inflation topics for research paper

122 Inflation Essay Topic Ideas & Examples

🏆 best inflation topic ideas & essay examples, 👍 good essay topics on inflation, ⭐ simple & easy inflation essay titles, 💡 interesting topics to write about inflation.

• Increasing Inflation Impact on Individuals In simpler terms, inflation is the rise in the cost of living due to an exaggerated increase in commodity prices. This is because the rate of savings will be lower than the inflation resulting in […]
• Inflation and Unemployment in the United States In the 21st century, there are so many issues in the economy of the United States. This is increasing the demand for skilled workers by the day as opposed to the unskilled.
• The Current Impact of Inflation and Unemployment on Germany’s Political/Economic System It is notable to recognize the fact that the rate of savings in the nation is quite high causing a dip in the rate of inflation.
• The Price Deviations and Inflation Rates As seen from the table, the price deviations and inflation rates vary significantly depending on the item, season, and any global events that affect the economy.
• The Economic Disparity and Inflation It is essential to emphasize that the economic consequences of the pandemic are severe and are due in the main to inflation.
• US Economy: Navigating Debt, Inflation, and Recession Risks Today, the US is the world’s largest debtor and also the largest economy, market, and investor. Household debt can become a severe problem for the economy if exceeds their dead and accumulated wealth.
• Unemployment Rate: Impact on GDP and Inflation In such a way, the scenario shows it is vital to preserve the balance and avoid decisions focusing on only one aspect of the economy.
• The Inflation Dynamics in the Canadian Context According to the report, the economy only functions well when inflation is stable and predictable and is in an unhealthy state otherwise.inflation has been stable in the country over the last 25 years because of […]
• “Expected and Realized Inflation…” by Binder & Kamdar At the same time, the key focus of adaptive expectations is on the past rates of realized inflation and the factors that caused it.
• Inflation at the International Monetary Fund Anchoring inflation expectations, which is a condition in which inflation is regarded near the Central Bank target and typically matches what consumers anticipate, is one of the other possible measures. The pandemic appears to be […]
• How the Federal Reserve Controls Inflation According to the author of the article, the crisis became the impetus for developing new strategies for controlling the level of inflation.
• Fiscal Policy and Inflation in Canada According to the report, in order to protect the country from the long-lasting consequences of the COVID-19 pandemic and the recently emerged effects of the Russian-Ukraine war, Canadian policy-makers implemented fiscal policies, but their efficacy […]
• Walmart Has Been Negatively Impacted by Inflation The employment issues caused by the pandemic and increased prices for goods handling forced the company to consider the option of automation for business processes.
• “Inflation Hits the Fastest Pace Since 1981, at 8.5% Through March” by Koeze Further on, the predictions reveal that the inflation rate is expected to stabilize due to a decrease in the price of used cars and apparel.
• Inflation Rates and the Value of the Dollar Projected Social Security benefits at the retirement age of 65 years are 48,580 The current age is 25 years Retirement age is 65 years =40 years The annual inflation rate is at 3% Utilizing the […]
• Inflation and High-Interest Rates When a company borrows in a country with higher interest rates, the risk of inflation and currency depreciation grows, but the debt of this company is the same.
• Inflation’s Impact on Fixed Income By taking a diversified approach to fixed income investing, investors can better manage the risks associated with interest rates as well as inflation and increase the yield in their bond portfolios.
• How Economic Crises Affect Inflation Beliefs A feature of the article is the study by the authors of the consequences of inflationary crises and comparison with pre-existing crises to calculate the level of the crisis as a whole.
• Inflation: What Is It and Inflation in the USA Inflation is an increase in the general price level of goods, works, and services of the country’s population and businesses or an extended period. This kind of inflation is considered the best because it occurs […]
• Inflation Crisis in China for Financial Managers As a financial manager running my company, the rise in prices of commodities will decrease the purchasing power of the foreign currency used by investors and potential customers across the globe.
• Unemployment and Inflation Relation However, the level of unemployment and its prevailing types can differ significantly depending on the state of the economies of countries and the policies they use to combat unemployment.
• Unanticipated and Participated Inflation The first inflation outcome refers to income recipients hurt by inflation as there is a forcible price level increase that does not coincide with their income increase proportionally.
• Interest Rate and Inflation Impact on Exchange Rate The second observation point to be made pertains to the differences in the exchange rate of NZDUSD among the two viable.
• Inflation and Deflation Effects on the US and Saudi Stock Markets Inflation is traditionally defined as a consistent rise in the price rates within a specific industry or in the entire economy of the state, which is triggered by a rapid increase in demand: “Inflation is […]
• Treasury Inflation-Protected Security Refers This ensures that the real rate of interest is determined beforehand, and it adjusts automatically to the increase in the inflation rate.
• Significance of Inflation to Corporate Finance The argument goes on that with elevated inflation rates, there is always a chance to cut down on interest rates as compared to instances when the inflation rates are low and interest rates need to […]
• UAE and GCC Economic Analysis: Inflation and Unemployment This is explained by the fact that UAE is less dependent on oil trade, hence, the inflation and unemployment rate in the UAE is lower in comparison with the countries of GCC.
• Government Spending Stimulation in the Fight Against Inflation The equilibrium point is a point where the value of is money adjusted thereby creating an equilibrium in the quantity of money supplied and that of the quantity of money demanded.
• How the Inflation Gauge Was Faulty in the Past In other words, the goal of the CPI, when prices change, is to measure the percentage change if the spending by the consumers to be as well off as they were before.
• Inflation in the US Business Industry Inflation can be measured in the following ways; Monetary inflation; caused by increase in the increase in the amount of money in circulation in an economy.
• How Should Monetary Authorities React to Higher Inflation Therefore, the best alternative for monetary authorities to react to higher inflation is to reduce its regulatory influence in private enterprises and banks and limit the amount of money supply in the country. Therefore, the […]
• Gasoline Prices, Rates of Unemployment, Inflation, and Economic Growth The data which has been queried from the database are related to gasoline prices in California, the unemployment rate in the US, the inflation rate in the US, and Real GDP.
• Federal Reserve System: Inflation The article ‘Inflation and the Federal Reserve’ by Richard Cook; this source can be used to describe the central threat of inflation and identify the principal steps to be developed by central banks, government, and […]
• The US and the Philippines: Unemployment and Inflation In cyclical terms, this rising inflation is actually the product and not the cause of these record-high oil prices and the idea that the U.S.had failed to think of the above-discussed alternatives to the energy […]
• Interest Rate and Inflation in Netherlands As interest rate rises, demand for debt falls as cost of capital will increase and growth rate declined. As, more funds is shifting to Market B, central bank may raise the bank rate to stabilise […]
• Monetary Policy in an Economy: Inflation Inflation can be defined as a persistent rise in the general level of prices or alternatively, a persistent fall in the value of money.
• Dollarization the Main Tool to Reduce Inflation In general, wouldollarization’ means the substitution of hard currency for the domestic currency as the medium of exchange, and above all as the store of value, in a large part of the domestic economy, so […]
• Inflation Targeting in Emerging Countries Inflation Targeting is the public announcement of numerical targets for inflation for the year. Some emerging market countries that engage in inflation targeting have gone too far in the limitation of exchange rate flexibility, with […]
• Future Inflation and Growth Figures The increase in real GDP in the first half of 2007 was the same as that in the second half of 2006: at an annual rate of 2.25%.
• “Inflation Rise Hits US Consumers” BBC Article The main focus of the article’s concern is the inflation rise that US economics experiences now and the impact it has on US consumer spending.
• Inflation Dynamics: Mistakes in the Forecaster’s Behavior In this case, the authors of the article pay attention to the evaluation of the Phillips curve and understanding its advantages and drawbacks.
• Inflation Effect on Japan’s and Mexico’s Economies Thus, the study aimed to establish the influence of inflation and FDI on the GDP of a developed country, developing country, and the world.
• Inflation and Deflation and Their Outcomes That is the money in the hands of the consumers is more causing an increase in the aggregate demand. On the other side, the lender of the money loses some value of the money given […]
• Saudi Arabia and Inflation: Past, Present, Future What is the role of the Saudi Central Bank about inflation? What is the historical inflation trend in Saudi?
• Inflation Expectations: Households and Forecasters The New Keynesian formula that the authors of the paper were trying to create, in its turn was supposed to provide justification for the lack of forecast efficacy in determining the changes in inflation rates, […]
• Inflation Targeting in Emerging Economies Debates supporting the concept of inflation targeting are premised on the idea that recession remains as a greater challenge relative to the state of high inflation. The basis of inflation targeting is always to monitor […]
• The Federal Reserve and the Inflation Problem Louis in 2005, it was noted that the economic hero of the inflationary decades was the then chairman of the Fed, Paul Volcker.
• Asset Bubbles and Policy Response: A Historical View In the 1990s and the early years of the 21st century, the federal reserve policy makers opted to adopt the mop-up-after strategy-policy of letting the bubbles burst and then mopping up there after.
• Inflation Tradeoffs and the Phillips Curve In the findings, Lucas concluded that the there is a direct relationship and variance in the tradeoff between full employment and inflation rate at a particular level of input in the countries studied.
• Unemployment and Inflation Issues In most cases, if one is suffering structural unemployment, it is as a result of improvement in a certain area, or a change in the way things are done.
• Fluctuations in Inflation and Employment Debate surrounded what is termed the multiplier effect: are they higher for tax cuts or government spending, the differences in multiplier effect from different tax cuts, Incentive impact from tax cuts.
• Inflation in the 1970s In such a case, the reduced injections into the circular flow of the economy trim down the demand, which reduces inflation, and the general growth of the economy reduces significantly.
• Inflation Causes: Structuralism and Monetarism One of the features of this kind of inflation is a rapid rise in the price level with the currency loosing its value.
• The Relationship Between Money Supply and Inflation It is evidenced that changing the money supply through the central banks leads to a control of the inflationary situations in the same economy.
• The Effects of Inflation Targeting In theory inflation targeting is straightforward: the impending rate of inflation is predicted by the central bank, later on it is juxtaposed with the target rates which the government considers as appropriate for the economy […]
• The Euro Zone’s Rising Inflation and Unemployment Rate However, the euro zone found itself in a predicament from late 2009 after the economic downturns that faced some countries in the euro zone.
• Inflation Tax – Printing More Money to Cover the War Expenses The subsequent encroachment of inflation diminishes the value of money hence even if people had more money, the value of their cash was meaningless, a phenomenon similar to tax collection, which reduces the total amount […]
• Economic Condition of Singapore: Inflation Hits 5.2% in March Some of the effects of high inflation rate that has been felt in the economy are the increase of the housing prices, and cost of fuel increased by approximately 5%, thereby increasing the cost of […]
• Inflation Is Here to Stay, as Prices Will Always Go Up Monetary policy refers to the actions pursued by the central bank of a country to regulate the amount of money supply in the economy.
• Inflation in the United Kingdom According to the Bank of England, inflation occurs when the demand exceeds the ability by the economy’s capacity to produce goods and services.
• Effect of a Permanent Increase in Oil Price on Inflation and Output During the same year, the alterations in the price of oil were activated by a change in the supply of the same commodity in the market place.
• Consumer Price Index: Measuring Inflation In this case, the volumes of money being circulated exceeds the supply of goods and services in the same market thus leading to an upward adjustment of prices in order to absorb the extra monies […]
• The Cause of China’s Inflation The supply is affected by the increase of prices of food in the global market, whereby, the Chinese government finds it difficult to satisfy the food demand of the increasing population of the Chinese population.
• China’s Economic Growth and Inflation On the road to becoming the second largest economy, China has experienced growth rates of about 10% in the last 30 years making it to top the list of the fastest growing economies.
• Evaluate Government Policies to Reduce the Rate of Inflation The rate of inflation is the adjustment in the index of price in a single year to a new one expressed in percentages.
• Problem of China’s Inflation With the increase in oil prices, energy costs have increased, and this has resulted into an increase in the prices of products manufactured in the industries. In 2009 the government made a policy to increase […]
• Inflation in Saudi Arabia This paper, using the quarterly data from 1980 to 2010, examines the causes behind the inflation in Saudi, its effects, and the effectiveness of the counter-strategies and policies the Saudi government has put in place […]
• Inflation Rates in Sweden The recession of the early 1990s was largely responsible for the drop in inflation rates. As per the theoretical model of money supply and inflation, increases in money supply will lead to inflationary pressures.
• China Currency Policy and Inflation The sphere of inflation in China relates to the consumer price index which has recorded a rising orientation in the near past.
• Current News of Economics: The Global Inflation Inflation has affected the total demand for goods and services in the economy, thus exceeding the supply. This means that you would have to pay more for the same amount of goods and services you […]
• Analysis of Unemployment and Inflation in the United States This was at the height of the recession that continues to grapple the country with major negative implications in the economy.
• GDP, Unemployment, Inflation, and Economic Growth
• Absolute and Relative Anti-Inflation Reputation: Evidence From the Bond Markets
• World Inflation and Monetary Accommodation in Eight Countries
• Can Demography Improve Inflation Forecasts? The Case of Sweden
• Accounting for Post-Crisis Inflation and Employment: A Retro Analysis
• Unravelling India’s Inflation and Policy Puzzles
• Inflation and Financial Market Performance: What Have We Learned in the Past Ten Years?
• Administered Inflation and Business Pricing: Another Look
• The Historical Relationship Between Inflation and Political Rebellion: What It Might Teach Us About Neoliberalism
• America’s Only Peacetime Inflation: The 1970s
• Sectoral Inflation and the Phillips Curve: What Has Changed Since the Great Recession?
• Analyzing the Relationship Between Inflation Rate and Per Capita GDP Growth
• Banks, Lies, and Bricks: The Determinants of Home Value Inflation in Spain During the Housing Boom
• Capacity Utilization and Unemployment Rates: Are They Complements or Substitutes?
• Fast vs. Gradual Policies to Control Inflation
• Bond Market Inflation Expectations in Industrial Countries: Historical Comparisons
• Inflation and Monetary Velocity in Latin America
• What Drives the Relationship Between Inflation and Price Dispersion: Market Power vs. Price Rigidity
• How Much Did Speculation Contribute to the Recent Food Price Inflation?
• MAPI: Model for Analysis and Projection of Inflation in France
• Budget Deficit, Inflation, and Debt Sustainability: Evidence From Turkey
• Monetarism: Printing Money to Curb Inflation
• Capacity Constraints, Inflation, and the Transmission Mechanism: Forward-Looking vs. Myopic Policy Rules
• When Did Inflation Expectations in the Euro Area De-Anchor?
• Capturing the Link Between M3 Growth and Inflation in the Euro Zone
• Implementing Monetary Cooperation Through Inflation Targeting
• German Great Inflation: Summary & Analysis
• The Maastricht Inflation Criterion: On the Effect of the European Union Expansion
• What Unemployment Rates Tell Us About the Future Inflation
• Applying Foreign Exchange Interventions as an Additional Instrument Under Inflation Targeting: The Case of Ukraine
• China’s Economic Slowdown and International Inflation Dynamics
• The Impact of Inflation Targeting on the Real Economy of Developing and Emerging Countries
• Effects of Inflation on Business: The Good and the Bad
• U.S. Inflation Dynamics: What Drives Them Over Different Frequencies
• Structural Inflation and the 1994 ‘Monetary’ Crisis in China
• Macroeconomic Aggregate Model for Analysis of Inflation and Stabilization of the Russian Economy
• Cyclical vs. Acyclical Inflation: A Deeper Dive
• The Inflation-Output Nexus: Empirical Evidence From India, Brazil, and South Africa
• Forecasting Inflation Using Constant Gain Least Squares
• Stopping Hyperinflation: Lessons From the German Inflation Experience of the 1920s
• Modeling and Forecasting Inflation in Japan
• Globalization and Inflation Dynamics: The Impact of Increased Competition
• The Relationship Between Inflation and Economic Growth: A Multi-Country Empirical Analysis
• How Does Monetary Policy Influence Inflation and Employment?
• Assessing the Gap Between Observed and Perceived Inflation in the Euro Area
• Unanticipated Inflation, Devaluation, and Output in Latin America
• Inflation and Economic Growth Nexus in BRICS: Evidence From ARDL Bound Testing Approach
• Bootstrapping Covariate Unit Root Tests: An Application to Inflation Rates
• Fiscal Dominance and Inflation Targeting: Lessons From Brazil
• Inflation and the Gig Economy: E-Tailing and Self-Employment Rise in Disrupting the Phillips Curve
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100 Inflation Essay Topic Ideas & Examples

