Erik Hurst followed up on a comment made by the discussant, John Haltiwanger. He posited that the increasing dispersion in revenue productivity across firms might arise because of differential shifts in the composition of the labor force based on human capital accumulation. Haltiwanger added that the source of dispersion in revenues per employee could also be the result of misallocation deriving from a learning process. The adoption of information and communications technologies across firms is a slow process, which would result in staggered revenue productivity gains across firms, he argued. The authors agreed that markup dispersion—themain source of productivity differentials according to their results—provides a good explanation, but it is not exhaustive. Other possible channels, like human capital, and the resulting misallocations are worth investigating, they said. The authors also sympathized with the discussant’s suggestion to extend their analysis to the sectoral level and to employ a nested CES production structure. They agreed that this methodologymight be useful to shed more light on the role of big firms in driving aggregate productivity growth. The discussion ended with a brief question by Haltiwanger on the surge of IPOs in the 1990s. He askedwhether the authors had researched their role in contributing to subsequent growth. The authors answered that they did not perform the exercise but that their framework could be employed for answering the question.
Erik Hurst跟进了讨论者John Haltiwanger的评论。他认为，企业之间收入生产率的日益分散可能是由于基于人力资本积累的劳动力组成的差异变化。Haltiwanger补充道，每位员工收入分散的来源也可能是学习过程中分配不当的结果。他认为，企业间采用信息和通信技术是一个缓慢的过程，这将导致企业间收入和生产力的交错增长。作者们一致认为，根据他们的研究结果，标记分散是生产力差异的主要来源，这提供了一个很好的解释，但并不是详尽无遗的。他们表示，其他可能的渠道，如人力资本，以及由此产生的分配不当值得调查。作者还赞同讨论者的建议，将他们的分析扩展到部门层面，并采用嵌套的消费电子产品生产结构。他们一致认为，这种方法可能有助于进一步阐明大公司在推动总生产力增长方面的作用。讨论结束时，Haltiwanger就20世纪90年代IPO的激增提出了一个简短的问题。他询问作者是否研究了他们在促进后续成长中的作用。作者回答说，他们没有进行练习，但他们的框架可以用来回答这个问题。
{"title":"Discussion","authors":"","doi":"10.1086/712327","DOIUrl":"https://doi.org/10.1086/712327","url":null,"abstract":"Erik Hurst followed up on a comment made by the discussant, John Haltiwanger. He posited that the increasing dispersion in revenue productivity across firms might arise because of differential shifts in the composition of the labor force based on human capital accumulation. Haltiwanger added that the source of dispersion in revenues per employee could also be the result of misallocation deriving from a learning process. The adoption of information and communications technologies across firms is a slow process, which would result in staggered revenue productivity gains across firms, he argued. The authors agreed that markup dispersion—themain source of productivity differentials according to their results—provides a good explanation, but it is not exhaustive. Other possible channels, like human capital, and the resulting misallocations are worth investigating, they said. The authors also sympathized with the discussant’s suggestion to extend their analysis to the sectoral level and to employ a nested CES production structure. They agreed that this methodologymight be useful to shed more light on the role of big firms in driving aggregate productivity growth. The discussion ended with a brief question by Haltiwanger on the surge of IPOs in the 1990s. He askedwhether the authors had researched their role in contributing to subsequent growth. The authors answered that they did not perform the exercise but that their framework could be employed for answering the question.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"308 - 308"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42702249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hubmer, Krusell, and Smith use a heterogeneous agent model to quantify the sources ofwealth inequality in the United States since 1960. They find that the substantial decline of US tax progressivity is a key driver of wealth inequality in the United States. Two other keymodel features are (1) allowing for heterogeneous returns and (2) portfolio heterogeneity. My comments focus on the threemain determinants thatHubmer et al. emphasize, which I agree are quite important, and then discuss a few other drivers ofwealth inequality that strikeme asfirst order andworthy of more analysis and discussion in future work.
