Adam M. Guren, A. McKay, Emi Nakamura, J. Steinsson
Recent empirical work uses variation across cities or regions to identify the effects of economic shocks of interest to macroeconomists. The interpretation of such estimates is complicated by the fact that they reflect both partial equilibrium and local general equilibrium effects of the shocks. We propose an approach for recovering estimates of partial equilibrium effects from these cross-regional empirical estimates. The basic idea is to divide the cross-regional estimate by an estimate of the local fiscal multiplier, which measures the strength of local general equilibrium amplification. We apply this approach to recent estimates of housing wealth effects based on city-level variation, and derive conditions under which the adjustment is exact. We then evaluate its accuracy in a richer general equilibrium model of consumption and housing. The paper also reconciles the positive cross-sectional correlation between house price growth and construction with the notion that cities with larger price volatility have lower housing supply elasticities using a model in which housing supply elasticities are more dispersed in the long run than in the short run.
{"title":"What Do We Learn from Cross-Regional Empirical Estimates in Macroeconomics?","authors":"Adam M. Guren, A. McKay, Emi Nakamura, J. Steinsson","doi":"10.1086/712321","DOIUrl":"https://doi.org/10.1086/712321","url":null,"abstract":"Recent empirical work uses variation across cities or regions to identify the effects of economic shocks of interest to macroeconomists. The interpretation of such estimates is complicated by the fact that they reflect both partial equilibrium and local general equilibrium effects of the shocks. We propose an approach for recovering estimates of partial equilibrium effects from these cross-regional empirical estimates. The basic idea is to divide the cross-regional estimate by an estimate of the local fiscal multiplier, which measures the strength of local general equilibrium amplification. We apply this approach to recent estimates of housing wealth effects based on city-level variation, and derive conditions under which the adjustment is exact. We then evaluate its accuracy in a richer general equilibrium model of consumption and housing. The paper also reconciles the positive cross-sectional correlation between house price growth and construction with the notion that cities with larger price volatility have lower housing supply elasticities using a model in which housing supply elasticities are more dispersed in the long run than in the short run.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"175 - 223"},"PeriodicalIF":7.7,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46763611","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}
In this paper, Debortoli, Galı́, and Gambetti offer compelling empirical evidence that extraordinary actions taken by the Federal Reserve were able to shield the macroeconomy from many of the policy constraints associated with the zero lower bound (ZLB) on nominal interest rates. As Debortoli et al. argue, if these extraordinary actions had been ineffective, the United States would have witnessed a change in the volatility of macro aggregates and a change in their response to specific nonfinancial shocks. Yet volatility and impulse responses remained largely unchanged during the ZLB period. There is no one more qualified than the paper’s first discussant to discuss the ZLB, the Fed’s actions, and their effects on the macroeconomy. With this in mind, I will offer no comments of substance about this excellent paper, beyond the observation that I am in agreement with Debortoli et al.’s overall empirical conclusions. Instead, I will focus my comments on a methodological issue: statistical inference in sign-restricted structural vector autoregressions (SVARs), which is one of the methods used in Debortoli et al.’s paper. Sign-restricted SVARs are an increasingly popular method for estimating dynamic causal effects in macroeconomics. Many researchers use a variant of Uhlig’s (2005) Bayes method for imposing these sign restrictions and conducting inference. Thismethod has both strengths and weaknesses. The strengths are widely recognized by macroeconomists but the weaknesses far less so. This discussion explains and highlights these weaknesses. I make two initial comments. First, Debortoli et al. use a sophisticated time-varying SVAR identified by both long-run equality restrictions and shorter-run sign restrictions. To keep things simple, I will focus on a time-invariant SVAR. Second, there is nothing original in my comments beyond a few numerical calculations. Sign-restricted SVARs are a special