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Inflation is a key economic indicator that affects the purchasing power of consumers and the overall health of an economy. As such, it is a popular topic for essays and research papers in economics, finance, and related fields. If you are looking for inspiration for your next inflation essay, look no further. Here are 100 inflation essay topic ideas and examples to help you get started:

• The causes and effects of inflation
• The relationship between inflation and unemployment
• The impact of inflation on interest rates
• The role of the Federal Reserve in controlling inflation
• The differences between demand-pull and cost-push inflation
• The effects of hyperinflation on a country's economy
• The impact of inflation on fixed income earners
• The relationship between inflation and the stock market
• The effects of inflation on real estate prices
• The impact of inflation on international trade
• The role of inflation expectations in shaping economic behavior
• The effects of inflation on poverty and income inequality
• The impact of inflation on retirement savings
• The relationship between inflation and economic growth
• The effects of inflation on consumer spending
• The role of inflation in shaping monetary policy decisions
• The impact of inflation on business investment
• The effects of inflation on government finances
• The relationship between inflation and currency exchange rates
• The impact of inflation on the cost of living
• The effects of inflation on social welfare programs
• The role of inflation in causing economic recessions
• The impact of inflation on international competitiveness
• The effects of inflation on the environment
• The relationship between inflation and financial stability
• The role of inflation in shaping government policy decisions
• The impact of inflation on entrepreneurship and innovation
• The effects of inflation on consumer confidence
• The relationship between inflation and technological advancement
• The impact of inflation on the healthcare industry
• The effects of inflation on the education sector
• The role of inflation in shaping consumer behavior
• The impact of inflation on the agricultural sector
• The relationship between inflation and social mobility
• The effects of inflation on urban development
• The role of inflation in shaping labor market dynamics
• The impact of inflation on small businesses
• The effects of inflation on the tourism industry
• The relationship between inflation and government regulations
• The impact of inflation on infrastructure development
• The role of inflation in shaping energy policy
• The effects of inflation on the manufacturing sector
• The relationship between inflation and the digital economy
• The impact of inflation on the gig economy
• The effects of inflation on the sharing economy
• The role of inflation in shaping consumer preferences
• The impact of inflation on the automotive industry
• The relationship between inflation and the housing market
• The effects of inflation on the retail sector
• The impact of inflation on the hospitality industry
• The role of inflation in shaping supply chain dynamics
• The effects of inflation on the fashion industry
• The relationship between inflation and the art market
• The impact of inflation on the entertainment industry
• The effects of inflation on the music industry
• The role of inflation in shaping the sports industry
• The relationship between inflation and the gaming industry
• The impact of inflation on the film industry
• The effects of inflation on the publishing industry
• The role of inflation in shaping the food and beverage industry
• The impact of inflation on the beauty and personal care industry
• The effects of inflation on the health and wellness industry
• The relationship between inflation and the pharmaceutical industry
• The impact of inflation on the technology industry
• The effects of inflation on the telecommunications industry
• The role of inflation in shaping the media industry
• The relationship between inflation and the advertising industry
• The impact of inflation on the e-commerce industry
• The effects of inflation on the transportation industry
• The role of inflation in shaping the logistics industry
• The impact of inflation on the energy industry
• The effects of inflation on the renewable energy industry
• The relationship between inflation and the oil and gas industry
• The impact of inflation on the mining industry
• The effects of inflation on the construction industry
• The role of inflation in shaping the real estate industry
• The relationship between inflation and the property market
• The impact of inflation on the architecture and design industry
• The effects of inflation on the engineering industry
• The role of inflation in shaping the manufacturing industry
• The effects of inflation on the aerospace industry
• The relationship between inflation and the defense industry
• The impact of inflation on the security industry
• The effects of inflation on the law enforcement industry
• The role of inflation in shaping the healthcare industry
• The impact of inflation on the medical devices industry
• The effects of inflation on the biotechnology industry
• The role of inflation in shaping the life sciences industry
• The impact of inflation on the education industry
• The effects of inflation on the e-learning industry
• The relationship between inflation and the edtech industry
• The impact of inflation on the publishing industry
• The effects of inflation on the media and entertainment industry
• The role of inflation in shaping the sports and recreation industry
• The relationship between inflation and the leisure and travel industry
• The impact of inflation on the tourism and hospitality industry
• The effects of inflation on the food and beverage industry
• The role of inflation in shaping the retail and consumer goods industry

These are just a few examples of the many possible topics you could explore in an inflation essay. Whether you are interested in the macroeconomic implications of inflation or its effects on specific industries, there is no shortage of interesting and important questions to investigate. So pick a topic that interests you, do some research, and start writing!

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114 Inflation Essay Topics

🏆 best essay topics on inflation, ✍️ inflation essay topics for college, 👍 good inflation research topics & essay examples, 🎓 most interesting inflation research titles, ❓ essay questions on inflation.