{"title":"Comment","authors":"Owen M. Zidar","doi":"10.1086/712333","DOIUrl":"https://doi.org/10.1086/712333","url":null,"abstract":"Hubmer, Krusell, and Smith use a heterogeneous agent model to quantify the sources ofwealth inequality in the United States since 1960. They find that the substantial decline of US tax progressivity is a key driver of wealth inequality in the United States. Two other keymodel features are (1) allowing for heterogeneous returns and (2) portfolio heterogeneity. My comments focus on the threemain determinants thatHubmer et al. emphasize, which I agree are quite important, and then discuss a few other drivers ofwealth inequality that strikeme asfirst order andworthy of more analysis and discussion in future work.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"456 - 467"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41939637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the late 1970s, the US earnings distribution has experienced profound changes. Among these changes, two of the most well-known are the increasing share of total earnings that accrues to top earners (i.e., individuals in the top 1% or top 0.1% of the earnings distribution) and the continued relative absence of women from this top-earning group. This latter phenomenon is commonly referred to as the glass ceiling, the emergence of which has spurred both debate over the appropriate policy response as well as active research into its primary causes. However, progress on both fronts has been hampered by the scarcity of empirical evidence from nationally representative data on the gender structure at the top of the earnings distribution. Our goal in this paper is to provide this necessary empirical evidence on the glass ceiling, using newer and better data than have been previously available. In doing so, we also revisit several important questions about top earners of both genders: the dynamics of their earnings, their industry composition, their age and cohort composition, and the evolution of earnings for lifetime top earners. Our interest in top earners is motivated by their disproportionately large influence on the aggregate economy. This influence operates through at least three channels. First, top earners are crucial economic actors. In the United States, individuals in the top 1% of the income distribution earn approximately 15% of aggregate before-tax income and pay about 40% of individual income taxes—more than one and a half times the amount paid by the bottom 90 percentiles—and 50% of all corporate income tax. Because this group includes virtually all high-level managers
{"title":"The Glass Ceiling and the Paper Floor: Changing Gender Composition of Top Earners since the 1980s","authors":"Fatih Guvenen, Greg Kaplan, Jae Song","doi":"10.1086/712328","DOIUrl":"https://doi.org/10.1086/712328","url":null,"abstract":"Since the late 1970s, the US earnings distribution has experienced profound changes. Among these changes, two of the most well-known are the increasing share of total earnings that accrues to top earners (i.e., individuals in the top 1% or top 0.1% of the earnings distribution) and the continued relative absence of women from this top-earning group. This latter phenomenon is commonly referred to as the glass ceiling, the emergence of which has spurred both debate over the appropriate policy response as well as active research into its primary causes. However, progress on both fronts has been hampered by the scarcity of empirical evidence from nationally representative data on the gender structure at the top of the earnings distribution. Our goal in this paper is to provide this necessary empirical evidence on the glass ceiling, using newer and better data than have been previously available. In doing so, we also revisit several important questions about top earners of both genders: the dynamics of their earnings, their industry composition, their age and cohort composition, and the evolution of earnings for lifetime top earners. Our interest in top earners is motivated by their disproportionately large influence on the aggregate economy. This influence operates through at least three channels. First, top earners are crucial economic actors. In the United States, individuals in the top 1% of the income distribution earn approximately 15% of aggregate before-tax income and pay about 40% of individual income taxes—more than one and a half times the amount paid by the bottom 90 percentiles—and 50% of all corporate income tax. Because this group includes virtually all high-level managers","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"309 - 373"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46511088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The distribution of wealth in most countries for which there is reliable data is strikingly uneven. There is also recent work suggesting that the wealth distribution has undergone significant movements