There is a rapidly growing recent literature that analyzes the rise of market power over the last 4 decades in the United States and in many other economies. The study of market power is, of course, not new and is arguably as old as the study of economics itself. But the renewed interest is its scope, particularly the role for macroeconomics. Much of the recent literature focuses on measuring market power throughout the economy and on its quantitative macroeconomic implications. Many macroeconomic models from monetary economics, over trade and urban economics, to labor have predictions that hinge on the degree of market power that firms have. Themonetary transmissionmechanism in theNewKeynesianmodels, for example, crucially depends onmarkups and the extent to which the market power of firms is pervasive throughout the economy. The challenge, therefore, is to find appropriate ways to measure market power for a representative sample of the universe of firms in the economy. The paper by Esteban Rossi-Hansberg, Pierre Sarte, andNico Trachter draws attention to an important and hitherto understudied issue in this literature, the dichotomy between national and local measures of concentration. The main idea is that the degree of concentration of firms at the national level is very different than what it is at a local level, be it the state, the metropolitan area, or the ZIP code. They find a baffling fact: all measures of local concentration show a declining trend, whereas measures of national concentration show an increasing trend. This is an important observation and I sympathizewith the premise of investigating market power at different levels of aggregation and for different subeconomies of the macro economy. After all, to understand the macro economywe need to understand the micro origins. The paper
{"title":"Comment","authors":"J. Eeckhout","doi":"10.1086/712318","DOIUrl":"https://doi.org/10.1086/712318","url":null,"abstract":"There is a rapidly growing recent literature that analyzes the rise of market power over the last 4 decades in the United States and in many other economies. The study of market power is, of course, not new and is arguably as old as the study of economics itself. But the renewed interest is its scope, particularly the role for macroeconomics. Much of the recent literature focuses on measuring market power throughout the economy and on its quantitative macroeconomic implications. Many macroeconomic models from monetary economics, over trade and urban economics, to labor have predictions that hinge on the degree of market power that firms have. Themonetary transmissionmechanism in theNewKeynesianmodels, for example, crucially depends onmarkups and the extent to which the market power of firms is pervasive throughout the economy. The challenge, therefore, is to find appropriate ways to measure market power for a representative sample of the universe of firms in the economy. The paper by Esteban Rossi-Hansberg, Pierre Sarte, andNico Trachter draws attention to an important and hitherto understudied issue in this literature, the dichotomy between national and local measures of concentration. The main idea is that the degree of concentration of firms at the national level is very different than what it is at a local level, be it the state, the metropolitan area, or the ZIP code. They find a baffling fact: all measures of local concentration show a declining trend, whereas measures of national concentration show an increasing trend. This is an important observation and I sympathizewith the premise of investigating market power at different levels of aggregation and for different subeconomies of the macro economy. After all, to understand the macro economywe need to understand the micro origins. The paper","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"151 - 166"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49258601","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}
Greg Kaplan opened the discussion by inquiring about the goal of the estimation and the nature of the counterfactual exercises carried out in the paper. He expressed concern that the exercises that study the response of households to changes in prices are treating an equilibrium object—namely, house prices—as a parameter. AlisdairMcKay responded that individual households treat house prices as exogenous. Thus, it is appropriate to treat house prices as a parameter in a partial equilibrium setting and study how sensitive household decisions are to that parameter. As a following step, it is then possible to aggregate housing decisions. He agreed that a real-world counterpart of this exercise is not immediate because it would consider an average of different housing parameters across individuals. Kaplan agreed on the scope of the exercise in the context of the model, but he pointed out that we need to believe that the assumptions made in the model also hold in the real world. He also emphasized the importance of clearly stating the relevant real-world counterfactual at the core of the analysis that is being carried out. Jón Steinsson argued that it was important to distinguish clearly between two separate objects. The first is what is measured in the data, which is clearly defined by the empirical specification. The second is the interpretation of the measured object. A theoretical model can provide an interpretation for the object measured in the data. He highlighted that housing is not exogenous in the model presented in the paper, yet it corresponds to the object observed in the data. Adam Guren further pointed out that the home price shock is similar to a foreign demand shock and that the effect of such a shock on consumption is instrumental to correctly calibrating the housing wealth effect in a general equilibrium model.
