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Kopenhāgenas Biznesa skolas profesora Andersa Sērensena prezentācija

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Kopenhāgenas Biznesa skolas profesora Andersa Sērensena prezentācija par automatizācijas un mākslīgā intelekta izaicinājumiem.

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Kopenhāgenas Biznesa skolas profesora Andersa Sērensena prezentācija

  1. 1. Labour Markets in the Baltics: Challenges Ahead, October 2, 2019 AI, Automation and the Future of Work: A Challenge or an opportunity for the Baltic States? Professor Anders Sørensen Department of Economics Copenhagen Business School Denmark
  2. 2. Automation has a large potential => Many jobs will potentially disappear
  3. 3. 3 McKinsey Global Institute: Around half of all working hours in the global workforce can be automated with existing technologies. • Sector variation (Manufacturing high/business service lower) • Unskilled and short education are more likely to be affected • Middle-income jobs are more likely to be displaced. Automation has a large potential
  4. 4. Technology adoption important for potential to be realized Even though potential is high, technology adoption seems to be low and slow.
  5. 5. 5 Technology adoption – incl. Baltic Countries Source: International Federation of Robotics Robots per million hours worked by employees - 2015 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00
  6. 6. 6 Technology adoption – across firms, over time Automation score – 2005 and 2010 The share of production processes that are performed automatically; not manually 0 10 20 30 40 50 60 1 (1,2] (2,3] (3,4] (4,5] Percent 2005 2010 Source: AIM-data, (Kromann and Sørensen, 2019)
  7. 7. Why is technology adoption low and slow if productivity and profitability increase? Driver: Firms that are exposed to high competitive pressure from (e.g., China) automate Barrier: Lack of knowledge may be a constraint for new technologies.
  8. 8. 8 • Lack of knowledge about new technologies: • Observations from firm visits: • Firms seemed to lack the necessary skills to investigate needs and possibilities for automation. • New collected survey data: • Self-scored measure of process innovation and “objective” automation score are not “in sync”. Barriers to technology adoption
  9. 9. 9 Self scored measure Low High Total Low 27 11 38 High 37 25 62 Total 64 36 100 Automation score Automation score and self scored measure not “in sync” Self scored automation exceeds the automation score
  10. 10. What are the effects on the labor market? Low at present …. Yet Technology adoption takes time but may speed up
  11. 11. 11 • Labour-saving technologies: • Reduces the required labour input per unit output, • An offsetting output expansion effect through reducing costs/increasing quality. • Danish panel-data: • Manufacturing firms with high investments in automation have higher employment growth that firms with low investments in automation. Employment – Firm studies – Survey data
  12. 12. 12 Employment – Firm studies – Survey data
  13. 13. There may be an important trade-off between industry and education policy • Policies targeted at technology adoption • Education policies for upgrading the work force
  14. 14. 14 Educational attainment – population – Denmark 23% 18% 6% 6% 38% 34% 31% 40% 2009 2019 Primary education Upper secondary education Vocational Education and Training Further education
  15. 15. 15 • High potential for automation => Many jobs may disappear • However, technology adoption seems to be low and slow. • Why? Driver and Barrier • What are the effects on the labor market? • Low, at present ... but may speed up if technology adoption takes off • There may be an important trade-off: • Industry policy targeted at lacking knowledge about technologies • Education policies for upgrading the work force Summary
  16. 16. 16 Acemoglu, D. and P. Restrepo (2019), “Robots and Jobs: Evidence from US Labor Markets”, forthcoming, Journal of Political Economy Bloom, N., M. Draca, and J. Van Reenen (2016). “Trade Induced Technical Change: The Impact of Chinese Imports on Innovation, IT and Productivity”, Review of Economic Studies, 83(1), 87-117. Borjas, G. J, and R. B. Freeman (2019), “From Immigrants to Robots: The Changing Locus of Substitutes from Workers”, in RSF: The Russell Sage Foundation Journal of the Social Sciences, Issue on Improving Employment and Earnings in Twenty-First Century Labor Markets, edited by Erica L. Groshen and Harry J. Holzer, forthcoming 2019. The Tuborg Research Centre For Globalisation and Firms and McKinsey & Company ”A future that works: the impact of automation in Denmark”, April 2017 McKinsey Global Institute “Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages, November 2017 Kromann, L., and A. Sørensen (2019), “Automation, Performance, and International Competition: A Firm-Level Comparison of Process Innovation” forthcoming, Economic Policy Kromann, L., N. Malchow-Møller, J.R. Skaksen, and A. Sørensen (2019), “Automation and Productivity – A Cross- country, Cross-industry Comparison”, forthcoming, Industrial and Corporate Change, 2019, https://doi.org/10.1093/icc/dtz039 References
  17. 17. Extra slide
  18. 18. 18 Technology adoption – Baltic Countries • Lithuania • Latvia • Estonia • Denmark • EU28 Share of firms that have used the advanced technology 2017/2018 Source: EUROSTAT 0 10 20 30 40 50 0 1 2 3 4 5 6 EU28 EU28EU28 Pct. of firms Big data analysis geolocation Big data analyse social media Advanced cloud computing 3D printing Industry robots All Firms Manufacturing firms EU28 EU28

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