• The Inflation Impact on Society
• Inflation: Causes, Problems, Impacts on Economy
• Malaysia’s Inflation
• A Political Cartoon About Canada’s Inflation by MacKay
• The Mechanisms of Inflation
• Income Inflation: Absorption Costing vs. Variable
• Inflation in the Real Estate Industry
• Inflation and Control Policies in the United Kingdom Inflation is a highly contentious issue. This is due to its economic implications. Inflation has the potential of crippling a country’s economy.
• The Problem of Inflation: Crucial Aspects Of primary importance is the recognition that inflation is not an unnatural or harmful mechanism for a country’s economy.
• Nominal Anchor: Monetary Targeting and Inflation This paper set out to explore various pros and cons of the three anchors commonly used by the central Banks namely monetary targeting, inflation targeting, and exchange rates targeting.
• How to Cure Inflation: Summary Inflation leads to increased prices. Although wages grow, the taxes increase. In the end, people do not have more money to spend on goods.
• Inflation in the UK The Bank of England raised its interest rates in response to the inflation increase and forecast inflation to reach a value just above two percent in two years.
• How Raising Interest Rates Helps Fight Inflation and High Prices There is a mutual relationship between business and government where the government regulates the environment in which business operates.
• Exploration of Price Increase and Inflation Over the Years The paper discusses how inflation affected prices in 2021 and previous periods of inflation. It helps scientists understand how important inflation is.
• The Impact of Taxation and Inflation in the U.S. Carl Szabo’s article “Democrats want you to keep paying more” from the RealClear Policy website discusses how the current government makes Americans pay more.
• Real vs. Inflationary Growth: What’s the Difference? Real growth and inflation are associated with the Gross Domestic Product (GDP). GDP is the total market worth of a nation’s products and services during a specific period.
• The Ethical Repercussions Inflation Has Had on Businesses This paper analyzes the ethical repercussions inflation has had on businesses and the impacts that inflation has had on consumers and other businesses.
• The Unemployment and Inflation Causes in Australia The change in the Australian 2021 indicator of unemployment is the representation of cyclical unemployment since it lasted less than a year.
• Grades Inflation and Educational Services Quality In the modern education system, the quality of educational services has become the most relevant topic. The rating system aims to improve the differentia of academic performance.
• Inflation, Oil Prices, and How the President May Influence Them Inflation and oil prices are actual modern themes, as they are directly connected with the incomes and wealth of most people.
• Inflation and Increase in Money Supply Even though the increase in the money supply might stimulate the economy, it is a dangerous strategy, and the Federal Reserve has to act with caution.
• Inflation and Consumer Price Indexes The paper provides an example of a country that has implemented hyperinflation and explains the impact of this economic policy on the economy.
• The US Federal Reserve on Employment and Inflation The paper analyzes the statement of Federal Reserve, how it affects the economy, why the general public criticizes it, and what the future looks like if strategic changes are made.
• Federal Reserve System, Inflation, and Wage-Price Spiral An important indicator that can cause a policy shift toward a more stringent monetary policy would be the wage-price spiral.
• The United States Inflation Rate Inflation is expressed primarily in the depreciation of money, which depreciates in relation to gold, commodities, and foreign currencies.
• Measuring Inflation: Article Analysis Fluctuating in a seemingly unpredictable way, inflation rates are shaped by a range of factors, one of which is the change in the cost of living.
• Measuring Inflation: Consumer Price Index The article examines the consumer price index as the main instrument for measuring inflation in the United States, analyzes its advantages and disadvantages.
• Inflation and Unemployment in Bavaria Considering the normal state of the economy and the existing level of employment close to full, the President of Bavaria is not recommended to pursue an expansionary fiscal policy.
• Balance of Payments, Inflation and Exchange Rate Balance of payments, inflation, and the exchange rate are the main driving forces of the UK economy, as well as in other countries.
• Inflation in India and China The growth of the Asian economies, more specifically China and India have allowed the examination of inflation in both countries.
• Zimbabwe Inflation Now Over 1 Million Percent In Zimbabwe, the national-average real wages are raised well above their historical trends, but faster inflation has reduced real wages back to trend levels.
• China Faces Inflation Pressure Inflation is essentially the rise in general price of goods and services over a period of time in economics. It is more commonly referred to as price inflation in now.
• Inflation in the United Kingdom’s Economy Global Positioning System is a system comprised of satellites capable of broadcasting certain signals used primarily for navigation in both the private and the military sectors.
• Mugabenomics as a Cause of Inflation in Zimbabwe The paper outlines the primary challenges of Zimbabwe economic system and provides a consistent account of inefficient economic strategies that disrupt the country’s well-being.
• Today’s Inflation and the Great Inflation of the 1970s
• Accelerating Inflation and the Distribution of Household Savings Incentives
• Perceived Inflation and Reality: Understanding the Difference
• Alternative Instruments for Hedging Inflation Risk in the Banking Industry
• Making Sense of Consumers’ Inflation Perceptions and Expectations
• Evaluating Inflation Targeting Based on the Distribution of Inflation and Inflation Volatility
• Global Inflation Dynamics: Regularities and Forecasts
• Contract Duration, Inflation Uncertainty, and the Welfare Effects of Inflation
• Advanced Economy Inflation: The Role of Global Factors
• Central Banks’ Inflation Forecasts: The Problem of Conditioning on Fixed Short-Term Interest Rates
• Who Is Suffering the Most From Rising Inflation?
• Conflict Inflation: Estimating the Contributions to Wage Inflation in Australia During the 1990s
• Adaptive Models and Heavy Tails With an Application to Inflation Forecasting
• The Main Strategies to Deal With Inflation in Business
• Does Inflation Harm Economic Growth?
• High Inflation and the Nominal Anchors of an Open Economy
• Euro Area Inflation: Aggregation Bias and Convergence
• Adopting Inflation Targeting: Practical Issues for Emerging Market Countries
• Causality Nexus Between Economic Growth, Inflation, and Innovation
• Analyzing Inflation: Monetary and Real Theories
• Deflating Inflation Expectations: The Implications of Inflation’s Simple Dynamics
• Forecasting Inflation: The Relevance of Higher Moments
• Inflation and Financial Market Performance: What Have We Learned in the Last Ten Years
• Commodity Prices and Inflation in the Middle East, North Africa, and Central Asia
• Analyzing the Connection Between Inflation and Unemployment
• Administered Prices, Inflation, and the Business Cycle
• Core Inflation and Inflation Targeting in a Developing Economy
• Baffling Inflation: Cost-push Inflation Theories in the Late 1950s United States
• America’s Historical Experience With Low Inflation
• Right Balance Between Growth and Inflation
• Admissible Monetary Aggregates and UK Inflation Targeting
• Estimating the Optimal Inflation Target From Trends in Relative Prices
• Causality Between Inflation and Inflation Uncertainty in South Africa
• Analyzing Factors Affecting U.S. Food Price Inflation
• Forecasting Inflation Using Economic Indicators: The Case of France
• Central Bank Independence and Inflation: Good News and Bad News
• Deflation and Inflation Trends in Japan
• Anticipated Inflation and Interest Rates in an Open Economy
• Financial Conditions and Density Forecasts for US Output and Inflation
• Arch and Structural Breaks in United States Inflation
• Has U.S. Inflation Really Become Harder to Forecast?
• Challenges for Adopting Inflation Targeting Regime in Egypt
• Demographic Transition and Inflation in Emerging Economies
• High Inflation: Resource Misallocations and Growth Effects
• Australian Wage and Price Inflation: 1971-1994
• Consumer Attitudes and the Epidemiology of Inflation Expectations
• Food Inflation and the Consumption Patterns of U.S. Households
• Inflation and Asset Returns in a Monetary Economy
• Discovering the Link Between Inflation Rates and Inflation Uncertainty
• Inflation and Deflationary Biases in Inflation Expectations
• What Are the Reasons for High and Persistent Inflation in the Country?
• How Does Inflation Affect Savings and Investment?
• How Is Inflation Used as a Measure of Economic Performance?
• What Is the Monetarist View of Inflation?
• Does Inflation Lead to a Rapid Increase in Unemployment?
• Whom Does Inflation Hurt the Most?
• What Measures of Inflation Are Used Today?
• Why Does Increased Interest Rate Not Increase Inflation?
• Why Does an Increase in the “Cost of Money” Not Mean an Increase in Inflation?
• What Methods Are Used to Control Inflation?
• What Happens to the Equilibrium Interest Rate When Inflation Is Expected to Decrease?
• Why Are Economists Concerned About Inflation?
• How Can Government Policies Be Used to Reduce Inflation in a Country?
• What Is Natural Rate of Inflation?
• How Can Cost-Push and Demand-Pull Inflation Be Caused by a Fall in the Exchange Rate?
• What Is the Condition of Low and Stable Inflation Called?
• How Does Inflation Affect Cash Flows?
• In Which Decade Was the Highest Rate of Inflation in the United States?
• Does Inflation Always Benefit Debtors and Hurt Creditors?
• Can the Introduction of a New Banknote Cause Inflation?
• How Does the Government Directly and Indirectly Create Inflation?
• What Are the Macroeconomic Effects of Inflation?
• How Does Unexpected Inflation Affect Creditors, Debtors, and Savers?
• Can Inflation Occur With Non-fiat Money?
• What Is the Effect of Inflation on Our Financial Assets?
• Why Does Inflation Harmful to the Economy?
• Could Inflation Be a Problem for Some Low- And Middle-Income Countries?
• What Does the Phillips Curve Show During Inflation?
• How Can an Inflation Tax Explain the Creation of Inflation by a Central Bank?
• What Type of Inflation Will Cause Stagflation?

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These essay examples and topics on Inflation were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on January 8, 2024 .

Inflation is the rate of increase in the prices of goods and services in an economy over a specified time period. Stable and moderate levels of inflation are a natural part of a healthy, expanding economy. Inflation that is too high, too low, or deflationary typically reflects imbalances in the economy that can cause longer-term economic instability and hardship for people, businesses, and communities.

As part of the Federal Reserve’s dual mandate to promote maximum employment and price stability, inflation is a major area of ongoing research at the SF Fed. This page features a collection of content on inflation, including topics such as goods inflation, services inflation, housing inflation, and inflation expectations.

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Essays on Inflation

Inflation essay topics and outline examples, essay title 1: understanding inflation: causes, effects, and economic policy responses.

Thesis Statement: This essay provides a comprehensive analysis of inflation, exploring its root causes, the economic and societal effects it generates, and the various policy measures employed by governments and central banks to manage and mitigate inflationary pressures.

• Introduction
• Defining Inflation: Concept and Measurement
• Causes of Inflation: Demand-Pull, Cost-Push, and Monetary Factors
• Effects of Inflation on Individuals, Businesses, and the Economy
• Inflationary Policies: Central Bank Actions and Government Interventions
• Case Studies: Historical Inflationary Periods and Their Consequences
• Challenges in Inflation Management: Balancing Growth and Price Stability

Essay Title 2: Inflation and Its Impact on Consumer Purchasing Power: A Closer Look at the Cost of Living

Thesis Statement: This essay focuses on the effects of inflation on consumer purchasing power, analyzing how rising prices affect the cost of living, household budgets, and the strategies individuals employ to cope with inflation-induced challenges.

• Inflation's Impact on Prices: Understanding the Cost of Living Index
• Consumer Behavior and Inflation: Adjustments in Spending Patterns
• Income Inequality and Inflation: Examining Disparities in Financial Resilience
• Financial Planning Strategies: Savings, Investments, and Inflation Hedges
• Government Interventions: Indexation, Wage Controls, and Social Programs
• The Global Perspective: Inflation in Different Economies and Regions

Essay Title 3: Hyperinflation and Economic Crises: Case Studies and Lessons from History

Thesis Statement: This essay explores hyperinflation as an extreme form of inflation, examines historical case studies of hyperinflationary crises, and draws lessons on the devastating economic and social consequences that result from unchecked inflationary pressures.

• Defining Hyperinflation: Thresholds and Characteristics
• Case Study 1: Weimar Republic (Germany) and the Hyperinflation of 1923
• Case Study 2: Zimbabwe's Hyperinflationary Collapse in the Late 2000s
• Impact on Society: Currency Devaluation, Poverty, and Social Unrest
• Responses and Recovery: Stabilizing Currencies and Rebuilding Economies
• Preventative Measures: Policies to Avoid Hyperinflationary Crises

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Inflation’s Last Half Mile: Higher for Longer?

• Randal J. Verbrugge

Will inflation quickly return to the FOMC’s target of 2 percent? I explore this question through the lens of the Verbrugge and Zaman (2023) model—the VZ model—a structural model whose forecasts are competitive with hard-to-beat forecasting models. The time it takes to get to the target depends on the persistence of inflation, and theory gives mixed signals about whether inflation persistence is currently high or low. The VZ model distinguishes between two sources of inflation persistence, extrinsic and intrinsic, and implies that inflation has high intrinsic persistence. If the extrinsic forces that have lately been pushing down inflation, notably, the resolution of supply chain issues, have run their course, then the last half mile could take several years.

The views authors express in Economic Commentary are theirs and not necessarily those of the Federal Reserve Bank of Cleveland or the Board of Governors of the Federal Reserve System. The series editor is Tasia Hane. This paper and its data are subject to revision; please visit clevelandfed.org  for updates.

Introduction

During 2023, US inflation fell rapidly, with four-quarter PCE inflation coming in at 2.7 percent, beating the January 2023 Blue Chip Economic Indicators (BCEI) Consensus 1 expectation of 3.2 percent. But in 2024:Q1, quarterly inflation readings moved up. Was this just a transitory blip? Will inflation resume its rapid downward progress? The majority view among forecasters, as measured by the May 2024 Blue Chip Financial Indicators Consensus, is that inflation will indeed continue to fall apace, leaving four-quarter PCE inflation near 2 percent by the middle of next year. There are several reasons this may happen. Given that new-tenant rent inflation has been subdued for the past year, and that new-tenant rents feed into all rents with about a year lag (Adams et al., 2024), housing services inflation appears poised to fall notably. Additionally, the inflation expectations of households and firms have come down a lot over the past year. 2 But there is debate about how fast inflation will fall going forward. In particular, in 2024:Q1, quarterly PCE inflation surged to 3.4 percent, and core PCE inflation came in even higher, at 3.7 percent. Shelter inflation continued to remain quite elevated in the April CPI report; and given this report, the Cleveland Fed’s nowcasting model (May 15 reading) currently predicts that quarterly PCE inflation will be near 3 percent in 2024:Q2.

This Economic Commentary thus asks if getting inflation from where it is now down to the Federal Open Market Committee’s 2 percent target will take notably longer than most forecasters expect. To answer this question, I first review what economic theory has to say about this question. As theory does not lead to a definitive answer, I next use a recent empirical model in Verbrugge and Zaman (2023) (the VZ model) as a lens through which to view inflation dynamics. In particular, I use the model to distinguish between inflation’s intrinsic dynamics, that is, how inflation generally behaves when it is not being driven by big shocks, and its extrinsic or inherited dynamics, or what happens to inflation as a result of its being hit by big shocks. 3 I also compare the VZ model’s predictions to those of two other models that are considered in the forecasting literature to be “hard to beat.” Neither of these other models distinguishes between intrinsic and extrinsic dynamics.

The analysis suggests that the intrinsic dynamics of inflation are very persistent. It also suggests that, going forward, inflation will be mainly governed by its intrinsic dynamics. Hence, according to this analysis, inflation could take several years to return to its target. 4

What Does Theory Have to Say about Persistence of Inflation?

The inflation literature identifies two categories of persistence: intrinsic persistence and inherited or extrinsic persistence (see, for example, Fuhrer, 2006, or Kurozumi and Van Zandweghe, 2023). Extrinsic persistence arises from persistence in the “external” driving forces of inflation, such as production costs or an overheated labor market. Intrinsic persistence arises from the internal dynamics of price-setting and wage-setting decisions and from the way that inflation expectations are formed. High intrinsic persistence can arise from such sources as indexation or other contract assumptions (Fuhrer and Moore, 1995; Christiano et al., 2005), strategic complementarities in price setting, wage-price spirals, or backward-looking or rule-of-thumb price setters. 5 In addition, “learning” or other “imperfect information” models of expectation formation can lead to high intrinsic persistence (Erceg and Levin, 2003; Branch and Evans, 2006); some salient information imperfections are discussed below. Inflation persistence is likely to vary over time.

