a pivotal role in the academic journey of students pursuing degrees in economics or related fields. These topics serve as the foundation for research, analysis, and the development of critical thinking skills. 

Selecting the right economic project topic is crucial, as it can significantly impact the success of your research and the depth of your understanding of economic principles. 

In this blog, we’ll guide you through the process of choosing the right economic project topic, explore different categories of topics, and provide tips for a successful research journey.

How To Select Economic Project Topics?

Table of Contents

Before diving into the categories of economic project topics, it’s essential to understand the process of selecting a topic that aligns with your interests, expertise, and available resources. Here’s a closer look at how to choose the right topic:

Identifying Your Interests and Expertise

Passion for your research topic can be a powerful motivator. Consider areas within economics that genuinely interest you. 

Do you have a fascination with microeconomic concepts like market dynamics and consumer behavior, or are you more drawn to macroeconomic issues like fiscal and monetary policies? Identifying your interests will make the research process more enjoyable and rewarding.

Moreover, leveraging your expertise can lead to a more fruitful research experience. If you have a background in a specific industry or possess unique skills, it may be wise to select a topic that aligns with your strengths. 

Your existing knowledge can provide valuable insights and a competitive edge in your research.

Assessing the Relevance and Timeliness of Topics

Economic research should address current and relevant issues in the field. To ensure the significance of your project, consider the timeliness of the topic. 

Are you exploring an emerging economic trend, or does your research address a longstanding issue that still requires attention?

Additionally, think about the broader implications of your research. How does your chosen topic contribute to the existing body of knowledge in economics? 

Assessing the relevance and potential impact of your research can help you choose a topic that resonates with both academic and real-world audiences.

Considering Available Resources and Data

Practicality is a crucial factor in selecting an economic project topic. Assess the availability of resources and data required for your research. Do you have access to relevant datasets, surveys, or academic journals that support your chosen topic? 

It’s essential to ensure that the necessary resources are accessible to facilitate your research process effectively.

400+ Economic Project Topics: Category-Wise

Economic project topics encompass a wide range of areas within the field. Here are four major categories to explore:

100+ Microeconomics Project Topics

• The impact of advertising on consumer behavior.
• Price elasticity of demand for luxury goods.
• Analyzing market structure in the tech industry.
• Consumer preferences for sustainable products.
• The economics of online streaming services.
• Factors affecting pricing strategies in the airline industry.
• The role of information asymmetry in used car markets.
• Microeconomics of fast fashion and its environmental effects.
• Behavioral economics in food choices and obesity.
• The impact of minimum wage on small businesses.
• Market competition and pharmaceutical drug prices.
• Monopoly power in the pharmaceutical industry.
• Economic analysis of the gig economy.
• Elasticity of demand for healthcare services.
• Price discrimination in the hotel industry.
• Consumer behavior in the sharing economy.
• Economic analysis of e-commerce marketplaces.
• The economics of ride-sharing services like Uber.
• Factors influencing the demand for organic foods.
• Game theory and strategic pricing in oligopolistic markets.
• Microeconomics of the coffee industry.
• Analyzing the effects of tariffs on imported goods.
• Price elasticity of demand for electric vehicles.
• The economics of artificial intelligence and job displacement.
• Behavioral economics in the stock market.
• Impact of advertising on children’s consumer choices.
• Monopolistic competition in the smartphone industry.
• Economic analysis of the video game industry.
• The role of patents in pharmaceutical pricing.
• Price discrimination in the airline industry.
• Analyzing consumer behavior in the luxury fashion industry.
• The economics of addiction and substance abuse.
• Market structure in the online advertising industry.
• Price elasticity of demand for energy-efficient appliances.
• Economic analysis of the fast-food industry.
• The impact of product recalls on consumer trust.
• Factors influencing consumer choices in the beer industry.
• Microeconomics of the music streaming industry.
• Behavioral economics and food labeling.
• Economic analysis of the fitness and wellness industry.
• The economics of organic farming and sustainability.
• Analyzing the demand for mobile app-based services.
• Price discrimination in the entertainment industry.
• Economic analysis of subscription box services.
• Consumer preferences for eco-friendly packaging.
• Game theory in online auction markets.
• Analyzing the effects of congestion pricing.
• The economics of university tuition and student loans.
• Microeconomics of the fashion resale market.
• Behavioral economics in online shopping cart abandonment.
• Market structure in the pharmaceutical distribution.
• Analyzing the economics of cryptocurrency.
• Economic analysis of the real estate market.
• Price elasticity of demand for streaming music services.
• Consumer choices in the electric vehicle market.
• The economics of food delivery services.
• Monopoly power in the cable television industry.
• Factors influencing consumer decisions in the cosmetics industry.
• Behavioral economics and charitable donations.
• Economic analysis of the online dating industry.
• The impact of healthcare regulations on prices.
• Price discrimination in the cruise line industry.
• Economic analysis of the fashion resale market.
• Analyzing the effects of subsidies on agriculture.
• Consumer preferences for eco-friendly transportation.
• Market structure in the book publishing industry.
• Microeconomics of the craft beer industry.
• Behavioral economics and impulse buying.
• Price elasticity of demand for video game consoles.
• Economic analysis of the coffee shop industry.
• The economics of mobile payment systems.
• Analyzing consumer choices in the fast-food breakfast market.
• Monopolistic competition in the smartphone app industry.
• Factors influencing consumer decisions in the beauty industry.
• Behavioral economics in the context of online reviews.
• Economic analysis of the organic skincare industry.
• The impact of government regulations on tobacco prices.
• Price discrimination in the movie theater industry.
• Microeconomics of the subscription box industry.
• Analyzing the effects of trade barriers on agricultural exports.
• Consumer preferences for sustainable fashion.
• Market structure in the video game console industry.
• The economics of mobile app monetization.
• Price elasticity of demand for streaming television services.
• Economic analysis of the organic food industry.
• Behavioral economics and the psychology of pricing.
• Analyzing consumer choices in the electric scooter market.
• Monopoly power in the cable internet service industry.
• Factors influencing consumer decisions in the wine industry.
• Economic analysis of the impact of product reviews on sales.
• The economics of online crowdfunding platforms.
• Price discrimination in the music festival industry.
• Microeconomics of the meal kit delivery industry.
• Behavioral economics and the impact of discounts on purchasing behavior.
• Analyzing the effects of trade agreements on global supply chains.
• Consumer preferences for sustainable home appliances.
• Market structure in the online marketplace for handmade goods.
• The economics of esports and gaming tournaments.
• Price elasticity of demand for online streaming subscriptions.
• Economic analysis of the fast-casual restaurant industry.
• The impact of government subsidies on renewable energy prices.

100+ Macroeconomics Project Topics

• The impact of fiscal policy on economic growth.
• Analyzing the effectiveness of monetary policy.
• Inflation targeting and its implications.
• The relationship between unemployment and inflation.
• Factors influencing exchange rates.
• The effects of globalization on income inequality.
• Assessing the economic consequences of trade wars.
• The role of central banks in financial stability.
• Economic growth in emerging markets.
• Government debt and its impact on the economy.
• The economics of healthcare reform.
• Income distribution and poverty alleviation strategies.
• The economics of renewable energy adoption.
• The impact of automation on employment.
• Economic consequences of climate change.
• The economics of the gig economy.
• The Phillips Curve and its modern relevance.
• The economics of housing bubbles.
• Economic development in sub-Saharan Africa.
• The economics of education funding.
• The impact of technology on productivity growth.
• The role of the IMF in global financial stability.
• Economic consequences of Brexit.
• The economics of cryptocurrency.
• Economic implications of aging populations.
• The economics of natural disasters.
• The effects of income tax cuts on the economy.
• The relationship between economic freedom and growth.
• The role of infrastructure investment in economic development.
• The economics of health insurance markets.
• The impact of minimum wage laws on employment.
• The economics of food security.
• The effects of government subsidies on industries.
• The role of the World Bank in global development.
• Economic consequences of government regulation.
• The economics of corporate mergers.
• The relationship between government spending and economic growth.
• Economic effects of monetary policy on asset prices.
• The economics of social safety nets.
• The impact of income inequality on economic growth.
• The role of entrepreneurship in economic development.
• Economic consequences of trade deficits.
• The effects of financial deregulation.
• The economics of the opioid crisis.
• The relationship between economic growth and environmental sustainability.
• The impact of tax evasion on government revenue.
• Economic development in post-conflict regions.
• The economics of the sharing economy.
• The role of the World Trade Organization (WTO) in international trade.
• Economic consequences of government debt crises.
• The effects of population aging on healthcare systems.
• The economics of public-private partnerships.
• The impact of economic sanctions on countries.
• Economic implications of income tax reform.
• The role of venture capital in innovation.
• The economics of foreign aid.
• The relationship between education and economic growth.
• Economic effects of natural resource extraction.
• The economics of financial market crashes.
• The role of economic incentives in behavior.
• Economic consequences of currency devaluation.
• The effects of income tax progressivity on income distribution.
• The economics of income mobility.
• The impact of government subsidies on renewable energy.
• Economic development in post-communist countries.
• The economics of intellectual property rights.
• The relationship between government corruption and economic growth.
• Economic consequences of government budget deficits.
• The effects of financial globalization.
• The role of behavioral economics in policy-making.
• The economics of healthcare access.
• The impact of automation on manufacturing jobs.
• Economic implications of population growth.
• The economics of housing affordability.
• The relationship between monetary policy and asset bubbles.
• Economic effects of immigration policies.
• The role of economic forecasting in decision-making.
• The economics of taxation on multinational corporations.
• Economic development in the digital age.
• The impact of economic shocks on consumer behavior.
• Economic consequences of natural disasters.
• The effects of income inequality on social cohesion.
• The economics of financial innovation.
• The relationship between economic freedom and entrepreneurship.
• Economic implications of healthcare reform.
• The role of gender inequality in economic development.
• The economics of climate change mitigation.
• The impact of government regulations on small businesses.
• Economic development in the Middle East.
• The economics of consumer debt.
• The relationship between trade policy and national security.
• Economic consequences of housing market crashes.
• The effects of monetary policy on income distribution.
• The economics of sustainable agriculture.
• The role of economic sanctions in international diplomacy.
• Economic implications of corporate tax reform.
• The economics of innovation clusters.
• The impact of government procurement policies on industries.
• Economic development in post-apartheid South Africa.
• The relationship between economic inequality and political instability.

