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AI unboxed and jobs: A novel measure and firm-level evidence from three countries

Engberg, Erik ; Görg, Holger ; Lodefalk, Magnus LU ; Javed, Farrukh LU ; Längkvist, Martin ; Monteiro, Natália ; Kyvik Nordås, Hildegunn ; Pulito, Giuseppe ; Schroeder, Sarah and Tang, Aili (2024)
Abstract
We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We... (More)
We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groups of workers. (Less)
Abstract (Swedish)
We unbox developments in artificial intelligence (AI) to estimate how exposure to
these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We... (More)
We unbox developments in artificial intelligence (AI) to estimate how exposure to
these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groups of workers. (Less)
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organization
publishing date
type
Working paper/Preprint
publication status
published
subject
pages
44 pages
publisher
IZA Discussion Papers
language
English
LU publication?
yes
id
01c9660c-6481-491b-bf69-afbe7b4a8c09
alternative location
https://docs.iza.org/dp16717.pdf
date added to LUP
2026-04-20 14:04:02
date last changed
2026-04-22 12:27:55
@misc{01c9660c-6481-491b-bf69-afbe7b4a8c09,
  abstract     = {{We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groups of workers.}},
  author       = {{Engberg, Erik and Görg, Holger and Lodefalk, Magnus and Javed, Farrukh and Längkvist, Martin and Monteiro, Natália and Kyvik Nordås, Hildegunn and Pulito, Giuseppe and Schroeder, Sarah and Tang, Aili}},
  language     = {{eng}},
  note         = {{Working Paper}},
  publisher    = {{IZA Discussion Papers}},
  title        = {{AI unboxed and jobs: A novel measure and firm-level evidence from three countries}},
  url          = {{https://docs.iza.org/dp16717.pdf}},
  year         = {{2024}},
}