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Summary

100 Years of Rising Corporate Concentration
Author(s):
Spencer Yongwook Kwon, Harvard University
Yueran Ma, University of Chicago and NBER
Kaspar Zimmermann, Leibniz Institute for Financial Research SAFE
Discussant(s):
Thomas Philippon, New York University and NBER
Abstract:

Kwon, Ma, and Zimmermann collect data on the size distribution of U.S. corporate businesses for nearly 100 years. The researchers document that corporate concentration (e.g., asset share or sales share of the top 1%) in the U.S. economy has been increasing persistently over the past century. Across different industries, rising concentration was more pronounced in manufacturing, mining, and utilities before 1970s, and more pronounced in services, retail, and wholesale after 1970s. Kwon, Ma, and Zimmermann find that the timing and the degree of rising concentration in an industry align closely with the investment intensity in research and development and information technology. In addition, industries with higher increases in concentration also exhibit higher output growth. The evidence suggests that the long-run trends of rising corporate concentration reflect increasingly stronger economies of scale.

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Spending and Job Search Impacts of Expanded Unemployment Benefits: Evidence from Administrative Micro Data
Author(s):
Peter Ganong, University of Chicago and NBER
Fiona E. Greig, JP Morgan Chase Institute
Pascal J. Noel, University of Chicago and NBER
Daniel M. Sullivan, JPMorgan Chase Institute
Joseph S. Vavra, University of Chicago and NBER
Discussant(s):
Hilary W. Hoynes, University of California, Berkeley and NBER
Abstract:

How did the largest expansion of unemployment benefits in U.S. history affect household behavior? Using anonymized bank account data covering millions of households, Ganong, Greig, Noel, Sullivan, and Vavra provide new empirical evidence on the spending and job search responses to benefit changes during the pandemic and compare those responses to the predictions of benchmark structural models. Ganong, Greig, Noel, Sullivan, and Vavra find that spending responds more than predicted, while job search responds an order of magnitude less than predicted. In sharp contrast to normal times when spending falls after job loss, the researchers show that when expanded benefits are available, spending of the unemployed actually rises after job loss. Using quasi-experimental research designs, Ganong, Greig, Noel, Sullivan, and Vavra estimate a large marginal propensity to consume out of benefits. Notably, spending responses are large even for households who have built up substantial liquidity through prior receipt of expanded benefits. These large responses contrast with a theoretical prediction that spending responses should shrink with liquidity. Simple job search models predict a sharp decline in search in the wake of a substantial benefit expansion, followed by a sustained rebound when benefits expire. Ganong, Greig, Noel, Sullivan, and Vavra instead find that the job finding rate is quite stable. Moreover, Ganong, Greig, Noel, Sullivan, and Vavra document that recall plays an important role in driving job-finding dynamics throughout the pandemic. A model extended to fit these key features of the data implies small job search distortions from expanded unemployment benefits. Jointly, these spending and job finding facts suggest that benefit expansions during the pandemic were a more effective policy than predicted by standard structural models. Abstracting from general equilibrium effects, Ganong, Greig, Noel, Sullivan, and Vavra find that overall spending was 2.0-2.6 percent higher and employment only 0.2-0.4 percent lower as a result of the benefit expansions.

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The Diffusion of Disruptive Technologies
Author(s):
Nicholas Bloom, Stanford University and NBER
Tarek Alexander Hassan, Boston University and NBER
Aakash Kalyani, Boston University
Josh Lerner, Harvard University and NBER
Ahmed Tahoun, London Business School
Discussant(s):
Chad Syverson, University of Chicago and NBER
Abstract:

Bloom, Hassan, Kalyani, Lerner, and Tahoun identify novel technologies using textual analysis of patents, job postings, and earnings calls. Thier approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from the data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread geographically. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these pioneer locations are more likely to arise in areas with universities and high skilled labor pools.

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This paper was distributed as Working Paper 28999, where an updated version may be available.