100+ International Economics Project Topics

• Impact of Trade Wars on Global Economies
• Exchange Rate Determinants and Fluctuations
• The Role of Multinational Corporations in International Trade
• Effects of Brexit on International Trade
• Comparative Analysis of Free Trade Agreements
• Currency Manipulation and Its Consequences
• Economic Integration in the European Union
• Global Supply Chains and Vulnerabilities
• The Impact of China’s Belt and Road Initiative
• Trade Liberalization in Developing Countries
• Globalization and Income Inequality
• Economic Consequences of Economic Sanctions
• International Trade and Environmental Sustainability
• The Role of the World Trade Organization (WTO)
• Foreign Direct Investment and Economic Growth
• Exchange Rate Regimes: Fixed vs. Floating
• International Financial Crises and Their Causes
• NAFTA vs. USMCA: A Comparative Analysis
• The Effects of Tariffs on Import-Dependent Industries
• Trade and Economic Development in Africa
• Offshoring and Outsourcing in a Global Economy
• The Economics of Remittances
• Currency Wars and Competitive Devaluations
• International Trade and Intellectual Property Rights
• The Impact of Economic Openness on Inflation
• The Eurozone Crisis: Causes and Solutions
• Trade Imbalances and Their Consequences
• The Economics of International Migration
• Exchange Rate Volatility and Speculation
• The Silk Road: Historical and Modern Perspectives
• The Role of International Aid in Development
• Globalization and Cultural Homogenization
• International Trade and National Security
• The Economic Effects of Brexit on the EU
• Sovereign Debt Crises and Bailouts
• The Economics of Global Energy Markets
• International Trade and Human Rights
• The Asian Financial Crisis of 1997
• The Economics of International Tourism
• The Impact of Global Economic Institutions
• International Trade and Technological Innovation
• Comparative Advantage and Trade Theory
• Globalization and Income Redistribution
• International Trade and Agriculture
• The BRICS Countries in the Global Economy
• Exchange Rate Pegs and Currency Boards
• The Economics of Global Health Challenges
• International Trade and Gender Inequality
• The Effects of Economic Migration on Sending and Receiving Countries
• The Role of Non-Tariff Barriers in International Trade
• International Trade and Economic Development in Latin America
• The European Debt Crisis and Austerity Measures
• Globalization and Income Mobility
• The Impact of International Trade on Small and Medium-sized Enterprises (SMEs)
• The Economics of Regional Integration: ASEAN, Mercosur, etc.
• Trade Agreements and Dispute Resolution
• Exchange Rate Forecasting Models
• The Economics of Foreign Aid Allocation
• The Role of International Trade in Poverty Alleviation
• International Trade and Economic Freedom
• The Economics of International Banking
• Globalization and Income Convergence
• The Effects of Political Instability on International Trade
• Trade and Economic Development in South Asia
• The Role of Special Economic Zones (SEZs) in Trade
• International Trade and Labor Standards
• Economic Consequences of Trade Deficits
• The Economics of International Taxation
• Trade and Economic Development in the Middle East
• Globalization and Income Polarization
• The Impact of Global Value Chains (GVCs) on Trade
• International Trade and Health Care Systems
• The Economics of Bilateral vs. Multilateral Trade Agreements
• Trade and Economic Development in Southeast Asia
• Exchange Rate Parity Conditions
• The Economics of International Migration Policies
• The Role of Trade Facilitation Measures
• International Trade and Human Capital Development
• Globalization and Income Insecurity
• The Effects of Trade on Environmental Sustainability
• The Economics of Foreign Direct Investment (FDI) Incentives
• Trade and Economic Development in Eastern Europe
• The Role of Export Credit Agencies (ECAs) in Trade
• International Trade and Technological Transfer
• Globalization and Income Resilience
• The Impact of Global Economic Shocks
• Trade and Economic Development in Oceania
• Exchange Rate Risk Management Strategies
• The Economics of Foreign Exchange Reserves
• International Trade and Economic Geography
• The Role of Trade Promotion Agencies
• Globalization and Income Diversity
• The Effects of Exchange Rate Intervention
• International Trade and Financial Inclusion
• Trade and Economic Development in the Caribbean
• The Economics of Trade Agreements on Services
• The Role of Export Processing Zones (EPZs) in Trade
• International Trade and Income Mobility
• Globalization and Income Equality Policies
• The Impact of Trade Disputes on International Relations.

100+ Economic Policy Project Topics

• The impact of minimum wage laws on employment rates.
• The effectiveness of quantitative easing in stimulating economic growth.
• Analyzing the consequences of trade tariffs on international commerce.
• The role of government subsidies in shaping agricultural markets.
• The economic implications of healthcare reform policies.
• Examining the relationship between income inequality and economic growth.
• Evaluating the effects of corporate tax cuts on business investments.
• The impact of immigration policies on labor markets.
• Analyzing the economic consequences of climate change regulations.
• Assessing the effectiveness of financial regulations in preventing economic crises.
• The role of central banks in controlling inflation.
• The economic implications of universal basic income programs.
• Investigating the relationship between education spending and economic development.
• The impact of government debt on future generations.
• Analyzing the effects of fiscal stimulus packages on economic recovery.
• The role of monetary policy in addressing unemployment.
• Evaluating the economic consequences of government healthcare programs.
• The impact of exchange rate fluctuations on international trade.
• The economic implications of public-private partnerships in infrastructure development.
• Analyzing the effects of antitrust laws on competition in markets.
• The role of social welfare programs in poverty reduction.
• Evaluating the economic consequences of aging populations.
• The impact of housing policies on real estate markets.
• Investigating the relationship between foreign aid and economic development.
• The economic implications of globalization on income distribution.
• Analyzing the effects of regulatory capture in financial markets.
• The role of tax incentives in promoting renewable energy.
• Evaluating the economic consequences of healthcare privatization.
• The impact of immigration reform on labor market dynamics.
• Investigating the relationship between government debt and interest rates.
• The economic implications of trade liberalization agreements.
• Analyzing the effects of corporate social responsibility on profitability.
• The role of fiscal policy in addressing economic recessions.
• Evaluating the economic consequences of income tax reforms.
• The impact of technology policies on innovation and economic growth.
• Investigating the relationship between monetary policy and asset bubbles.
• The economic implications of minimum wage adjustments.
• Analyzing the effects of government regulations on the pharmaceutical industry.
• The role of foreign direct investment in economic development.
• Evaluating the economic consequences of healthcare cost containment measures.
• The impact of labor market policies on workforce participation.
• Investigating the relationship between exchange rates and export competitiveness.
• The economic implications of intellectual property rights protection.
• Analyzing the effects of fiscal austerity measures on economic stability.
• The role of government spending in stimulating economic growth.
• Evaluating the economic consequences of energy subsidies.
• The impact of trade agreements on job displacement.
• Investigating the relationship between infrastructure investment and productivity.
• The economic implications of financial market deregulation.
• Analyzing the effects of income tax credits on low-income families.
• The role of social safety nets in mitigating economic shocks.
• Evaluating the economic consequences of healthcare rationing.
• The impact of labor market flexibility on employment stability.
• Investigating the relationship between corporate governance and firm performance.
• The economic implications of government subsidies for renewable energy.
• Analyzing the effects of taxation on wealth distribution.
• The role of sovereign wealth funds in economic development.
• Evaluating the economic consequences of currency devaluation.
• The impact of government regulation on the gig economy.
• Investigating the relationship between foreign aid and political stability.
• The economic implications of healthcare privatization.
• Analyzing the effects of income inequality on social cohesion.
• The role of infrastructure investment in reducing transportation costs.
• Evaluating the economic consequences of carbon pricing policies.
• The impact of trade protectionism on domestic industries.
• Investigating the relationship between public education funding and student outcomes.
• The economic implications of housing affordability challenges.
• Analyzing the effects of labor market discrimination on wage gaps.
• The role of monetary policy in addressing asset price bubbles.
• Evaluating the economic consequences of financial market speculation.
• The impact of government procurement policies on small businesses.
• Investigating the relationship between population aging and healthcare expenditures.
• The economic implications of regional economic integration.
• Analyzing the effects of government subsidies on agricultural sustainability.
• The role of tax incentives in promoting technology startups.
• Evaluating the economic consequences of trade imbalances.
• The impact of healthcare cost containment measures on patient outcomes.
• Investigating the relationship between government debt and economic growth.
• The economic implications of housing market speculation.
• Analyzing the effects of labor unions on wage negotiations.
• The role of economic sanctions in shaping international relations.
• Evaluating the economic consequences of natural resource depletion.
• The impact of fiscal policy on income redistribution.
• Investigating the relationship between education quality and workforce productivity.
• The economic implications of government investment in green infrastructure.
• Analyzing the effects of income tax evasion on government revenue.
• The role of gender-based economic disparities in overall growth.
• Evaluating the economic consequences of healthcare fraud.
• The impact of public transportation policies on urban development.
• Investigating the relationship between corporate social responsibility and consumer behavior.
• The economic implications of government support for the arts and culture sector.
• Analyzing the effects of government subsidies on electric vehicles.
• The role of economic diplomacy in promoting international trade.
• Evaluating the economic consequences of financial market volatility.
• The impact of globalization on wage convergence or divergence.
• Investigating the relationship between economic sanctions and human rights violations.
• The economic implications of government investments in digital infrastructure.
• Analyzing the effects of government interventions in housing markets.
• The role of economic policies in addressing income mobility.
• Evaluating the economic consequences of occupational licensing regulations.

Popular Economic Project Topics

To inspire your research journey, here are some popular economic project topics within each category:

• Case Studies

1. Analyzing the Impact of COVID-19 on a Specific Industry: Examine how the pandemic affected industries like hospitality, aviation, or e-commerce.

2. Evaluating the Economic Effects of Tax Reforms: Investigate the consequences of recent tax policy changes on businesses, individuals, and government revenue.

• Research-Based Topics

1. Exploring the Relationship Between Inflation and Unemployment: Conduct empirical research to analyze the Phillips Curve and its relevance in the modern economy.

2. Investigating the Factors Influencing Consumer Spending Patterns: Use surveys and data analysis to understand what drives consumer spending behavior.

• Policy Analysis

1. Assessing the Effectiveness of a Recent Economic Stimulus Package: Evaluate the impact of government stimulus measures on economic recovery, employment, and inflation.

2. Examining the Pros and Cons of Minimum Wage Adjustments: Analyze the economic effects of changes in the minimum wage on low-wage workers, businesses, and overall employment.

Research Methodologies: Economic Project Topics

The methodology you choose for your economic project can significantly impact the outcomes of your research. Here are some common research approaches:

• Quantitative Research

Quantitative research involves collecting and analyzing numerical data. Common methods include:

1. Surveys and Questionnaires: Conduct surveys to gather data from respondents and use statistical analysis to draw conclusions.

2. Data Analysis and Regression Models: Employ statistical software to analyze datasets and establish relationships between variables using regression analysis.