over time, most recently with a large upward swing in dispersion in several AngloSaxon countries (Piketty 2014; Saez and Zucman 2016). For example, according to the estimates in Saez and Zucman (2016) for the United States, the share of overall wealth held by the top 1% has increased from around 25% in 1980 to more than 40% today; for the top 0.1% it has increased from less than 10% tomore than 20% over the same time period. The observed developments have generated strong reactions across the political spectrum. In his 2014 book Capital in the Twenty-First Century, Piketty is obviously motivated by the growing inequality in itself, but he also suggests that further increases in wealth concentration may lead to both economic and democratic instability. Conservatives in the United States have expressed worries as well: Is the American dream really still alive, or might it be that a large fraction of the population simply will no longer be able to productively contribute to society? Given, for example, that parental wealth and well-being are important determinants of children’s human capital accumulation, these are legitimate concerns regardless of one’s political views. These concerns, moreover, have stimulated the proposal and discussion of a number of possible changes in policy. The primary aim of the present paper is, instead of focusing on policy changes, to understand the determinants of the observed movements in wealth inequality. This aim is basic but well-motivated in light of the
在大多数有可靠数据的国家，财富的分配是极不平衡的。最近也有研究表明，随着时间的推移，财富分配经历了显著的变化，最近在几个盎格鲁-撒克逊国家，财富分布出现了大幅上升(Piketty 2014;Saez and Zucman 2016)。例如，根据Saez和Zucman(2016)对美国的估计，最富有的1%的人持有的总财富份额从1980年的25%左右增加到今天的40%以上;对于收入最高的0.1%的人来说，这一比例在同一时期从不到10%上升到20%以上。观察到的事态发展在整个政治领域引起了强烈反应。在2014年出版的《21世纪资本论》(Capital In the Twenty-First Century)一书中，皮凯蒂显然是受到日益加剧的不平等本身的推动，但他也指出，财富集中度的进一步提高可能会导致经济和民主的不稳定。美国的保守派也表达了担忧:美国梦真的还活着吗?还是说，很大一部分人口将不再能够为社会做出有成效的贡献?例如，鉴于父母的财富和幸福是儿童人力资本积累的重要决定因素，无论一个人的政治观点如何，这些都是合理的担忧。此外，这些关切刺激了关于若干可能的政策变化的建议和讨论。本文的主要目的不是关注政策变化，而是了解观察到的财富不平等运动的决定因素。这个目标是基本的，但很有动机的
{"title":"Sources of US Wealth Inequality: Past, Present, and Future","authors":"Joachim Hubmer, Per Krusell, Anthony A. Smith","doi":"10.1086/712332","DOIUrl":"https://doi.org/10.1086/712332","url":null,"abstract":"The distribution of wealth in most countries for which there is reliable data is strikingly uneven. There is also recent work suggesting that the wealth distribution has undergone significant movements over time, most recently with a large upward swing in dispersion in several AngloSaxon countries (Piketty 2014; Saez and Zucman 2016). For example, according to the estimates in Saez and Zucman (2016) for the United States, the share of overall wealth held by the top 1% has increased from around 25% in 1980 to more than 40% today; for the top 0.1% it has increased from less than 10% tomore than 20% over the same time period. The observed developments have generated strong reactions across the political spectrum. In his 2014 book Capital in the Twenty-First Century, Piketty is obviously motivated by the growing inequality in itself, but he also suggests that further increases in wealth concentration may lead to both economic and democratic instability. Conservatives in the United States have expressed worries as well: Is the American dream really still alive, or might it be that a large fraction of the population simply will no longer be able to productively contribute to society? Given, for example, that parental wealth and well-being are important determinants of children’s human capital accumulation, these are legitimate concerns regardless of one’s political views. These concerns, moreover, have stimulated the proposal and discussion of a number of possible changes in policy. The primary aim of the present paper is, instead of focusing on policy changes, to understand the determinants of the observed movements in wealth inequality. This aim is basic but well-motivated in light of the","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"391 - 455"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712332","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41671187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John Haltiwanger opened the discussion by bringing attention to the dataused in thepaper. The analysis useddata fromtheNational Establishment Time Series (NETS). The authors argued that the NETS is a reliable source because it aligns on several dimensions with another data set, the County Business Patterns (CBP). However, Haltiwanger argued that NETS overstates employment because of imputations. Further, NETS sales data are not reliable, as documented by Barnatchez, Crane, and Decker (“An Assessment of the National Establishment Times Series [NETS] Database,” Finance and Economics Discussion Series 2017 [2017]: 110). The authors recognized that there are imputations in NETS. However, they emphasized that their findings are