The document discusses the potential impacts of automation and AI on labor markets in the Baltic states. It finds that while around half of all jobs could potentially be automated, technology adoption has been low and slow. This is likely due to a lack of knowledge about new technologies among firms. While automation may reduce required labor for each unit of output, it can also lead to higher overall employment growth through lower costs and increased productivity. However, there is a trade-off between policies aimed at increasing technology adoption and policies focused on upgrading workforce skills through education.

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. Automation has a large potential
=> Many jobs will potentially
disappear

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. Technology adoption important for
potential to be realized
Even though potential is high,
technology adoption seems to be low
and slow.

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
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. 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
• 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
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. What are the effects on the labor
market?
Low at present …. Yet
Technology adoption takes time
but may speed up

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
Employment – Firm studies – Survey data

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
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
• 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
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. Extra slide

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