{"title":"Discussion","authors":"","doi":"10.1086/712324","DOIUrl":"https://doi.org/10.1086/712324","url":null,"abstract":"Greg Kaplan opened the discussion by inquiring about the goal of the estimation and the nature of the counterfactual exercises carried out in the paper. He expressed concern that the exercises that study the response of households to changes in prices are treating an equilibrium object—namely, house prices—as a parameter. AlisdairMcKay responded that individual households treat house prices as exogenous. Thus, it is appropriate to treat house prices as a parameter in a partial equilibrium setting and study how sensitive household decisions are to that parameter. As a following step, it is then possible to aggregate housing decisions. He agreed that a real-world counterpart of this exercise is not immediate because it would consider an average of different housing parameters across individuals. Kaplan agreed on the scope of the exercise in the context of the model, but he pointed out that we need to believe that the assumptions made in the model also hold in the real world. He also emphasized the importance of clearly stating the relevant real-world counterfactual at the core of the analysis that is being carried out. Jón Steinsson argued that it was important to distinguish clearly between two separate objects. The first is what is measured in the data, which is clearly defined by the empirical specification. The second is the interpretation of the measured object. A theoretical model can provide an interpretation for the object measured in the data. He highlighted that housing is not exogenous in the model presented in the paper, yet it corresponds to the object observed in the data. Adam Guren further pointed out that the home price shock is similar to a foreign demand shock and that the effect of such a shock on consumption is instrumental to correctly calibrating the housing wealth effect in a general equilibrium model.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"242 - 244"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47341663","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 NBER’s 35th Annual Conference on Macroeconomics brought together leading scholars to present, discuss, and debate six research papers on central issues in contemporary macroeconomics. In addition, Jeremy Stein, former governor of the Federal Reserve, led a thoughtprovoking discussion on whether the financial system is safer now than it was prior to the Great Recession. Given the pandemic, the conference took place via Zoom. Video recordings of the presentations of the papers and the after-dinner talk are all accessible on the web page of the NBER AnnualConference onMacroeconomics. These videosmake auseful complement to this volumeandmake the content of the conferencemorewidely accessible. This conference volume contains edited versions of the six papers presented at the conference. With one exception, each paper is followed by two written discussions by leading scholars and a summary of the debates that followed each paper. In the exception, there is only one written discussion. In their paper “Imperfect Macroeconomic Expectations: Evidence and Theory,” George-Marios Angeletos, Zhen Huo, and Karthik Sastry tackle a central question in macroeconomics: How should we model people’s expectations? For many years the answer to this question was widely viewed among mainstream macroeconomists as a settled issue. The answer was that people have rational expectations and common knowledge about the state of the economy. That consensus shattered under theweight of a large literature testing rational expectations, based in part on survey-based data. But what should we replace our simple benchmark model of expectations with? The paper by Angeletos et al.
{"title":"Editorial","authors":"M. Eichenbaum, Erik Hurst","doi":"10.1086/712310","DOIUrl":"https://doi.org/10.1086/712310","url":null,"abstract":"The NBER’s 35th Annual Conference on Macroeconomics brought together leading scholars to present, discuss, and debate six research papers on central issues in contemporary macroeconomics. In addition, Jeremy Stein, former governor of the Federal Reserve, led a thoughtprovoking discussion on whether the financial system is safer now than it was prior to the Great Recession. Given the pandemic, the conference took place via Zoom. Video recordings of the presentations of the papers and the after-dinner talk are all accessible on the web page of the NBER AnnualConference onMacroeconomics. These videosmake auseful complement to this volumeandmake the content of the conferencemorewidely accessible. This conference volume contains edited versions of the six papers presented at the conference. With one exception, each paper is followed by two written discussions by leading scholars and a summary of the debates that followed each paper. In the exception, there is only one written discussion. In their paper “Imperfect Macroeconomic Expectations: Evidence and Theory,” George-Marios Angeletos, Zhen Huo, and Karthik Sastry tackle a central question in macroeconomics: How should we model people’s expectations? For many years the answer to this question was widely viewed among mainstream macroeconomists as a settled issue. The answer was that people have rational expectations and common knowledge about the state of the economy. That consensus shattered under theweight of a large literature testing rational expectations, based in part on survey-based data. But what should we replace our simple benchmark model of expectations with? The paper by Angeletos et al.","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"xi - xvii"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42054725","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 is a nice