• Qualitative Research

Qualitative research focuses on understanding the underlying reasons, motivations, and perceptions of individuals or groups. Common methods include:

1. Interviews and Focus Groups: Conduct interviews or group discussions to gain insights into specific economic behaviors or attitudes.

2. Content Analysis: Analyze textual or visual data, such as documents, reports, or media, to identify themes and patterns.

• Mixed-Methods Research

Mixed-methods research combines both quantitative and qualitative approaches to provide a comprehensive understanding of economic phenomena. Researchers often collect numerical data alongside qualitative insights.

Tips for Successful Project Topic Selection

To ensure a successful research journey, keep these tips in mind:

• Narrowing Down Your Focus: While it’s essential to choose a topic you’re passionate about, make sure it’s specific enough to be manageable within the scope of your project.
• Staying Informed About Current Economic Events: Stay up-to-date with economic news and events to identify emerging trends and issues that may inspire your research.
• Seeking Guidance from Professors or Advisors: Don’t hesitate to seek advice from your professors or academic advisors. They can provide valuable insights and help you refine your research questions.

Selecting the right economic project topics is a critical step in your academic journey. By identifying your interests, considering the relevance and timeliness of topics, and assessing available resources, you can embark on a rewarding research journey. 

Whether you choose to delve into microeconomics, macroeconomics, international economics, or economic policy, remember that your research has the potential to contribute to the broader understanding of economic principles and their real-world applications.

Start your research journey today, and you’ll not only gain valuable knowledge but also make a meaningful contribution to the field of economics.

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Education Standards

Nebraska business, marketing and management standards.

Learning Domain: Marketing

Standard: Explain the types of economic systems.

Economic Systems - Create a Country Project

This lesson introduces students to the three main types of economic systems, command, market, and mixed. Students work with limited knowledge, not knowing about mixed systems until the very end. This allows students to see the pieces of command systems and market systems that are present in the United States and in their “ideal” economies.

Economic Systems

This lesson introduces students to the three main types of economic systems. Students work with limited knowledge, not knowing about mixed systems until the very end. This allows students to see the pieces of command systems and market systems that are present in the United States and in their “ideal” economies.

Part 1 - Hook

Part 2 - Direct Instruction on Command and Market Economies

Part 3 - Create Country Project

Part 4 - Direct instruction on Mixed Economies, Debrief Island Economies

Part 5 - Present Island Project

Part 6 - Rubric

An ancient artifact has been unearthed, giving you the ability to insert your own country anywhere in the world. Once you place your 20 square miles, the artifact goes dormant. Now is the time to get to work!

Students will work in pairs for this project. They may choose to use the country they started working on during the hook, or create a completely new one.

To have a successful country, you will need to answer the following questions.

• Name of the country and flag.
• Factors of Production:
• Land - Where is your country located? Do some research and list out the land that is available naturally in that area!
• Labor - Who will be allowed to move to your country? Will there be requirements on items such as their education level or ability  to work specific jobs?
• Capital - What capital will be needed? Will your businesses create them or will you import them?
• Entrepreneurship - What kind of business will your country have? What goods and services will be created? You will need entrepreneurs to start the businesses, and who will be the labor?
• Wants vs Needs: Will the government provide for any of its citizens wants and needs? Or is it up to each person? Will any basic needs be provided for? (Remember to think about things like education, insurance, safety net for people in need, etc).
• What is the role of government in your country?
• How will prices be set for goods and services? By the businesses? Or will the government set prices?
• What (if anything) will the government regulate?

Put everything together in a creative way, such as an infographic or commercial for your country! Have your own idea? Run it by the teacher!

New Learning/Debrief:  

Introduce students to mixed economic systems. Have students compare and contrast the systems.

• Include how they answer the three big questions, and provide students with pros and cons.
• Discuss  what pieces about the United States economy is command, market, and how we are a mixed economy.

Have them reflect on the type of system that they chose to create with the following questions.

• Identify some areas that your economic system has that are from a command system and some areas that are from a market system.
• Which system most closely resembles the one you designed (market, command, or mixed)? Define that system and explain how yours compares. Explain referring to how you answered the questions on your economy.
• Identify at least 3 strengths and 3 weaknesses to the economic system that you created. (I suggest looking up strengths and weaknesses of the various types of systems).
• In the past there has been a variety of economic systems, but today the most popular system is the mixed economy. Based on what you know about the United States and your created country, why do you think that is?

Presentation : Have groups present their islands. Audience members will fill out this sheet  while listening.

Grading Rubric:

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• Open access
• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Maximilian Kotz, Anders Levermann & Leonie Wenz

Institute of Physics, Potsdam University, Potsdam, Germany

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Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Wassily Leontief personal archive

Wassily Wassilyevich Leontief (1905-1999) was an American economist of Russian descent. He won the Nobel Prize in 1973. For over twenty years, he was the Henry Lee Professor of Economics at Harvard University. The Wassily Leontief personal archive documents Leontief's academic and professional activity from 1928 to 2001. The material is pertinent to the history of economics, especially the application of the input-output method of economic analysis and the integration of economic theory and statistical methods in mainstream economic science.

• Creation: 1928-2001 and undated
• Leontief, Wassily, 1906-1999 (Person)

Researcher Access

The Wassily Leontief personal archive is open for research use with the following exceptions: Access to Harvard University administrative records in this collection is restricted for 50 years. These records include correspondence between Harvard departments, staff and administrators; department records and committee records, including budgets, meeting minutes, notes, and planning documents; and financial and budget records (excluding salary information, which is restricted for 80 years). Student and personnel records are closed to research use for 80 years. This restriction covers records that include grades, student papers, applications, records related to academic advising, letters of recommendation, appointments and scholarships, and that document employment. Restricted records appear throughout the collection; restrictions are noted at the folder level. Restricted materials are housed in the following boxes:

• Wassily Leontief personal archive, Part I (material received prior to 1981)
• Boxes 96-135
• Wassily Leontief personal archive, Part II (material received 1987-1999)
• Boxes 161-162; Accession number: 11196
• Boxes 184-187; Accession number: 11228
• Boxes 223-227; Accession number: 12255
• Boxes 250-255; Accession number: 12654
• Boxes 280-281; Accession number: 13416
• Boxes 323-328; Accession number: 13712
• Boxes 339-340; Accession number: 13920

Restrictions on use:

Use restrictions are noted in the folder lists.

Scope and Contents

The Wassily Leontief personal archive documents the academic and professional career of Wassily Leontief as a teacher, writer, consultant, researcher, and entrepreneur from 1928 to 2001. The collection is organized into two sections. The first section consists of Leontief papers acquired by the Harvard University Archives prior to 1981. The second section consists of seven accessions acquired from 1987 to 1999. Each accession has been maintained as received and is described separately. Leontief was a prolific researcher, correspondent, and author, and thus much of the collection consists of correspondence, published and unpublished manuscripts, lectures, and course materials produced during his career. The collection is a valuable resource for economic research, particularly in the use of the input-output method of economic analysis in academia, private business, and governmental organizations in the United States and abroad in the second half of the twentieth century. The collection also illustrates the changing nature of the work of economists, particularly after World War II, in which the integration of economic theory and statistical methods came to dominate mainstream economic science.

The Wassily Leontief personal archive documents the academic and professional career of Wassily Leontief as a teacher, writer, consultant, researcher, and entrepreneur from 1928 to 2001. The collection is a valuable resource for economic research, particularly in the use of the input-output method of economic analysis in academia, private business, and governmental organizations in the United States and abroad in the second half of the twentieth century. The collection also illustrates the changing nature of the work of economists, particularly after World War II, in which the integration of economic theory and statistical methods came to dominate mainstream economic science. Leontief was a prolific researcher, correspondent, and author, and thus much of the collection consists of correspondence, published and unpublished manuscripts, lectures, and course materials produced during his career. Although the groups of material are generally well defined there is some overlap of subject matter. This collection chronicles Leontief’s interactions with mathematicians, economists, statisticians, social scientists, accountants, businessmen, and government officials in devising solutions to intricate problems related to economic analysis and the wide-spread adoption of input-output techniques in the United States, Europe, Latin America, Asia, and Africa. Correspondence documents Leontief’s involvement with professional organizations including the American Academy of Arts and Sciences and the American Economic Association, with government agencies such as the United States Bureau of Labor Statistics and the United States Air Force, and with learned societies including the National Science Foundation. Meeting minutes, reports, proposals and other records illuminate Leontief’s role in providing guidance on statistical and methodology problems related to the application of modern input-output analysis to the United Nations, the United States Environmental Protection Agency, the American Society of Mechanical Engineers, and the United States Department of Commerce. Leontief’s entrepreneurial activities promoting the use of input-output techniques for market research, business planning, and scientific management are illustrated in files related to the Council for Economic and Industry Research, the Institute for Interindustry Data, Arthur D. Little, the New York investment firm de Vegh and Company, and the Italian Ministry of Transportation.Leontief’s associations with the North Carolina School of Science and Mathematics, the Tolstoy Foundation, and the International Research and Exchanges Board illustrate his interest in educational and cultural philanthropic activities. Letters, telegrams, memoranda, meeting minutes, and reports document Leontief’s involvement in political and economic reform in Russia (or the Soviet Union) in the late 1980s and early 1990s; and his work with such organizations as the US-USSR Trade and Economic Council, the International Management Institute of Saint Petersburg, and the International Centre for Economic and Social Research. Reports, meeting minutes, and correspondence with colleagues in this collection demonstrate Leontief’s persistence in developing and applying modern techniques of economic analysis to the solution of economic problems including the reconstruction of the Italian and Japanese transportation systems, the planning of a bridge spanning the Straits of Gibraltar to connect Europe and Africa, and the formulation of transition strategies by which Russia could evolve from a centrally planned economy to a market economy. Conference files document Leontief’s promotion of the application of input-output analysis, especially to policy issues connected with economic and technological change. Included among these files are Leontief’s remarks on the economic and social impact of automation on employment, the impact of new technology on developed and less developed countries, and the effects of arms spending on economic development. Leontief’s directorship of the Harvard Economic Research Project on the structure of the American Economy (HERP) from 1948 to 1972 is chronicled in letters, memoranda, reports, and other records documenting the research projects performed at the HERP, the purchase of equipment and supplies, the construction of office and research facilities, and personnel assignments. Correspondence details Leontief’s interaction with faculty in developing economic studies at Harvard; and recounts his work on such Harvard committees as the Committee on Applied Mathematics, the Faculty Committee on Computation Facilities, and the Guest Lecturer Committee. Leontief’s Harvard teaching materials chiefly document clerical matters including the assignment of tutors, the purchase of books, the giving of grades, the preparation of examinations, readings for courses, and student research topics. Scattered among the teaching materials are reading lists, outlines, and lecture notes for Leontief’s courses on Marxian economics, mathematical economics, economic theory, the economy of the United States, Russian industrialization, and international trade. Letters, memoranda, and reports document Leontief’s association with New York University from 1975 to 1997, including his leadership of the Institute for Economic Analysis from 1978 to 1985. Many of the records document the application of input-output modelling to examinations of international trade relationships, the monetary structure of the United States, computer-based automation, and the economic impact of military spending. Teaching materials for the economic courses taught by Leontief at New York University primarily are administrative documenting student registration and the assignment of grades; there is very little lecture material included in the records. Correspondence includes descriptions of Leontief’s efforts to advance interdisciplinary cooperation between different departments, divisions, and schools within New York University; mentions his efforts to improve the economics curriculum; and documents his appeals for financial support for the Institute for Economic Analysis. Writings in this collection including published and unpublished manuscripts, reports, reprints, lectures, transcripts, research papers, and interviews document Leontief’s contributions to advances in economic theory and empirical analysis, particularly in the field of econometrics. Particularly highlighted are Leontief’s diverse research interests in such areas as monetary economic policy, economic forecasting, environmental economics, and the implications of technology and automation on an economy. Additionally, the records document Leontief’s interest in the impact of military expenditures on economic development and the transition of socialist countries (including the Soviet Union) from centrally planned to market economies. Leontief’s writings illuminate his refinement and application of input-output analysis techniques to the solution of economic problems, as a management tool in business planning, as a guide for economic planning and forecasting, and as a means of projecting long-term economic growth. Leontief’s writings reveal his conviction that the only valid test of economic research is its empirical significance and its practical application; that academic theories, although sometimes useful, are more likely to be dead-ends in economic research. A limited amount of biographical or personal materials including interviews, biographical sketches, and letters which contain descriptions of Leontief’s life and career are found in this collection. Of particular interest are photocopies of sketches by Leontief from 1944 to 1973, chiefly of Harvard professors drawn while Leontief was attending doctoral examinations in the Department of Economics. Professors include Alexander Gerschenkron (1908-1978), Seymour E. Harris (1897-1974), Gottfried von Haberler (1900-1995), Overton H. Taylor (1897-1987), and J. Keith Butters (1915-2006). This collection includes material written in English, French, German, Russian, Spanish, and Italian.