A Model of the Data Economy
Author(s):
Maryam Farboodi, Massachusetts Institute of Technology and NBER
Laura Veldkamp, Columbia University and NBER
Discussant(s):
Charles I. Jones, Stanford University and NBER
Abstract:

The rise of information technology and big data analytics has given rise to "the new economy." But are its economics new? This article constructs a dynamic equilibrium model where firms accumulate data, instead of capital. We incorporate three key features of data: 1) Data is a by-product of economic activity, 2) data is information used for prediction, and 3) uncertainty reduction enhances firm profitability. The model can explain why data-intensive goods or services, like apps, are given away for free, why many new firms are unprofitable and why some of the biggest firms in the economy profit primarily from selling data. While the transition dynamics of the data economy and a capital economy differ, the long-run dynamics are similar: Data has diminishing returns; comparative advantage dictates who produces what, and capital allocations are efficient. However, even in the long run, data creates new economic distortions, relative to social optimum.

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Task Based Discrimination
Author(s):
Erik Hurst, University of Chicago and NBER
Yona Rubinstein, London School of Economics
Kazuatsu Shimizu, Massachusetts Institute of Technology
Discussant(s):
Kevin Lang, Boston University and NBER
Abstract:

Hurst, Rubinstein, and Shimizu introduce a concept of task based discrimination to identify and quantify the impact of labor market discrimination, skills and task returns on the Black-White gaps in occupational sorting and wages over the past half century. At the heart of their framework is the idea that discrimination varies by the task requirement of each job. Using Census, ACS and NLSY data they find that in the early 1960s Black workers faced high barriers of entry into occupations requiring either complex analytical activities - Abstract - or interactions with customers and co-workers - Contact. Since then two different trends emerged. The barriers deterring entry of Black men into jobs requiring Contact tasks diminished sharply, reflecting a decline in labor market taste-based discrimination. However, over that time, substantial racial barriers remained with respect to jobs requiring Abstract tasks. Their structurally estimated model and reduced form estimates indicate that, during the last forty years, the narrowing of racial skill gaps and the decline in labor market discrimination were not large enough to offset the increasing returns to Abstract tasks which, on average, favored White workers relative to Black workers since the early 1980s. Their task based discrimination framework thus explains both the growing relative wages of Black workers between 1960 and 1980 and the stagnation in Blacks' relative wages since then.

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This paper was distributed as Working Paper 29022, where an updated version may be available.

The 2000s Housing Cycle With 2020 Hindsight: A Neo-Kindlebergerian View
Author(s):
Gabriel Chodorow-Reich, Harvard University and NBER
Adam Guren, Boston University and NBER
Timothy McQuade, University of California, Berkeley
Discussant(s):
Antoinette Schoar, Massachusetts Institute of Technology and NBER
Abstract:

With "2020 hindsight,'' the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound at both the national level and across cities. Chodorow-Reich, Guren, and McQuade argue this pattern reflects a larger role for fundamentally-rooted explanations than previously thought. The researchers construct a city-level long-run fundamental using a spatial equilibrium regression framework in which house prices are determined by local income, amenities, and supply. The fundamental predicts not only 1997-2019 price and rent growth but also the amplitude of the boom-bust-rebound and foreclosures. This evidence motivates their neo-Kindlebergerian model, in which an improvement in fundamentals triggers a boom-bust-rebound. Agents learn about the fundamentals by observing "dividends'' but become over-optimistic due to diagnostic expectations. A bust ensues when over-optimistic beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom-bust-rebound with a single fundamental shock and accounts quantitatively for cross-city patterns in the dynamics of prices and foreclosures.

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This paper was distributed as Working Paper 29140, where an updated version may be available.

Participants

Kevin Donovan, Yale University
Jianyu Lu, Central Bank of Chile
Davide Melcangi, Federal Reserve Bank of New York
Alvaro Ortiz, BBVA Research
Kaspar Zimmermann, Leibniz Institute for Financial Research SAFE

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