paper that uses data from the US Social Security Administration (SSA) to provide empirical evidence on gender differences in earnings. It focuses on differences in gender representation at the top 0.1% and the next 0.9% of the earnings distribution. It examines the persistence of an individual’s presence in these top percentiles, how age and industry composition matter, and gives some feel for life-cycle dynamics, all the while contrasting the presence of women versus men. The authors have access to a 10% representative sample of individual earnings histories from the SSA (constructed by selecting all individuals with the same last digit of a transformation of the social security number). This is a panel data set spanning 32 years: 1981–2012. There is basic demographic information available: age, sex, race, type of work (farm/ nonfarm, employment/self-employment), and earnings. The latter consists of wages and salaries, bonuses, and exercised stock options as reported in Box 1 on a W-2 form. For most of their analysis, they select from the 10% sample all individuals who in that year are between 25 and 60 years old and whose annual earnings exceed a minimum threshold (equivalent to 13 weeks, full-time, at one-half minimum wage). Among the advantages of a panel set are that one can, for example, study earnings over a number of years to smooth out temporary fluctuations, ask questions about persistence, and examine measures of lifetime income. What this data set sacrifices, however, is any rich information about other characteristics of these individuals such as their education, marital status and children, spousal attributes, and occupation other than that captured by broad industry categories. This makes it virtually
{"title":"Comment","authors":"Raquel Fernández","doi":"10.1086/712330","DOIUrl":"https://doi.org/10.1086/712330","url":null,"abstract":"This is a nice paper that uses data from the US Social Security Administration (SSA) to provide empirical evidence on gender differences in earnings. It focuses on differences in gender representation at the top 0.1% and the next 0.9% of the earnings distribution. It examines the persistence of an individual’s presence in these top percentiles, how age and industry composition matter, and gives some feel for life-cycle dynamics, all the while contrasting the presence of women versus men. The authors have access to a 10% representative sample of individual earnings histories from the SSA (constructed by selecting all individuals with the same last digit of a transformation of the social security number). This is a panel data set spanning 32 years: 1981–2012. There is basic demographic information available: age, sex, race, type of work (farm/ nonfarm, employment/self-employment), and earnings. The latter consists of wages and salaries, bonuses, and exercised stock options as reported in Box 1 on a W-2 form. For most of their analysis, they select from the 10% sample all individuals who in that year are between 25 and 60 years old and whose annual earnings exceed a minimum threshold (equivalent to 13 weeks, full-time, at one-half minimum wage). Among the advantages of a panel set are that one can, for example, study earnings over a number of years to smooth out temporary fluctuations, ask questions about persistence, and examine measures of lifetime income. What this data set sacrifices, however, is any rich information about other characteristics of these individuals such as their education, marital status and children, spousal attributes, and occupation other than that captured by broad industry categories. This makes it virtually","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"381 - 387"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42181466","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 authors build a quantitative framework, broad enough to nest different types of ideas proposed in behavioral macroeconomics, to help make sense of the survey evidence on expectations. The goal is not so much to understand survey evidence for its own sake, though that might be interesting. Nor is it to decide between a class of competing models, all of which work fairly well. Rather, the goal is to inform future directions for research in a field that at the moment seems a bit lost in a “wilderness.” In the end, using their approach, the authors are able to say that theweight of the evidence favors some types of models rather than others. The focus on survey evidence comes at a time of uncertainty about future directions inmacroeconomics.Many, including some of the authors in prior work, have noted serious failings with benchmark models. These failings have a solution, perhaps, within a different class of models, ones that do not impose rational expectations. But rational expectations are a powerful disciplining device, and this broader class seems impossibly large. The literature has, for several years, recognized the potential of survey evidence to itself be a disciplining device, filling some of the role that rational expectations used to, and do still, play. Barberis et al. (2015), for example, argue for using survey evidence to decide between competing models in financial economics. In macroeconomics, Coibion and Gorodnichenko (2015) argue that one need not have a model to interpret survey evidence: a positive sign in regressions of forecast errors on forecast revisions is
