recently. Many observers have noted warning signs of a recession, such as an inverted yield curve and volatile oil prices, while other indicators give hope for a soft landing. Although statistical models and professional forecasts imply a much-higher-than-normal probability of recession, there is still a lot of uncertainty about the outcome. Professional forecasters synthesize many types of information to predict future economic quantities, such as GDP or inflation, and they often assess the uncertainty associated with their predictions. In the case of recession prediction, for example, forecasters estimate the likelihood of a recession within the next 12 months. As of August 2023, a typical estimate of the probability of a US recession within 12 months is about 60%, and it has been above 50% since October 2022. While economic forecasters don’t have crystal balls and can only assign probabilities to various outcomes, their forecasts contain enough information for major corporations and organizations to purchase them. This raises questions: What variables do forecasters look at? How do these variables translate into forecasts? One could investigate forecast methods, but forecasters are naturally reluctant to publicly detail their methods. In addition, professional forecasters often supplement many sorts of statistical methods with their own judgment to account for special circumstances, such as unusual weather or changes to tax laws or financial regulations. These facts make it difficult to define forecasting techniques. An alternative to directly studying forecast methods would be to replicate forecasts using public information. In other words, instead of predicting output growth, inflation, probability of recession, or some other variable, one might try to replicate the past forecasts of the variable using public data. This essay investigates what variables professional forecasters appear to use to predict the probability of recession in the next 12 months. We obtain our target variable—the estimated probability of a US recession within 12 months— from Consensus Economics, which surveys economic forecasters and reports the results of their surveys as historical data series. The series for the estimated recession probability Modeling Professional Recession Forecasts
{"title":"Modeling Professional Recession Forecasts","authors":"Christopher J. Neely","doi":"10.20955/es.2023.21","DOIUrl":"https://doi.org/10.20955/es.2023.21","url":null,"abstract":"recently. Many observers have noted warning signs of a recession, such as an inverted yield curve and volatile oil prices, while other indicators give hope for a soft landing. Although statistical models and professional forecasts imply a much-higher-than-normal probability of recession, there is still a lot of uncertainty about the outcome. Professional forecasters synthesize many types of information to predict future economic quantities, such as GDP or inflation, and they often assess the uncertainty associated with their predictions. In the case of recession prediction, for example, forecasters estimate the likelihood of a recession within the next 12 months. As of August 2023, a typical estimate of the probability of a US recession within 12 months is about 60%, and it has been above 50% since October 2022. While economic forecasters don’t have crystal balls and can only assign probabilities to various outcomes, their forecasts contain enough information for major corporations and organizations to purchase them. This raises questions: What variables do forecasters look at? How do these variables translate into forecasts? One could investigate forecast methods, but forecasters are naturally reluctant to publicly detail their methods. In addition, professional forecasters often supplement many sorts of statistical methods with their own judgment to account for special circumstances, such as unusual weather or changes to tax laws or financial regulations. These facts make it difficult to define forecasting techniques. An alternative to directly studying forecast methods would be to replicate forecasts using public information. In other words, instead of predicting output growth, inflation, probability of recession, or some other variable, one might try to replicate the past forecasts of the variable using public data. This essay investigates what variables professional forecasters appear to use to predict the probability of recession in the next 12 months. We obtain our target variable—the estimated probability of a US recession within 12 months— from Consensus Economics, which surveys economic forecasters and reports the results of their surveys as historical data series. The series for the estimated recession probability Modeling Professional Recession Forecasts","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
past decade: specifically, from $69 trillion to $153 trillion between 2012 and 2021, a 220% rate of increase. It has continued to rapidly increase even after controlling for inflation and economic growth. This essay documents and decomposes the changes in national wealth for the past two decades and discusses the potential implications of these changes for the U.S. economy. We obtain U.S. wealth data from the Financial Accounts of the United States provided by the Federal Reserve System. Wealth is defined as the total nominal value of tangible assets controlled by various sectors in the U.S. economy.1 Roughly, there are four main sectors:
{"title":"The Recent Rise of U.S. National Wealth","authors":"YiLi Chien, A. Stewart","doi":"10.20955/es.2022.28","DOIUrl":"https://doi.org/10.20955/es.2022.28","url":null,"abstract":"past decade: specifically, from $69 trillion to $153 trillion between 2012 and 2021, a 220% rate of increase. It has continued to rapidly increase even after controlling for inflation and economic growth. This essay documents and decomposes the changes in national wealth for the past two decades and discusses the potential implications of these changes for the U.S. economy. We obtain U.S. wealth data from the Financial Accounts of the United States provided by the Federal Reserve System. Wealth is defined as the total nominal value of tangible assets controlled by various sectors in the U.S. economy.1 Roughly, there are four main sectors:","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84423419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
his essay explores the relationship between economic growth and the evolution of mortality across countries between 1960 and 2019. The data are from the World Bank’s World Development Indicators and Health Nutrition and Population Statistics; they are for a group of 86 countries for which there exist annual data on real gross domestic product (GDP) per capita and two measures of mortality—the crude death rate and life expectancy at birth. 1
{"title":"Mortality and Economic Growth","authors":"Guillaume Vandenbroucke","doi":"10.20955/es.2022.30","DOIUrl":"https://doi.org/10.20955/es.2022.30","url":null,"abstract":"his essay explores the relationship between economic growth and the evolution of mortality across countries between 1960 and 2019. The data are from the World Bank’s World Development Indicators and Health Nutrition and Population Statistics; they are for a group of 86 countries for which there exist annual data on real gross domestic product (GDP) per capita and two measures of mortality—the crude death rate and life expectancy at birth. 1","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81104795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
between White and Black households. Using the newest Survey of Consumer Finance (SCF) data, Bhutta et al. (2020) report that both the median and mean wealth of Black families are each less than 15% of those of White families. In addition, Kent and Ricketts (2021) use the same SCF data and further point out that Black families are much less likely to own various assets, such as homes, businesses, and financial and retirement assets, which implies that the participation rates for various financial vehicles could vary significantly across races.
