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The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)
April 2025
Working Paper Number:
CES-25-27
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications. We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains. Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding, while harming productivity and profitability in the short run. These losses are unevenly distributed, concentrating among older businesses while being mitigated by growth-oriented business strategies and within-firm spillovers. Dynamics, however, matter: earlier (pre-2017) adopters exhibit stronger growth over time, conditional on survival. Notably, among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses, revealing a specific channel through which intangible factors shape AI's impact. Taken together, these results provide novel evidence on the microfoundations of technology J-curves, identifying mechanisms and illuminating how and why they differ across firm types. These findings extend our understanding of modern General Purpose Technologies, explaining why their economic impact'exemplified here by AI'may initially disappoint, particularly in contexts dominated by older, established firms.
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The Intangible Divide: Why Do So Few Firms Invest in Innovation?
February 2025
Working Paper Number:
CES-25-15
Investments in software, R&D, and advertising have surged, nearing half of U.S. private nonresidential investment. Yet just a few hundred firms dominate this growth. Most firms, including large ones, regularly invest little in capitalized software and R&D, widening this 'intangible divide' despite falling intangible prices. Using comprehensive US Census microdata, we document these patterns and explore factors associated with intangible investment. We find that firms invest significantly less in innovation-related intangibles when their rivals invest more. One firm's investment can obsolesce rivals' investments, reducing returns. This negative pecuniary externality worsens the intangible divide, potentially leading to significant misallocation.
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Investigating the Effect of Innovation Activities of Firms on Innovation Performance: Does Firm Size Matter?
January 2025
Working Paper Number:
CES-25-04
Understanding the relationship between a firm's innovation activities and its performance has been of great interest to management scholars. While the literature on innovation activities is vast, there is a dearth of studies investigating the effect of key innovation activities of the firm on innovation outcomes in a single study, and whether their effects are dependent on the nature of firms, specifically firm size. Drawing from a longitudinal dataset from the Business Research & Development and Innovation Survey (BRDIS), and informed by contingency theory and resource orchestration theory, we examine the relationship between a firm's innovation activities - including its Research & Development (R&D) investment, securing patents, collaborative R&D, R&D toward new business areas, and grants for R&D - and its product innovation and process innovation. We also investigate whether these relationships are contingent on firm size. Consistent with contingency theory, we find a significant difference between large firms and small firms regarding how they enhance product innovation and process innovation. Large firms can improve product innovation by securing patents through applications and issuances, coupled with active participation in collaborative R&D efforts. Conversely, smaller firms concentrate their efforts on the number of patents applied for, directing R&D efforts toward new business areas, and often leveraging grants for R&D efforts. To achieve process innovation, a similar dichotomy emerges. Larger firms demonstrate a commitment to securing patents, engage in R&D efforts tailored to new business areas, and actively collaborate with external entities on R&D efforts. In contrast, smaller firms primarily focus on securing patents and channel their R&D efforts toward new business pursuits. This nuanced exploration highlights the varied strategies employed by large and small firms in navigating the intricate landscape of both product and process innovation. The results shed light on specific innovation activities as antecedents of innovation outcomes and demonstrate how the effectiveness of such assets is contingent upon firm size.
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Exploratory Report: Annual Business Survey Ownership Diversity and Its Association with Patenting and Venture Capital Success
October 2024
Working Paper Number:
CES-24-62
The Annual Business Survey (ABS) as the replacement for the Survey of Business Owners (SBO) serves as the principal data source for investigating business ownership of minorities, women, and immigrants. As a combination of SBO, the innovation questions formerly collected in the Business R&D and Innovation Survey (BRDIS), and an R&D module for microbusinesses with fewer than 10 employees, ABS opens new research opportunities investigating how ownership demographics are associated with innovation. One critical issue that ABS is uniquely able to investigate is the role that diversity among ownership teams plays in facilitating innovation or intermediate innovation outcomes in R&D-performing microbusinesses. Earlier research using ABS identified both demographic and disciplinary diversity as strong correlates to new-to-market innovation. This research investigates the extent to which the various forms of diversity also impact tangible innovation related intermediate outcomes such as the awarding of patents or securing venture capital financing for R&D. The other major difference with the earlier work is the focus on R&D-performing microbusinesses that are an essential input to radical innovation through the division of innovative labor. Evidence that disciplinary and/or demographic diversity affect the likelihood of receiving a patent or securing venture capital financing by small, high-tech start-ups may have implications for higher education, affirmative action, and immigration policy.
