{"title":"Comments and Discussion","authors":"Marianne Bertrand, Edward L. Glaeser","doi":"10.1353/eca.2022.0008","DOIUrl":"https://doi.org/10.1353/eca.2022.0008","url":null,"abstract":"","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"40 1","pages":"448 - 476"},"PeriodicalIF":5.9,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76188228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comments and Discussion","authors":"Gabriel Chodorow-reich, V. Ramey","doi":"10.1353/eca.2022.0011","DOIUrl":"https://doi.org/10.1353/eca.2022.0011","url":null,"abstract":"","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"24 1","pages":"50 - 69"},"PeriodicalIF":5.9,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76265148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Rennert, Brian C. Prest, W. Pizer, R. Newell, D. Anthoff, Cora Kingdon, Lisa Rennels, R. Cooke, A. Raftery, H. Ševčíková, F. Errickson
ABSTRACT:The social cost of carbon (SCC) is a crucial metric for informing climate policy, most notably for guiding climate regulations issued by the US government. Characterization of uncertainty and transparency of assumptions are critical for supporting such an influential metric. Challenges inherent to SCC estimation push the boundaries of typical analytical techniques and require augmented approaches to assess uncertainty, raising important considerations for discounting. This paper addresses the challenges of projecting very long-term economic growth, population, and greenhouse gas emissions, as well as calibration of discounting parameters for consistency with those projections. Our work improves on alternative approaches, such as nonprobabilistic scenarios and constant discounting, that have been used by the government but do not fully characterize the uncertainty distribution of fully probabilistic model input data or corresponding SCC estimate outputs. Incorporating the full range of economic uncertainty in the social cost of carbon underscores the importance of adopting a stochastic discounting approach to account for uncertainty in an integrated manner.
{"title":"The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates","authors":"K. Rennert, Brian C. Prest, W. Pizer, R. Newell, D. Anthoff, Cora Kingdon, Lisa Rennels, R. Cooke, A. Raftery, H. Ševčíková, F. Errickson","doi":"10.1353/eca.2022.0003","DOIUrl":"https://doi.org/10.1353/eca.2022.0003","url":null,"abstract":"ABSTRACT:The social cost of carbon (SCC) is a crucial metric for informing climate policy, most notably for guiding climate regulations issued by the US government. Characterization of uncertainty and transparency of assumptions are critical for supporting such an influential metric. Challenges inherent to SCC estimation push the boundaries of typical analytical techniques and require augmented approaches to assess uncertainty, raising important considerations for discounting. This paper addresses the challenges of projecting very long-term economic growth, population, and greenhouse gas emissions, as well as calibration of discounting parameters for consistency with those projections. Our work improves on alternative approaches, such as nonprobabilistic scenarios and constant discounting, that have been used by the government but do not fully characterize the uncertainty distribution of fully probabilistic model input data or corresponding SCC estimate outputs. Incorporating the full range of economic uncertainty in the social cost of carbon underscores the importance of adopting a stochastic discounting approach to account for uncertainty in an integrated manner.","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"2 1","pages":"223 - 305"},"PeriodicalIF":5.9,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76994795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhengyang Jiang, Hanno Lustig, Stijn Van Nieuwerburgh, M. Xiaolan
ABSTRACT:We use discounted cash flow analysis to measure the projected fiscal capacity of the US federal government. We apply our valuation method to the Congressional Budget Office (CBO) projections for the US federal government's primary deficits between 2022 and 2052 and projected debt outstanding in 2052. The discount rate for projected cash flows and future debt must include a GDP or market risk premium in recognition of the risk associated with future surpluses. Despite current low interest rates, we find that US fiscal capacity is more limited than commonly thought. Because of the back-loading of projected primary surpluses, the duration of the surplus claim far exceeds the duration of the outstanding Treasury portfolio. This duration mismatch exposes the government to the risk of rising interest rates, which would trigger the need for higher tax revenue or lower spending. Reducing this risk by front-loading primary surpluses requires a major fiscal adjustment.