{"title":"The Large Gap in Stock Market Participation Between Black and White Households","authors":"J. Bennett, YiLi Chien","doi":"10.20955/es.2022.7","DOIUrl":"https://doi.org/10.20955/es.2022.7","url":null,"abstract":"between White and Black households. Using the newest Survey of Consumer Finance (SCF) data, Bhutta et al. (2020) report that both the median and mean wealth of Black families are each less than 15% of those of White families. In addition, Kent and Ricketts (2021) use the same SCF data and further point out that Black families are much less likely to own various assets, such as homes, businesses, and financial and retirement assets, which implies that the participation rates for various financial vehicles could vary significantly across races.","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79981148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inflation, Part 2: How Do We Construct and Choose an Index?","authors":"Carlos Garriga, Devin Werner","doi":"10.20955/es.2022.16","DOIUrl":"https://doi.org/10.20955/es.2022.16","url":null,"abstract":"","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86216303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
demic, and the U.S. economy has substantially recovered. The unemployment rate was 3.9% in December 2021, and real GDP growth has remained above 2% since 2020:Q2. Despite these positive indicators, there is growing sentiment that the pandemic has inflicted long-lasting effects on the labor market. Specifically, a recent, anecdotal impediment to employers has been the lack of available workers; news sources, political pundits, and economists alike are calling it the “Great Resignation”—a phrase stemming from the idea that, since the economy’s recovery, a large quantity of workers have quit their jobs. People generally view the Great Resignation in a negative way, with the underlying connotation that quits are mostly harmful to the economy and that people do not want to work anymore. However, we should be careful when discussing the growing number of quits because it does not necessarily imply a worker has left the labor market or even entered unemployment; many quits are due to workers switching jobs.
{"title":"The Great Resignation vs. The Great Reallocation: Industry-Level Evidence","authors":"S. Birinci, Aaron Amburgey","doi":"10.20955/es.2022.4","DOIUrl":"https://doi.org/10.20955/es.2022.4","url":null,"abstract":"demic, and the U.S. economy has substantially recovered. The unemployment rate was 3.9% in December 2021, and real GDP growth has remained above 2% since 2020:Q2. Despite these positive indicators, there is growing sentiment that the pandemic has inflicted long-lasting effects on the labor market. Specifically, a recent, anecdotal impediment to employers has been the lack of available workers; news sources, political pundits, and economists alike are calling it the “Great Resignation”—a phrase stemming from the idea that, since the economy’s recovery, a large quantity of workers have quit their jobs. People generally view the Great Resignation in a negative way, with the underlying connotation that quits are mostly harmful to the economy and that people do not want to work anymore. However, we should be careful when discussing the growing number of quits because it does not necessarily imply a worker has left the labor market or even entered unemployment; many quits are due to workers switching jobs.","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86249659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Production of durable goods relies on complex global value chains (GVCs), where several stages in the production process are outsourced. While GVCs allow firms to benefit from specialization, there are risks, as firms are exposed to both domestic and foreign shocks. The unprecedented events of the COVID-19 recession, with a global health crisis that led governments around the world to implement containment policies, put pressure on supply chains and shipping networks—at that same time demand for these goods increased rapidly.1 Supply disruptions, combined with a shift in demand toward durable goods, resulted in supply and demand mismatches and bottlenecks. In this essay, we document the unprecedented nature of these supply and demand mismatches. Figure 1 plots data from IHS Markit on world manufacturing backlogs between January 2003 and December 2021. Backlogs measure monthly changes in the number of unfulfilled new orders. The index values are relative to the previous month: 50 represents no change, values below 50 indicate bottlenecks have loosened (i.e., there are fewer unfilled orders and unused production capacity), and values above 50 indicate bottlenecks have tightened (i.e., there are more unfulfilled Supply Chain Disruptions During the COVID-19 Recession