A generic equation describing the dynamics of inflation helps us understand and distinguish intrinsic from extrinsic persistence. In the following equation, inflation today depends upon inflation expectations, inflation’s own lags, and exogenous driving forces:

π t = a + c π t + s e x p e c t + ∑ j = 1 n b j π t − j + ∑ k = 1 m d k X k , t + e t

where  π t stands for inflation at time t , a is a constant, c is a parameter value, there are n parameter values denoted by b j , there are m parameter values denoted by d k , π t + s e x p e c t stands for the expectation of inflation s periods in the future, there are m driving forces of inflation, denoted by X k,t , and there is a random shock e t . If the sum of the b j coefficients is large, or c is both large and π t + s e x p e c t is typically close to last period’s inflation reading, then inflation will have a lot of intrinsic persistence. Inflation will have a lot of extrinsic persistence over a given period if one or more of the X k,t is then experiencing a very persistent fluctuation and its corresponding coefficient d k is large. In like manner, inflation can appear to be non persistent over a period of time if it is being driven during that period by a powerful non persistent driving force X k,t .

This brings us to today. Many economists believe that the recent sharp increase and decrease in inflation was caused by X factors: the recent runup being caused by a sharp increase in supply constraints combined with a demand shock (Sheremirov, 2022; di Giovanni et al., 2023) and perhaps abetted by an overheated labor market, and the recent decrease largely driven by sharp improvements in supply conditions (possibly abetted by the labor market cooling that has happened so far). It is tempting to think that inflation will keep moving downward at its recent pace, but given that both the Federal Reserve Bank of New York’s global supply chain pressure index and the PPI for core intermediate goods have moved from deeply negative readings to near zero or positive readings of late, it seems likely that the downward pressure from these aforementioned sources is nearly over. Absent such exogenous favorable downward forces, inflation’s movements in the near future will be largely driven by its intrinsic persistence.

To understand inflation dynamics, it helps to use a good measure of inflation. Measuring inflation is not easy, and official measures are noisy. For the rest of this Economic Commentary , I will generally focus on trimmed-mean PCE inflation, which removes noise from headline PCE inflation (to better focus on the signal) in a statistically sound manner. Historically, it has been a more accurate indicator of the medium-term trend in inflation than has core PCE inflation (Mertens, 2016; Dolmas and Koenig, 2019; Verbrugge, 2022). For instance, when core PCE inflation and trimmed-mean PCE inflation diverge, it is core PCE inflation that adjusts to eliminate the gap (Verbrugge, 2022).

I first look at economic theory for guidance as to whether intrinsic persistence of inflation is high or low at the present moment. This theory is divided. There are two sorts of theoretical arguments suggesting that right now inflation may have low persistence. First, some theories suggest that the responsiveness of inflation to changes in labor market tightness (in other words, the Phillips curve) may be particularly strong right now; if so, continued small reductions in labor market tightness may result in large inflation decreases. Higher inflation seems to strengthen the Phillips curve (Hadjini, 2023; Dedola et al., 2023), and the Phillips curve has been found to be nonlinear in labor market tightness: inflation responds more to overheating than to slack (Filardo, 1998; Ashley and Verbrugge, 2023; Gitti, 2024), and the labor market may still be overheated today. Second, deviations of inflation expectations from the FOMC’s 2 percent target may not be persistent today or even strongly anchored at that target. Why? In many imperfect information models, inattention leads inflation expectations to be more persistent (Afrouzi, 2023; Hubert and Ricco, 2018). Further, there is more attention to inflation when inflation is high (Weber et al., 2023; Korenok et al., 2023; Braitsch and Mitchell, 2023). As such, recently higher inflation may be increasing attention to inflation, hence reducing its persistence. Relatedly, when inflation is high, more households are exposed to information on monetary policy (Knotek et al., 2024), a situation which could lead them to expect a rapid return of inflation to target.

But there are also some theoretical reasons to think that today inflation may have high intrinsic persistence. Since inflation has come down so much, attention to inflation may have already waned (Weber et al., 2023; Korenok, Munro, and Chen, 2023), and the Phillips curve may have already weakened. 6 On the flip side of this argument, increased attention to inflation has a wide range of effects. One pertinent theoretical mechanism is that high inflation makes workers more likely to realize that their wage growth has not kept pace with inflation, and thus they are more likely to demand wage increases in response to recent inflation; this series of realization and demand can ignite wage-price spirals (Borio et al., 2023). The nonlinearity in the Phillips curve cuts both ways: since labor market tightness (measured, for instance, by the vacancy–unemployment ratio) has eased so much already, there may be little remaining benefit to further easing in the labor market. Other imperfect information models of inflation suggest that inflation is likely to be more persistent today (Pfauti, 2023). 7 Finally, downward rigidity in prices or wages can reduce the speed at which inflation subsides. That there are competing theories indicates that theory alone does not give us a clear answer to our question.

Empirical Evidence

As noted above, inflation in 2024:Q1 picked up. Looking at the components of core PCE, over the past three months, the deceleration in housing services prices has stalled near 6 percent; core services excluding housing inflation has picked up, averaging over 5 percent; and core goods inflation, which had previously been strongly negative, has averaged +1.3 percent. Trimmed-mean PCE inflation in 2024:Q1 was 3.6 percent. What about other inflation indicators? Wage inflation has also remained well above prepandemic levels, and above levels that some economists view as consistent with 2 percent inflation. So far in 2024, according to the May 2024 report of the National Federation of Independent Businesses survey, the proportion of respondents who reported raising prices, at 24, is nearly double the rate in 2019; likewise, the proportion of respondents planning to raise prices, at 31, is 9 percentage points higher than its average in 2019.

Persistence in inflation may be time-varying, and there is some evidence suggesting that inflation may be quite persistent today. Estimated persistence in inflation has risen of late (Almazura and Sbordone, 2023; Kiley, 2023). 8 Quantile regressions indicate that higher inflation implies higher persistence (Ghysels et al., 2018; Mitchell and Zaman, 2023). In general, higher inflation kicks off persistent effects: Blanco et al. (2022) document that, worldwide, inflation tends to stay persistently high after inflation initially surges (see also Borio et al., 2023, and Pfauti, 2023).

I turn to the VZ model to study the historical evidence. Doing so highlights episodes during which inflation’s dynamics were dominated by its intrinsic persistence, which seems to be rather high. Then, in a simple exercise, I compare the intrinsic persistence of the VZ model to that of two other models. This comparison suggests that the VZ model’s intrinsic persistence, which lies between that of the other two models, seems to be appropriate. Finally, I provide forecasts from the three models going forward.

The VZ model is a four-equation model with a nonlinear Phillips curve. Its inflation variable is trimmed-mean PCE inflation, modeled in gap form as deviations from the Survey of Professional Forecasters 10-year PCE inflation forecast. Such inflation-gap modeling follows good practice in the inflation forecasting literature (Verbrugge and Zaman, 2024) and also serves to impose anchored long-run inflation expectations (in the sense that if the model is stationary, inflation must return to its expectation). The inflation equation has three extrinsic ( X ) drivers: an overheated labor market driver, a recessionary labor market driver, and a supply shocks driver. 9

The model is estimated on data from 1985–2019. Estimating the inflation equation allows me to determine the amount of “force,” by quarter, that has been exerted on trimmed-mean PCE inflation by these X drivers over the entire sample. If X O L M , t − 1 represents the overheated labor market term, and β ^ O L M is the estimated coefficient, then β ^ O L M X O L M , t − 1 represents the force exerted by the overheated labor market on inflation at time t . I add up this force to the forces associated with the other two X variables, rescale the resulting time series to provide visual clarity, and then plot this series in orange in Figure 1. This represents extrinsic force on inflation.

When extrinsic force is weak or small, then inflation is mostly determined by its intrinsic dynamics. I use an ad hoc rule of thumb to classify when extrinsic force is weak: when this force is less than one standard deviation in magnitude. In yellow dashed lines, I depict the one-standard-deviation bounds of the extrinsic force series. When the orange line lies between the yellow lines, extrinsic force is weak; and when it is outside the yellow lines, extrinsic force is strong. Thus, for example, between 1987:Q4 and 1990:Q3, extrinsic drivers were applying more than a one-standard-deviation amount of upward force on inflation; similarly, between 2008:Q4 and 2010:Q3, extrinsic drivers exerted strong downward force on inflation. Notice that over the recent period (2020:Q3–2023:Q3), extrinsic force was quite strong, but also notice that in 2023:Q4, extrinsic force returned to its normal, weak levels, suggesting that going forward, inflation will be governed by its intrinsic dynamics.

Figure 1 also plots, in light gray, the four-quarter trimmed-mean PCE inflation gap. I argue above that when extrinsic force is weak, inflation’s dynamics are governed by intrinsic persistence. There are five periods during which extrinsic force was weak for more than two quarters; inflation realizations corresponding to such periods are depicted in blue. If intrinsic persistence is generally low, then we should expect the inflation gap to move quickly to zero during these periods.

So how high is intrinsic persistence? The above decomposition of history into periods when inflation is facing high extrinsic force versus low extrinsic force suggests that intrinsic persistence in inflation is rather high indeed, and, moreover, inflation seems somewhat prone to “head fakes,” times when inflation moves one way but then reverses itself. Looking at things episode by episode, from 1985:Q1 to 1987:Q3, inflation showed no signs of moving towards its long-run expectation. From 1990:Q4 to 1994:Q3, inflation moved away from its expectations, and then it moved sideways. From 1996:Q1 to 1997:Q2, there was a head fake: inflation was moving up, then dropped markedly, and then rebounded. From 2002:Q4 to 2004:Q1, inflation moved further away from its long-run expectation (this episode was part of a head fake). Finally, 2011:Q4 to 2017:Q3, inflation essentially moved sideways. And more generally, over the post-Global Financial Crisis episode, inflation experienced a head fake: inflation rose sharply between 2010:Q2 and 2012:Q1 but then fell back notably by 2013:Q2. 10

All told, this exploration of the history of trimmed-mean PCE inflation suggests two things. First, historically, head fakes are not uncommon; second, again historically, intrinsic persistence appears to be very high.

Evidence from Comparing Forecasting Models

What do forecasting models tell us about the risks that inflation may remain higher for longer? Forecasting models differ on whether they condition upon or abstract from X factors. Each forecasting model has a different implied or inherent level of persistence of inflation that may be too high or too low. A model that abstracts from X factors will implicitly assume that the average historical persistence of the X -factor influence on inflation is actually part of inflation’s inherent persistence.

In this section, I look at some representative historical forecasts from three different models over recent history (2010–2019). The purpose of this comparison is not to provide a general test of these models’ forecasting ability, since standard forecast comparison tests of these models over various time periods are provided elsewhere (see, for example, Verbrugge and Zaman, 2023). Instead, these forecasts are intended to demonstrate visually each model’s implied level of intrinsic persistence in inflation, something which can help inform judgments about inflation prospects going forward.

The first two models are both univariate models that are considered in the forecast literature to be hard to beat: the Stock and Watson (2007) UCSV model and the Faust and Wright (2013) model (the FW model). The third model is the aforementioned VZ model. 11

I provide illustrative forecasts from each of the three models at two different points in this period: 2010:Q3 and 2013:Q1. 12 According to the VZ model decomposition above, at both points (and throughout their forecasts), X forces played little role, so at these points one can compare how each model matches the intrinsic persistence of inflation. As will be seen, using just two forecast points suffices to demonstrate the general behavior, and inherent persistence, of the three models. Figure 2 depicts the inflation realization in black. As can be seen, the UCSV forecasts, in gray, are flat, that is, close to simple random walk forecasts; this model features very high persistence that results in a very poor forecast in 2010:Q3 and a passable forecast in 2013:Q1. The FW model, in yellow, has much less persistence and projects very rapid returns to near 2 percent. In 2010:Q3, the FW forecast initially looks great—until inflation sinks back to 1.5 percent starting in early 2012. This forecast, in other words, completely misses the head fake. The 2013:Q1 FW forecast also returns to target too quickly. In short, the FW model seems to have too little persistence over this period. In contrast, the VZ model, in orange, does fairly well in capturing the dynamics of inflation at both forecast points, including not being fooled by the head fake. The tentative conclusion I draw from this exercise is that the UCSV model may have too much intrinsic persistence, the FW model seems to have too little intrinsic persistence, and the VZ model appears to have about the right amount of intrinsic persistence.