{"title":"Measuring US Fiscal Capacity Using Discounted Cash Flow Analysis","authors":"Zhengyang Jiang, Hanno Lustig, Stijn Van Nieuwerburgh, M. Xiaolan","doi":"10.2139/ssrn.4058541","DOIUrl":"https://doi.org/10.2139/ssrn.4058541","url":null,"abstract":"ABSTRACT:We use discounted cash flow analysis to measure the projected fiscal capacity of the US federal government. We apply our valuation method to the Congressional Budget Office (CBO) projections for the US federal government's primary deficits between 2022 and 2052 and projected debt outstanding in 2052. The discount rate for projected cash flows and future debt must include a GDP or market risk premium in recognition of the risk associated with future surpluses. Despite current low interest rates, we find that US fiscal capacity is more limited than commonly thought. Because of the back-loading of projected primary surpluses, the duration of the surplus claim far exceeds the duration of the outstanding Treasury portfolio. This duration mismatch exposes the government to the risk of rising interest rates, which would trigger the need for higher tax revenue or lower spending. Reducing this risk by front-loading primary surpluses requires a major fiscal adjustment.","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"5 1","pages":"157 - 229"},"PeriodicalIF":5.9,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89182948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT:We propose a new measure of the rate of poverty we call the supplemental expenditure poverty measure (SEPM), based on expenditure in the Consumer Expenditure Survey. It treats household expenditure as a measure of resources available to purchase the minimum bundle necessary to meet basic needs. Our measure differs from conventional income and consumption poverty in both concept and measurement, and it has advantages relative to both. Poverty rates using our basic measure are very close in level and recent trend to those of the most preferred income-based poverty rate produced by the US Census Bureau. But the SEPM poverty rate differs from the US Census Bureau measure at different levels of the poverty line. For example, the number of individuals living in either poor or almost poor households is 5 percentage points greater (about 16 million individuals) using our measure. We also construct an augmented measure that adds additional potential liquid resources. This "maximal resources" measure indicates that if disadvantaged households used up all their bank balances and maximized their credit card borrowing, 9.6 percent of the population (over 31 million individuals) would still be poor and unable to purchase the goods necessary for the basic needs of life.
摘要:本文基于消费者支出调查中的支出，提出了一种新的贫困率度量方法——补充支出贫困度量(SEPM)。它将家庭支出视为衡量可用于购买满足基本需要的最低限度必需品的资源。本文的计量方法在概念和计量方法上都与传统的收入贫困和消费性贫困有所不同，并具有相对于两者的优势。根据我们的基本衡量标准，贫困率在水平和近期趋势上与美国人口普查局(US Census Bureau)最优选的基于收入的贫困率非常接近。但SEPM的贫困率与美国人口普查局在不同贫困线水平上的测量结果不同。例如，使用我们的衡量标准，生活在贫困或近乎贫困家庭中的个人数量高出5个百分点(约1600万人)。我们还构建了一个增强措施，增加了额外的潜在液体资源。这一“最大资源”指标表明，如果弱势家庭用尽了他们所有的银行余额，并将信用卡借款最大化，9.6%的人口(超过3100万人)仍将处于贫困状态，无法购买基本生活所需的物品。
{"title":"The Supplemental Expenditure Poverty Measure: A New Method for Measuring Poverty","authors":"John M. Fitzgerald, R. Moffitt","doi":"10.1353/eca.2022.0017","DOIUrl":"https://doi.org/10.1353/eca.2022.0017","url":null,"abstract":"ABSTRACT:We propose a new measure of the rate of poverty we call the supplemental expenditure poverty measure (SEPM), based on expenditure in the Consumer Expenditure Survey. It treats household expenditure as a measure of resources available to purchase the minimum bundle necessary to meet basic needs. Our measure differs from conventional income and consumption poverty in both concept and measurement, and it has advantages relative to both. Poverty rates using our basic measure are very close in level and recent trend to those of the most preferred income-based poverty rate produced by the US Census Bureau. But the SEPM poverty rate differs from the US Census Bureau measure at different levels of the poverty line. For example, the number of individuals living in either poor or almost poor households is 5 percentage points greater (about 16 million individuals) using our measure. We also construct an augmented measure that adds additional potential liquid resources. This \"maximal resources\" measure indicates that if disadvantaged households used up all their bank balances and maximized their credit card borrowing, 9.6 percent of the population (over 31 million individuals) would still be poor and unable to purchase the goods necessary for the basic needs of life.","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"26 1","pages":"253 - 305"},"PeriodicalIF":5.