{"title":"Comment","authors":"M. Watson","doi":"10.1086/707179","DOIUrl":"https://doi.org/10.1086/707179","url":null,"abstract":"In this paper, Debortoli, Galı́, and Gambetti offer compelling empirical evidence that extraordinary actions taken by the Federal Reserve were able to shield the macroeconomy from many of the policy constraints associated with the zero lower bound (ZLB) on nominal interest rates. As Debortoli et al. argue, if these extraordinary actions had been ineffective, the United States would have witnessed a change in the volatility of macro aggregates and a change in their response to specific nonfinancial shocks. Yet volatility and impulse responses remained largely unchanged during the ZLB period. There is no one more qualified than the paper’s first discussant to discuss the ZLB, the Fed’s actions, and their effects on the macroeconomy. With this in mind, I will offer no comments of substance about this excellent paper, beyond the observation that I am in agreement with Debortoli et al.’s overall empirical conclusions. Instead, I will focus my comments on a methodological issue: statistical inference in sign-restricted structural vector autoregressions (SVARs), which is one of the methods used in Debortoli et al.’s paper. Sign-restricted SVARs are an increasingly popular method for estimating dynamic causal effects in macroeconomics. Many researchers use a variant of Uhlig’s (2005) Bayes method for imposing these sign restrictions and conducting inference. Thismethod has both strengths and weaknesses. The strengths are widely recognized by macroeconomists but the weaknesses far less so. This discussion explains and highlights these weaknesses. I make two initial comments. First, Debortoli et al. use a sophisticated time-varying SVAR identified by both long-run equality restrictions and shorter-run sign restrictions. To keep things simple, I will focus on a time-invariant SVAR. Second, there is nothing original in my comments beyond a few numerical calculations. Sign-restricted SVARs are a special","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"182 - 193"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43764639","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 Phillips (1958), economists have sought to estimate a Phillips curve relationship or a positive relation between inflation,pt, and ameasure of the output gap, xt. Although historically such a relationship could be easily detected, the Phillips curve appears to have flattened in the United Statesmore recently. Some authors have suggested that inflation does not depend on slack, that it is largely exogenous. This raises the key question: What changed? The answer to that question is critical for much of macroeconomics and in particular for monetary policy. With most central banks around the world seeking to stabilize inflation around a target level (e.g., 2% in the United States), it is crucial to understand the determinants of inflation and to knowwhether monetary policy can still affect inflation. Several potential explanations have been provided for the flattening of the Phillips curve. Some have suggested that structural changes in the economy in recent decades have played a significant role (e.g., Duca 2019). In many of models of sticky prices, more rigid prices than in the past or increases in market concentration and pricing power (De Loecker and Eeckhout 2017) could also result in a flattening of the Phillips curve. McLeay and Tenreyro argue instead that monetary policy itself is responsible for the flattening of the Phillips curve. The explanation is simple: If the central bank conducts optimal monetary policy, seeking to minimize deviations of inflation from target and output from potential output, then it should set its policy instruments to increase inflation when output is below potential and vice versa. It follows that optimal policy causes a negative correlation between inflation and the output gap. That negative correlation blurs in turn the positive correlation implied by the Phillips curve, so that in equilibrium, the correlation between
