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Growth is Getting Harder to Find, Not Ideas
April 2025
Working Paper Number:
CES-25-21
Relatively flat US output growth versus rising numbers of US researchers is often interpreted as evidence that "ideas are getting harder to find." We build a new 46-year panel tracking the universe of U.S. firms' patenting to investigate the micro underpinnings of this claim, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. Over our sample period, we find that researchers' patenting productivity is increasing, there is little evidence of any secular decline in high-quality patenting common to all firms, and the link between patents and growth is present, differs by type of idea, and is fairly stable. On the other hand, we find strong evidence of secular decreases in output unrelated to patenting, suggesting an important role for other factors. Together, these results invite renewed empirical and theoretical attention to the impact of ideas on growth. To that end, our patent-firm bridge, which will be available to researchers with approved access, is used to produce new, public-use statistics on the Business Dynamics of Patenting Firms (BDS-PF).
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Starting Up AI
March 2024
Working Paper Number:
CES-24-09R
Using comprehensive administrative data on business applications over the period 2004- 2023, we study business applications (ideas) and the resulting startups that aim to develop AI technologies or produce goods or services that use, integrate, or rely on AI. The annual number of new AI-related business applications is stable between 2004 and 2011, but begins to rise in 2012 with further increases from 2016 onward into the Covid-19 pandemic and beyond, with a large, discrete jump in 2023. The distribution of these applications is highly uneven across states and sectors. AI business applications have a higher likelihood of becoming employer startups compared to other applications. Moreover, businesses originating from these applications exhibit higher revenue, average wage, and labor share, but similar labor productivity and lower survival rate, compared to other businesses. While it is still early in the diffusion of AI, the rapid rise in AI business applications, combined with the better performance of resulting businesses in several key outcomes, suggests a growing contribution from AI-related business formation to business dynamism.
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AI Adoption in America: Who, What, and Where
September 2023
Working Paper Number:
CES-23-48R
We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of 'superstar' cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing 'AI divide' if early patterns persist.
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On The Role of Trademarks: From Micro Evidence to Macro Outcomes
March 2023
Working Paper Number:
CES-23-16R
What are the effects of trademarks on the U.S. economy? Evidence from comprehensive micro data on trademark registrations and outcomes for U.S. employer firms suggests that trademarks protect firm value and are linked to higher firm growth and marketing activity. Motivated by this evidence, trademarks are introduced in a general equilibrium framework to quantify their aggregate effects. Firms invest in product quality and engage in both informative and persuasive advertising to build a customer base subject to depreciation. Persuasive advertising induces a perception of higher quality. Firms can register trademarks to reduce customer depreciation and enhance product awareness. The model's predictions about trademark registrations, firm growth, and advertising expenditures align with the empirical evidence. The analysis shows that, compared to the counterfactual economy without trademarks, the U.S. economy with trademarks generates higher average product quality but lower variety, ultimately resulting in greater welfare and higher industry concentration. While informative advertising improves welfare, persuasive advertising reduces it. Nevertheless, the positive welfare impact of trademarks outweighs the negative effects of persuasive advertising.
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Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey
April 2022
Authors:
John Haltiwanger,
Lucia Foster,
Emin Dinlersoz,
Nikolas Zolas,
Daron Acemoglu,
Catherine Buffington,
Nathan Goldschlag,
Zachary Kroff,
David Beede,
Gary Anderson,
Eric Childress,
Pascual Restrepo
Working Paper Number:
CES-22-12R
This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES). The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. The adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and young firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and lower labor shares. In particular, the use of these technologies is associated with a 11.4% higher labor productivity, which accounts for 20'30% of the difference in labor productivity between large firms and the median firm in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor, but brought limited or ambiguous effects to their employment levels.