validated by several robustness checks. In addition, they noted that similar results hold when using a different data set, the Longitudinal Business Database (LBD). The next comments were related to the suitability of the HerfindahlHirschman Index (HHI) as ameasure ofmarket power at different levels of geographic aggregation. Jeffrey Campbell pointed out that the HHI is not a reliable measure of market power for large cities, where there are many firms in themarket and there is substantial variation across neighborhoods. He encouraged the authors to repeat their analysis for small towns. The authors responded that they replicated their results for different measures of concentration and that their findings are robust regardless of the measure considered. Erik Hurst questioned why the authors decided to start the analysis at the smallest area of aggregation, the ZIP-code level, as opposed to a larger area of aggregation, such as the county level. Seconding Campbell’s remark, the authors responded that themeasurement ofmarket power ismore reliable for smaller geographical
{"title":"Discussion","authors":"J. Haltiwanger","doi":"10.1086/712320","DOIUrl":"https://doi.org/10.1086/712320","url":null,"abstract":"John Haltiwanger opened the discussion by bringing attention to the dataused in thepaper. The analysis useddata fromtheNational Establishment Time Series (NETS). The authors argued that the NETS is a reliable source because it aligns on several dimensions with another data set, the County Business Patterns (CBP). However, Haltiwanger argued that NETS overstates employment because of imputations. Further, NETS sales data are not reliable, as documented by Barnatchez, Crane, and Decker (“An Assessment of the National Establishment Times Series [NETS] Database,” Finance and Economics Discussion Series 2017 [2017]: 110). The authors recognized that there are imputations in NETS. However, they emphasized that their findings are validated by several robustness checks. In addition, they noted that similar results hold when using a different data set, the Longitudinal Business Database (LBD). The next comments were related to the suitability of the HerfindahlHirschman Index (HHI) as ameasure ofmarket power at different levels of geographic aggregation. Jeffrey Campbell pointed out that the HHI is not a reliable measure of market power for large cities, where there are many firms in themarket and there is substantial variation across neighborhoods. He encouraged the authors to repeat their analysis for small towns. The authors responded that they replicated their results for different measures of concentration and that their findings are robust regardless of the measure considered. Erik Hurst questioned why the authors decided to start the analysis at the smallest area of aggregation, the ZIP-code level, as opposed to a larger area of aggregation, such as the county level. Seconding Campbell’s remark, the authors responded that themeasurement ofmarket power ismore reliable for smaller geographical","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"173 - 174"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47521869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Following up on Ricardo Reis’s discussion, Martin Eichenbaum asked a question on disagreement in models with heterogeneous beliefs, which sparked a general discussion on the topic. In practice, disagreement between agents seems to be very persistent. He asked whether the authors’ model and the modification suggested by the discussant were consistent with this observation.Alp Simsek secondedEichenbaum’s comment, pointing out that most models with dispersed information, including the authors’, have the implication that if an individualwere to elicit other agents’ expectations, shewould account for this new information. In otherwords, if average beliefs were part of the current information set, individuals would update their own beliefs in response. This does not seem to be supported by the data, he claimed. Simsek’s own empirical analysis of the Blue Chip Financial Forecasts in Ricardo Caballero and Alp Simsek (“Monetary Policy with OpinionatedMarkets” [Working Paper no. 27313, NBER, Cambridge, MA, 2020]) finds evidence in support of confident disagreement: past individual forecasts are abetter predictor for future individual forecasts than past consensus forecasts. This fact seems to indicate that forecasters have dogmatic beliefs and do not consider the other agents to have useful informationwhen forming expectations for the future. It is important to take this into account for understanding how agents respond to new information, he claimed. The authors answered to the remarks on the role of disagreement among agents arguing that it does not have material consequences for the implications of their theory. There are two arguments that support this conclusion. First, the actual level of information precision is irrelevant for shaping aggregate outcomes. It is the perceived level of precision