{"title":"Comment","authors":"M. Giannoni","doi":"10.1086/707182","DOIUrl":"https://doi.org/10.1086/707182","url":null,"abstract":"Since Phillips (1958), economists have sought to estimate a Phillips curve relationship or a positive relation between inflation,pt, and ameasure of the output gap, xt. Although historically such a relationship could be easily detected, the Phillips curve appears to have flattened in the United Statesmore recently. Some authors have suggested that inflation does not depend on slack, that it is largely exogenous. This raises the key question: What changed? The answer to that question is critical for much of macroeconomics and in particular for monetary policy. With most central banks around the world seeking to stabilize inflation around a target level (e.g., 2% in the United States), it is crucial to understand the determinants of inflation and to knowwhether monetary policy can still affect inflation. Several potential explanations have been provided for the flattening of the Phillips curve. Some have suggested that structural changes in the economy in recent decades have played a significant role (e.g., Duca 2019). In many of models of sticky prices, more rigid prices than in the past or increases in market concentration and pricing power (De Loecker and Eeckhout 2017) could also result in a flattening of the Phillips curve. McLeay and Tenreyro argue instead that monetary policy itself is responsible for the flattening of the Phillips curve. The explanation is simple: If the central bank conducts optimal monetary policy, seeking to minimize deviations of inflation from target and output from potential output, then it should set its policy instruments to increase inflation when output is below potential and vice versa. It follows that optimal policy causes a negative correlation between inflation and the output gap. That negative correlation blurs in turn the positive correlation implied by the Phillips curve, so that in equilibrium, the correlation between","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"256 - 266"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49155189","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}
Chang-Tai Hsieh opened the general discussionwith a question onmeasurement. The authors’model can be used to back out ameasure of skillbiased technical change (SBTC), he noted. However, this measure is model dependent and its units vary with the parametrization, he argued. Hsieh asked the authors about a possible empirical counterpart to this measure. The authors agreed that their measure of quality or SBTC is model dependent. However, they argued that there is a formal equivalence between their model with SBTC and existing models with capital and skill complementarities (but no SBTC), such as Per Krusell, Lee E. Ohanian, José-Vı́ctor Rı́os-Rull, and Giovanni L. Violante (“Capital-Skill Complementarity and Inequality: A Macroeconomic Analysis,” Econometrica 68, no. 5 [2000]: 1029–53), for which units are somewhat more interpretable. In those models, changes in the price of capital increase the complementarity between capital and high-skill labor. This mechanism amplifies the response to shocks, they noted, just like the “trading up” phenomenon in their model. Richard Blundell argued that the market for childcare constitutes a good case study of trading up. Childcare is a nontradable, low-skill good, he noted. However, the demand for this service has increased over time as women’s incomes improved. In appearance, this runs counter to the authors’ premise. But in reality, the composition of the demand for childcare changed, Blundell said. As incomes grew, so did the demand for skilled childcare. Blundell noted that the literature on the subject typically allows for variable quality in the production of care, much like in the authors’ paper. The authors agreed with Blundell’s comment. They added that childcare is a particularly interesting service in that it could be produced at home as well. Allowing for choices at the extensive margin would be an interesting extension, they said.
{"title":"Discussion","authors":"G. Violante","doi":"10.1086/707188","DOIUrl":"https://doi.org/10.1086/707188","url":null,"abstract":"Chang-Tai Hsieh opened the general discussionwith a question onmeasurement. The authors’model can be used to back out ameasure of skillbiased technical change (SBTC), he noted. However, this measure is model dependent and its units vary with the parametrization, he argued. Hsieh asked the authors about a possible empirical counterpart to this measure. The authors agreed that their measure of quality or SBTC is model dependent. However, they argued that there is a formal equivalence between their model with SBTC and existing models with capital and skill complementarities (but no SBTC), such as Per Krusell, Lee E. Ohanian, José-Vı́ctor Rı́os-Rull, and Giovanni L. Violante (“Capital-Skill Complementarity and Inequality: A Macroeconomic Analysis,” Econometrica 68, no. 5 [2000]: 1029–53), for which units are somewhat more interpretable. In those models, changes in the price of capital increase the complementarity between capital and high-skill labor. This mechanism amplifies the response to shocks, they noted, just like the “trading up” phenomenon in their model. Richard Blundell argued that the market for childcare