{"title":"Comment","authors":"Jessica A. Wachter","doi":"10.1086/712314","DOIUrl":"https://doi.org/10.1086/712314","url":null,"abstract":"The authors build a quantitative framework, broad enough to nest different types of ideas proposed in behavioral macroeconomics, to help make sense of the survey evidence on expectations. The goal is not so much to understand survey evidence for its own sake, though that might be interesting. Nor is it to decide between a class of competing models, all of which work fairly well. Rather, the goal is to inform future directions for research in a field that at the moment seems a bit lost in a “wilderness.” In the end, using their approach, the authors are able to say that theweight of the evidence favors some types of models rather than others. The focus on survey evidence comes at a time of uncertainty about future directions inmacroeconomics.Many, including some of the authors in prior work, have noted serious failings with benchmark models. These failings have a solution, perhaps, within a different class of models, ones that do not impose rational expectations. But rational expectations are a powerful disciplining device, and this broader class seems impossibly large. The literature has, for several years, recognized the potential of survey evidence to itself be a disciplining device, filling some of the role that rational expectations used to, and do still, play. Barberis et al. (2015), for example, argue for using survey evidence to decide between competing models in financial economics. In macroeconomics, Coibion and Gorodnichenko (2015) argue that one need not have a model to interpret survey evidence: a positive sign in regressions of forecast errors on forecast revisions is","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"87 - 98"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44925004","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, Guren, McKay, Nakamura, and Steinsson (Guren et al.) advocate using regional cross-sectional empirical estimates to measure microeconomic parameters such as the marginal propensity to consume out of housing wealth. The “divide-by-multiplier”method recovers the microeconomic parameter by dividing the regional response to a shock by a “demandmultiplier” estimated separately as the regional response to a government spending shock. This method has many potential applications in regional data. (Full disclosure: in Chodorow-Reich, Nenov, and Simsek [2019] I use it to measure the stock market wealth effect on consumption.) Guren et al. provide a comprehensive yet accessible theoretical exposition and a numerical evaluation in the context of a model of housing wealth. I divide my comments into four parts. First, I provide background on literatures using regional data in macroeconomics and on demand multipliers. Second, Guren et al. apply the method to a shock to preferences for housing. This choice introduces complications because housing is both part of wealth and an expenditure item, and both construction and consumer spending may respond to changes in house prices. I illustrate the relationship using a simpler yet still fully microfounded model with pure wealth shocks. Third, I use thismodel tomakemymain critique of the paper or, more accurately, of the paper’s title. A casual reader might infer that the divide-by-multipliermethod provides the only validway to interpret regional empirical estimates. My simple model illustrates an additional property of regional effects of demand shocks, namely, that they provide a lower bound for the appropriately defined aggregate effect of the shock. I conclude by reviewing other recent approaches to interpreting
{"title":"Comment","authors":"Gabriel Chodorow-reich","doi":"10.1086/712322","DOIUrl":"https://doi.org/10.1086/712322","url":null,"abstract":"In this paper, Guren, McKay, Nakamura, and Steinsson (Guren et al.) advocate using regional cross-sectional empirical estimates to measure microeconomic parameters such as the marginal propensity to consume out of housing wealth. The “divide-by-multiplier”method recovers the microeconomic parameter by dividing the regional response to a shock by a “demandmultiplier” estimated separately as the regional response to a government spending shock. This method has many potential applications in regional data. (Full disclosure: in Chodorow-Reich, Nenov, and Simsek [2019] I use it to measure the stock market wealth effect on consumption.) Guren et al. provide a comprehensive yet accessible theoretical exposition and a numerical evaluation in the context of a model of housing wealth. I divide my comments into four parts. First, I provide background on literatures using regional data in macroeconomics and on demand multipliers. Second, Guren et al. apply the method to a shock to preferences for housing. This choice introduces complications because housing is both part of wealth and an expenditure item, and both construction and consumer spending may respond to changes in house prices. I illustrate the relationship using a simpler yet still fully microfounded model with pure wealth shocks. Third, I use thismodel tomakemymain critique of the paper or, more accurately, of the paper’s title. A casual reader might infer that the divide-by-multipliermethod provides the only validway to interpret regional empirical estimates. My simple model illustrates an additional property of regional effects of demand shocks, namely, that they provide a lower bound for the appropriately defined aggregate effect of the shock. I conclude by reviewing other recent approaches to interpreting","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"224 - 231"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43532847","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}