In Figure 3, I provide forecasts from these three models. Each model conditions on the 2024:Q1 inflation readings; for instance, four-quarter trimmed-mean PCE inflation was then at 3.1 percent. The VZ model conditions on the May Blue Chip Economic Indicators unemployment rate forecast, which projects that unemployment will converge to its long-run forecast by the end of 2024. In keeping with this, as noted above, the VZ model decomposition suggests that extrinsic force on inflation is fairly weak at present, so inflation may be governed by its intrinsic persistence. (As a reminder, neither the FW nor the UCSV model distinguishes between extrinsic and intrinsic persistence.)

As one would expect from Figure 2, the FW model predicts a fairly rapid decline in trimmed-mean PCE inflation, such that it sees inflation at 2.1 percent by 2025:Q2. This forecast is similar to the Blue Chip PCE inflation forecast; but recall that the FW model may have too little persistence. The UCSV model sees inflation picking up to a 3.6 percent pace, and then moving sideways; but recall that this model may have too much persistence. Finally, the VZ model predicts that inflation remains near its current level of 3.1 percent in 2024:Q2, followed by gradual deceleration in prices. In 2025:Q2, when the FW model sees inflation at 2.1 percent, the VZ model conversely sees inflation at 2.7 percent. And according to this model, inflation does not fall to near to 2 percent until mid-2027. Taken together, these model-based forecasts indicate notable upside risk to forecasts that see inflation back to 2 percent by spring of next year.

Inflation fell rapidly last year, and many forecasters expect it to return to the FOMC’s 2 percent longer run target by spring of next year. But, as explained above, the Verbrugge and Zaman (2023) model suggests that this conclusion may be premature. There are both theoretical and empirical reasons to think that, absent X factors such as continued favorable supply shocks or strong productivity gains, the last half-mile could well take several years.

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• Di Giovanni, Julian, Ṣebnem Kalemli-Özcan, Alvaro Silva, and Muhammed Yildirim. 2023. “Pandemic-Era Inflation Drivers and Global Spillovers.” Working paper 31887. National Bureau of Economic Research. https://doi.org/10.3386/w31887 .
• Hubert, Paul, and Giovanni Ricco. 2018. “Imperfect Information in Macroeconomics.” Revue de l’OFCE 157 (3): 181–196. https://doi.org/10.3917/reof.157.0181 .
• Kiley, Michael T. 2023. “A (Bayesian) Update on Inflation and Inflation Persistence.” FEDS Notes. Board of Governors of the Federal Reserve System. https://doi.org/10.17016/2380-7172.3349 .
• Knotek II, Edward S., James Mitchell, Mathieu Pedemonte, and Taylor Shiroff. 2024. “The Effects of Interest Rate Increases on Consumers’ Inflation Expectations: The Roles of Informedness and Compliance.” Working paper 24-01. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202401 .
• Korenok, Oleg, David Munro, and Jiayi Chen. 2023. “Inflation and Attention Thresholds.” Review of Economics and Statistics , November, 1–28. https://doi.org/10.1162/rest_a_01402 .
• Kurozumi, Takushi, and Willem Van Zandweghe. 2023. “A Theory of Intrinsic Inflation Persistence.” Journal of Money, Credit and Banking 55 (8): 1961–2000. https://doi.org/10.1111/jmcb.13066 .
• Lebow, David, and Ekaterina Peneva. 2024. “Inflation Perceptions During the Covid Pandemic and Recovery.” FEDS Notes. Board of Governors of the Federal Reserve System. https://doi.org/10.17016/2380-7172.3439 .
• Mertens, Elmar. 2016. “Measuring the Level and Uncertainty of Trend Inflation.” The Review of Economics and Statistics 98 (5): 950–967. https://doi.org/10.1162/REST_a_00549 .
• Mitchell, James, and Saeed Zaman. 2023. “The Distributional Predictive Content of Measures of Inflation Expectations.” Working paper 23-31. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202331 .
• Pfajfar, Damjan, and Emiliano Santoro. 2013. “News on Inflation and the Epidemiology of Inflation Expectations.” Journal of Money, Credit and Banking 45 (6): 1045–1067. https://doi.org/10.1111/jmcb.12043 .
• Rapach, David. 2024. “Is the Last Mile More Arduous?” Policy Hub 2024 (1). Federal Reserve Bank of Atlanta. https://doi.org/10.29338/ph2024-1 .
• Reis, Ricardo. 2023. “Four Mistakes in the Use of Measures of Expected Inflation.” AEA Papers and Proceedings 113 (May): 47–51. https://doi.org/10.1257/pandp.20231033 .
• Schwartzman, Felipe F. 2023. “Untangling Persistent Inflation: Understanding the Factors at Work.” Economic Brief 23-31. Federal Reserve Bank of Richmond. https://www.richmondfed.org/publications/research/economic_brief/2023/eb_23-31 .
• Sheremirov, Viacheslav. 2022. “Are the Demand and Supply Channels of Inflation Persistent? Evidence from a Novel Decomposition of PCE Inflation.” Current Policy Perspectives. Federal Reserve Bank of Boston. https://www.bostonfed.org/publications/current-policy-perspectives/2022/are-the-demand-and-supply-channels-of-inflation-persistent.aspx .
• Stock, James H., and Mark W. Watson. 2007. “Why Has U.S. Inflation Become Harder to Forecast?” Journal of Money, Credit and Banking 39 (s1): 3–33. https://doi.org/10.1111/j.1538-4616.2007.00014.x .
• Stock, James H., and Mark W. Watson. 2010. “Modeling Inflation After the Crisis.” In Macroeconomic Challenges: The Decade Ahead . Jackson Hole Economic Policy Symposium. Federal Reserve Bank of Kansas City. https://www.kansascityfed.org/Jackson%20Hole/documents/3111/2010-Stock-Watson_final.pdf .
• Verbrugge, Randal J. 2022. “Is It Time to Reassess the Focal Role of Core PCE Inflation in Assessing the Trend in PCE Inflation?” Economia 45 (89): 73–101. https://doi.org/10.18800/economia.202201.004 .
• Verbrugge, Randal J., and Saeed Zaman. 2021. “Whose Inflation Expectations Best Predict Inflation?” Economic Commentary , no. 2021-19 (October). https://doi.org/10.26509/frbc-ec-202119 .
• Verbrugge, Randal J., and Saeed Zaman. 2023. “The Hard Road to a Soft Landing: Evidence from a (Modestly) Nonlinear Structural Model.” Energy Economics 123 (July): 106733. https://doi.org/10.1016/j.eneco.2023.106733 .
• Verbrugge, Randal J., and Saeed Zaman. 2024. “Improving Inflation Forecasts Using Robust Measures.” International Journal of Forecasting 40 (2): 735–745. https://doi.org/10.1016/j.ijforecast.2023.05.003 .
• Verbrugge, Randal J., and Saeed Zaman. Forthcoming. “Post-COVID Inflation Dynamics: Higher for Longer.” Journal of Forecasting . https://doi.org/10.1002/for.3070 .
• Weber, Michael, Bernardo Candia, Hassan Afrouzi, Tiziano Ropele, Rodrigo Lluberas, Serafin Frache, Brent H. Meyer, et al. 2023. “Tell Me Something I Don’t Already Know: Learning in Low and High-Inflation Settings.” Working paper 31485. National Bureau of Economic Research. https://doi.org/10.3386/w31485 .
• Zaman, Saeed. 2022. "A Unified Framework to Estimate Macroeconomic Stars." Working Paper No. 21-23R. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202123r .
• Wolters Kluwer Legal and Regulatory Solutions US Blue Chip Financial Forecasts. Return to 1
• Households’ year-ahead inflation expectations (University of Michigan Surveys of Consumers, February 2024) came down in December from 4.5 percent to 3 percent and have remained there; firms’ expectations (Cleveland Fed Survey of Firms’ Inflation Expectations, January 2024) have been declining steadily since October 2022 and are now at 3.4 percent, close to their 2018 levels. Return to 2
• “How inflation generally behaves” is influenced by prevailing monetary policy. However, the VZ model does not include any interest rate variable. Thus, implicitly its predictions assume that, going forward, monetary policy will be conducted in the same way that it has been historically. Monetary policy is often represented by policy rules; see the Cleveland Fed’s Simple Monetary Policy Rules ( https://www.clevelandfed.org/indicators-and-data/simple-monetary-policy-rules ). Return to 3
• Rapach (2024) also studies the last mile. That paper focuses on how much tightening will be required to finish the job and does not disagree that it might take a long time. Return to 4
• Schwartzman (2023) discusses sources of intrinsic and extrinsic persistence in more detail. Return to 5
• Taking the Crust et al. (2023) estimates at face value, current inflation levels would suggest that the Phillips curve is “still reasonably strong” at present. However, other estimates disagree; for instance, Forbes et al. (2022) find that the Phillips curve weakens considerably when inflation is below 3 percent. Return to 6
• Inflation expectations today remain a big risk. Despite decades of research, there remains debate on how inflation expectations are actually formed, whether more or better information has much influence upon household inflation expectations or moves them closer to those of professionals (D’Acunto et al., 2023; Pfajfar and Santoro, 2013; Lebow and Peneva, 2024), or, indeed, whose inflation expectations actually matter for inflation (Coibion and Gorodnichenko, 2015; Binder, 2015; Verbrugge and Zaman, 2021; Candia et al., 2023; Mitchell and Zaman, 2023; Reis, 2023). Return to 7
• Estimates from Zaman (2022) also indicate a rise in inflation persistence over the last few years. Return to 8
• The first two drivers are distinct components of the unemployment rate gap; the last driver is the PPI for core intermediate goods. See Verbrugge and Zaman (2023) for more details. The VZ inflation equation fits the data well, as will be evident below. Return to 9
• Note that all the periods identified in blue have a negative inflation gap; the inflation gap itself has been mostly negative over this period. It is possible that intrinsic persistence in inflation is lower when the inflation gap is positive. Return to 10
• Phillips curve models usually perform poorly in forecast comparisons; see, for instance, Verbrugge and Zaman (2024). However, the full VZ structural model is competitive with hard-to-beat benchmarks like the UCSV model. Its predecessor, Ashley and Verbrugge (2023), outperforms the UCSV and random-walk models on pre-COVID-19 data. None of the three models includes monetary policy variables; thus, all implicitly condition on monetary policy’s being conducted in the same way that it has been historically (see endnote 3). Return to 11
• All models are estimated using data only up to the forecast point. The VZ model conditions on X variables, but at both forecast points, both labor supply variables exert no influence. For the purposes of this exercise, an AR(2) model was used to forecast its PPI variable. This forecast quickly mean-reverts. However, stripping the PPI variable out of the model resulted in fairly similar forecasts and does not alter the qualitative conclusions. To further explore model performance, I produced a forecast from this VZ-sans-PPI model for the 2007:Q1–2019:Q4 period. In this forecast, the model could observe the other two X variables but could not see any inflation data after 2006:Q4. Over the entire 12 years, the root mean squared error was a mere 0.18 percent. Return to 12

Suggested Citation

Verbrugge, Randal J. 2024. “Inflation’s Last Half Mile: Higher for Longer?” Federal Reserve Bank of Cleveland,  Economic Commentary  2024-09. https://doi.org/10.26509/frbc-ec-202409

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• Published: 03 June 2024

Scientific integrity and U.S. “Billion Dollar Disasters”

• Roger Pielke Jr 1 , 2  

npj Natural Hazards volume  1 , Article number:  12 ( 2024 ) Cite this article

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• Climate-change impacts
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For more than two decades, the U.S. National Oceanic and Atmospheric Administration (NOAA) has published a count of weather-related disasters in the United States that it estimates have exceeded one billion dollars (inflation adjusted) in each calendar year starting in 1980. The dataset is widely cited and applied in research, assessment and invoked to justify policy in federal agencies, Congress and by the U.S. President. This paper performs an evaluation of the dataset under criteria of procedure and substance defined under NOAA’s Information Quality and Scientific Integrity policies. The evaluation finds that the “billion dollar disaster” dataset falls short of meeting these criteria. Thus, public claims promoted by NOAA associated with the dataset and its significance are flawed and at times misleading. Specifically, NOAA incorrectly claims that for some types of extreme weather, the dataset demonstrates detection and attribution of changes on climate timescales. Similarly flawed are NOAA’s claims that increasing annual counts of billion dollar disasters are in part a consequence of human caused climate change. NOAA’s claims to have achieved detection and attribution are not supported by any scientific analysis that it has performed. Given the importance and influence of the dataset in science and policy, NOAA should act quickly to address this scientific integrity shortfall.

