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Industry Linkages from Joint Production
January 2023
Working Paper Number:
CES-23-02
I develop a theory of joint production to quantify aggregate economies of scope. In US manufacturing data, increased export demand in one industry raises a firm's sales in its other industries that share knowledge inputs like R&D and software. I estimate that knowledge inputs contribute to economies of scope through their scalability and partial non-rivalry within the firm. On average a 10 percent increase in output in one industry lowers prices in other industries by 0.4 percent. Such economies of scope manifest disproportionately among knowledge proximate industries and imply large spillover impacts of recent US-China trade policy on producer prices.
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Structural Change Within Versus Across Firms: Evidence from the United States
June 2022
Working Paper Number:
CES-22-19
We document the role of intangible capital in manufacturing firms' substantial contribution to
non-manufacturing employment growth from 1977-2019. Exploiting data on firms' 'auxiliary' establishments, we develop a novel measure of proprietary in-house knowledge and show that it
is associated with increased growth and industry switching. We rationalize this reallocation in a
model where irms combine physical and knowledge inputs as complements, and where producing
the latter in-house confers a sector-neutral productivity advantage facilitating within-firm structural
transformation. Consistent with the model, manufacturing firms with auxiliary employment pivot towards services in response to a plausibly exogenous decline in their physical input prices.
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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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Capital Investment and Labor Demand
February 2022
Working Paper Number:
CES-22-04
We study how bonus depreciation, a policy designed to lower the cost of capital, impacted investment and labor demand in the US manufacturing sector. Difference-in-differences estimates using restricted-use US Census Data on manufacturing establishments show that this policy increased both investment and employment, but did not lead to wage or productivity gains. Using a structural model, we show that the primary effect of the policy was to increase the use of all inputs by lowering overall costs of production. The policy further stimulated production employment due to the complementarity of production labor and capital. Supporting this conclusion, we nd that investment is greater in plants with lower labor costs. Our results show that recent policies that incentivize capital investment do not lead manufacturing plants to replace workers with machines.
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Two-sided Search in International Markets
January 2022
Working Paper Number:
CES-22-02
We develop a dynamic model of international business-to-business transactions in which sellers and buyers search for each other, with the probability of a match depending on both individual and aggregate search effort. Fit to customs records on U.S. apparel imports, the model captures key cross-sectional and dynamic features of international buyer-seller relationships. We use the model to make several quantitative inferences. First, we calculate the search costs borne by heterogeneous importers and exporters. Second, we provide a structural interpretation for the life cycles of importers and exporters as they endogenously acquire and lose foreign business partners. Third, we pursue counterfactuals that approximate the phaseout of the Agreement on Textiles and Clothing (the 'China shock") and the IT revolution. Lower search costs can significantly improve consumer welfare, but at the expense of importer pro ts. On the other hand, an increase in the population of foreign exporters can congest matching to the extent of dampening or even reversing the gains consumers enjoy from access to extra varieties and more retailers.
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The Industrial Revolution in Services
October 2021
Working Paper Number:
CES-21-34
The U.S. has experienced an industrial revolution in services. Firms in service industries, those where output has to be supplied locally, increasingly operate in more markets. Employment, sales, and spending on fixed costs such as R&D and managerial employment have increased rapidly in these industries. These changes have favored top firms the most and have led to increasing national concentration in service industries. Top firms in service industries have grown entirely by expanding into new local markets that are predominantly small and mid-sized U.S. cities. Market concentration at the local level has decreased in all U.S. cities but by significantly more in cities thatwere initially small. These facts are consistent with the availability of a new menu of fixed-cost-intensive technologies in service sectors that enable adopters to produce at lower marginal costs in any markets. The entry of top service firms into new local markets has led to substantial unmeasured productivity growth, particularly in small markets.
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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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Twisting the Demand Curve: Digitalization and the Older Workforce
November 2020
Working Paper Number:
CES-20-37
This paper uses U.S. Census Bureau panel data that link firm software investment to worker earnings. We regress the log of earnings of workers by age group on the software investment by their employing firm. To unpack the potential causal factors for differential software effects by age group we extend the AKM framework by including job-spell fixed effects that allow for a correlation between the worker-firm match and age and by including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, software capital raises earnings at a rate that declines post age 50 to about zero after age 65. By contrast, the effects of non-IT equipment investment on earnings increase for workers post age 50. The difference between the software and non-IT equipment effects suggests that our results are attributable to the technology rather than to age-related bargaining power. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and the earnings in high-wage firms relative to low-wage firms, and may thus widen earnings inequality within and across firms.
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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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Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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