{"title":"Relation of the Directors to the Work and Publications of the NBER","authors":"","doi":"10.1086/667777","DOIUrl":"https://doi.org/10.1086/667777","url":null,"abstract":"","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"26 1","pages":"vii - viii"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/667777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42511369","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 paper by Guvenen, Kaplan, and Song provides many useful insights into the evolution of top earners’ earnings and the dynamics of the gender gap at the top of the earning distribution. The authors have access to a representative sample of 10% of individual earnings from the US Social Security Administration (SSA) between 1981 and 2012. The panel structure of the data allows them to follow individuals over time, shedding light on the persistence of top earners and addressing some of the shortcomings of the literature that uses cross-sectional data. Theyfind several interesting results. First, in 2012, the share ofwomen among the top 0.1% of earners is only 11% and among the top 1% only 18%. This gap was even larger earlier in the sample, suggesting a slow positive trend toward the reduction of the gender gap, with themajority of the gain concentrated in the 1980s and the 1990s. Second, the paper unveils a very large turnover over the life cycle among top earners, for both men andwomen, and highlights that gender improvements coincidewith a larger persistence over time ofwomen at the top of the income distribution. Although in the 1980swomenwere twice as likely asmen to drop out from the top earners, in recent years the probability of men and women of droppingout hasbecome similar. Third, industryandagevariationsprovide further insights on the dynamics of the gender gap. The authors do not find any obvious relationship between the evolution of income across industries and the mitigation of the gender gap over time, whereas the distribution of age among top earners shows significant changes, with new cohorts of women making inroads into the top 1% earlier in their lifetime than previous cohorts (this results does not hold for the top 0.1% earners).
{"title":"Comment","authors":"Paola Sapienza","doi":"10.1086/712329","DOIUrl":"https://doi.org/10.1086/712329","url":null,"abstract":"The paper by Guvenen, Kaplan, and Song provides many useful insights into the evolution of top earners’ earnings and the dynamics of the gender gap at the top of the earning distribution. The authors have access to a representative sample of 10% of individual earnings from the US Social Security Administration (SSA) between 1981 and 2012. The panel structure of the data allows them to follow individuals over time, shedding light on the persistence of top earners and addressing some of the shortcomings of the literature that uses cross-sectional data. Theyfind several interesting results. First, in 2012, the share ofwomen among the top 0.1% of earners is only 11% and among the top 1% only 18%. This gap was even larger earlier in the sample, suggesting a slow positive trend toward the reduction of the gender gap, with themajority of the gain concentrated in the 1980s and the 1990s. Second, the paper unveils a very large turnover over the life cycle among top earners, for both men andwomen, and highlights that gender improvements coincidewith a larger persistence over time ofwomen at the top of the income distribution. Although in the 1980swomenwere twice as likely asmen to drop out from the top earners, in recent years the probability of men and women of droppingout hasbecome similar. Third, industryandagevariationsprovide further insights on the dynamics of the gender gap. The authors do not find any obvious relationship between the evolution of income across industries and the mitigation of the gender gap over time, whereas the distribution of age among top earners shows significant changes, with new cohorts of women making inroads into the top 1% earlier in their lifetime than previous cohorts (this results does not hold for the top 0.1% earners).","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"374 - 380"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42703081","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}
Understanding the determinants of innovation and productivity growth is a core open area of economics research. Although enormous progress has beenmade in theoretical models of innovation accompanied by an increasing use of firm-level data to quantify the nature of innovation and productivity, many challenges remain. A key challenge is that much of the research using firm-level data has focused on firms with observable measures of the inputs into innovation (e.g., research and development [R&D] expenditures) and direct measures of the success of innovations (e.g., patents). This approach focuses on a relativelynarrow subset offirms and sectors where such observables are relevant. Most firms do not report R&D expenditures or patents. It is implausible that only the firms with these observable measures of innovation are responsible for the observed fluctuations in productivity growth from innovation. As evidence of this, a National Academy of Sciences report (Brown et al. 2005) highlighting the limitations of R&Ddata reported that one of themost innovative firms in retail trade, Walmart, reports no R&D expenditures in its 10-K reports. This paper takes an indirect approach to identifying innovation activity that overcomes these limitations. Using an innovative growth accounting framework that is motivated by a quality laddermodel of innovation, this paper uses data on the employment growth rate distribution for the universe of private sector, nonfarm (hereafter private sector for short) establishments to quantify the contribution of creative destruction (CD), own innovation, and new varieties. The authors accomplish this important objective by using the Longitudinal Business Database (LBD) that tracks the employment dynamics, including entry and exit,firm size, andfirmage of