constitutes a good case study of trading up. Childcare is a nontradable, low-skill good, he noted. However, the demand for this service has increased over time as women’s incomes improved. In appearance, this runs counter to the authors’ premise. But in reality, the composition of the demand for childcare changed, Blundell said. As incomes grew, so did the demand for skilled childcare. Blundell noted that the literature on the subject typically allows for variable quality in the production of care, much like in the authors’ paper. The authors agreed with Blundell’s comment. They added that childcare is a particularly interesting service in that it could be produced at home as well. Allowing for choices at the extensive margin would be an interesting extension, they said.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"337 - 339"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48772270","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}
Borella, De Nardi, and Yang (2019) tackle an important question. They consider two cohorts of white, non-college-educated Americans: (i) those born between 1936 and 1945 (referred to as the 1940s cohort), and (ii) those born between 1956 and 1965 (referred to as the 1960s cohort). They consider three differences in the opportunities afforded to these cohorts: (i) potential wages, (ii) life expectancy, and (iii) out-of-pocket medical expenses. And they ask how these three differences in opportunities affected three differences in outcomes across the two cohorts: (i) labor supply, (ii) savings, and (iii) welfare. The authors reach a provocative conclusion. They write: “Our results thus indicate that the group of white, non-college-educated people born in the 1960s cohort, which comprises about 60% of the population of the same age, experienced large negative changes in wages, large increases in medical expenses, and large decreases in life expectancy and would have been much better off if they had faced the corresponding lifetime opportunities of the 1940s birth cohort.” If correct, this finding is worth repeating. Despite all the technological advances in health care, communication, and transportation; despite the progress that has been made on gender equality; despite the massive increase in international trade; despite iPhones and the internet; despite the fact that real gross domestic product per capita has grown by more than a factor of 2.5 in the 50 years from 1965 to 2015; and despite all these perceived improvements in life, more than half of the US population would have been better off had they been born 20 years earlier. In the following section, Iwill offer some casual observations of changes in the US economy over this time period that might make one skeptical that the 1940s cohort really was better off than the 1960s cohort. To shed light on the authors’ pessimistic conclusions, I will then explain why the authors’ assumptions about each of the three changing opportunities that
Borella, De Nardi和Yang(2019)解决了一个重要的问题。他们考虑了两组没有受过大学教育的白人美国人:(i) 1936年至1945年出生的人(被称为“1940年代”)，(ii) 1956年至1965年出生的人(被称为“1960年代”)。他们考虑了提供给这些群体的机会的三个差异:(一)潜在工资，(二)预期寿命，(三)自费医疗费用。他们还询问了机会的这三种差异是如何影响两组人群结果的三种差异的:(i)劳动力供给，(ii)储蓄和(iii)福利。作者得出了一个发人深省的结论。他们写道:“因此，我们的研究结果表明，出生在20世纪60年代的白人，没有受过大学教育的人，约占同龄人口的60%，经历了工资的大幅负变化，医疗费用的大幅增加，预期寿命的大幅下降，如果他们能面对40年代出生的人相应的一生机会，他们会过得更好。”如果正确的话，这一发现值得重复。尽管在医疗保健、通讯和交通方面取得了所有的技术进步;尽管在性别平等方面取得了进展;尽管国际贸易大幅增长;尽管有iphone和互联网;尽管从1965年到2015年的50年间，实际人均国内生产总值(gdp)增长了2.5倍以上;尽管生活有了这些明显的改善，但超过一半的美国人如果早出生20年，境况会更好。在下一节中，我将提供一些对这段时间内美国经济变化的偶然观察，这些变化可能会让人怀疑40年代的人真的比60年代的人过得更好。为了阐明作者的悲观结论，我将解释为什么作者对三个变化机会中的每一个的假设
{"title":"Comment","authors":"Greg Kaplan","doi":"10.1086/707175","DOIUrl":"https://doi.org/10.1086/707175","url":null,"abstract":"Borella, De Nardi, and Yang (2019) tackle an important question. They consider two cohorts of white, non-college-educated Americans: (i) those born between 1936 and 1945 (referred to as the 1940s cohort), and (ii) those born between 1956 and 1965 (referred to as the 1960s cohort). They consider three differences in the opportunities afforded to these cohorts: (i) potential wages, (ii) life expectancy, and (iii) out-of-pocket medical expenses. And they ask how these three differences in opportunities affected three differences in outcomes across the two cohorts: (i) labor supply, (ii) savings, and (iii) welfare. The authors reach a provocative conclusion. They write: “Our results thus indicate that the group of white, non-college-educated people born in the 1960s cohort, which comprises about 60% of the population of the same age, experienced large negative changes in wages, large increases in medical expenses, and large decreases in life expectancy and would have been much better off if