CREAT: Census Research Exploration and Analysis Tool

Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey

April 2022

Abstract

Document Tags and Keywords

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey' are listed below in order of similarity.

• Working Paper

  🔥

  
  
  Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
  

  December 2020

  Authors: Lucia Foster, Emin Dinlersoz, Kristina McElheran, Nikolas Zolas, Erik Brynjolfsson, Catherine Buffington, Nathan Goldschlag, Zachary Kroff, David Beede

  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.
  View Full Paper PDF

  ---

• Working Paper

  🔥

  
  
  Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey
  

  March 2024

  Authors: John Haltiwanger, Lucia Foster, Emin Dinlersoz, Catherine Buffington, Nathan Goldschlag, Zachary Kroff, Kathryn Bonney, Cory Breaux, Keith Savage

  Working Paper Number:

  CES-24-16R

  Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.
  View Full Paper PDF

  ---

• Working Paper

  
  
  AI Adoption in America: Who, What, and Where
  

  September 2023

  Authors: Lucia Foster, Emin Dinlersoz, Kristina McElheran, Nikolas Zolas, Erik Brynjolfsson, Zachary Kroff, J. Frank Li

  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.
  View Full Paper PDF

  ---

• Working Paper

  
  
  Starting Up AI
  

  March 2024

  Authors: Emin Dinlersoz, Nikolas Zolas, Can Dogan

  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.
  View Full Paper PDF

  ---

• Working Paper

  
  
  The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)
  

  April 2025

  Authors: Kristina McElheran, Erik Brynjolfsson, Mu-Jeung Yang, Zachary Kroff

  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.
  View Full Paper PDF

  ---

• Working Paper

  
  
  The Effect Of Technology Use On Productivity Growth
  

  April 1996

  Authors: Robert H Mcguckin, Mary L Streitwieser, Mark E Doms

  Working Paper Number:

  CES-96-02

  This paper examines the relationship between the use of advanced technologies and productivity and productivity growth rates. We use data from the 1993 and 1988 Survey of Manufacturing Technology (SMT) to examine the use of advanced (computer based) technologies at two different points in time. We are also able to combine the survey data with the Longitudinal Research Database (LRD) to examine the relationships between plant performance, plant characteristics, and the use of advanced technologies. In addition, a subset of these plants were surveyed in both years, enabling us to directly associate changes in technology use with changes in plant productivity performance. The main findings of the study are as follows. First, diffusion is not the same across the surveyed technologies. Second, the adoption process is not smooth: plants added and dropped technologies over the six-year interval 1988-93. In fact, the average plant showed a gross change of roughly four technologies in achieving an average net increase of less than one new technology. In this regard, technology appears to be an experience good: plants experiment with particular technologies before deciding to add additional units or drop the technology entirely. We find that establishments that use advanced technologies exhibit higher productivity. This relationship is observed in both 1988 and 1993 even after accounting for other important factors associated with productivity: size, age, capital intensity, labor skill mix, and other controls for plant characteristics such as industry and region. In addition, the relationship between productivity and advanced technology use is observed both in the extent of technologies used and the intensity of their use. Finally, while there is some evidence that the use of advanced technologies is positively related to improved productivity performance, the data suggest that the dominant explanation for the observed cross-section relationship is that good performers are more likely to use advanced technologies than poorly performing operations.
  View Full Paper PDF

  ---

• Working Paper

  
  
  The Characteristics and Geographic Distribution of Robot Hubs in U.S. Manufacturing Establishments
  

  March 2023

  Authors: Javier Miranda, Erik Brynjolfsson, Robert Seamans, Catherine Buffington, Nathan Goldschlag, J. Frank Li

  Working Paper Number:

  CES-23-14

  We use data from the Annual Survey of Manufactures to study the characteristics and geography of investments in robots across U.S. manufacturing establishments. We find that robotics adoption and robot intensity (the number of robots per employee) is much more strongly related to establishment size than age. We find that establishments that report having robotics have higher capital expenditures, including higher information technology (IT) capital expenditures. Also, establishments are more likely to have robotics if other establishments in the same Core-Based Statistical Area (CBSA) and industry also report having robotics. The distribution of robots is highly skewed across establishments' locations. Some locations, which we call Robot Hubs, have far more robots than one would expect even after accounting for industry and manufacturing employment. We characterize these Robot Hubs along several industry, demographic, and institutional dimensions. The presence of robot integrators and higher levels of union membership are positively correlated with being a Robot Hub.
  View Full Paper PDF

  ---

• Working Paper

  
  
  Productivity, Investment in ICT and Market Experimentation: Micro Evidence from Germany and the U.S.
  

  February 2003

  Authors: John Haltiwanger, Ron Jarmin, Thorsten Schank

  Working Paper Number:

  CES-03-06

  In this paper, we examine the relationship between the use of advanced technologies, such as information and communications technologies (ICT), and related business practices and outcomes such as productivity, employment, the skill mix of the workforce and wages using micro data for the U.S. and Germany. . We find support to the idea that U.S. businesses engage in experimentation in a variety of ways not matched by their German counterparts. In particular, there is greater experimentation amongst young US businesses and there is greater experimentation among those actively changing their technology. This experimentation is evidenced in a greater dispersion in productivity and in related key business choices, like the skill mix and Internet access for workers. We also find that the mean impact of adopting new technology is greater in U.S. than in Germany. Putting the pieces together suggests that U.S. businesses choose a higher mean, higher variance strategy in adopting new technology.
  View Full Paper PDF

  ---

• Working Paper

  
  
  Innovation and Appropriability: Revisiting the Role of Intellectual Property
  

  March 2022

  Authors: Timothy Simcoe, Filippo Mezzanotti

  Working Paper Number:

  CES-22-09

  It is more than 25 years since the authors of the Yale and Carnegie surveys studied how firms seek to protect the rents from innovation. In this paper, we revisit that question using a nationally representative sample of firms over the period 2008-2015, with the goal of updating and extending a set of stylized facts that has been influential for our understanding of the economics of innovation. There are five main findings. First, while patenting firms are relatively uncommon in the economy, they account for an overwhelming share of R&D spending. Second, utility patents are considered less important than other forms of IP protection, like trade secrets, trademarks, and copyrights. Third, industry differences explain a great deal of the level of firms' engagement with IP, with high-tech firms on average being more active on all forms of IP. Fourth, we do not find any significant difference in the use of IP strategies across firms at different points of their life cycle. Lastly, unlike age, firms of different size appear to manage IP significantly differently. On average, larger firms tend to engage much more extensively in the protection of IP, and this pattern cannot be easily explained by differences in the type of R&D or innovation produced by a firm. We also discuss the implications of these findings for innovation research and policy.
  View Full Paper PDF

  ---

• Working Paper

  
  
  The Annual Survey of Entrepreneurs: An Introduction
  

  November 2015

  Authors: Lucia Foster, Patrice Norman

  Working Paper Number:

  CES-15-40R

  The Census Bureau continually seeks to improve its measures of the U.S. economy as part of its mission. In some cases this means expanding or updating the content of its existing surveys, expanding the use of administrative data, and/or exploring the use of privately collected data. When these options cannot provide the needed data, the Census Bureau may consider fielding a new survey to fill the gap. This paper describes one such new survey, the Annual Survey of Entrepreneurs (ASE). Innovations in content, format, and process are designed to provide high-quality, timely, frequent information on the activities of one of the important drivers of economic growth: entrepreneurship. The ASE is collected through a partnership of the Census Bureau with the Kauffman Foundation and the Minority Business Development Agency. The first wave of the ASE collection started in fall of 2015 (for reference period 2014) and results will be released in summer 2016. Qualified researchers on approved projects will be able to access micro data from the ASE through the Federal Statistical Research Data Center (FSRDC) network.
  View Full Paper PDF

  ---