{
  "title": "Labor Market Exposure to AI: Cross-country Differences and Distributional Implications",
  "publication": "IMF Working Papers, October 4, 2023",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2023/10/04/labor-market-exposure-to-ai-cross-country-differences-and-distributional-implications-539656",
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  "summary": "This paper examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs).",
  "sections": [
    {
      "heading": "Study overview and methodology",
      "content": "- Authors: Carlo Pizzinelli, Augustus J Panton, Marina Mendes Tavares, Mauro Cazzaniga, Longji Li\n- Date: October 4, 2023\n- Research focus: Examines the impact of Artificial Intelligence (AI) on labor markets in Advanced Economies (AEs) and Emerging Markets (EMs).\n- Methodological contribution: Proposes an extension to a standard measure of AI exposure that accounts for AI's potential as either a complement or a substitute for labor; complementarity reflects lower risks of job displacement.\n- Data: Worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa)."
    },
    {
      "heading": "Key findings — cross-country patterns",
      "content": "- Unadjusted AI exposure varies substantially across countries.\n- Advanced Economies (AEs) face higher unadjusted exposure than Emerging Markets (EMs) due to a higher employment share in professional and managerial occupations.\n- When accounting for potential complementarity, differences in exposure across countries are more muted."
    },
    {
      "heading": "Key findings — within-country distributional patterns",
      "content": "- Women face greater occupational exposure to AI at both high and low complementarity.\n- Highly educated workers face greater occupational exposure to AI at both high and low complementarity.\n- Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity."
    },
    {
      "heading": "Subject tags and keywords",
      "content": "- Subject: Artificial intelligence, Education, Employment, Labor, Labor markets, Technology\n- Keywords: Artificial intelligence, educated worker, Emerging Markets, Employment, employment share, exposure to AI, impact of artificial intelligence, labor market exposure, Labor markets, Occupations\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2023/10/04/labor-market-exposure-to-ai-cross-country-differences-and-distributional-implications-539656"
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    "Authors: Carlo Pizzinelli, Marina Mendes Tavares, Mauro Cazzaniga, Longji Li",
    "Published: October 4, 2023",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400254802.001",
    "Authors: Carlo Pizzinelli, Augustus J Panton, Marina Mendes Tavares, Mauro Cazzaniga, Longji Li",
    "Date: October 4, 2023",
    "Research focus: Examines the impact of Artificial Intelligence (AI) on labor markets in Advanced Economies (AEs) and Emerging Markets (EMs).",
    "Methodological contribution: Proposes an extension to a standard measure of AI exposure that accounts for AI's potential as either a complement or a substitute for labor; complementarity reflects lower risks of job displacement.",
    "Data: Worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa).",
    "Unadjusted AI exposure varies substantially across countries.",
    "Advanced Economies (AEs) face higher unadjusted exposure than Emerging Markets (EMs) due to a higher employment share in professional and managerial occupations.",
    "When accounting for potential complementarity, differences in exposure across countries are more muted.",
    "Women face greater occupational exposure to AI at both high and low complementarity.",
    "Highly educated workers face greater occupational exposure to AI at both high and low complementarity.",
    "Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity.",
    "Subject: Artificial intelligence, Education, Employment, Labor, Labor markets, Technology",
    "Keywords: Artificial intelligence, educated worker, Emerging Markets, Employment, employment share, exposure to AI, impact of artificial intelligence, labor market exposure, Labor markets, Occupations",
    "**Working Paper**"
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