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Introduction.

In the late 1990s, the U.S. National Oceanic and Atmospheric Administration (NOAA) began publishing a tally of weather and climate disasters that each resulted in more than $1 billion in damage, noting that the time series had become “one of our more popular web pages” 1 . Originally, the data was reported in current-year U.S. dollars. In 2011, following criticism that the dataset was misleading, NOAA modified its methods to adjusted historical losses to constant-year dollars by accounting for inflation ( https://www.washingtonpost.com/blogs/capital-weather-gang/post/2011-billion-dollar-weather-disaster-record-legit-or-bad-economics/2012/01/12/gIQADocztP_blog.html ).

By 2023, the billion dollar disaster time series had become a fixture in NOAA’s public outreach, was highlighted by the U.S. government’s U.S. Global Change Research Program (USGCRP) as a “climate change indicator” ( https://storymaps.arcgis.com/collections/ad628a4d3e7e4460b089d9fe96b2475d?item=1 ), was a cited as evidence in support of a “key message” of the Fifth U.S. National Climate Assessment showing that “extreme events are becoming more frequent and severe” ( https://nca2023.globalchange.gov/chapter/2/ ). The time series is often cited in policy settings as evidence of the effects of human-caused climate change to increase the frequency and intensity of extreme weather events and associated economic damage, including in federal agencies, Congress and by the U.S. President ( https://www.congress.gov/bill/118th-congress/house-bill/598/text ; https://www.whitehouse.gov/briefing-room/statements-releases/2023/11/14/fact-sheet-biden-harris-administration-releases-fifth-national-climate-assessment-and-announces-more-than-6-billion-to-strengthen-climate-resilience-across-the-country ). In addition to being widely cited in justifications of policy, as of March, 2024, NOAA’s billion dollar dataset has been cited in almost 1000 articles according to Google Scholar ( https://scholar.google.com/scholar?hl=en&as_sdt=0%2C6&q=%22billion+dollar+disasters%22&btnG= ).

This paper evaluates the billion dollar disaster time series by applying criteria of NOAA’s Information Quality and Scientific Integrity policies. The evaluation finds that billion dollar disaster time series fails to meet NOAA’s criteria for “information quality,” specifically, NOAA’s criteria of traceability, transparency, presentation, and substance.

Thus, the billion dollar disaster dataset is not simply an insufficient basis for claims of the detection and attribution of changes in climate variables (or a consequence of such changes), but the dataset is inappropriate for use in such research. Throughout, I use the terms “detection” and “attribution” as defined by the Intergovernmental panel on Climate Change (IPCC) 2 . Climate data should be the basis for claims of detection and attribution of changes in climate variables, not economic loss data. Because of the shortfalls in scientific integrity documented in this evaluation, policy makers and the public have been misinformed about extreme events and disasters in the United States.

Evaluation of policy or program performance is among the most common and influential practices in applied policy research. Policy evaluation tells us if actions by government programs and agencies are meeting their stated goals and provides insight into reasons for successes and failures. As such, evaluation offers important input that empowers policy makers to correct course and supports efforts by the public to hold governments democratically accountable. A systematic evaluation includes four distinct intellectual tasks 3 , 4 : (a) identification of goals to be achieved, (b) metrics which can be used to assess progress (or lack thereof) with respect to goals, (c) data or evidence related to such metrics, and finally, if possible, (d) judgments of responsibility for observed outcomes.

NOAA’s billion dollar disaster time series is considered a “fundamental research communication” under the Public Communications order of NOAA’s parent agency, the Department of Commerce ( https://www.osec.doc.gov/opog/dmp/daos/dao219_1.html ). NOAA defines a “fundamental research communication” to be “official work regarding the products of basic or applied research in science and engineering, the results of which ordinarily are published and shared broadly within the scientific community” ( https://www.noaa.gov/sites/default/files/legacy/document/2021/Feb/202-735-D.pdf ). NOAA further identifies an important subset of “fundamental research communications” to be “influential information,” which “means information the agency reasonably can determine will have or does have a clear and substantial impact on important public policies or private sector decisions” ( https://www.noaa.gov/organization/information-technology/policy-oversight/information-quality/information-quality-guidelines ). The billion dollar disaster dataset is also what the Office of Management and Budget defines as “Influential Scientific Information” ( https://www.govinfo.gov/content/pkg/FR-2005-01-14/pdf/05-769.pdf ).

NOAA’s Information Quality and Scientific Integrity policies set forth the criteria to be used for evaluating “fundamental research communications,” including the subset of “influential information.” Specifically, NOAA’s Information Quality Guidelines identify three criteria of information quality: utility, objectivity, and integrity ( https://www.noaa.gov/organization/information-technology/policy-oversight/information-quality/information-quality-guidelines ).

Utility refers to “the usefulness of research to its intended users, including the public,” with an emphasis on “transparency.” NOAA’s Scientific Integrity Policy provides further guidance: “Transparency, traceability, and integrity at all levels are required” in order for the agency “to achieve” its mission ( https://www.noaa.gov/sites/default/files/legacy/document/2021/Feb/202-735-D.pdf ).

Traceability: “The ability to verify sources, data, information, methodology, results, assessments, research, analysis, conclusions or other evidence to establish the integrity of findings.”

Transparency: “Characterized by visibility or accessibility of information.”

Objectivity refers to presentation and substance:

Presentation: “includes whether disseminated information is presented in an accurate, clear, complete, and unbiased manner and in a proper context.”

Substance: “involves a focus on ensuring accurate, reliable, and unbiased information. In a scientific, financial, or statistical context, the original and supporting data shall be generated, and the analytic results shall be developed, using sound statistical and research methods.”

Integrity refers to “security ‑ the protection of information from unauthorized access or revision, to ensure that the information is not compromised through corruption or falsification.” Integrity will not be further considered as part of this evaluation.

NOAA’s Scientific Integrity Policy also states that it will “ensure that data and research used to support policy decisions undergo independent peer review by qualified experts” ( https://sciencecouncil.noaa.gov/scientific-integrity-commons/sic-integrity-policy/ ). OMB requires that agencies develop “a transparent process for public disclosure of peer review planning, including a Web-accessible description of the peer review plan that the agency has developed for each of its forthcoming influential scientific disseminations” ( https://www.govinfo.gov/content/pkg/FR-2005-01-14/pdf/05-769.pdf ). There is no such plan in place for the NOAA “billion dollar” dataset and the methods, which have evolved over time, and results have not been subject to any public or transparent form of peer review.

The evaluation conducted here thus focuses on traceability and transparency (as elements of utility) and presentation and substance (as elements of objectivity).

Traceability and transparency

The NOAA billion dollar disaster dataset is intransparent in many ways, including its sources, input data and methodologies employed to produce results. The intransparency includes elements of event loss estimation, additions to and subtractions of events from the database, and adjustments made to historical loss estimates. There have been an unknown number of versions of the dataset, which have not been documented or made publicly available. Changes are made to the dataset more frequently than annually, suggesting that there have been many dozens of versions of the dataset over the past decades. Replication of the dataset or changes made to it is thus not possible by any independent researcher, as is verification or evaluation of the dataset itself.

Seven examples illustrate the lack of transparency and lack of traceability.

First, NOAA states that it utilizes more than “a dozen sources” to “help capture the total, direct costs (both insured and uninsured) of the weather and climate events” ( https://www.ncei.noaa.gov/access/billions/faq ). However, NOAA does not specifically identify these sources in relation to specific events, how its estimates are derived from these sources, or the estimates themselves. Almost all data sources that NOAA cites that it relies on for loss estimates are public agencies that produce data released to the public. Insured losses for specific events are aggregated and typically made available to the public, such as by the Florida Office of Insurance Regulation ( https://www.floir.com/home ). Aggregated data provides no information on specific businesses or individuals.

NOAA also states that it includes in it loss estimates various indirect losses such as business interruption, wildfire suppression and others. NOAA does not provide the data or methods for its estimation of such indirect losses. Smith and Matthews 5 (who also have created and maintained the dataset as NOAA employees) also identify livestock feeding costs as a function of national feedstock trends as a variable used in compiling the dataset. Livestock feeding costs are not considered a disaster cost in conventional disaster accounting methods (such as by NOAA Storm Data or SHELDUS), as these are not direct losses due to a local or regional extreme event, but rather an estimate of national market changes in commodity prices which are influenced by many more factors than an extreme event. It is unclear what other measures of indirect costs are included in the NOAA tabulation.

Second, consider the case of Hurricane Idalia, which made landfall in the Big Bend Region of Florida in late September 2023. Initial catastrophe model estimates suggested insured losses of $2.5 to 5 billion ( https://www.insurancejournal.com/news/national/2023/09/05/738970.htm ). The initial NOAA estimate reported on its billion dollar disaster website in the immediate aftermath of the storm was $2.5 billion. However, actual insured losses have been far less than was estimated in the storm’s aftermath, totaling officially about $310 million through mid-November 2023 ( https://www.floir.com/home/idalia ). The historical practice of NOAA’s National Hurricane Center for estimating total direct hurricane damage was to double insured losses to arrive at an estimate of total direct losses 6 . Even accounting for some additional insurance claims to be made, it is unlikely that Idalia would reach $1 billion in total direct losses under the NHC methodology. Yet by December 2023 NOAA had increased its loss estimate for Idalia to $3.6 billion. What is the basis for NOAA’s estimate of Idalia’s total losses being ~12 times insured losses? That is unknown.

Third, similarly unknown is why historical events are periodically added and removed from the dataset. For instance, from a version of the dataset available in December 2022 to an update published in July 2023, 10 new events were added and 3 were deleted (Fig. 1 ). A later comparison with yet another version of the dataset indicates 4 additional historical events were added (not shown in Fig. 1 ). There is no documentation or justification for such changes, I am only aware of them through the happenstance of downloading the currently available dataset at different times.

Undocumented changes to disaster counts made by NOAA between two different versions of the billion dollar disaster dataset, one downloaded in 2022 and another in 2023.

Fourth, a comparison of event loss estimates from the 2022 dataset and the 2023 version shows that each individual event has been adjusted by a different amount. According to NOAA, the only annual adjustment acknowledged is for inflation based on the Consumer Price Index (CPI). From 2022 to 2023, most of the adjustments made to individual events are between 4.5% and 6% but nine events are adjusted from 6.6% to 145%, and one is a reduction of about 75%. An annual adjustment for CPI should be constant across all events. No documentation is provided to explain these various adjustments and why they are unique to each event.

Fifth, NOAA states that they perform “key transformations” of loss data estimates by “scaling up insured loss data to account for uninsured and underinsured losses, which differs by peril, geography, and asset class.” NOAA makes no details available on the methodology or basis for such transformations, nor their impact on loss estimates, nor how these transformations may change over time.

Similarly, Smith and Matthews 5 reference an overall bias correction that has been applied to the dataset, as well as an additional correction for crop insurance losses. Smith and Katz 6 reference other adjustments, such as an adjustment to U.S. flood insurance participation rates, but neither the methodologies nor results of these various adjustments are documented, nor has the baseline data to which the adjustments are applied. Table 3 from Smith and Katz 7 suggests an open-ended formulaic approach to loss estimation, but none of the data that would be used in such formulas is available. Nor is it clear that NOAA currently applies the formula to loss estimation. If so, it should be straightforward to provide sources, data and methods for each iteration of the dataset.

Sixth, the number of smaller disasters ranging from $1 to $2 billion was fairly constant from 1980 to 2007 and then sharply increased starting in 2008 (Fig. 2 ). NOAA states that “we introduce events into the time series as they “inflate” their way above $1B in costs in today’s dollars. Every year, this leads to the introduction of several new events added from earlier in the time series” ( https://sciencecouncil.noaa.gov/scientific-integrity-commons/sic-integrity-policy/ ). However, the December 2023 dataset shows a net change of zero events from $1-2 billion for the period of 1980–2000 and a net increase of such 2 events from 2001–2023. NOAA’s statement that it elevates disasters from <1 billion in losses to the billion dollar disaster database also indicates that NOAA has another dataset with sub-billion dollar events that is not publicly available.

Increasing disaster counts costing $1-2 billion in a version of NOAA’s 2023 dataset.

The sharp discontinuity in the counts of $1-2 billion events starting in 2008 is suggestive of a change in disaster accounting methods, however, the lack of transparency into the creation of the dataset makes it impossible to know the reasons that may underlie this discontinuity.