they had faced the corresponding lifetime opportunities of the 1940s birth cohort.” If correct, this finding is worth repeating. Despite all the technological advances in health care, communication, and transportation; despite the progress that has been made on gender equality; despite the massive increase in international trade; despite iPhones and the internet; despite the fact that real gross domestic product per capita has grown by more than a factor of 2.5 in the 50 years from 1965 to 2015; and despite all these perceived improvements in life, more than half of the US population would have been better off had they been born 20 years earlier. In the following section, Iwill offer some casual observations of changes in the US economy over this time period that might make one skeptical that the 1940s cohort really was better off than the 1960s cohort. To shed light on the authors’ pessimistic conclusions, I will then explain why the authors’ assumptions about each of the three changing opportunities that","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"127 - 136"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41738709","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}
There is by now a huge literature on the increase in the college premium and other dimensions of inequality in the United States and many other Western nations (see Acemoglu andAutor [2011] for an overview of this literature). As I discuss in the following text, the focal explanation in this literature is that technological changes of the last 4 decades have increased the demand for skills and have pushed up premia to different kinds of skills, college education among them (though other factors including globalization and changes in labormarket institutions have also contributed to these trends). The paper by Jaimovich, Rebelo, Wong, and Zhang tackles an important topic anddevelops a relatively underresearched line of inquirywithin this broad literature. The main idea is that a major contributor to the increase in the demand for skills has been “trading up” (the authors’ term) by households to higher-quality products as they have become richer. Higher-quality products are argued to bemore intensive in skilled labor. As a result, this process has naturally brought a higher demand for skills as a by-product of economic growth. This is an important idea, and one I sympathize with a lot. The paper also has a noteworthy original contribution in providing compelling motivating evidence. It estimates product quality froma variety of sources, links these to establishment-level demand for skills from the microdata of the Occupational Employment Statistics (OES) data set of the Bureau of Labor Statistics, and verifies that higher-quality products are more skill intensive than products of lower quality. This empirical work alone is worth more than the price of admission. But the paper does not fully deliver on this very promising research agenda. The reason why it fails to do that is interesting and instructive. It is because it follows a methodology I call quantitative Friedmanite modeling. This approach combines Friedman’s (1953/2008) famous
{"title":"Comment","authors":"D. Acemoglu","doi":"10.1086/707187","DOIUrl":"https://doi.org/10.1086/707187","url":null,"abstract":"There is by now a huge literature on the increase in the college premium and other dimensions of inequality in the United States and many other Western nations (see Acemoglu andAutor [2011] for an overview of this literature). As I discuss in the following text, the focal explanation in this literature is that technological changes of the last 4 decades have increased the demand for skills and have pushed up premia to different kinds of skills, college education among them (though other factors including globalization and changes in labormarket institutions have also contributed to these trends). The paper by Jaimovich, Rebelo, Wong, and Zhang tackles an important topic anddevelops a relatively underresearched line of inquirywithin this broad literature. The main idea is that a major contributor to the increase in the demand for skills has been “trading up” (the authors’ term) by households to higher-quality products as they have become richer. Higher-quality products are argued to bemore intensive in skilled labor. As a result, this process has naturally brought a higher demand for skills as a by-product of economic growth. This is an important idea, and one I sympathize with a lot. The paper also has a noteworthy original contribution in providing compelling motivating evidence. It estimates product quality froma variety of sources, links these to establishment-level demand for skills from the microdata of the Occupational