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88729203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT:This paper illustrates how the Congressional Budget Office's (CBO's) July 2021 baseline budget projections would have differed if the agency had used two alternative economic scenarios. The high-sixth scenario is based on the average values of projections for several variables—including inflation and the growth of gross domestic product after removing the effects of inflation (real GDP)—from the six Blue Chip forecasters (about one-sixth of the total) with the highest average interest rate projections. The low-sixth scenario is based on the average values of projections for the same variables from the six Blue Chip forecasters with the lowest average interest rate projections. Using its simplified model of how macroeconomic changes would affect the federal budget, the CBO found that projected deficits would be $2.1 trillion larger from 2022 to 2031 under the high-sixth scenario (totaling $13.8 trillion) than under the low-sixth scenario ($11.7 trillion). Despite a greater amount of debt in dollar terms under the high-sixth scenario, federal debt held by the public as a percentage of GDP would total about 101 percent at the end of 2031 under both scenarios.
{"title":"Budgetary Implications of Economic Scenarios with Higher and Lower Interest Rates","authors":"P. Swagel","doi":"10.1353/eca.2022.0013","DOIUrl":"https://doi.org/10.1353/eca.2022.0013","url":null,"abstract":"ABSTRACT:This paper illustrates how the Congressional Budget Office's (CBO's) July 2021 baseline budget projections would have differed if the agency had used two alternative economic scenarios. The high-sixth scenario is based on the average values of projections for several variables—including inflation and the growth of gross domestic product after removing the effects of inflation (real GDP)—from the six Blue Chip forecasters (about one-sixth of the total) with the highest average interest rate projections. The low-sixth scenario is based on the average values of projections for the same variables from the six Blue Chip forecasters with the lowest average interest rate projections. Using its simplified model of how macroeconomic changes would affect the federal budget, the CBO found that projected deficits would be $2.1 trillion larger from 2022 to 2031 under the high-sixth scenario (totaling $13.8 trillion) than under the low-sixth scenario ($11.7 trillion). Despite a greater amount of debt in dollar terms under the high-sixth scenario, federal debt held by the public as a percentage of GDP would total about 101 percent at the end of 2031 under both scenarios.","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"37 1","pages":"233 - 249"},"PeriodicalIF":5.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74342182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COMMENT BY GARY GORTON Decentralized finance (DeFi) is a blockchain-based set of smart contracts that executes financial transactions without a centralized authority. It relies on member agents jointly making decisions. It is a large and growing sector of crypto space that has the potential to significantly disrupt the financial sector. “Disruptive” in the sense of Christensen (2011), it is a new technology that will reduce or eliminate the need for some set of skills or technologies. For example, the advent of personal computers disrupted the typewriter market. So, the question is: Will DeFi significantly disrupt banking? Regulators and academics need to understand this space because while DeFi is only embryonic currently, it will grow and morph. Makarov and Schoar clearly and comprehensively summarize the ongoing developments, giving us an introduction to this space. Their overview is important because there is bewildering terminology that is little understood by many. It is important to keep in mind that we are in the very early days of blockchain, DeFi, smart contracts, and stablecoins. These early days are somewhat like these two examples: in 1899, there were 30 American car companies, and by the end of the next decade an additional 485 had started up. But this number dropped from 253 in 1908 to only 44 by 1929 and three companies—Ford, General Motors, and Chrysler—accounted for
