Artificial Intelligence and Firm-Level Productivity

Czarnitzki, Dirk; Fernández, Gastón P.; Rammer, Christian

doi:10.2139/ssrn.4049824

Cited by 13 publications

(16 citation statements)

References 48 publications

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“…However, after controlling for training and other confounding factors, educational attainments -proxied by the share of employees with a university degree -do not exhibit a statistically significant link with AI use. 42 Overall, this evidence suggests that while generic skills are relevant, ICT skills constitute a necessary complementary asset to use AI algorithms. More in-depth ICT skills, as proxied by the presence of ICT specialists or ICT training, may be particularly relevant in this early stage of AI diffusion.…”

Section: Factors Complementary To Ai Usementioning

confidence: 94%

“…The variable "Training of Employees" equals 1 if the firm invests in employees' training a part of personnel expenditure greater than the sector median, 0 otherwise. The variable "Skilled Employees" takes value 1 when at least 50% of employees have at least a university degree, 0 otherwise 42. See alsoCalvino et al (2022[1]) for additional evidence about tertiary education and AI use focusing on the United Kingdom.…”

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confidence: 99%

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A portrait of AI adopters across countries

2023

OECD Science, Technology and Industry Working Papers

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Working PapersOECD Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed are those of the authors. Working Papers describe preliminary results or research in progress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcomed, and may be sent to Directorate for

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Section: Factors Complementary To Ai Usementioning

confidence: 94%

mentioning

confidence: 99%

A portrait of AI adopters across countries

2023

OECD Science, Technology and Industry Working Papers

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“…To best capture the extensive margin of AI use across industries, we combine usage rates across AI-related technologies. That said, our findings largely represent variation in the use of "machine learning" in production.3 Young firms are disproportionately single-unit, and hence straightforward to situate geographically.4 Studies of AI use and impacts outside of the United States have been on the rise, including in Europe(Czarnitzki et al, 2023;Hoffreumon et al, 2023), China(Beraja et al, 2023;Lu et al, 2023), Canada(Alexopoulos & Cohen, 2018), and in the context of international tradeGoldfarb & Trefler, 2019).5 See alsoAlcacer and Delgado (2016),Delgado et al (2010), andForman et al (2005), inter alia.6 For example,Akcigit et al (2022),Botelho et al (2021),Catalini et al (2019), and Lerner and Nanda (2020).7 SeeBrynjolfsson and Milgrom (2013) for more on complementarity theory and a review of the literature.8 Such details are captured via responses from "primary owners" to the ABS, which excludes publicly traded firms, estates, trusts, government and tribal entities, associations, membership clubs, cooperatives and foreign entities. 9 Specifically, we recalculate the sample weights by stratifying the firms in the 2017 LBD and our final sample of firms in the ABS on firm size, age, and industry.…”

mentioning

confidence: 84%

“…Recent research into AI's potential impacts has relied on O*NET task descriptions (Brynjolfsson et al, 2018; Eloundou et al, 2023; Felten et al, 2021), online job descriptions (Acemoglu et al, 2022; Alekseeva et al, 2021; Babina et al, 2024; Goldfarb et al, 2023), or combining the latter with patents and/or research publications (Bessen et al, 2021; Webb, 2019). Direct firm‐level measures of AI use are rare, with exceptions in Europe, where samples are smaller (e.g., Czarnitzki et al, 2023; Hoffreumon et al, 2023). Studies of AI in other countries, such as Canada and China, have had to rely on yet different measurement approaches (Alexopoulos & Cohen, 2018; Beraja et al, 2023).…”

Section: Motivation and Prior Workmentioning

confidence: 99%

AI adoption in America: Who, what, and where

McElheran,

Li,

Brynjolfsson

et al. 2024

Economics Manag Strategy

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We study the early adoption and diffusion of five artificial intelligence (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, 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 in startups displaying indicators of high‐growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth‐oriented business strategies. Early AI 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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“…Where AI increases productivity, this should lead to more trade as firms are more competitive (Bailey et al ., 2023; Melitz & Redding, 2014). Indeed, it is already the case that firms most adept at using AI are more productive than non‐AI adopting firms (Czarnitzki et al ., 2023). AI could also lead to more product variety and trade (Trefler & Sun, 2023).…”

Section: Why Foundational Ai Mattersmentioning

confidence: 99%

The Impact of Foundational AI on International Trade, Services and Supply Chains in Asia

Meltzer

2023

Asian Economic Policy Review

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Foundational artificial intelligence (AI) is a general purpose technology that will affect economic activity across industries, with potentially significant impacts on jobs and international trade. This could include automation of routine and nonroutine white‐collar jobs, which reduces international trade where these jobs are on‐shored. However, where AI compliments existing jobs and leads to higher productivity, this could open up new opportunities for international trade in services. How foundational AI affects international trade will also depend on how it is regulated. Governments are moving to regulate AI, which could become a barrier to international trade. Geopolitics could also undermine opportunities for AI to deepen globalization and trade in AI as the USA and China restrict trade in the technology needed to build and run foundational AI systems. Regulating the impact of AI on international trade is an increasing focus in free trade agreements (FTAs) and digital economy agreements (DEAs). The World Trade Organization (WTO) Joint Statement Initiative (JSI) e‐commerce negotiations could also support trade in AI. Yet, there is much more that trade policy could do to minimize barriers to trade in AI.

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Artificial Intelligence and Firm-Level Productivity

Cited by 13 publications

References 48 publications

A portrait of AI adopters across countries

A portrait of AI adopters across countries

AI adoption in America: Who, what, and where

The Impact of Foundational AI on International Trade, Services and Supply Chains in Asia

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