Employment Statistics (OES) data set of the Bureau of Labor Statistics, and verifies that higher-quality products are more skill intensive than products of lower quality. This empirical work alone is worth more than the price of admission. But the paper does not fully deliver on this very promising research agenda. The reason why it fails to do that is interesting and instructive. It is because it follows a methodology I call quantitative Friedmanite modeling. This approach combines Friedman’s (1953/2008) famous","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"1997 7","pages":"317 - 330"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41263314","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}
Thomas Philippon opened the general discussion by commenting on the benefits of competition among localities in China. He referred to the findings of Joel Mokyr (A Culture of Growth: The Origins of the Modern Economy [Princeton, NJ: Princeton University Press, 2018]) on the link between political fragmentation and growth in Europe. Philippon recalled that, focusing on the period from 1500 to 1700, Mokyr found that political fragmentation preventedmonarchs from impeding innovation. He pointed out that political fragmentation is particularly beneficial when combined with cultural unity, free trade, and absence of war. Philippon noted that China shares these three characteristics and that competition among localities seems to foster innovation, just like political fragmentation did in Europe a few centuries ago. Frederic Mishkin spoke next. He noted that China experienced an important rural exodus. This reallocation of labor potentially increased productivity, he argued, by reassigning it from unproductive activities in the countryside to productive, capital-intensive ones in cities. Mishkin asked the authors to which extent reallocation could explain China’s growth over the past decades. The authors argued that the evidence suggests that labor reallocation played a minor role, at least over the past two decades. In particular, they pointed out that real wages grew 6% to 7% on average over an extended period of time, which does not fit the labor reallocation narrative. The authors emphasized the role of capital misallocation instead, referring to existing work of theirs. In Chong-En Bai, Chang-Tai Hsieh, and Zheng Michael Song (“The Long Shadow of China’s Fiscal Expansion,” Brookings Papers on Economic Activity 47, no. 2 [2016]: 129–81), they found that capital misallocation has increased over the past 10 years. Part of this misallocation is imputed to the response to the Great Recession, they argued. Local governments circumvented institutional constraints on borrowing by setting
Thomas Philippon在一般性讨论开始时评论了中国地方间竞争的好处。他提到了Joel Mokyr（《增长的文化：现代经济的起源》[新泽西州普林斯顿：普林斯顿大学出版社，2018年]）关于欧洲政治分裂与增长之间联系的研究结果。Philippon回忆道，聚焦于1500年至1700年这段时间，Mokyr发现政治分裂阻止了Monachs阻碍创新。他指出，政治分裂与文化团结、自由贸易和没有战争相结合尤其有益。Philippon指出，中国有这三个特点，地方之间的竞争似乎促进了创新，就像几个世纪前欧洲的政治分裂一样。弗雷德里克·米什金接着发言。他指出，中国经历了一次重要的农村人口外流。他认为，这种劳动力的重新分配可能会提高生产力，将其从农村的非生产性活动重新分配到城市的生产性、资本密集型活动。米什金询问作者，再分配在多大程度上可以解释中国过去几十年的增长。作者认为，证据表明，劳动力再分配至少在过去二十年中发挥了次要作用。他们特别指出，在很长一段时间内，实际工资平均增长6%至7%，这不符合劳动力再分配的说法。相反，作者强调了资本错配的作用，并参考了他们现有的工作。在Chong En Bai、Chang Tai Hsieh和Zheng Michael Song（“中国财政扩张的长期阴影”，Brookings Papers on Economic Activity 47，no.2[2016]129-81）中，他们发现资本错配在过去10年中有所增加。他们认为，这种分配不当的部分原因是对大衰退的反应。地方政府通过设定
{"title":"Discussion","authors":"Thomas Philippon, D. Acemoglu","doi":"10.1086/707192","DOIUrl":"https://doi.org/10.1086/707192","url":null,"abstract":"Thomas Philippon opened the general discussion by commenting on the benefits of competition among localities in China. He referred to the findings of Joel Mokyr (A Culture of Growth: The Origins of the Modern Economy [Princeton, NJ: Princeton University Press, 2018]) on the link between political fragmentation and growth in Europe. Philippon recalled that, focusing on the period from 1500 to 1700, Mokyr found that political fragmentation preventedmonarchs from impeding innovation. He pointed out that political fragmentation is particularly beneficial when combined with cultural unity, free trade, and absence of war. Philippon noted that China shares these three characteristics and that competition among localities seems to foster innovation, just like political fragmentation did in Europe a few centuries ago. Frederic Mishkin spoke next. He noted that China experienced an important rural exodus. This reallocation of labor potentially increased productivity, he argued, by reassigning it from unproductive activities in the countryside to productive, capital-intensive ones in cities. Mishkin asked the authors to which extent reallocation could explain China’s growth over the past