{"title":"Comment","authors":"J. Haltiwanger","doi":"10.1086/712326","DOIUrl":"https://doi.org/10.1086/712326","url":null,"abstract":"Understanding the determinants of innovation and productivity growth is a core open area of economics research. Although enormous progress has beenmade in theoretical models of innovation accompanied by an increasing use of firm-level data to quantify the nature of innovation and productivity, many challenges remain. A key challenge is that much of the research using firm-level data has focused on firms with observable measures of the inputs into innovation (e.g., research and development [R&D] expenditures) and direct measures of the success of innovations (e.g., patents). This approach focuses on a relativelynarrow subset offirms and sectors where such observables are relevant. Most firms do not report R&D expenditures or patents. It is implausible that only the firms with these observable measures of innovation are responsible for the observed fluctuations in productivity growth from innovation. As evidence of this, a National Academy of Sciences report (Brown et al. 2005) highlighting the limitations of R&Ddata reported that one of themost innovative firms in retail trade, Walmart, reports no R&D expenditures in its 10-K reports. This paper takes an indirect approach to identifying innovation activity that overcomes these limitations. Using an innovative growth accounting framework that is motivated by a quality laddermodel of innovation, this paper uses data on the employment growth rate distribution for the universe of private sector, nonfarm (hereafter private sector for short) establishments to quantify the contribution of creative destruction (CD), own innovation, and new varieties. The authors accomplish this important objective by using the Longitudinal Business Database (LBD) that tracks the employment dynamics, including entry and exit,firm size, andfirmage of","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"296 - 307"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45729314","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}
Authors Rossi-Hansberg, Sarte, and Trachter have delivered a focused, definitive paper, part of a broader research program on structural change in US product and labor markets. The subject of the paper is concentration. Measurement of concentration starts from the market definition. Concentration depends on the pattern of market shares within a market, defined by a product, a geographic region, and a time period. Amarket is a specification of the product, region, and time such that a hypothetical monopolist would have market power—it could elevate its price substantially abovemarginal cost. The connection to concentration is the general belief that the actual market power of the firms selling in a market depends on the number and importance of the competitors selling in that market. Themost widely usedmeasure of concentration—and the one used in this paper—is the Herfindahl-Hirschman Index (HHI), the sum of the squared market shares of the sellers in a market. This measure is sensitive to both the number of sellers and their market shares, and it is a sufficient statistic for the elevation of price over marginal cost in certain oligopoly models. However, other models, especially those involving collusion, predict substantial exercise ofmarket powerwithpricesmarked up well above marginal cost, in the face of HHI measures suggesting rather less price elevation. Some markets are national or international. The products in these markets have low transport cost across the country relative to price. A substantial body of research has found high and rising concentration in many national markets, primarily for manufactured goods. The paper confirms that finding. The paper does not pursue the key question of
{"title":"Comment","authors":"R. Hall","doi":"10.1086/712319","DOIUrl":"https://doi.org/10.1086/712319","url":null,"abstract":"Authors Rossi-Hansberg, Sarte, and Trachter have delivered a focused, definitive paper, part of a broader research program on structural change in US product and labor markets. The subject of the paper is concentration. Measurement of concentration starts from the market definition. Concentration depends on the pattern of market shares within a market, defined by a product, a geographic region, and a time period. Amarket is a specification of the product, region, and time such that a hypothetical monopolist would have market power—it could elevate its price substantially abovemarginal cost. The connection to concentration is the general belief that the actual market power of the firms selling in a market depends on the number and importance of the competitors selling in that market. Themost widely usedmeasure of concentration—and the one used in this paper—is the Herfindahl-Hirschman Index (HHI), the sum of the squared market shares of the sellers in a market. This measure is sensitive to both the number of sellers and their market shares, and it is a sufficient statistic for the elevation of price over marginal cost in certain oligopoly models. However, other models, especially those involving collusion, predict substantial exercise ofmarket powerwithpricesmarked up well above marginal cost, in the face of HHI measures suggesting rather less price elevation. Some markets are national or international. The products in these markets have low transport cost across the country relative to price. A substantial body of research has found high and rising concentration in many national markets, primarily for manufactured goods. The paper confirms that finding. The paper does not pursue the key question of","PeriodicalId":51680,"journal":{"name":"Nber Macroeconomics Annual","volume":"35 1","pages":"167 - 172"},"PeriodicalIF":7.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44295694","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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