This section is organized in seven series, as follows. The Correspondence Series spans Leontief’s career from 1928 to 1975. It consists mainly of professional correspondence with his colleagues in the United States and other countries. The records illustrate the growth of quantitative economics and demonstrate the role Leontief played in devising solutions to intricate problems related to economic analysis including the efficient allocation and utilization of economic resources. The letters in this series document the widespread adoption of Leontief’s input-output method of economic analysis in the United States, Japan, France, Holland, and Italy; in socialist countries such as the Soviet Union, Hungary, Poland, Romania, East Germany, Czechoslovakia, and China; and in Latin America, Asia, and Africa. Leontief’s relationship with the Harvard Economic Research Project (HERP) is chronicled in the correspondence files. Letters include discussions of the purchase of equipment and supplies, the construction of office and research facilities, staff related research projects, and Leontief’s participation at conferences. Moreover, Leontief’s ongoing interest in the Soviet Union, the communist system, and Eastern Europe is highlighted in Russian Research Center files. Additionally, the De Vegh and Company and Arthur D. Little files, illustrate Leontief’s efforts to persuade American businessmen to take a more rational view of the development of the American economy, applying input-output techniques in finding solutions to market and economic problems. There are also files detailing Leontief’s presidency of the American Economic Association (1968-1971), his winning the Nobel Prize in Economics Award in 1973, Leontief’s participation at conferences, and Leontief’s committee work, both at Harvard University and elsewhere. The Writings Series documents Leontief’s contributions to the field of economics from 1928 to 1973 and includes manuscripts, reports, reprints and other publications. The wide range of topics in economic theory reflects Leontief’s interests over many decades, beginning with his work on economic forecasting, including the application of input-output techniques to the solution of economic problems, and later on, focusing more specifically on disarmament, economic growth and planning, and problems related to the economic development in socialist countries, including the Soviet Union. Other aspects of Leontief’s career can be found in files which form the Teaching Materials Series, most of which contain course outlines, reading lists, and syllabi related to Leontief’s Harvard University economic courses (1936-1956). These files chiefly document clerical matters related to the assignment of tutors, the purchase of books, the giving of grades, and the preparation of examinations. Letters, memoranda, and reports in the Fourteenth Pugwash Conferences on Science and World Affairs Series document Leontief’s interaction with scholars and public figures meeting together to discuss ways to reduce the danger of armed conflict and enhance global security. Leontief’s study of interindustry relations is highlighted in the Input-Output Charts Series which contains tables, graphs, and diagrams presumably created by Leontief in collaboration with the United States Bureau of Labor Statistics. The General Information by and about Wassily Leontief Series and Correspondence, Manuscript, Research Files, and Teaching Materials Series contain a limited amount of Leontief biographical or personal materials, including historical sketches, newspaper clippings, magazine articles, and personal correspondence. Also, photocopies of sketches drawn by Leontief (1944-1973), chiefly of Harvard professors while Leontief was attending doctoral examinations in the Department of Economics are found in the Sketches by Wassily Leontief Series.

This section is organized in seven series, as acquired by the Harvard University Archives. After leaving Harvard in 1975, Leontief periodically sent additional papers to the Harvard University Archives in installments from 1987 to 1999. The Correspondence series’ in these additional papers were primarily arranged by country, organization, and personal name. The files document Leontief’s professional relationships with policy makers, diplomats, businessmen, scholars, government officials, and scientists in the United States and abroad including the Soviet Union, Japan, Spain, and Italy; as well as philanthropic foundations, government agencies, corporations, and international organizations, chiefly from 1975 to 2001. Letters, memoranda, reports, meeting minutes, and other records illuminate the actual application of input-output analysis to the solution of practical economic problems in the final decades of twentieth century. Leontief’s exchanges with colleagues highlight the many socioeconomic changes occurring in society in the late twentieth century due to advances in technology and globalization, the trend towards neoliberalism, and the increasing social and economic difficulties found in developing countries. The correspondence files also illustrate Leontief’s involvement in educational and cultural activities, including his association with the North Carolina School of Science and Mathematics, a school which provides education to young scholars in science, mathematics, and the humanities; and the Tolstoy Foundation, a group involved in the resettlement of Russian refugees, education, and training. In addition, letters, memoranda, and meeting minutes chronicle Leontief’s relationship with New York University, particularly at the Institute of Economic Analysis, promoting the use of input-output analysis. Working papers and publication materials include Leontief’s manuscript drafts, lectures, book reviews, encyclopedia entries, and letters-to-the-editor, principally written in the 1980s and 1990s. In some cases, the files include Leontief’s correspondence between his editors and publishers. Leontief’s writings reveal that he wrote extensively on the effects of technology on employment and income distribution, monetary phenomena, globalization and economic disparities between less developed and highly developed countries, private enterprise in the Soviet Union, and the application of input-output analysis methodology to the solving economic problems. Teaching materials including reading lists, syllabi, and outlines for Leontief’s Harvard University courses (1953-1975) in input-output analysis, economic theory, and the growth and structure of the American economy are also found in these papers. Most of the teaching material related to Leontief’s economic courses at New York University (1975-1999) are administrative in nature, documenting student registration and the assignment of grades. Conference files chiefly document the arrangements made for Leontief’s attendance at conferences; some of the files contain the lectures Leontief presented at the meetings. Conference themes illuminate some of the important issues that were of concern to economists, including the political, cultural, and economic issues confronting the Soviet Union and Eastern Europe in the 1980s and 1990s, the impact of technological change on economic and social conditions, the effect of arms spending on economic development, and the use of input-output techniques in economic forecasting and modelling. This collection includes material written in English, French, German, Russian, Spanish, and Italian.

Additional Description

Biographical / historical.

Wassily Wassilyevich Leontief (1905-1999) was the Henry Lee Professor of Economics at Harvard University from 1953 to 1975. Leontief won the Nobel Committee's Nobel Memorial Prize in Economic Sciences in 1973 “for the development of the input-output method and for its application to important economic problems.” Leontief’s principal fields of interest were pure theory and empirical quantitative analysis; his major contribution to economics was his input-output method of analysis which measures how changes in one economic sector may have an effect on other sectors. The input-output method’s greatest value is as a planning device for both private firms and governments to anticipate demand for goods and services and to predict the ripple effects among various sectors of the economy. Born in Munich, Germany, in 1905 to Russian parents, Leontief spent his childhood in St. Petersburg, Russia. Leontief received his degree of Learned Economist from the University of Leningrad in 1925. After studying at the University of Berlin (PhD 1928), Leontief accepted a position as research economist at the Institute of World Economics at the University of Kiel (1927-1928), studying the effects of supply and demand curves on the steel industry. It was in Germany that Leontief began examining the need for a dynamic model of general equilibrium to explain the behavior of supply, demand, and prices in a whole economy. In 1929 Leontief went to China, where he worked as an economic advisor to the Ministry of Railways. He moved to the National Bureau of Economic Research in New York in 1931 and to the Department of Economics at Harvard University in 1932. During World War II while teaching full-time at Harvard, Leontief served as a consultant to the United States Department of Labor, where he applied his input-output system of analysis to problems created by the impending shift from a war to a peacetime economy; and as Chief of the Russian Economics Subdivision of the Office of Strategic Services where he produced reports regarding the Soviet Union as a cooperative ally during the war and assessments of the Soviet international position in the postwar period. At Harvard, Leontief’s research focused on developing a general equilibrium theory capable of understanding the structure and operation of economic systems. Leontief held that economics was an empirical and applied science and that academic theories, although sometimes useful, needed to be supported by sound statistical data. In 1932, Leontief compiled the first input-output tables of the American economy for the years 1919 and 1929. In 1936, he published his first input-output paper demonstrating the importance of input-output economic analysis; and in 1941 published his first major book demonstrating his general equilibrium theory, The Structure of American Economy, 1919-1929: An Empirical Application of Equilibrium Analysis. Continuing his work on the development of input-output theory and its applications to economic problems, Leontief received several promotions, and became a Professor of Economics in 1946. In 1948 Leontief founded the Harvard Economic Research Project on the structure of the American Economy, serving as its director until 1973. The Project was dedicated to input-output research. Leontief played a critical role in the application of quantitative methods to economic theory and practice after World War II. Using newly emerging computer technology, Leontief applied his input-output method to the exploration of the economic impact of defense cuts, the cost of pollution abatement, and the effects of trade liberalization on the economy. Leontief input-output models were used to study the environmental implications of promoting the development of less developed countries, the relationships of interindustry transactions, and world income inequality. Moreover, Leontief explored the possibility of using input-output analysis as a means of enabling more efficient national economic planning. Of particular interest to economists was the Leontief Paradox in which Leontief demonstrated that United States imports were more capital intensive than United States exports, a contradiction of contemporary economic orthodoxy, and which initiated a reappraisal of trade theory and econometric techniques among economists. Leontief’s input-output method became a major field of economic research. International conferences were held on the subject, bibliographic references on input-output research were compiled, textbooks on input-output analysis were published, and at least fifty countries adopted the use of input-output tables to administer national economic activity, including the United States, the Soviet Union, the Common Market countries, and Japan. In 1975, Leontief left Harvard to become a Professor of Economics at New York University, where he founded the Institute for Economic Analysis, serving as its director until 1985. The Institute was dedicated to research in input-output analysis. In 1983, Leontief was named a university professor and two years later appointed a senior scholar. At New York University, Leontief continued work on improving his input-output method applying it to examinations of various problems including the impact of automation on workers, the influence of capital and labor upon the selection of alternative technologies, and the social and economic implications of military spending. The relationship of environmental disruption and economic growth and the effect of modernization on income distribution were also investigated. In the late 1980s and first half of the 1990s, Leontief became more involved in Soviet Russian affairs lending assistance to economists, engineers, managers, businessmen, and ordinary citizens during Russia’s transition from a centrally planned economy to a market economy. Leontief was active in maintaining and developing scientific and cultural exchanges and cooperation between the United States and Soviet Russia. Moreover, many academic and other organizations in the United States, Europe, and Japan, concerned with their work in Soviet Russia sought his advice. Through numerous interviews published in Russian journals and newspapers, and at meetings with political and economic leaders, Leontief became widely known on matters related to economic reform in Soviet Russia, including the introduction of input-output methodologies for the development of transition mechanisms needed to restructure the Soviet economy. In 1991, the International Centre for Economic and Social Research, Leontief Centre, was established in St. Petersburg, Russia, to guide regional and local authorities and support market reforms in Russia. Leontief received many prestigious awards throughout his career including the Bernhard-Harms Prize from the Institute of World Economics at the University of Kiel (1970). Additionally, Leontief was a member of several professional societies including the Econometric Society (President, 1950), the American Economic Association (President, 1970), and the British Association for the Advancement of Science (President, 1976). A prolific writer, Leontief was the author or co-author of more than 200 economic papers. Leontief married Estelle Helena Marks in 1932. The couple had one daughter: Svetlana Leontief Alpers (born 1936). Leontief died on February 5, 1999.