decades. The authors argued that the evidence suggests that labor reallocation played a minor role, at least over the past two decades. In particular, they pointed out that real wages grew 6% to 7% on average over an extended period of time, which does not fit the labor reallocation narrative. The authors emphasized the role of capital misallocation instead, referring to existing work of theirs. In Chong-En Bai, Chang-Tai Hsieh, and Zheng Michael Song (“The Long Shadow of China’s Fiscal Expansion,” Brookings Papers on Economic Activity 47, no. 2 [2016]: 129–81), they found that capital misallocation has increased over the past 10 years. Part of this misallocation is imputed to the response to the Great Recession, they argued. Local governments circumvented institutional constraints on borrowing by setting","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"395 - 397"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46859826","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}
Macroeconomics has become interested inmarket power.A series of studies over the past few years has documented a set of possibly interrelated, broad-based, and decades-long trends: increased market concentration, higher profit rates, higher measured price-cost markups, decreased investment rates, reduced firm entry and factor market dynamism, and a fall in labor’s share of income. If one wanted to offer a single, plausibleon-its-face explanation for these trends, it would be reasonable to argue that there has been a broad increase in market power among producers in the economy. This interest in market power extends beyond just productmarkets. Characterizing the role ofmonopsony, especially in the labor market, is an active research area as well. However, there are potential alternative explanations for many of the trends described earlier. These include a growing role for intangible capital in production, increases in product market substitutability due to the expansion of trade or decreases in consumer search costs, and other shifts in production technologies that have increased returns to scale. Moreover, a set of studies has offered evidence for these mechanisms—in case studies, certainly, but in more broadly scoped empirical settings as well. I view the goal of the Covarrubias, Gutiérrez, and Philippon paper as trying to bring together andmake sense of those many data patterns and conflicting stories. On the theory side, the paper shows how a commonly used class of models captures many of the proposed explanations for the aforementioned data trends, and it uses such models to point to possible empirical tests to discriminate among these explanations. On the empirical side, it applies these tests in an attempt to identify the most likely explanation for the data trends. (Though as I note later, the collage of
{"title":"Comment","authors":"C. Syverson","doi":"10.1086/707171","DOIUrl":"https://doi.org/10.1086/707171","url":null,"abstract":"Macroeconomics has become interested inmarket power.A series of studies over the past few years has documented a set of possibly interrelated, broad-based, and decades-long trends: increased market concentration, higher profit rates, higher measured price-cost markups, decreased investment rates, reduced firm entry and factor market dynamism, and a fall in labor’s share of income. If one wanted to offer a single, plausibleon-its-face explanation for these trends, it would be reasonable to argue that there has been a broad increase in market power among producers in the economy. This interest in market power extends beyond just productmarkets. Characterizing the role ofmonopsony, especially in the labor market, is an active research area as well. However, there are potential alternative explanations for many of the trends described earlier. These include a growing role for intangible capital in production, increases in product market substitutability due to the expansion of trade or decreases in consumer search costs, and other shifts in production technologies that have increased returns to scale. Moreover, a set of studies has offered evidence for these mechanisms—in case studies, certainly, but in more broadly scoped empirical settings as well. I view the goal of the Covarrubias, Gutiérrez, and Philippon paper as trying to bring together andmake sense of those many data patterns and conflicting stories. On the theory side, the paper shows how a commonly used class of models captures many of the proposed explanations for the aforementioned data trends, and it uses such models to point to possible empirical tests to discriminate among these explanations. On the empirical side, it applies these tests in an attempt to identify the most likely explanation