Seventh, a comparison of 2023 CPI-adjusted official losses of NOAA’s National Hurricane Center (NHC)20 to the loss estimates of the 2023 NOAA billion dollar dataset (BDD), for significant hurricanes shows large differences (Table 1 ).

The NOAA billion dollar disaster estimates are in all cases except Hurricane Andrew substantially higher than the CPI-adjusted estimates based on the official estimates of NHC. There is no obvious pattern to the differences and the lack of methodological and data transparency makes it impossible to understand why there are such large differences and why these differences vary by such a great deal.

These seven examples indicate clearly that the NOAA billion dollar dataset fails with respect to NOAA’s scientific integrity criteria of traceability and transparency. The many issues and questions raised above cannot be answered because it is impossible to verify sources, data or methodology to establish the integrity of findings. These seven examples are just a small subset of issues that I have raised in public forums about the provenance, methods, and publicly communicated results of the application of these methods. The billion dollar dataset thus does not meet NOAA’s requirement that data be transparent and traceable.

Presentation and substance

Even in the absence of the issues documented above, the NOAA billion dollar disaster dataset is potentially misleading, because it has been represented by NOAA and U.S. government officials as evidence of the detection of trends in extreme weather phenomena and the attribution of those trends to human-caused climate change due to the emission of greenhouse gases.

For instance:

The NOAA official responsible for overseeing the dataset claimed that the dataset showed: “Climate change is supercharging many of these extremes that can lead to billion-dollar disasters” ( https://www.cbsnews.com/news/noaa-billion-dollar-weather-disasters-2022-hurricane-ian-drought/ ).

At the press conference where the 2022 dataset was released, the NOAA Administrator claimed that the dataset indicated that, “Climate change is creating more and more intense extreme events that cause significant damage” ( https://www.npr.org/2023/01/12/1148633707/extreme-weather-fueled-by-climate-change-cost-the-u-s-165-billion-in-2022 ).

In 2021 the U.S. Department of Treasury identified increasing billion dollar disasters as evidence of the effects of climate change on financial risks ( https://home.treasury.gov/system/files/261/FSOC-Climate-Report.pdf ).

The Fifth U.S. National Climate Assessment cited the NOAA dataset as evidence that “Climate change is not just a problem for future generations, it’s a problem today,” and claimed that the dataset, in part, demonstrated “the increasing frequency and severity of extreme events” due in part to “human-caused climate change” ( https://nca2023.globalchange.gov/chapter/2/ ).

In 2023, President Biden attributed weather and climate-related disaster costs in the U.S. in 2022 to climate change, citing the NOAA dataset: “[C]limate change related extreme weather events still pose a rapidly intensifying threat – one that costs the U.S. at least $150 billion each year … This year set a record for the number of climate disasters that cost the United States over $1 billion. The United States now experiences a billion-dollar disaster approximately every three weeks on average, compared to once every four months during the 1980s” ( https://www.whitehouse.gov/briefing-room/statements-releases/2023/11/14/fact-sheet-biden-harris-administration-releases-fifth-national-climate-assessment-and-announces-more-than-6-billion-to-strengthen-climate-resilience-across-the-country/ ).

The point here is not to call into question the reality or importance of human-caused climate change – it is real, and it is important. Rather, the question is whether the NOAA billion dollar disaster time series provides evidence of detection or attribution of changes in the climate of extreme weather events in the United States, as frequently claimed.

Economic loss data is not suitable for detection and attribution of trends in extreme weather events because losses involve more than just climatic factors. It is well understood that a disaster occurs at the intersection of an extreme event and a vulnerable and exposed society (IPCC) 8 . NOAA acknowledges that a combination of risk, vulnerability and exposure is necessary for a disaster to occur ( https://www.ncei.noaa.gov/access/billions/faq ), but it fails to take any of these factors into account in its methodologies prior to making claims of detection and attribution. Of note, NOAA performs such a GDP normalization for disasters at the state level but does not do so for its national billion dollar disaster database. In a June, 2023 insurance industry Webinar, the lead scientist responsible for the NOAA dataset identified the absence of a national GDP-based normalization to be a major challenge for interpreting the database, and suggested that this would be added to the dataset in the future ( https://www.catmanagers.org/event-details/put-past-losses-in-their-proper-context-1 ). Smith and Katz 7 explain that “the billion-dollar dataset is only adjusted for the CPI over time, not currently incorporating any changes in exposure (e.g., as reflected by shifts in wealth or population)”.

Over time, population and wealth have increased dramatically in the United States (and globally), meaning that when an extreme climate or weather event occurs, there is more to be damaged and invariably, more damage occurs even if there is no underlying trend in the frequency or intensity of extreme weather. Consequently, there is a large literature that seeks to “normalize” historical loss data to account for changes in exposure and vulnerability (e.g., a recent literature review identified more than 60 such papers 9 , other relevant studies discuss the importance of the spatial dimensions of land use change 10 , 11 , 12 , 13 ).

A common approach to disaster normalization adjusts historical losses based on GDP, as a proxy for increasing population and wealth 14 , 15 , 16 , 17 , 18 . Figure. 3 shows loss per disaster in the NOAA 2023 dataset as a percentage of US GDP ( https://fred.stlouisfed.org/series/RGDPNAUSA666NRUG ). According to a simple linear trend, losses per disaster are down by about 80% since 1980, as a proportion of GDP. This is likely due to a combination of actual decreasing losses as a proportion of GDP, as has been documented in many rich countries, as well as the sharp increase in small disasters included in NOAA’s dataset (see Fig. 2 ).

Losses per disaster in NOAA’s billion dollar disaster dataset (the version downloaded in July 2023), 1980 to 2022.

In comparison, weather and climate disasters losses as a percentage of U.S. GDP, show no increase over the period of record, which is 1990–2019 based on these data (Fig. 4 ).

Sources: Spatial Hazard Events and Losses Database for the United States (SHELDUS) at Arizona State University, which has made public aggregate losses from 1990 to 2019. Data on GDP from the U.S. Office of Management and Budget.

Other, more sophisticated and granular approaches to the normalization of U.S. weather and climate related disaster losses robustly confirm the aggregate downward trend in losses, once population growth and wealth are properly accounted 6 , 18 , 19 , 20 , 21 , 22 . Hurricane, flood and tornado losses have all decreased as a proportion of GDP on climate time scales, and as these are responsible for the majority of direct losses, so too have aggregate disaster losses.

NOAA’s failure to consider changes in exposure and vulnerability is significant. Consider for example Hurricane Andrew in 1992. The NOAA 2023 billion dollar disaster loss estimate for Andrew is $58.9 billion, but a 2023 normalized loss estimate is more than twice that at $119.9 billion (based on Weinkle et al.). For comparison, in 2022, Swiss Reinsurance estimated that a recurrence of Hurricane Andrew would result in $120 billion in total damage ( https://www.abcactionnews.com/news/price-of-paradise/experts-say-modern-day-hurricane-andrew-could-cost-florida-120-billion ). Thus, these estimates differ by ~100%.

By adjusting for inflation, but not for changes in exposure or vulnerability, the NOAA billion dollar dataset introduces a bias into the time series, as the upwards trend in losses in the billion dollar disaster time series is a result of growth in population and wealth, and not climate trends. As Smith and Katz 7 acknowledged more than a decade ago of the increase in billion dollar disasters, “the magnitude of such increasing trends is greatly diminished when applied to data normalized for exposure.”

Thus, any claim that the NOAA billion dollar disaster dataset indicates worsening weather or worsening disasters is incomplete at best and misleading at worst. When U.S. disaster losses are considered in the context of exposure changes it becomes clear that as the absolute costs of disasters has increased, the impact relative to the economy has diminished over past decades, which is exactly the opposite of claims made by NOAA, the U.S. National Climate Assessment, the USGCRP, and the president of the United States, among many others.

The most appropriate data for investigating detection and attribution of changes in climate variables will always be climate data, and not economic data. IPCC has assessed research on the detection and attribution of trends in extreme weather events and has only low confidence in the emergence of signals of climate-impact drivers for river floods, heavy precipitation and pluvial flood, landslide, drought, fire weather, tropical cyclones, hail, severe wind storms and heavy snowfall 2 – that is, each of the elements of the billion dollar disaster dataset. The IPCC does express confidence in some regions in the detection and attribution of changes in heat extremes and in extreme precipitation 2 , neither of which is an element of the billion dollar disaster database. The IPCC is explicit in warning against conflating changes in extreme precipitation with changes in pluvial flooding 2 .

NOAA makes strong claims of detection and attribution contrary to the conclusions of the IPCC but provides no analyses in support of these claims. For instance, NOAA states of its time series:

“The increases in population and material wealth over the last several decades are an important factor for higher damage potential. These trends are further complicated by the fact that many population centers and infrastructure exist in vulnerable areas like coasts and river floodplains, while building codes are often insufficient in reducing damage from extreme events. Climate change is also playing a role in the increasing frequency of some types of extreme weather that lead to billion-dollar disasters.”

However, NOAA makes no effort to quantify the roles of increasing population and material wealth, nor does it substantiate its claims that climate change has increased the frequency of some types of extreme weather.

NOAA does not acknowledge a large literature on disaster “normalization” that seeks to quantify the roles of population, material wealth, mitigation, building practices, etc. on increasing losses and also ignores literature on the detection and attribution of trends in various forms of extreme weather 2 , 9 .

Thus, any claim that the NOAA billion dollar disaster dataset indicates the detection trends in climate variables and the attribution of those trends to human-caused climate change is contrary to the most recent assessment of the IPCC. NOAA has provided no evidence or research to support claims that human-caused changes in climate are driving the increase in billion dollar disaster counts. Similarly, the opposite claim, that increasing billion dollar disasters are evidence of changes in the frequency of some extreme events resulting from human-caused climate change is also unsupported. NOAA’s claims are also circular – one claim is that climate change causes increasing billion dollar disasters and the second claim is that increasing billion dollar disasters indicate climate change. The billion dollar dataset fails to meet NOAA’s criteria of presentation and substance.

To summarize: the NOAA billion dollar disaster dataset falls short of NOAA’s guidelines for scientific integrity. The shortfalls documented here are neither small nor subtle. They represent a departure from NOAA’s long-term history of scientific integrity and excellence, which has saved countless lives and supported the nation’s economy.

Identifying the reasons why NOAA’s billion dollar disaster dataset has departed so significantly from the agency’s own standards of scientific integrity goes well beyond the scope of this paper. However, the steps necessary to bring the dataset back into conformance with NOAA’s information quality criteria are straightforward ( https://www.noaa.gov/organization/information-technology/policy-oversight/information-quality/information-quality-guidelines ):

Publish all data, including all versions of the dataset;

Document and publish baseline loss estimates and their provenance;

Clearly describe all methodologies employed to adjust baseline data;

Document every change made to the dataset, give each successive version of the dataset a unique name, and publish all version of the data;

Maintain all historical versions of the dataset in a publicly accessible archive;

Subject the methods and results to annual peer review by experts, including economists and others with subject matter expertise, who are independent of NOAA. Make the peer review reports public;

Align NOAA’s practices with federal government policies for disseminating statistical information that are applied to other agencies ( https://www.federalregister.gov/documents/2002/06/04/02-13892/federal-statistical-organizations-guidelines-for-ensuring-and-maximizing-the-quality-objectivity );

Align claims with IPCC methods and standards for any claims of detection and attribution, or justify why the claims are at odds with those of the IPCC.

NOAA is a crucially important agency that sits at the intersection of science, policy and politics. It has a long and distinguished history of providing weather, climate, water, ocean and other data to the nation. These data have saved countless lives, supported the economy and enabled significant scientific research. The agency is far too important to allow the shortfalls in scientific integrity documented in this paper to persist. Fortunately, science and policy are both self-correcting.

Policy evaluation

The analysis in this paper follows the logic of policy evaluation, which compares policy implementation with respect to criteria, with a goal of identifying progress or lack thereof towards goals (sources). Identifying progress requires identification of specific metrics of progress and data relevant to those metrics.

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Reexamining Misinformation: How Unflagged, Factual Content Drives Vaccine Hesitancy

Research from the Computational Social Science Lab finds that factual, vaccine-skeptical content on Facebook has a greater overall effect than “fake news,” discouraging millions from the COVID-19 shot.

By Ian Scheffler, Penn Engineering 

What threatens public health more, a deliberately false Facebook post about tracking microchips in the COVID-19 vaccine that is flagged as misinformation, or an unflagged, factual article about the rare case of a young, healthy person who died after receiving the vaccine?