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Are Immigrants More Innovative? Evidence from Entrepreneurs
November 2023
Working Paper Number:
CES-23-56
We evaluate the contributions of immigrant entrepreneurs to innovation in the U.S. using linked survey-administrative data on 199,000 firms with a rich set of innovation measures and other firm and owner characteristics. We find that not only are immigrants more likely than natives to own businesses, but on average their firms display more innovation activities and outcomes. Immigrant owned firms are particularly more likely to create completely new products, improve previous products, use new processes, and engage in both basic and applied R&D, and their efforts are reflected in substantially higher levels of patents and productivity. Immigrant owners are slightly less likely than natives to imitate products of others and to hire more employees. Delving into potential explanations of the immigrant-native differences, we study other characteristics of entrepreneurs, access to finance, choice of industry, immigrant self-selection, and effects of diversity. We find that the immigrant innovation advantage is robust to controlling for detailed characteristics of firms and owners, it holds in both high-tech and non-high-tech industries and, with the exception of productivity, it tends to be even stronger in firms owned by diverse immigrant-native teams and by diverse immigrants from different countries. The evidence from nearly all measures that immigrants tend to operate more innovative and productive firms, together with the higher share of business ownership by immigrants, implies large contributions to U.S. innovation and growth.
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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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Patents, Innovation, and Market Entry
September 2023
Working Paper Number:
CES-23-45
Do patents facilitate market entry and job creation? Using a 2014 Supreme Court decision that limited patent eligibility and natural language processing methods to identify invalid patents, I find that large treated firms reduce job creation and create fewer new establishments in response, with no effect on new firm entry. Moreover, companies shift toward innovation aimed at improving existing products consistent with the view that patents incentivize creative destruction.
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Where Have All the "Creative Talents" Gone?
Employment Dynamics of US Inventors
April 2023
Working Paper Number:
CES-23-17
How are inventors allocated in the US economy and does that allocation affect innovative capacity? To answer these questions, we first build a model where an inventor with a new idea has the possibility to work for an entrant or incumbent firm. Strategic considerations encourage the incumbent to hire the inventor, offering higher wages, and then not implement her idea. We then combine data on 760 thousand U.S. inventors with the LEHD data. We find that when an inventor is hired by an incumbent, their earnings increases by 12.6 percent and their innovative output declines by 6 to 11 percent.
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An Examination of the Informational Value of Self-Reported Innovation Questions
October 2022
Working Paper Number:
CES-22-46
Self-reported innovation measures provide an alternative means for examining the economic performance of firms or regions. While European researchers have been exploiting the data from the Community Innovation Survey for over two decades, uptake of US innovation data has been much slower. This paper uses a restricted innovation survey designed to differentiate incremental innovators from more far-ranging innovators and compares it to responses in the Annual Survey of Entrepreneurs (ASE) and the Business R&D and Innovation Survey (BRDIS) to examine the informational value of these positive innovation measures. The analysis begins by examining the association between the incremental innovation measure in the Rural Establishment Innovation Survey (REIS) and a measure of the inter-industry buying and selling complexity. A parallel analysis using BRDIS and ASE reveals such an association may vary among surveys, providing additional insight on the informational value of various innovation profiles available in self-reported innovation surveys.
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Measuring the Characteristics and Employment Dynamics of U.S. Inventors
September 2022
Working Paper Number:
CES-22-43
Innovation is a key driver of long run economic growth. Studying innovation requires a clear view of the characteristics and behavior of the individuals that create new ideas. A general lack of rich, large-scale data has constrained such analyses. We address this by introducing a new dataset linking patent inventors to survey, census, and administrative microdata at the U.S. Census Bureau. We use this data to provide a first look at the demographic characteristics, employer characteristics, earnings, and employment dynamics of inventors. These linkages, which will be available to researchers with approved access, dramatically increases the scope of what can be learned about inventors and innovative activity.
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