for the data trends. (Though as I note later, the collage of","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"55 - 61"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43377579","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 skill premium and inequality, more generally, have increased dramatically in the United States since 1980; see the top panel of figure 1. This rise has coincided with a substantial increase in the relative supply of skilled workers; see the bottom panel of figure 1. To the extent that relative supply and demand shape relative prices, these patterns reveal a sizable skill-biased shift in relative demand. A large literature across a range of subfields within economics investigates the roles of various economic forces in generating such a shift. This literature emphasizes in particular two broad categories of observable shocks: a fall in the quality-adjusted cost of capital equipment that is relatively more substitutable for less skilled labor (including computers, software, industrial robots, etc.) and demand shocks biased toward jobs that are relatively intensive in skilled labor (induced by international trade, offshoring, structural transformation, etc.). One central goal of this broad literature is to quantify how important each shock is in explaining the evolution of the skill premium andhowmuch remains unexplained (often referred to as “skill-biased technological change”). “Trading Up and the Skill Premium” does a good job of empirically motivating the potential importance of a particular channel that has not featured prominently (or at all) in this literature: a within-industry version of the link between structural transformation and inequality. The authors provide evidence that higher-income consumers disproportionately purchase higher-quality varieties within industries and that higher-quality varieties within industries are skill intensive. This evidence suggests that an increase in incomewill generate a skill-biased demand shock (i.e., an increase in relative expenditure on skill-intensive varieties at fixed prices) within industries. Themain point of our discussion is that this first pass at quantification is missing two key elements. First, the connection between the model
{"title":"Comment","authors":"Jonathan Vogel","doi":"10.1086/707186","DOIUrl":"https://doi.org/10.1086/707186","url":null,"abstract":"The skill premium and inequality, more generally, have increased dramatically in the United States since 1980; see the top panel of figure 1. This rise has coincided with a substantial increase in the relative supply of skilled workers; see the bottom panel of figure 1. To the extent that relative supply and demand shape relative prices, these patterns reveal a sizable skill-biased shift in relative demand. A large literature across a range of subfields within economics investigates the roles of various economic forces in generating such a shift. This literature emphasizes in particular two broad categories of observable shocks: a fall in the quality-adjusted cost of capital equipment that is relatively more substitutable for less skilled labor (including computers, software, industrial robots, etc.) and demand shocks biased toward jobs that are relatively intensive in skilled labor (induced by international trade, offshoring, structural transformation, etc.). One central goal of this broad literature is to quantify how important each shock is in explaining the evolution of the skill premium andhowmuch remains unexplained (often referred to as “skill-biased technological change”). “Trading Up and the Skill Premium” does a good job of empirically motivating the potential importance of a particular channel that has not featured prominently (or at all) in this literature: a within-industry version of the link between structural transformation and inequality. The authors provide evidence that higher-income consumers disproportionately purchase higher-quality varieties within industries and that higher-quality varieties within industries are skill intensive. This evidence suggests that an increase in incomewill generate a skill-biased demand shock (i.e., an increase in relative expenditure on skill-intensive varieties at fixed prices) within industries. Themain point of our discussion is that this first pass at quantification is missing two key elements. First, the connection between the model","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"34 1","pages":"331 - 336"},"PeriodicalIF":7.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/707186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42706785","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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