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Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
December 2020
Working Paper Number:
CES-20-40
We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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An Anatomy of U.S. Firms Seeking Trademark Registration
April 2018
Working Paper Number:
CES-18-22
This paper reports on the construction of a new dataset that combines data on trademark applications and registrations from the U.S. Patent and Trademark Office with data on firms from the U.S. Census Bureau. The resulting dataset allows tracking of various activity related to trademark use and protection over the life-cycle of firms, such as the first application for a trademark registration, the first use of a trademark, and the renewal, assignment, and cancellation of trademark registrations. Facts about firm-level trademark activity are documented, including the incidence and timing of trademark registration filings over the firm life-cycle and the connection between firm characteristics and trademark applications. We also explore the relation of trademark application filing to firm employment and revenue growth, and to firm innovative activity as measured by R&D and patents.
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An 'Algorithmic Links with Probabilities' Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition
March 2016
Working Paper Number:
CES-16-15
Patents are a useful proxy for innovation, technological change, and diffusion. However, fully exploiting patent data for economic analyses requires patents be tied to measures of economic activity, which has proven to be difficult. Recently, Lybbert and Zolas (2014) have constructed an International Patent Classification (IPC) to industry classification crosswalk using an 'Algorithmic Links with Probabilities' approach. In this paper, we utilize a similar approach and apply it to new patent classification schemes, the U.S. Patent Classification (USPC) system and Cooperative Patent Classification (CPC) system. The resulting USPC-Industry and CPC-Industry concordances link both U.S. and global patents to multiple vintages of the North American Industrial Classification System (NAICS), International Standard Industrial Classification (ISIC), Harmonized System (HS) and Standard International Trade Classification (SITC). We then use the crosswalk to highlight changes to industrial technology composition over time. We find suggestive evidence of strong persistence in the association between technologies and industries over time.
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A Portrait of Firms that Invest in R&D
January 2016
Working Paper Number:
CES-16-41
We focus on the evolution and behavior of firms that invest in research and development (R&D). We build upon the cross-sectional analysis in Foster and Grim (2010) that identified the characteristics of top R&D spending firms and follow up by charting the behavior of these firms over time. Our focus is dynamic in nature as we merge micro-level cross-sectional data from the Survey of Industrial Research and Development (SIRD) and the Business Research & Development and Innovation Survey (BRDIS) with the Longitudinal Business Database (LBD). The result is a panel firm-level data set from 1992 to 2011 that tracks firms' performances as they enter and exit the R&D surveys. Using R&D expenditures to proxy R&D performance, we find the top R&D performing firms in the U.S. across all years to be large, old, multinational enterprises. However, we also find that the composition of R&D performing firms is gradually shifting more towards smaller domestic firms with expenditures being less sensitive to scale effects. We find a high degree of persistence for these firms over time. We chart the history of R&D performing firms and compare them to all firms in the economy and find substantial differences in terms of age, size, firm structure and international activity; these differences persist when looking at future firm outcomes.
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INTERNATIONAL PATENTING STRATEGIES WITH HETEROGENEOUS FIRMS
September 2014
Working Paper Number:
CES-14-28
This paper analyzes how firms decide where to patent in a heterogeneous firm model of trade with endogenous rival entry. In the model, innovating firms compete with rival firms on price, where rivals force the innovating firm to reduce markups and lower the innovating firm's probability of obtaining monopolistic profits. Patenting allows the innovating firm to reduce the number of rival rms by increasing their fixed overhead costs, thereby providing higher expected profits and increased markups from reduced competition. Countries with higher states of technology, more competition and better patent protection have a greater proportion of entrants who patent. Industries tend to follow a U-shaped pattern of patenting where industries with high heterogeneity in production and low substitution, along with industries with low heterogeneity in production and high substitution patent more frequently. Using a generalized framework of the model, I estimate market-based measures of country-level patent protection, which when compared with other IP indices, suggests that not enough international patenting is taking place. Finally, I test the predictions of the model using a newly available technology-to-industry concordance on bilateral patent flows and show that firms are increasingly sensitive to foreign IP protection. Countries that choose to maximize their IP protection can increase the number of foreign patents by almost 10%.
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