Job automation: the local dimension
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Presentation on job automation made at the Workshop on Territorial Mapping and Modelling of Education and Skills across EU Regions, held on 11 October 2018 in Brussels, Belgium. Presentation by David Bartolini, OECD Local employment, skills and social innovation division, CFE. More information: http://www.oecd.org/cfe/leed/local-employment.htm
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Job automation: the local dimension
- 1. THE LOCAL DIMENSION OF JOB AUTOMATION David Bartolini Senior Economist, OECD
- 2. LEED flagship publication • Launched on 18th September 2018 • LEED flagship biennial publication Table of content Executive summary Chapter 1: The local dimension of job automation Chapter 2: The geography of non-standard work Chapter 3: Fostering social inclusion in local labour markets Country profiles Available at http://www.oecd.org/publications/job- creation-and-local-economic-development-26174979.htm 2
- 3. • Frey & Osborne (2013), 47% of jobs at risk of automation in USA • OECD 14% of jobs at high risk of automation (USA, 10%) Country level estimates varies according to methodology 0 10 20 30 40 50 60 70 High risk of automation Significant risk of change
- 4. Subnational approach Task-based approach using PIAAC (Nedelkoska & Quintini, 2018) Risk of automation per occupation and per country Employment in those occupations by TL2 regions
- 5. Large differences in the impact of automation Percentage of jobs at high risk of automation, highest and lowest performing region, 2016 Source: OECD calculations based on Labour Force Surveys Oslo and Akershus Helsinki-Uusimaa Stockholm Delaware London Prague CapitalR. Île-de-France FlemishBrabant Lazio Ontario Madrid SouthernandEastern EastAustria Mazovia Attica WesternSlovenia BratislavaRegion HedmarkandOppland EasternandNorthernFinland SmalandwithIslands Nevada NorthernIreland CentralMoravia SouthernDenmark Champagne-Ardenne WestFlanders Marche NewfoundlandandLabrador Murcia Border,MidlandandWestern WestAustria Swietokrzyskie CentralGreece EasternSlovenia WestSlovakia 0 5 10 15 20 25 30 35 40 45 50 Bottom region Top region
- 6. What regional characteristics explain the regional difference? Below 25% Between 25% and 40% Above 40% Shareofjobsathighriskof automation Share of the workforce with tertiary education 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Below 40% Between 40% and 80% Above 80% Share of the population living in Functional Urban Areas 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Below 25% Between 25% and 40% Above 40% Share of employment in the tradable sector
- 7. • Yes, in 80% of regions it is happening • Employment trends 2011-16 • By occupation Is automation actually happening? 25% 26% 27% 28% 29% 30% 2011 2012 2013 2014 2015 2016 SHAREOFTOTALEMPLOYMENT Low-risk occupations High-risk occupations Employment trend in the most and least risk occupations
- 8. In most regions high-risk occupations are replaced by low- risk ones 60% 10% 22% 9% Creating jobs, predominantly in less risky occupations Creating jobs, predominantly in riskier occupations Losing jobs, predominantly in riskier occupations Losing jobs, predominantly in less risky occupations Help workers transition to better jobs Help firms transition to digital economy Help workers transition to better jobs and spur job creation Need employment to complement regional development policies Policy
- 9. Example 1: job losses in high-risk occupations are offset by job created in low-risk occupations 9Source: OECD calculations based on EU Labour Force Survey Mazowieckie (Poland) Employment growth (2011-16): 2.1% Lower risk 26 – Legal, Social and Cultural professionals 31 – Science and Engineering associate professionals Higher risk 83 – Drivers and mobile plant operators 91 – Cleaners and helpers
- 10. Example 2: jobs are mainly created in high-risk occupations Lower Normandy (France) Employment growth (2011-16): 2.2% 10 Lower risk 13 – Production and specialised services managers 24- Business and administration professionals Higher risk 93 – Labourers in construction, manufacturing and transport 81 – Stationary plants and machine operators
- 11. Example 3: regions decreasing employment in high-risk occupations 11 Source: OECD calculations based on Quintini (2018, forthcoming) and EU Labour Force Survey Catalonia (Spain) Employment growth (2011-16): -0.6%
- 12. Example 4: Regions losing mainly low-risk occupations 12 Source: OECD calculations based on Quintini (2018, forthcoming) and EU Labour Force Survey Abruzzo (Italy) Employment growth (2011-16): -2.7%
- 13. What policy response? – Trade offs: • Boosting productivity may require introducing more automation in sectors exposed to international competition (tradable) with the consequence of (short-run) technological unemployment • Creating jobs today in high-risk occupations may expose workers to future risks and hinder the transition to a more digital economy that is essential to improve regional competitiveness – These trade-offs are more or less severe according to the regional economy – Policy responses should aims at helping all workers and places to seize the opportunities from automation and digitalisation
- 14. • What types of jobs are created and in which sectors? – The rise on non-standard contracts – The importance of the tradable sector for productivity and competitiveness • Are people and places ready to benefit from technological progress? – Workers can react by upskilling, moving to another sector/occupation or moving to another region – How can policy make sure all can benefit from technological progress? Concluding remarks