According to Duncan J. Watts, Stevens University Professor in Computer and Information Science at Penn Engineering and Director of the Computational Social Science (CSS) Lab , along with David G. Rand, Erwin H. Schell Professor at MIT Sloan School of Management, and Jennifer Allen, 2024 MIT Sloan School of Management Ph.D. graduate and incoming CSS postdoctoral fellow, the latter is much more damaging. “The misinformation flagged by fact-checkers was 46 times less impactful than the unflagged content that nonetheless encouraged vaccine skepticism,” they conclude in a new paper in Science. 

Historically, research on “fake news” has focused almost exclusively on deliberately false or misleading content, on the theory that such content is much more likely to shape human behavior. But, as Allen points out, “When you actually look at the stories people encounter in their day-to-day information diets, fake news is a miniscule percentage. What people are seeing is either no news at all or mainstream media.” 

“Since the 2016 U.S. presidential election, many thousands of papers have been published about the dangers of false information propagating on social media,” says Watts. “But what this literature has almost universally overlooked is the related danger of information that is merely biased. That’s what we look at here in the context of COVID vaccines.” 

In the study, Watts, one of the paper’s senior authors, and Allen, the paper’s first author, used thousands of survey results and AI to estimate the impact of more than 13,000 individual Facebook posts. “Our methodology allows us to estimate the effect of each piece of content on Facebook,” says Allen. “What makes our paper really unique is that it allows us to break open Facebook and actually understand what types of content are driving misinformed-ness.” 

One of the paper’s key findings is that “fake news,” or articles flagged as misinformation by professional fact-checkers, has a much smaller overall effect on vaccine hesitancy than unflagged stories that the researchers describe as “vaccine-skeptical,” many of which focus on statistical anomalies that suggest that COVID-19 vaccines are dangerous. 

“Obviously, people are misinformed,” says Allen, pointing to the low vaccination rates among U.S. adults, in particular for the COVID-19 booster vaccine, “but it doesn’t seem like fake news is doing it.” One of the most viewed URLs on Facebook during the time period covered by the study, at the height of the pandemic, for instance, was a true story in a reputable newspaper about a doctor who happened to die shortly after receiving the COVID-19 vaccine. 

That story racked up tens of millions of views on the platform, multiples of the combined number of views of all COVID-19-related URLs that Facebook flagged as misinformation during the time period covered by the study. “Vaccine-skeptical content that’s not being flagged by Facebook is potentially lowering users’ intentions to get vaccinated by 2.3 percentage points,” Allen says. “A back-of-the-envelope estimate suggests that translates to approximately 3 million people who might have gotten vaccinated had they not seen this content.”

Despite the fact that, in the survey results, fake news identified by fact-checkers proved more persuasive on an individual basis, so many more users were exposed to the factual, vaccine-skeptical articles with clickbait-style headlines that the overall impact of the latter outstripped that of the former. 

“Even though misinformation, when people see it, can be more persuasive than factual content in the context of vaccine hesitancy,” says Allen, “it is seen so little that these accurate, ‘vaccine-skeptical’ stories dwarf the impact of outright false claims.” 

As the researchers point out, being able to quantify the impact of misleading but factual stories points to a fundamental tension between free expression and combating misinformation, as Facebook would be unlikely to shut down mainstream publications. “Deciding how to weigh these competing values is an extremely challenging normative question with no straightforward solution,” the authors write in the paper. 

Allen points to content moderation that involves the user community as one possible means to address this challenge. “Crowdsourcing fact-checking and moderation works surprisingly well,” she says. “That’s a potential, more democratic solution.” 

With the 2024 U.S. Presidential election on the horizon, Allen emphasizes the need for Americans to seriously consider these tradeoffs. “The most popular story on Facebook in the lead-up to the 2020 election was about military ballots found in the trash that were mostly votes for Donald Trump,” she notes. “That was a real story, but the headline did not mention that there were nine votes total, seven of them for Trump.” 

This study was conducted at the University of Pennsylvania’s School of Engineering and Applied Science, the Annenberg School for Communication and the Wharton School, along with the Massachusetts Institute of Technology Sloan School of Management, and was supported by funding from Alain Rossmann.

This article originally appeared on the Penn Engineering Blog.

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The Federal Reserve Bank of Cleveland and the Office of Financial Research invite the submission of research and policy-oriented papers for the 2024 Financial Stability Conference on November 21–22, 2024. The conference will be held in person in Cleveland, Ohio, and virtually.

Markets and institutions, increasingly interconnected, are being challenged by the dizzying pace of changes in the financial system, accelerating the buildup of risk and threats to solvency. Regulatory adaptations add another layer of complexity to the issue. Increasingly sophisticated algorithms and the rise of generative artificial intelligence may create new vulnerabilities across the system as banks, nonbank financial institutions, and financial markets exploit nascent opportunities. The twelfth annual conference will explore how firms and markets can become resilient or even antifragile and how regulators can encourage and accommodate needed changes.

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An Analysis of Pandemic-Era Inflation in 11 Economies

In a collaborative project with ten central banks, we have investigated the causes of the post-pandemic global inflation, building on our earlier work for the United States. Globally, as in the United States, pandemic-era inflation was due primarily to supply disruptions and sharp increases in the prices of food and energy; however, and in sharp contrast to the 1970s, the inflationary effects of these supply shocks have not been persistent, in part due to the credibility of central bank inflation targets. As the effects of supply shocks have subsided, tight labor markets, and the rises in nominal wages, have become relatively more important sources of inflation in many countries. In several countries, including the United States, curbing wage inflation and returning price inflation to target may require a period of modestly higher unemployment.

We thank the Peterson Institute for International Economics and the Hutchins Center for Fiscal and Monetary Policy at the Brookings Institution for research support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Study models how ketamine’s molecular action leads to its effects on the brain

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Ketamine, a World Health Organization Essential Medicine, is widely used at varying doses for sedation, pain control, general anesthesia, and as a therapy for treatment-resistant depression. While scientists know its target in brain cells and have observed how it affects brain-wide activity, they haven’t known entirely how the two are connected. A new study by a research team spanning four Boston-area institutions uses computational modeling of previously unappreciated physiological details to fill that gap and offer new insights into how ketamine works.

“This modeling work has helped decipher likely mechanisms through which ketamine produces altered arousal states as well as its therapeutic benefits for treating depression,” says co-senior author Emery N. Brown , the Edward Hood Taplin Professor of Computational Neuroscience and Medical Engineering at The Picower Institute for Learning and Memory at MIT, as well as an anesthesiologist at Massachusetts General Hospital and a professor at Harvard Medical School.

The researchers from MIT, Boston University (BU), MGH, and Harvard University say the predictions of their model, published May 20 in Proceedings of the National Academy of Sciences , could help physicians make better use of the drug.

“When physicians understand what's mechanistically happening when they administer a drug, they can possibly leverage that mechanism and manipulate it,” says study lead author Elie Adam , a research scientist at MIT who will soon join the Harvard Medical School faculty and launch a lab at MGH. “They gain a sense of how to enhance the good effects of the drug and how to mitigate the bad ones.”

Blocking the door

The core advance of the study involved biophysically modeling what happens when ketamine blocks the “NMDA” receptors in the brain’s cortex — the outer layer where key functions such as sensory processing and cognition take place. Blocking the NMDA receptors modulates the release of excitatory neurotransmitter glutamate.

When the neuronal channels (or doorways) regulated by the NMDA receptors open, they typically close slowly (like a doorway with a hydraulic closer that keeps it from slamming), allowing ions to go in and out of neurons, thereby regulating their electrical properties, Adam says. But, the channels of the receptor can be blocked by a molecule. Blocking by magnesium helps to naturally regulate ion flow. Ketamine, however, is an especially effective blocker.

Blocking slows the voltage build-up across the neuron’s membrane that eventually leads a neuron to “spike,” or send an electrochemical message to other neurons. The NMDA doorway becomes unblocked when the voltage gets high. This interdependence between voltage, spiking, and blocking can equip NMDA receptors with faster activity than its slow closing speed might suggest. The team’s model goes further than ones before by representing how ketamine’s blocking and unblocking affect neural activity.

“Physiological details that are usually ignored can sometimes be central to understanding cognitive phenomena,” says co-corresponding author Nancy Kopell , a professor of mathematics at BU. “The dynamics of NMDA receptors have more impact on network dynamics than has previously been appreciated.”

With their model, the scientists simulated how different doses of ketamine affecting NMDA receptors would alter the activity of a model brain network. The simulated network included key neuron types found in the cortex: one excitatory type and two inhibitory types. It distinguishes between “tonic” interneurons that tamp down network activity and “phasic” interneurons that react more to excitatory neurons.

The team’s simulations successfully recapitulated the real brain waves that have been measured via EEG electrodes on the scalp of a human volunteer who received various ketamine doses and the neural spiking that has been measured in similarly treated animals that had implanted electrode arrays. At low doses, ketamine increased brain wave power in the fast gamma frequency range (30-40 Hz). At the higher doses that cause unconsciousness, those gamma waves became periodically interrupted by “down” states where only very slow frequency delta waves occur. This repeated disruption of the higher frequency waves is what can disrupt communication across the cortex enough to disrupt consciousness.

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But how? Key findings

Importantly, through simulations, they explained several key mechanisms in the network that would produce exactly these dynamics.

The first prediction is that ketamine can disinhibit network activity by shutting down certain inhibitory interneurons. The modeling shows that natural blocking and unblocking kinetics of NMDA-receptors can let in a small current when neurons are not spiking. Many neurons in the network that are at the right level of excitation would rely on this current to spontaneously spike. But when ketamine impairs the kinetics of the NMDA receptors, it quenches that current, leaving these neurons suppressed. In the model, while ketamine equally impairs all neurons, it is the tonic inhibitory neurons that get shut down because they happen to be at that level of excitation. This releases other neurons, excitatory or inhibitory, from their inhibition allowing them to spike vigorously and leading to ketamine’s excited brain state. The network’s increased excitation can then enable quick unblocking (and reblocking) of the neurons’ NMDA receptors, causing bursts of spiking.

Another prediction is that these bursts become synchronized into the gamma frequency waves seen with ketamine. How? The team found that the phasic inhibitory interneurons become stimulated by lots of input of the neurotransmitter glutamate from the excitatory neurons and vigorously spike, or fire. When they do, they send an inhibitory signal of the neurotransmitter GABA to the excitatory neurons that squelches the excitatory firing, almost like a kindergarten teacher calming down a whole classroom of excited children. That stop signal, which reaches all the excitatory neurons simultaneously, only lasts so long, ends up synchronizing their activity, producing a coordinated gamma brain wave.

“The finding that an individual synaptic receptor (NMDA) can produce gamma oscillations and that these gamma oscillations can influence network-level gamma was unexpected,” says co-corresponding author Michelle McCarthy , a research assistant professor of math at BU. “This was found only by using a detailed physiological model of the NMDA receptor. This level of physiological detail revealed a gamma time scale not usually associated with an NMDA receptor.”

So what about the periodic down states that emerge at higher, unconsciousness-inducing ketamine doses? In the simulation, the gamma-frequency activity of the excitatory neurons can’t be sustained for too long by the impaired NMDA-receptor kinetics. The excitatory neurons essentially become exhausted under GABA inhibition from the phasic interneurons. That produces the down state. But then, after they have stopped sending glutamate to the phasic interneurons, those cells stop producing their inhibitory GABA signals. That enables the excitatory neurons to recover, starting a cycle anew.

Antidepressant connection?

The model makes another prediction that might help explain how ketamine exerts its antidepressant effects. It suggests that the increased gamma activity of ketamine could entrain gamma activity among neurons expressing a peptide called VIP. This peptide has been found to have health-promoting effects, such as reducing inflammation, that last much longer than ketamine’s effects on NMDA receptors. The research team proposes that the entrainment of these neurons under ketamine could increase the release of the beneficial peptide, as observed when these cells are stimulated in experiments. This also hints at therapeutic features of ketamine that may go beyond antidepressant effects. The research team acknowledges, however, that this connection is speculative and awaits specific experimental validation.

“The understanding that the subcellular details of the NMDA receptor can lead to increased gamma oscillations was the basis for a new theory about how ketamine may work for treating depression,” Kopell says.

Additional co-authors of the study are Marek Kowalski, Oluwaseun Akeju, and Earl K. Miller.

The work was supported by the JPB Foundation; The Picower Institute for Learning and Memory; The Simons Center for The Social Brain; the National Institutes of Health; George J. Elbaum ’59, SM ’63, PhD ’67; Mimi Jensen; Diane B. Greene SM ’78; Mendel Rosenblum; Bill Swanson; and annual donors to the Anesthesia Initiative Fund.

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