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Grassroots Design Meets Grassroots Innovation: Rural Design Orientation and Firm Performance
March 2024
Working Paper Number:
CES-24-17
The study of grassroots design'applying structured, creative processes to the usability or aesthetics of a product without input from professional design consultancies'remains under investigated. If design comprises a mediation between people and technology whereby technologies are made more accessible or more likely to delight, then the process by which new grassroots inventions are transformed into innovations valued in markets cannot be fully understood. This paper uses U.S. data on the design orientation of respondents in the 2014 Rural Establishment Innovation Survey linked to longitudinal data on the same firms to examine the association between design, innovation, and employment and payroll growth. Findings from the research will inform questions to be investigated in the recently collected 2022 Annual Business Survey (ABS) that for the first time contains a Design module.
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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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Research and/or Development? Financial Frictions and Innovation Investment
August 2023
Working Paper Number:
CES-23-39
U.S. firms have reduced their investment in scientific research ('R') compared to product development ('D'), raising questions about the returns to each type of investment, and about the reasons for this shift. We use Census data that disaggregates 'R' from 'D' to study how US firms adjust their innovation investments in response to an external increase in funding cost. Companies with greater demand for refinancing during the 2008 financial crisis, made larger cuts to R&D investment. This reduction in R&D is achieved almost entirely by reducing investment in research. Development remains essentially unchanged. If other firms patenting similar technologies must refinance, however, then Development investment declines. We interpret the latter result as evidence of technological competition: firms are reluctant to cut Development expenditures when that could place them at a disadvantage compared to potential rivals.
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The Changing Firm and Country Boundaries of US Manufacturers in Global Value Chains
July 2023
Working Paper Number:
CES-23-38
This paper documents how US firms organize goods production across firm and country boundaries. Most US firms that perform physical transformation tasks in-house using foreign manufacturing plants in 2007 also own US manufacturing plants; moreover manufacturing comprises their main domestic activity. By contrast, 'factoryless goods producers' outsource all physical transformation tasks to arm's-length contractors, focusing their in-house efforts on design and marketing. This distinct firm type is missing from standard analyses of manufacturing, growing in importance, and increasingly reliant on foreign suppliers. Physical transformation 'within-the-firm' thus coincides with substantial physical transformation 'within-the-country,' whereas its performance 'outside-the-firm' often also implies 'outside-the-country.' Despite these differences, factoryless goods producers and firms with foreign and domestic manufacturing plants both employ relatively high shares of US knowledge workers. These patterns call for new models and data to capture the potential for foreign production to support domestic innovation, which US firms leverage around the world.
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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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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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Development of Survey Questions on Robotics Expenditures and Use in U.S. Manufacturing Establishments
October 2018
Working Paper Number:
CES-18-44
The U.S. Census Bureau in partnership with a team of external researchers developed a series of questions on the use of robotics in U.S. manufacturing establishments. The questions include: (1) capital expenditures for new and used industrial robotic equipment in 2018, (2) number of industrial robots in operation in 2018, and (3) number of industrial robots purchased in 2018. These questions are to be included in the 2018 Annual Survey of Manufactures. This paper documents the background and cognitive testing process used for the development of these questions.
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Automation, Labor Share, and Productivity:
Plant-Level Evidence from U.S. Manufacturing
September 2018
Working Paper Number:
CES-18-39
This paper provides new evidence on the plant-level relationship between automation, labor and capital usage, and productivity. The evidence, based on the U.S. Census Bureau's Survey of Manufacturing Technology, indicates that more automated establishments have lower production labor share and higher capital share, and a smaller fraction of workers in production who receive higher wages. These establishments also have higher labor productivity and experience larger long-term labor share declines. The relationship between automation and relative factor usage is modelled using a CES production function with endogenous technology choice. This deviation from the standard Cobb-Douglas assumption is necessary if the within-industry differences in the capital-labor ratio are determined by relative input price differences. The CES-based total factor productivity estimates are significantly different from the ones derived under Cobb-Douglas production and positively related to automation. The results, taken together with earlier findings of the productivity literature, suggest that the adoption of automation may be one mechanism associated with the rise of superstar firms.
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Innovation, Productivity Dispersion, and Productivity Growth
February 2018
Working Paper Number:
CES-18-08
We examine whether underlying industry innovation dynamics are an important driver of the large dispersion in productivity across firms within narrowly defined sectors. Our hypothesis is that periods of rapid innovation are accompanied by high rates of entry, significant experimentation and, in turn, a high degree of productivity dispersion. Following this experimentation phase, successful innovators and adopters grow while unsuccessful innovators contract and exit yielding productivity growth. We examine the dynamic relationship between entry, productivity dispersion, and productivity growth using a new comprehensive firm-level dataset for the U.S. We find a surge of entry within an industry yields an immediate increase in productivity dispersion and a lagged increase in productivity growth. These patterns are more pronounced for the High Tech sector where we expect there to be more innovative activities. These patterns change over time suggesting other forces are at work during the post-2000 slowdown in aggregate productivity.
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