Footnote on Leontief's birthdate

Most of the biographical sources on Leontief give his year of birth as 1906, however, in 2006 a new possible date was uncovered. (Pavlova, Natal'ia Iu. and Svetlana A. Kaliadina, translated and annotated by Claus Wittich "The Family of W. W. Leontief in Russia" Economic Systems Research Vol. 18, Iss. 4, 2006). This source indicates that Leontief was born in 1905 in Munich, Germany rather than in 1906 in St. Petersburg. The confusion surrounding his birthdate stems from the fact that although his birth was registered in Munich, Germany in 1905 where he was born, when his parents returned to St. Petersburg the following year they registered the birth a second time (with the Orthodox Church as all births were then registered) as having taken place in 1906 in St. Petersburg. Though Leontief might well have known the true date early on in his life, the fiction of 1906, St. Petersburg was maintained on all necessary documents from then on until the 1940s when Leontief claimed that he had recently learned the true 1905 birth date from his parents. Since the 1906 date had been accepted by the Federal Bureau of Investigation, government agencies concerned with security clearances, and had been used on his United States passport, Leontief continued to indicate 1906 as his birthdate to avoid confusion. Leontief's daughter, Svetlana Alpers has reported that it was on the occasion of his 90th birthday, in 1995 (rather than 1996) that Leontief broke the news of his true birthdate to his immediate family.

Wassily Wassilyevich Leontief (1906-1999) was the Henry Lee Professor of Economics at Harvard University from 1953 to 1975. Leontief won the Nobel Committee's Nobel Memorial Prize in Economic Sciences in 1973. Leontief’s principal fields of interest were pure theory and empirical quantitative analysis; his major contribution to economics was his input-output method of analysis which measures how changes in one economic sector may have an effect on other sectors. The input-output method’s greatest value is as a planning device for both private firms and governments to anticipate demand for goods and services and to predict the ripple effects among various sectors of the economy. Born on August 5, 1906 in St. Petersburg, Russia, Leontief received his degree of Learned Economist from the University of Leningrad in 1925. After studying at the University of Berlin (PhD 1928), Leontief accepted a position as research economist at the Institute of World Economics at the University of Kiel (1927-1928), studying the effects of supply and demand curves on the steel industry. It was in Germany that Leontief began examining the need for a dynamic model of general equilibrium to explain the behavior of supply, demand, and prices in a whole economy. In 1929 Leontief went to China, where he worked as an economic advisor to the Ministry of Railways. He moved to the National Bureau of Economic Research in New York in 1931 and to the Department of Economics at Harvard University in 1932. During World War II while teaching full-time at Harvard, Leontief served as a consultant to the United States Department of Labor, where he applied his input-output system of analysis to problems created by the impending shift from a war to a peacetime economy; and as Chief of the Russian Economics Subdivision of the Office of Strategic Services where he produced reports regarding the Soviet Union as a cooperative ally during the war and assessments of the Soviet international position in the postwar period. At Harvard, Leontief’s research focused on developing a general equilibrium theory capable of understanding the structure and operation of economic systems. Leontief held that economics was an empirical and applied science and that academic theories, although sometimes useful, needed to be supported by sound statistical data. In 1932, Leontief compiled the first input-output tables of the American economy for the years 1919 and 1929. In 1936, he published his first input-output paper demonstrating the importance of input-output economic analysis; and in 1941 published his first major book demonstrating his general equilibrium theory, The Structure of American Economy, 1919-1929: An Empirical Application of Equilibrium Analysis. Continuing his work on the development of input-output theory and its applications to economic problems, Leontief received several promotions, and became a Professor of Economics in 1946. In 1948 Leontief founded the Harvard Economic Research Project on the structure of the American Economy, serving as its director until 1973. The Project was dedicated to input-output research. Leontief played a critical role in the application of quantitative methods to economic theory and practice after World War II. Using newly emerging computer technology, Leontief applied his input-output method to the exploration of the economic impact of defense cuts, the cost of pollution abatement, and the effects of trade liberalization on the economy. Leontief input-output models were used to study the environmental implications of promoting the development of less developed countries, the relationships of interindustry transactions, and world income inequality. Moreover, Leontief explored the possibility of using input-output analysis as a means of enabling more efficient national economic planning. Of particular interest to economists was the Leontief Paradox in which Leontief demonstrated that United States imports were more capital intensive than United States exports, a contradiction of contemporary economic orthodoxy, and which initiated a reappraisal of trade theory and econometric techniques among economists. Leontief’s input-output method became a major field of economic research. International conferences were held on the subject, bibliographic references on input-output research were compiled, textbooks on input-output analysis were published, and at least fifty countries adopted the use of input-output tables to administer national economic activity, including the United States, the Soviet Union, the Common Market countries, and Japan. In 1975, Leontief left Harvard to become a Professor of Economics at New York University, where he founded the Institute for Economic Analysis, serving as its director until 1985. The Institute was dedicated to research in input-output analysis. In 1983, Leontief was named a university professor and two years later appointed a senior scholar. At New York University, Leontief continued work on improving his input-output method applying it to examinations of various problems including the impact of automation on workers, the influence of capital and labor upon the selection of alternative technologies, and the social and economic implications of military spending. The relationship of environmental disruption and economic growth and the effect of modernization on income distribution were also investigated. In the late 1980s and first half of the 1990s, Leontief became more involved in Soviet Russian affairs lending assistance to economists, engineers, managers, businessmen, and ordinary citizens during Russia’s transition from a centrally planned economy to a market economy. Leontief was active in maintaining and developing scientific and cultural exchanges and cooperation between the United States and Soviet Russia. Moreover, many academic and other organizations in the United States, Europe, and Japan, concerned with their work in Soviet Russia sought his advice. Through numerous interviews published in Russian journals and newspapers, and at meetings with political and economic leaders, Leontief became widely known on matters related to economic reform in Soviet Russia, including the introduction of input-output methodologies for the development of transition mechanisms needed to restructure the Soviet economy. In 1991, the International Centre for Economic and Social Research, Leontief Centre, was established in St. Petersburg, Russia, to guide regional and local authorities and support market reforms in Russia. Leontief received many prestigious awards throughout his career including the Bernhard-Harms Prize from the Institute of World Economics at the University of Kiel (1970). Additionally, Leontief was a member of several professional societies including the Econometric Society (President, 1950), the American Economic Association (President, 1970), and the British Association for the Advancement of Science (President, 1976). A prolific writer, Leontief was the author or co-author of more than 200 economic papers. Leontief married Estelle Helena Marks in 1932. The couple had one daughter: Svetlana Eugenia Alpers (born 1936). Leontief died on February 5, 1999.

Organization of the collection

The Wassily Leontief personal archive is described in two parts

Acquisition information

The Wassily Leontief personal archive was acquired by the Harvard University Archives through donation. Acquisitions from the early 1970s were classified and described in the Harvard University shelflist prior to 1981. After leaving Harvard in 1975, Leontief periodically sent additional papers to the Archives. These acquisitions are as follows:

• Accession number: 11196; 1987 August 17
• Accession number: 11228; 1987 September 21
• Accession number: 12255; 1991 August 6
• Accession number: 12654; 1993 April 1
• Accession number: 13416; 1996 September 11
• Accession number: 13712; 1998 January 26
• Accession number: 13920; 1999 March 16

Related material

In the Harvard University Archives

• Harvard Economic Research Project. American Economic Data, 1919-1939 (UAV 347.xx) : contains economic data related to Leontief's studies of the structure of the American economy.
• Harvard Economic Research Project. Records of the Harvard Economic Research Project, 1947-1972 (UAV 347.xx) : contains the correspondence of Director Wassily Leontief and Associate Director Elizabeth W. Gilboy.
• Harvard Economic Research Project. Records of the International Conference on Input-Output Techniques, 1961 September 11-15 (UAV 347.xx) : contains records from the 1961 International Conference on Input-Output Techniques held in Geneva, Switzerland.
• Harvard University. Photograph Collection: Portraits, approximately 1852 - approximately 2004 (HUP) : contains photographs of Wassily Leontief, 1942 : http://nrs.harvard.edu/urn-3:HUL.ARCH:hua04006
• Harvard University. Records of the Office of News and Public Affairs, photographs, 1913-1994 (UAV 605.xx) : contains photographs of Wassily Leontief, 1946-1973 : http://nrs.harvard.edu/urn-3:HUL.ARCH:hua04003
• Hoffenberg, Marvin, 1914-. Marvin Hoffenberg personal archive [unprocessed accessions], 1941-2012 (Accession 2016.292) : the accession documents Hoffenberg's research centering on the use of empirical quantitative analysis, including the application of input-output methodology to problems related to public policy; includes correspondence with Leontief.