{"title":"Supply Chain Disruptions During the COVID-19 Recession","authors":"Ana Maria Santacreu, J. LaBelle","doi":"10.20955/es.2022.2","DOIUrl":"https://doi.org/10.20955/es.2022.2","url":null,"abstract":"Production of durable goods relies on complex global value chains (GVCs), where several stages in the production process are outsourced. While GVCs allow firms to benefit from specialization, there are risks, as firms are exposed to both domestic and foreign shocks. The unprecedented events of the COVID-19 recession, with a global health crisis that led governments around the world to implement containment policies, put pressure on supply chains and shipping networks—at that same time demand for these goods increased rapidly.1 Supply disruptions, combined with a shift in demand toward durable goods, resulted in supply and demand mismatches and bottlenecks. In this essay, we document the unprecedented nature of these supply and demand mismatches. Figure 1 plots data from IHS Markit on world manufacturing backlogs between January 2003 and December 2021. Backlogs measure monthly changes in the number of unfulfilled new orders. The index values are relative to the previous month: 50 represents no change, values below 50 indicate bottlenecks have loosened (i.e., there are fewer unfilled orders and unused production capacity), and values above 50 indicate bottlenecks have tightened (i.e., there are more unfulfilled Supply Chain Disruptions During the COVID-19 Recession","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86582053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Miguel Faria e Castro, Pascal Paul, Juan M. Sánchez
retains interest or popularity over time. In the context of finance and banking, the term is also used to describe a situation in which banks try to revive a loan that is on the verge of default by granting further loans to the same borrower. As economic activity came to a standstill in early 2020, governments worldwide tried to prevent mass bankruptcies and layoffs by providing firms with subsidized credit. A few months into the COVID-19 pandemic, concerns emerged that banks would “evergreen” loans. Like government programs, evergreening could help stabilize the economy in the short run, but it could also contribute to the creation and survival of less-productive “zombie firms,” tying up resources and inputs that other firms could more productively employ.
{"title":"Loan Evergreening: Recent Evidence from the U.S.","authors":"Miguel Faria e Castro, Pascal Paul, Juan M. Sánchez","doi":"10.20955/es.2022.26","DOIUrl":"https://doi.org/10.20955/es.2022.26","url":null,"abstract":"retains interest or popularity over time. In the context of finance and banking, the term is also used to describe a situation in which banks try to revive a loan that is on the verge of default by granting further loans to the same borrower. As economic activity came to a standstill in early 2020, governments worldwide tried to prevent mass bankruptcies and layoffs by providing firms with subsidized credit. A few months into the COVID-19 pandemic, concerns emerged that banks would “evergreen” loans. Like government programs, evergreening could help stabilize the economy in the short run, but it could also contribute to the creation and survival of less-productive “zombie firms,” tying up resources and inputs that other firms could more productively employ.","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90651917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inflation, Part 1: What Is it, Exactly?","authors":"Carlos Garriga, Devin Werner","doi":"10.20955/es.2022.15","DOIUrl":"https://doi.org/10.20955/es.2022.15","url":null,"abstract":"","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84454004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
terms of educational attainment and racial composition. Research spurred by renewed interest in these differences finds that the neighborhood a child grows up in can influence adult outcomes, such as college attainment and income (see, e.g., Chetty et al., 2019, and Fogli and Guerrieri, 2019). In this essay, we group U.S. neighborhoods based on the characteristics of their residents and find that most can be organized into one of five distinct groups. In our next essay, we further explore how other economic relationships differ across the neighborhood types.
{"title":"Neighborhood Types and Demographics","authors":"Victoria Gregory, E. Harding","doi":"10.20955/es.2022.12","DOIUrl":"https://doi.org/10.20955/es.2022.12","url":null,"abstract":"terms of educational attainment and racial composition. Research spurred by renewed interest in these differences finds that the neighborhood a child grows up in can influence adult outcomes, such as college attainment and income (see, e.g., Chetty et al., 2019, and Fogli and Guerrieri, 2019). In this essay, we group U.S. neighborhoods based on the characteristics of their residents and find that most can be organized into one of five distinct groups. In our next essay, we further explore how other economic relationships differ across the neighborhood types.","PeriodicalId":11402,"journal":{"name":"Economic Synopses","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82584225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：