{"title":"Discussion","authors":"Valmik Prabhu, Haozhi Qi","doi":"10.1086/712316","DOIUrl":"https://doi.org/10.1086/712316","url":null,"abstract":"Following up on Ricardo Reis’s discussion, Martin Eichenbaum asked a question on disagreement in models with heterogeneous beliefs, which sparked a general discussion on the topic. In practice, disagreement between agents seems to be very persistent. He asked whether the authors’ model and the modification suggested by the discussant were consistent with this observation.Alp Simsek secondedEichenbaum’s comment, pointing out that most models with dispersed information, including the authors’, have the implication that if an individualwere to elicit other agents’ expectations, shewould account for this new information. In otherwords, if average beliefs were part of the current information set, individuals would update their own beliefs in response. This does not seem to be supported by the data, he claimed. Simsek’s own empirical analysis of the Blue Chip Financial Forecasts in Ricardo Caballero and Alp Simsek (“Monetary Policy with OpinionatedMarkets” [Working Paper no. 27313, NBER, Cambridge, MA, 2020]) finds evidence in support of confident disagreement: past individual forecasts are abetter predictor for future individual forecasts than past consensus forecasts. This fact seems to indicate that forecasters have dogmatic beliefs and do not consider the other agents to have useful informationwhen forming expectations for the future. It is important to take this into account for understanding how agents respond to new information, he claimed. The authors answered to the remarks on the role of disagreement among agents arguing that it does not have material consequences for the implications of their theory. There are two arguments that support this conclusion. First, the actual level of information precision is irrelevant for shaping aggregate outcomes. It is the perceived level of precision","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"112 - 114"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47382225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This insightful paper byGuren,McKay,Nakamura, and Steinsson (Guren et al.) contributes to the literature that seeks to translate effects estimated on data at one level of geographic aggregation to another level of geographic aggregation. It can also be seen as a companion paper to their paper on the effects of variations of housingwealth on consumption (Guren et al. forthcoming). The current paper develops a very clever method for recovering “partial equilibrium” effects from regressions using variation across subnational units, such as cities and states. It then applies thismethod to estimating the marginal propensity to consume out of housing wealth. Separately, it offers a solution to a puzzle that has arisen with respect to the widely used Saiz (2010) instrument used for house prices.
这篇由Guren、McKay、Nakamura和Steinsson (Guren et al.)撰写的有见地的论文为寻求将一个地理聚集水平的数据估计效应转化为另一个地理聚集水平的文献做出了贡献。它也可以被视为他们关于住房财富变化对消费影响的论文的配套论文(Guren等人即将出版)。目前的论文开发了一种非常聪明的方法，可以利用城市和州等次国家单位的变化，从回归中恢复“部分平衡”效应。然后将该方法应用于估算住房财富的边际消费倾向。另外，它为广泛使用的用于房价的Saiz(2010)工具所产生的难题提供了解决方案。
{"title":"Comment","authors":"V. Ramey","doi":"10.1086/712323","DOIUrl":"https://doi.org/10.1086/712323","url":null,"abstract":"This insightful paper byGuren,McKay,Nakamura, and Steinsson (Guren et al.) contributes to the literature that seeks to translate effects estimated on data at one level of geographic aggregation to another level of geographic aggregation. It can also be seen as a companion paper to their paper on the effects of variations of housingwealth on consumption (Guren et al. forthcoming). The current paper develops a very clever method for recovering “partial equilibrium” effects from regressions using variation across subnational units, such as cities and states. It then applies thismethod to estimating the marginal propensity to consume out of housing wealth. Separately, it offers a solution to a puzzle that has arisen with respect to the widely used Saiz (2010) instrument used for house prices.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"232 - 241"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46695485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We document a new fact about expectations: in response to the main shocks driving the business cycle, expectations under-react initially but over-shoot later on. We show how previous, seemingly conflicting, evidence can be understood as different facets of this fact. We finally explain what the cumulated evidence means for macroeconomic theory. There is little support for theories emphasizing under-extrapolation or two close cousins of it, cognitive discounting and level-K thinking. Instead, the evidence favors the combination of dispersed, noisy information and over-extrapolation.