In the Leontief Centre, St. Petersburg, Russia

• The International Centre for Social and Economic Research - Leontief Centre was established in 1991 by Wassily Leontief and the Mayor of St. Petersburg Anatoly Sobchak to implement market reforms for Russia, provide counseling and guidance to regional and local authorities, and foster effective mechanisms for strategic management.

Sources for Leontief Biography

• Alpers, Svetlana. Roof Life. New Haven, Connecticut: Yale University Press, 2013.
• Bergson, Abram. Wassily Leontief, 5 August 1906-5 February 1999. Proceedings of the American Philosophical Society 144 (December 2000) : 465-468.
• Dorfman, Robert. Wassily Leontief’s Contribution to Economics. The Scandinavian Journal of Economics 75 (December 1973) : 430-449.
• Wood, John Cunningham and Michael McLure, ed. Vol. 1, Wassily Leontief: Critical Assessments of Leading Economists. London: Routledge, 2001.

Inventory update

This document last updated 2022 June 24.

• Carter, Anne P., 1925-
• De Vegh, Imre, 1906-
• Gilboy, Elizabeth W. (Elizabeth Waterman), 1903-1973.
• Leontief, Wassily, 1906-1999.
• Schumpeter, Elizabeth Boody.

General note

• American Economic Association.
• Arthur D. Little, Inc.
• Harvard University -- Department of Economics.
• Harvard University -- Department of Economics -- Faculty.
• Harvard University -- Economics.
• Harvard University -- Russian Research Center.
• Harvard University -- Society of Fellows.
• Harvard Economic Research Project.
• New York University -- Department of Economics.
• New York University -- Institute for Economic Analysis.
• North Carolina -- School of Science and Mathematics.
• Pugwash Conference on Science and World Affairs -- (14th -- 1965 -- Venice, Italy)
• Tolstoy Foundation (U.S.)
• United Nations -- Economic assistance.
• United States -- Bureau of Labor Statistics.
• United States -- Office of Strategic Services.
• Armed Forces -- Appropriations and expenditures.
• Automation -- Economic aspects -- United States.
• Business cycles -- Mathematical models -- History.
• Business forecasting.
• Capitalism.
• Disarmament -- Economic aspects.
• East-West trade.
• Economic assistance, American -- Developing countries.
• Economic development.
• Economic development -- Russia (Federation)
• Economic development -- Soviet Union.
• Economics -- Study and teaching.
• Economics -- United States -- History -- 20th century.
• Economists -- United States -- Biography.
• Employment (Economic theory) -- Mathematical models.
• Employment forecasting -- United States -- Mathematical models.
• Export marketing -- United States.
• Forecasting -- Methodology.
• Industries -- United States.
• Input-output analysis.
• Input-output analysis -- Congresses.
• Input-output tables -- United States.
• International economic relations.
• Italy -- Economic conditions.
• Japan -- Economic policy.
• Labor supply -- Effect of automation on -- United States.
• Nobel Prizes.
• Nobel Prize winners.
• Perestroĭka.
• Social change -- United States.
• Socialism -- Communist countries.
• Soviet Union -- Economic policy.
• Technology and society.
• United States -- Economic conditions -- 1918-1945.
• United States -- Economic conditions -- 1945-
• United States -- Statistics.
• War -- Economic aspects.

Formats and genres

• Audiocassettes.
• Audiotapes.
• Bibliographies.
• Black-and-white photographs.
• Charts (graphic documents)
• Color photographs.
• Conference proceedings.
• Consulting.
• Correspondence.
• Curriculum vitae.
• Entrepreneurs.
• Floppy disks.
• Letters of recommendation.
• Manuscripts (document genre)
• Newspaper clippings.
• Publications.
• Research notes.
• Researchers.
• Résumés (personnel records)

Processing Information

The Wassily Leontief personal archive was first classified and described in the Harvard University Archives shelflist prior to 1981. Early accessions were delivered to the Archives in installments and processed at various times throughout the years; twenty-one separate call numbers were created. From July 2015 to March 2016 Dominic P. Grandinetti reprocessed these early accessions, maintaining the order of the papers as found with minimal rearrangement. Reprocessing involved the consolidation of all call numbers under HUG 4517, rehousing of materials in appropriate archival containers, the establishment of series and subseries hierarchy, and the creation of this finding aid. Restricted materials were separated into boxes 96 through 135. As part of this finding aid, the archivist created a listing for superseded call numbers to help researchers in identifying materials noted in previous citations. The listing (located at the end of this finding aid) provides references from the superseded call number to new box and folder numbers. Processing for accessions acquired from 1987 to 1999 was completed in September 2016. These accessions were not merged or organized as a whole. Each accession is described separately, and titled according to the month and year of acquisition. Restricted materials were separated at the end of each accession. Whenever possible titles were transcribed from or derived using termilogy from inventories found in each box of each accession. Researchers should note that material within each accession may overlap with and/or relate to material found in other accessions.In all respects, the archivist attempted to retain and preserve the original arrangement and existing relationships of the documents. Physical rearrangement was minimal. Leontief’s original folder titles were retained; any folder titles and dates supplied by the archivist appear in brackets. Copies of personal checks and credit card receipts were removed and shredded.

Related Names

Administrative information, repository details.

Part of the Harvard University Archives Repository

Holding nearly four centuries of materials, the Harvard University Archives is the principal repository for the institutional records of Harvard University and the personal archives of Harvard faculty, as well as collections related to students, alumni, Harvard-affiliates and other associated topics. The collections document the intellectual, cultural, administrative and social life of Harvard and the influence of the University as it emerged across the globe.

Collection organization

Leontief, Wassily, 1906-1999. Wassily Leontief personal archive, 1928-2001 and undated. HUG 4517, Harvard University Archives.

Cite Item Description

Leontief, Wassily, 1906-1999. Wassily Leontief personal archive, 1928-2001 and undated. HUG 4517, Harvard University Archives. https://id.lib.harvard.edu/ead/hua02016/catalog Accessed April 24, 2024.

Once you have compiled a list of material you would like to consult in the reading room, please contact the Harvard University Archives at [email protected] .

• 21 hours ago

Career Navigation Q&A for Leaders and Policymakers

The following content comes from the report Unlocking Economic Prosperity: Career Navigation in a Time of Rapid Change . The Project on Workforce at Harvard and the National Fund for Workforce Solutions collaborated to conduct applied research that included convening focus groups, interviewing subject matter experts, conducting a scan of the National Fund network, hosting feedback sessions, and facilitating a roundtable discussion. Unlocking Economic Prosperity is informed by this approach, and draws on the perspectives of over 60 individuals from education, workforce development, research, and frontline work. 

What is career navigation? 

Career navigation involves acquiring knowledge, making informed, personally-relevant plans, and integrating and negotiating education, training, and work actions throughout one’s career. This includes self-assessment to identify skills, interests, values, and goals, along with career exploration to discover alternative pathways. Pathway mapping charts a course towards career goals, while skill and credential acquisition and job placement and advancement ensure progress. Effective navigation relies on informed decision-making and adapting to changing circumstances. While often a challenging and non-linear journey, career navigation is essential for making informed career decisions amidst evolving labor market demands. 

What drives career navigation success? 

The core drivers of career navigation success are identified as:

Information Access and Accuracy: Access to accurate information about education and career opportunities, including pathways and economic outcomes data, is crucial. Inaccurate information can lead to suboptimal career choices, highlighting the importance of external information in conjunction with internal personal goals and traits.

Skills and Credentials: Navigation skills and qualities, including adaptability, foundational skills, and job-specific skills and credentials, are vital for career progression.

Social Capital: Social relationships, networks, and engagement significantly impact an individual’s values, exposure, connections, and support, influencing their career trajectory.

Wraparound Resources and Supports: The availability of wraparound resources such as coaching, finances, technology, transportation, and child care plays a crucial role in supporting individuals in their career journeys.

Social Structures and Ecosystems: Social systems, including public and private policies, economic conditions, discrimination, and racism, shape individuals' career opportunities and experiences.

What are the types of career navigation programs and what does research tell us about them?

Types of career navigation programs include navigation courses, career exposure initiatives, and intensive experiential programs . Research suggests that while career navigation courses facilitate skill development and provide instruction in job search processes, their impact on adults' career journeys is inconclusive. For instance, a study on a job search assistance demonstration reported varying outcomes across different locations. Similarly, participation in college-to-career navigation courses showed mixed results in terms of academic outcomes and career planning.

On the other hand, intensive experiential programs, such as Year Up and Project QUEST, have demonstrated more consistent effectiveness. These programs integrate skill attainment, career immersion, and wraparound support, leading to notable improvements in participants' earnings and educational credentials. Research by the Clearinghouse for Labor Evaluation and Research identified several programs that significantly increased youth's earnings over time, emphasizing substantial time commitment and integrated job placement services as key factors contributing to their success.

Despite the upfront costs associated with intensive programs, studies have shown that the returns on investment outweigh the expenditures, benefiting both individuals and society. Additionally, career exposure programs, particularly for youth, have been shown to positively impact career decision-making and self-efficacy. While research on exposure programs' impact on adults is limited, evidence suggests that they can improve attitudes and intentions toward specific career paths, especially when targeted at high school students.

👉 Check out these related resources from The Project on Workforce:

The Workforce Almanac Data Portal: Mapping the workforce development sector. The Almanac is an interactive directory offering a comprehensive view of ~17,000 workforce training providers across the United States.

The College-to-Jobs Initiative: Exploring the intersection of higher education and the workforce. The C2J Map visualizes data on colleges and employment; Our C2J Playbook reviews programs that connect college students to good jobs.

How can organizations improve navigation programs and systems? 

These evidence-based principles can help improve career navigation programs and systems:

Communicate information and pathways in clear, accessible, and relevant ways: Present labor market information clearly and accessibly, considering language and format. Utilize social media platforms and traditional media to enhance engagement.

Integrate opportunities for career exposure and social capital development: Offer exposure to diverse professionals and careers to broaden perspectives and enhance networking. Ensure representation of professionals matching the demographics of job seekers.

Build foundational skills and navigation skills : Focus on building non-cognitive skills like resilience and adaptability alongside traditional job search processes. Incorporate experiential learning and skills coaching.