{"title":"Comments and Discussion","authors":"Adam M. Guren, Joseph Gyourko","doi":"10.1353/eca.2022.0020","DOIUrl":"https://doi.org/10.1353/eca.2022.0020","url":null,"abstract":"COMMENT BY GARY GORTON Decentralized finance (DeFi) is a blockchain-based set of smart contracts that executes financial transactions without a centralized authority. It relies on member agents jointly making decisions. It is a large and growing sector of crypto space that has the potential to significantly disrupt the financial sector. “Disruptive” in the sense of Christensen (2011), it is a new technology that will reduce or eliminate the need for some set of skills or technologies. For example, the advent of personal computers disrupted the typewriter market. So, the question is: Will DeFi significantly disrupt banking? Regulators and academics need to understand this space because while DeFi is only embryonic currently, it will grow and morph. Makarov and Schoar clearly and comprehensively summarize the ongoing developments, giving us an introduction to this space. Their overview is important because there is bewildering terminology that is little understood by many. It is important to keep in mind that we are in the very early days of blockchain, DeFi, smart contracts, and stablecoins. These early days are somewhat like these two examples: in 1899, there were 30 American car companies, and by the end of the next decade an additional 485 had started up. But this number dropped from 253 in 1908 to only 44 by 1929 and three companies—Ford, General Motors, and Chrysler—accounted for","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"79 1","pages":"345 - 366"},"PeriodicalIF":5.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75744872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT:Compared with previous recessions, the recession induced by COVID-19 had a greater impact on women's employment and labor force participation relative to men. But the big divide was less between men and women than it was between the more and the less educated. Contrary to many accounts, women did not exit the labor force in large numbers, and they did not greatly decrease their hours of work. The aggregate female labor force participation rate did not plummet. That said, the ability to balance caregiving and work differed greatly by education, occupation, and race. The more educated could work from home. Those who began the period employed in various in-person service occupations and establishments experienced large reductions in employment. Black women experienced a more negative impact beyond other factors considered, and the health impact of COVID-19 is a probable reason. The estimation of the pandemic's impact depends on the counterfactual used. The real story of women during the pandemic is that employed women who were educating their children and working adult daughters who were caring for their parents were stressed because they were in the labor force, not because they left.
{"title":"Understanding the Economic Impact of COVID-19 on Women","authors":"Claudia Goldin","doi":"10.3386/w29974","DOIUrl":"https://doi.org/10.3386/w29974","url":null,"abstract":"ABSTRACT:Compared with previous recessions, the recession induced by COVID-19 had a greater impact on women's employment and labor force participation relative to men. But the big divide was less between men and women than it was between the more and the less educated. Contrary to many accounts, women did not exit the labor force in large numbers, and they did not greatly decrease their hours of work. The aggregate female labor force participation rate did not plummet. That said, the ability to balance caregiving and work differed greatly by education, occupation, and race. The more educated could work from home. Those who began the period employed in various in-person service occupations and establishments experienced large reductions in employment. Black women experienced a more negative impact beyond other factors considered, and the health impact of COVID-19 is a probable reason. The estimation of the pandemic's impact depends on the counterfactual used. The real story of women during the pandemic is that employed women who were educating their children and working adult daughters who were caring for their parents were stressed because they were in the labor force, not because they left.","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"61 1","pages":"139 - 65"},"PeriodicalIF":5.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86242592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comments and Discussion","authors":"S. Albanesi, Jane Olmstead-Rumsey","doi":"10.1353/eca.2022.0024","DOIUrl":"https://doi.org/10.1353/eca.2022.0024","url":null,"abstract":"","PeriodicalId":51405,"journal":{"name":"Brookings Papers on Economic Activity","volume":"21 1","pages":"111 - 139"},"PeriodicalIF":5.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82708905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","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：