{"title":"Imperfect Macroeconomic Expectations: Evidence and Theory","authors":"G. Angeletos, Zhengxin Huo, Karthik A. Sastry","doi":"10.1086/712313","DOIUrl":"https://doi.org/10.1086/712313","url":null,"abstract":"We document a new fact about expectations: in response to the main shocks driving the business cycle, expectations under-react initially but over-shoot later on. We show how previous, seemingly conflicting, evidence can be understood as different facets of this fact. We finally explain what the cumulated evidence means for macroeconomic theory. There is little support for theories emphasizing under-extrapolation or two close cousins of it, cognitive discounting and level-K thinking. Instead, the evidence favors the combination of dispersed, noisy information and over-extrapolation.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"1 - 86"},"PeriodicalIF":7.7,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45845620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A remarkable fact about the historical US business cycle is that, after unemployment reached its peak in a recession, and a recovery begins, the annual reduction in the unemployment rate is stable at around one tenth of the current level of unemployment. We document this fact in a companion paper, Hall and Kudlyak (2020a). Here, we consider explanations for the surprising consistency of recoveries. We show that the evolution of the labor market from recession to recovery involves more than the direct effect of persistent unemployment of job-losers from the recession shock -- unemployment during the recovery is above normal for people who did not lose jobs during the recession. We explore models of the labor market's self-recovery that imply gradual working off of unemployment following a recession shock. We emphasize the feedback from high unemployment to the forces driving job creation. These models also explain why the recovery of market-wide unemployment is so much slower than the rate at which individual unemployed workers find new jobs. The reasons include the fact that the path that individual job-losers follow back to stable employment often includes several brief interim jobs.
{"title":"Why Has the US Economy Recovered So Consistently from Every Recession in the Past 70 Years?","authors":"Robert E. Hall, Marianna Kudlyak","doi":"10.1086/718588","DOIUrl":"https://doi.org/10.1086/718588","url":null,"abstract":"A remarkable fact about the historical US business cycle is that, after unemployment reached its peak in a recession, and a recovery begins, the annual reduction in the unemployment rate is stable at around one tenth of the current level of unemployment. We document this fact in a companion paper, Hall and Kudlyak (2020a). Here, we consider explanations for the surprising consistency of recoveries. We show that the evolution of the labor market from recession to recovery involves more than the direct effect of persistent unemployment of job-losers from the recession shock -- unemployment during the recovery is above normal for people who did not lose jobs during the recession. We explore models of the labor market's self-recovery that imply gradual working off of unemployment following a recession shock. We emphasize the feedback from high unemployment to the forces driving job creation. These models also explain why the recovery of market-wide unemployment is so much slower than the rate at which individual unemployed workers find new jobs. The reasons include the fact that the path that individual job-losers follow back to stable employment often includes several brief interim jobs.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"36 1","pages":"1 - 55"},"PeriodicalIF":7.7,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44725087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent work highlights a falling entry rate of new firms and a rising market share of large firms in the United States. To understand how these changing firm demographics have affected growth, we decompose productivity growth into the firms doing the innovating. We trace how much each firm innovates by the rate at which it opens and closes plants, the market share of those plants, and how fast its surviving plants grow. Using data on all nonfarm businesses from 1982–2013, we find that new and young firms (ages 0 to 5 years) account for almost one-half of growth – three times their share of employment. Large established firms contribute only one-tenth of growth despite representing one-fourth of employment. Older firms do explain most of the speedup and slowdown during the middle of our sample. Finally, most growth takes the form of incumbents improving their own products, as opposed to creative destruction or new varieties.
{"title":"Innovative Growth Accounting","authors":"Peter J. Klenow, Huiyu Li","doi":"10.1086/712325","DOIUrl":"https://doi.org/10.1086/712325","url":null,"abstract":"Recent work highlights a falling entry rate of new firms and a rising market share of large firms in the United States. To understand how these changing firm demographics have affected growth, we decompose productivity growth into the firms doing the innovating. We trace how much each firm innovates by the rate at which it opens and closes plants, the market share of those plants, and how fast its surviving plants grow. Using data on all nonfarm businesses from 1982–2013, we find that new and young firms (ages 0 to 5 years) account for almost one-half of growth – three times their share of employment. Large established firms contribute only one-tenth of growth despite representing one-fourth of employment. Older firms do explain most of the speedup and slowdown during the middle of our sample. Finally, most growth takes the form of incumbents improving their own products, as opposed to creative destruction or new varieties.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"245 - 295"},"PeriodicalIF":7.7,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42633607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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