Design culturally-relevant approaches: Tailor interventions to the unique needs and experiences of participants from specific racial and ethnic minorities. Provide targeted support to address challenges related to discrimination and socio-environmental factors.

Use high-touch services that meet individuals where they are: Provide personalized interaction and maintain frequent engagement with participants. Deliver services in accessible locations, such as community centers, to meet individuals where they are.

Provide financial and wraparound support: Address financial constraints and inequitable access to work-related supports by offering services like advising, transportation, and childcare. Prioritize the provision of wraparound services to enhance program effectiveness.

Pursue community and intergenerational partnerships that build trust: Develop trust by building partnerships with marginalized populations and co-creating programs with community leaders. Engage families through intergenerational approaches to drive change.

Leverage AI to personalize pathways: Utilize AI to personalize career navigation, improve efficiency, and enhance job fit. Implement strong policies to mitigate biases and ensure equitable outcomes.

Collect disaggregated data and embed research and evaluation: Measure program effectiveness by collecting and analyzing disaggregated economic outcomes data. Embed research and evaluation measures to assess what works and for whom, guiding investment and scaling decisions.

Center equity by recruiting and elevating individuals from under-resourced communities: Recruit individuals from under-resourced communities to inform program design and amplify their voices. Address barriers to advancement by tackling issues related to access, skills, social capital, resources, and discrimination through coordinated actions across sectors.

How can policymakers help improve career navigation systems? 

Policy makers can play a crucial role in improving career navigation systems by implementing the following strategies:

Embedding public workforce services: Expand the reach of career services by situating them in community centers, schools, prisons, and other state offices. This ensures accessibility for all individuals, beyond those who typically interact with American Job Centers (AJCs).

Investing in and professionalizing career coaching: Allocate funding to develop standardized training and certification programs for career coaches. Professionalizing the field and offering competitive compensation will address the shortage of career services professionals.

Adopting outcome-based metrics: Shift public workforce accountability metrics to prioritize earnings growth over time and emphasize career progression. This ensures that individuals have access to longer training programs leading to quality jobs and promotes equity by setting racial equity goals and reporting disaggregated outcomes.

Aligning and simplifying eligibility requirements: Streamline eligibility requirements across government programs to reduce administrative burden and facilitate access to services. Adjusting eligibility criteria to automatically qualify individuals receiving means-tested public benefits for WIOA programs could simplify enrollment.

Providing universal access to career coaching and lifelong upskilling: Consider providing training funds for every individual to access career support throughout their lifetimes. This addresses the urgent need for reskilling and career transition support in the face of rapid technological innovation.

Collecting and communicating disaggregated, longitudinal outcomes data: Link unemployment data with education and training outcomes to better understand the economic impacts of different training options. Communicate this information clearly to empower individuals with the data needed to make informed decisions and address inequities through targeted interventions.

How can employers improve career navigation systems for their employees? 

Employers can contribute to improving career systems within their organizations by implementing the following strategies:

Develop transparent, skills-based career pathways: Employees can work with their employers to create clear and accessible career ladders that map to skills rather than artificial barriers like degrees. This involves articulating the skills required for each role, understanding employees' existing skills, and providing necessary training programs to bridge skill gaps.

Provide training to middle managers: Employees can advocate for training programs aimed at middle managers to enhance their skills in providing clear feedback and career mentoring. By empowering middle managers with these tools, organizations can foster a culture of coaching and support for career development.

Advocate for upskilling programs: Employees can advocate for upskilling programs designed around their needs, considering factors like time constraints and financial barriers. By communicating the availability and benefits of such programs, employees can ensure their peers are aware of opportunities for career advancement.

Track and analyze worker mobility: Employees can encourage their organizations to develop and apply metrics to track worker mobility, disaggregated by race and gender. By monitoring career trajectories and setting internal goals for equity and mobility, organizations can address disparities and promote a more inclusive work environment.

👉 Check out these other Skills-Related Resources from The Project on Workforce:

The Harvard Skills Lab : Creating tools to measure higher-order skills like teamwork, leadership, and decision making.

Skills-Based Hiring: The Long Road from Pronouncements to Practice : This report identifies where the reality of Skills-Based Hiring is lagging well-meaning ambitions, and shows which companies are getting it right.

How can educators and training providers improve career navigation systems? 

Educators and training providers can enhance career navigation systems by implementing the following strategies:

Provide personalized career coaching: Offer personalized, high-touch career coaching services to all students, connecting their interests, education, and career goals. This coaching should be provided by professional career coaches or supported educators and trainers, fostering trusting relationships and guiding students away from low-wage pathways that perpetuate social inequities.

Integrate foundational and navigation skills: Prioritize the development of foundational and navigation skills critical to career success, such as decision-making, adaptability, resilience, self-efficacy, and communication skills. These skills should be taught effectively in the classroom and through work-based learning experiences to empower students in navigating their careers successfully.

Expose students to diverse career paths: Offer opportunities for career exposure early in the education pipeline, exposing students to various career paths from an early age and continuing throughout high school and postsecondary education. Bring diverse professionals into the classroom to broaden students' career exploration and understanding of labor market outcomes.

Facilitate structured cohort learning and networking: Integrate social networking and cohort learning opportunities into programs to help students build social capital essential for career navigation. Educators and organizations should assist students in developing networking competencies and provide a supportive community to encourage their success.

Utilize technology for personalized guidance: Leverage emerging technology tools, such as AI, to "nudge" students and personalize their education and career pathways at scale. Educators can use AI to develop personalized experiential learning opportunities and assist students in developing essential skills tailored to their individual needs.

How can intermediaries and other organizations improve career navigation systems?

Workforce boards and nonprofit organizations can enhance career navigation systems by implementing the following strategies:

Actively recruit individuals from underrepresented communities: Workforce boards and nonprofit organizations should actively recruit individuals from under-resourced communities for career services and programs. By focusing on active outreach and connection with learners and workers from these communities, intermediaries can ensure equitable access to career opportunities.

Build trusted, intergenerational community partnerships: Foster trusted, intergenerational community partnerships that embed coaching services within community-based organizations. Career coaching embedded in such organizations is conducive to building trust and providing culturally relevant services. Strengthening community networks across generations can enhance social capital and support career navigation efforts.

Provide career exploration opportunities and self-assessments to adult workers: Offer career exploration opportunities and self-assessment tools to adult workers. While these experiences are often provided to students, they are equally crucial for adult workers to make informed choices, especially in the face of rapid technological change. Providing such resources empowers workers to navigate their careers effectively.

Uplift worker voice and empower collective agency: Ensure that workforce boards and nonprofit organizations provide human-centered services by asking learners and workers what they want for themselves. It's essential to build individual agency and empower collective worker agency by including frontline workers in decision-making and program delivery. By involving workers in shaping services, intermediaries can better meet their needs and aspirations.

How can philanthropists help improve career navigation systems? 

Philanthropists can contribute to improving career navigation systems through the following actions:

Invest in innovative career navigation models: Philanthropic organizations can serve as a "research and development fund" for innovative career navigation models. By funding new programs, including high-touch, high-tech models that may not align with public funding opportunities, philanthropy can stimulate innovation in the field. Pairing funding with rigorous evaluation helps build an evidence base to inform public policies and resource allocation.

Support leaders with lived experience: Philanthropists should actively support individuals and organizations led by people with lived experience in low-wage work who face structural barriers. It's crucial to ensure that leaders from these populations are involved in community discussions, equipped with resources, and empowered to inform policy priorities. This involvement helps advance equity in the field and ensures that the voices of marginalized communities are heard and valued.

Advance research in career navigation: Philanthropy can contribute to advancing research in the field of career navigation to develop knowledge around best practices and policies. Identifying and filling gaps in understanding career pathways is essential for developing more effective practices for policymakers, employers, and educators. By devoting resources to gather data and evidence, philanthropy can support the development of evidence-based strategies to improve career navigation systems.

👉 Get updates on new research from The Project on Workforce:

📚 Our latest research on education and employment.

🙋‍♀️ Invites to virtual events for workforce leaders and practitioners.

📈 What our team is reading and listening to on important workforce trends.

Suggested Citation

Joseph B. Fuller, Kerry McKittrick, et al. (Fall 2023). Unlocking Economic Prosperity: Career Navigation in a Time of Rapid Change. Published by the Harvard Kennedy School. https://www.pw.hks.harvard.edu/post/career-navigation

About the Authors

Joseph B. Fuller is Professor of Management Practice in General Management at the Harvard Business School, where he co-leads the school’s Managing the Future of Work project. He also co-chairs The Project on Workforce at Harvard and is a Senior Non-Resident Fellow at the American Enterprise Institute.

Kerry McKittrick is the Co-Director of the Project on Workforce at Harvard University’s Malcolm Wiener Center for Social Policy.

Ali Epstein is a Research Project Coordinator at the Project on Workforce at Harvard University’s Malcolm Wiener Center for Social Policy.

Sherry Seibel has a Master’s in Education from the Harvard Graduate School of Education and served as a Research Assistant and Summer Fellow for the Project on Workforce at Harvard.

Cole Wilson has a Master’s in Education from the Harvard Graduate School of Education and served as a Research Assistant and Summer Fellow for the Project on Workforce at Harvard.

Vasundhara Dash is a Master’s in Public Administration candidate at the Harvard Kennedy School and a Research Assistant at the Project on Workforce at Harvard.

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Gender pay gap in U.S. hasn’t changed much in two decades

The gender gap in pay has remained relatively stable in the United States over the past 20 years or so. In 2022, women earned an average of 82% of what men earned, according to a new Pew Research Center analysis of median hourly earnings of both full- and part-time workers. These results are similar to where the pay gap stood in 2002, when women earned 80% as much as men.

As has long been the case, the wage gap is smaller for workers ages 25 to 34 than for all workers 16 and older. In 2022, women ages 25 to 34 earned an average of 92 cents for every dollar earned by a man in the same age group – an 8-cent gap. By comparison, the gender pay gap among workers of all ages that year was 18 cents.

While the gender pay gap has not changed much in the last two decades, it has narrowed considerably when looking at the longer term, both among all workers ages 16 and older and among those ages 25 to 34. The estimated 18-cent gender pay gap among all workers in 2022 was down from 35 cents in 1982. And the 8-cent gap among workers ages 25 to 34 in 2022 was down from a 26-cent gap four decades earlier.

The gender pay gap measures the difference in median hourly earnings between men and women who work full or part time in the United States. Pew Research Center’s estimate of the pay gap is based on an analysis of Current Population Survey (CPS) monthly outgoing rotation group files ( IPUMS ) from January 1982 to December 2022, combined to create annual files. To understand how we calculate the gender pay gap, read our 2013 post, “How Pew Research Center measured the gender pay gap.”

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting response rates. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

In addition to findings about the gender wage gap, this analysis includes information from a Pew Research Center survey about the perceived reasons for the pay gap, as well as the pressures and career goals of U.S. men and women. The survey was conducted among 5,098 adults and includes a subset of questions asked only for 2,048 adults who are employed part time or full time, from Oct. 10-16, 2022. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used in this analysis, along with responses, and its methodology .

The  U.S. Census Bureau has also analyzed the gender pay gap, though its analysis looks only at full-time workers (as opposed to full- and part-time workers). In 2021, full-time, year-round working women earned 84% of what their male counterparts earned, on average, according to the Census Bureau’s most recent analysis.

Much of the gender pay gap has been explained by measurable factors such as educational attainment, occupational segregation and work experience. The narrowing of the gap over the long term is attributable in large part to gains women have made in each of these dimensions.

Related: The Enduring Grip of the Gender Pay Gap

Even though women have increased their presence in higher-paying jobs traditionally dominated by men, such as professional and managerial positions, women as a whole continue to be overrepresented in lower-paying occupations relative to their share of the workforce. This may contribute to gender differences in pay.

Other factors that are difficult to measure, including gender discrimination, may also contribute to the ongoing wage discrepancy.

Perceived reasons for the gender wage gap

When asked about the factors that may play a role in the gender wage gap, half of U.S. adults point to women being treated differently by employers as a major reason, according to a Pew Research Center survey conducted in October 2022. Smaller shares point to women making different choices about how to balance work and family (42%) and working in jobs that pay less (34%).

There are some notable differences between men and women in views of what’s behind the gender wage gap. Women are much more likely than men (61% vs. 37%) to say a major reason for the gap is that employers treat women differently. And while 45% of women say a major factor is that women make different choices about how to balance work and family, men are slightly less likely to hold that view (40% say this).

Parents with children younger than 18 in the household are more likely than those who don’t have young kids at home (48% vs. 40%) to say a major reason for the pay gap is the choices that women make about how to balance family and work. On this question, differences by parental status are evident among both men and women.

Views about reasons for the gender wage gap also differ by party. About two-thirds of Democrats and Democratic-leaning independents (68%) say a major factor behind wage differences is that employers treat women differently, but far fewer Republicans and Republican leaners (30%) say the same. Conversely, Republicans are more likely than Democrats to say women’s choices about how to balance family and work (50% vs. 36%) and their tendency to work in jobs that pay less (39% vs. 30%) are major reasons why women earn less than men.

Democratic and Republican women are more likely than their male counterparts in the same party to say a major reason for the gender wage gap is that employers treat women differently. About three-quarters of Democratic women (76%) say this, compared with 59% of Democratic men. And while 43% of Republican women say unequal treatment by employers is a major reason for the gender wage gap, just 18% of GOP men share that view.

Pressures facing working women and men

Family caregiving responsibilities bring different pressures for working women and men, and research has shown that being a mother can reduce women’s earnings , while fatherhood can increase men’s earnings .

Employed women and men are about equally likely to say they feel a great deal of pressure to support their family financially and to be successful in their jobs and careers, according to the Center’s October survey. But women, and particularly working mothers, are more likely than men to say they feel a great deal of pressure to focus on responsibilities at home.

About half of employed women (48%) report feeling a great deal of pressure to focus on their responsibilities at home, compared with 35% of employed men. Among working mothers with children younger than 18 in the household, two-thirds (67%) say the same, compared with 45% of working dads.

When it comes to supporting their family financially, similar shares of working moms and dads (57% vs. 62%) report they feel a great deal of pressure, but this is driven mainly by the large share of unmarried working mothers who say they feel a great deal of pressure in this regard (77%). Among those who are married, working dads are far more likely than working moms (60% vs. 43%) to say they feel a great deal of pressure to support their family financially. (There were not enough unmarried working fathers in the sample to analyze separately.)

About four-in-ten working parents say they feel a great deal of pressure to be successful at their job or career. These findings don’t differ by gender.

Gender differences in job roles, aspirations

Overall, a quarter of employed U.S. adults say they are currently the boss or one of the top managers where they work, according to the Center’s survey. Another 33% say they are not currently the boss but would like to be in the future, while 41% are not and do not aspire to be the boss or one of the top managers.

Men are more likely than women to be a boss or a top manager where they work (28% vs. 21%). This is especially the case among employed fathers, 35% of whom say they are the boss or one of the top managers where they work. (The varying attitudes between fathers and men without children at least partly reflect differences in marital status and educational attainment between the two groups.)

In addition to being less likely than men to say they are currently the boss or a top manager at work, women are also more likely to say they wouldn’t want to be in this type of position in the future. More than four-in-ten employed women (46%) say this, compared with 37% of men. Similar shares of men (35%) and women (31%) say they are not currently the boss but would like to be one day. These patterns are similar among parents.

Note: This is an update of a post originally published on March 22, 2019. Anna Brown and former Pew Research Center writer/editor Amanda Barroso contributed to an earlier version of this analysis. Here are the questions used in this analysis, along with responses, and its methodology .

What is the gender wage gap in your metropolitan area? Find out with our pay gap calculator

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Carolina Aragão is a research associate focusing on social and demographic trends at Pew Research Center

Women have gained ground in the nation’s highest-paying occupations, but still lag behind men

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Home » za » questions » Economic systems are the means Assignment Answers

QUESTION ONE [20] “Economic systems are the means by which countries and governments organize, allocate and distribute goods, services and resources.” In terms of the above statement, compare and contrast the different economic systems. Include in your answer examples of contemporary economic systems. QUESTION TWO [20] For each of the following independent events, explain with the aid of a diagram, the effect on the equilibrium price and quantity of MP3 players (such as the iPod) if:

The price of a personal computer falls (10) The price of an MP3 download rises (10)

QUESTION THREE [30] Provide the elasticity coefficients for the following categories of price elasticity of demand. Include in your answer, using diagrams to motivate your answer, an explanation of its implication for total revenue for a business. Elastic demand (10) Inelastic demand (10) Unit elastic demand (10)

Answers to Above Questions on Economics

Answer 1: The role of the economic system is crucial in which governments organise, allocate and distribute goods and services and perform the management of resources. Economic systems are also of different types and the three important points include capitalism, socialism and mixed economies.

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The global supply of equities is shrinking – here's what you need to know

Fewer companies are choosing to list their shares on global stock exchanges. Image:  REUTERS/Staff

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Emma Charlton

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Blockchain is affecting economies, industries and global issues

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:.

• Fewer companies are choosing to list their shares on global stock exchanges.
• There’s been a $120 billion reduction in public equities this year, a third consecutive year of decline.
• One of the factors is the clouded economic outlook, which the latest World Economic Forum survey of chief economists says is being compounded by geopolitical risks.

Investing in shares can be a rewarding way to build wealth over time.

Anyone who is fortunate enough to have a bit of spare cash can consult the financial pages, pick specific companies to invest in and buy shares in certain companies with the hope that their value will increase. It’s an enticing way to generate returns that outpace inflation.

But that dynamic has changed in the past few decades, with fewer companies choosing to list their shares on stock exchanges, and make them available to a wide audience. This shift means savers have fewer options to choose from, and risk losing out on opportunities to invest in some of the world’s most innovative and fastest growing companies.

Have you read?

These charts show which businesses are driving the eu economy, how to increase europe’s competitiveness in the new global economy, the world is drowning in data. why don't we trade it like on a stock exchange.

Why is the supply of public equity shrinking?

There’s been a net reduction of $120 billion in public equities this year, according to JPMorgan analysts, a figure that dwarfs last year's $40bn decrease and marks the third consecutive year of decline.

JPMorgan's data suggests persistent uncertainty among companies worldwide. Share buybacks – when companies repurchase their own shares from shareholders in the market – have stayed stable, while share offerings have fallen, meaning there are fewer shares available overall.

The World Economic Forum’s Centre for the Fourth Industrial Revolution Network has built a global community of central banks, international organizations and leading blockchain experts to identify and leverage innovations in distributed ledger technologies (DLT) that could help usher in a new age for the global banking system.

We are now helping central banks build, pilot and scale innovative policy frameworks for guiding the implementation of DLT, with a focus on central bank digital currencies (CBDCs) . DLT has widespread implications for the financial and monetary systems of tomorrow, but decisions about its use require input from multiple sectors in order to realize the technology’s full potential.

“Over the next four years, we should expect to see many central banks decide whether they will use blockchain and distributed ledger technologies to improve their processes and economic welfare. Given the systemic importance of central bank processes, and the relative freshness of blockchain technology, banks must carefully consider all known and unknown risks to implementation.”

Our Central Banks in the Age of Blockchain community is an initiative of the Platform for Shaping the Future of Technology Governance: Blockchain and Digital Assets.

Read more about our impact , and learn how you can join this first-of-its-kind initiative.

Less incentive to list shares on an exchange

There’s also a global trend of a decline in the number of companies listing on stock exchanges, according to Duncan Lamont, Head of Strategic Research at Schroders.

There has been a drop of nearly 75% of companies listed on the main market of the London Stock Exchange between the 1960s and the end of 2022, he says. Similar data for public companies in Germany shows a drop of more than 40% since 2007, and in the US, there’s been around a 40% drop since 1996.

London’s stock market has lost 25% of its companies in the past decade, according to Bloomberg, citing data from the London Stock Exchange that includes the main and junior markets.

One of the factors is that there’s a much larger pool of alternatives for company executives to turn to when they need money.

The Alternative Investments market, that includes hedge funds, real estate, digital assets, private credit and private equity, grew to $26.1 trillion in the first quarter of 2023 , up from $25.5 trillion at the end of 2022, according to JPMorgan's Nikolaos Panigirtzoglou.

And that means there’s more for company managers to weigh when deciding where to look for investment. Listing on the stock market comes with both positive and negative factors.

Global uncertainty makes executives cautious

Global economic prospects remain subdued and fraught with uncertainty, according to the latest World Economic Forum survey of chief economists . Geopolitical risks are compounding that uncertainty as fragmentation clouds the outlook, the report said.

Almost seven out of ten (69%) of chief economists are expecting the pace of geoeconomic fragmentation to accelerate this year.

“Uncertainty that dominated the outlook over the last year continues to cloud near-term economic developments,” the Forum report said. “56% of chief economists expect the global economy to weaken over the next year, but another 43% foresee unchanged or stronger conditions.”

It seems that the factors underpinning the shrinking availability of stocks are likely to persist.

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World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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North Macedonia votes for president in test before parliamentary poll

Voters in North Macedonia went to the polls on Wednesday to elect the president in a litmus test for a parliamentary election next month in which a nationalist opposition is seeking to unseat the ruling centre-left Social Democrats.