Estimating the risk of automation at the national and local level - Beatriz JAMBRINA CANSECO
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Presentation by Beatriz JAMBRINA CANSECO, OECD LEED from the OECD capacity building seminar “A workforce for the future - Designing strong local strategies for better jobs and skills”, 28-29 Nov 2017, Venice, Italy. More information: http://oe.cd/CBSVenice2018
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Estimating the risk of automation at the national and local level - Beatriz JAMBRINA CANSECO
- 1. ESTIMATING THE RISK OF AUTOMATION AT THE NATIONAL AND LOCAL LEVEL Beatriz Jambrina Canseco, Organisation for Economic Co-operation and Development (OECD)
- 2. MEASURING AUTOMATION AT THE NATIONAL LEVEL
- 3. Methodology at the national level Starting from the method developed by Frey and Osborne (2013): • Expert assessment of risk of automation in 70 occupations • Technological bottlenecks: perception and manipulation, creative intelligence and social intelligence tasks • Out-of-sample estimation for the rest of the 702 occupations OECD applied this method to the OECD Adult Skills Survey (PIAAC). Steps: • Using Canadian data from PIAAC • Identified the same occupations using ISCO 2008 (4-digit) • Identified similar bottlenecks • Out-of-sample prediction for jobs in different countries 3Source: Quintini (2017)
- 4. 0 10 20 30 40 50 60 70 Across the OECD, around 14% of workers have a high probability of being automated Share of jobs at significant risk (50-70%) and of high risk (>70%) of automation, by country, % SIGNIFICANT RISK HIGH RISK Source: Quintini (2017)
- 5. The risk of automation and key individual and job characteristics Highest risk in routine jobs with low skill and education requirement BUT low risk applies to a broad range from professionals to social workers Automation mostly affects manufacturing industry and agriculture BUT some service sectors are highly automatable too. No evidence of polarisation or rising risk at the high end: automation risk declines with skills, education and hourly wages Young people are the most at risk of automation, followed by older workers, with disappearing student jobs and entry positions. Source: Quintini (2017)
- 6. HOW TO MEASURE AUTOMATION AT THE REGIONAL/LOCAL LEVEL?
- 7. • Based on OECD estimates of national risk of automation • Using data on local employment per occupation category • As a test, analysis has been developed for the United Kingdom Measuring automation at the local level Source: The Economist, Jan 18th 2014
- 8. The percentage of occupations at high risk of automation differs across countries… Data source: Quintini (2017)
- 9. … but also between local areas within the same country Inner London – West ≈ 5.8% ≈ Norwegian average North & North East Lincolnshire ≈ 15% ≈ Italian average
- 10. Part of the automatability trend has to do with manufacturing employment…
- 11. … although not with population density
- 12. What does this mean for the local level? “The full picture, as in all technological revolutions, emerges only if both – the better life for those who can adjust themselves and the suffering of those who are pushed out – are seen together and at the same time” Peter Drucker
- 13. Next steps • Expand country analysis and refine the methodology to estimate risk of automation at the regional level • Examine job creation opportunities emerging from automation • ‘Job Creation and Local Economic Development 2018’ will feature a chapter on the future of work at the local level Questions for discussion • Has regional analysis been developed within your country on the impacts of the future of work? • Are there any policies in place in your local area or at the country level that are designed to tackle the challenges associated with automation? Next steps and questions for discussion
Editor's Notes
- Technological bottlenecks: Perception and manipulation: Finger dexterity, manual dexterity and working in a cramped workspace or getting used to awkward positions Creative intelligence: Originality and fine arts Social intelligence: Social perceptiveness, negotiation, persuasion, and assisting and caring for others Identify FO tasks and create correspondence between O*NET training data to PIAAC training data I create a correspondence between the 70 occupations in FO’s training data and occupations in the ISCO 2008 4-digit classification There are 71 matches among the 440 ISCO 4-digit occupations Some FO occupations do not have a match, and others have more than one Occupations without a match: dishwashers, parking lot attendants, technical writers, legal assistants, gaming dealers, farm labor contractors, claims adjusters. FO tasks are also not fully covered either (see next slide). There are no matches for: (a) finger dexterity, (b) working awkward positions, (c) fine arts, and most importantly (d) caring for and assisting others. The last one is the most important missing variable because it affects a large population working in healthcare and services and is one of the most predictive tasks of difficulty to automate in the BIBB data. I focus on FO only because three categories of ALM tasks are fully missing (routine cognitive, routine manual and non-routine manual) Potential consequence of using a subset of FO tasks: overstate the automatability of jobs
- Accounting for individual variation reduces the estimated risk of automation; Across all countries, 14% of workers have higher than 70% probability of being automated Most jobs will change somewhat as a result of automation: 32% are at risk of significant change Bottom line: estimates vary across studies, depending on the technique used, but tend to converge to a much smaller figure than FO when individual variation in skills use at work is accounted for
- Automation is found to mainly affect the manufacturing industry and agriculture, although a number of service sectors, such as postal and courier services, land transport and food services are highly automatable too. However, no support is found for the hypothesis that automation has started affecting high-skilled jobs. The occupations with the highest estimated automatability typically only require basic to low level of education. At the other end of the spectrum, the least automatable occupations almost all require professional training and/or tertiary education. • There is no indication that the wave of near future automation will be polarising, i.e. affecting middle-income jobs in a more pronounced way, or that it is affecting high-income, highly-educated professionals. Overall, despite recurrent arguments that automation may start to adversely affect selected highly skilled occupations, this prediction is not supported by the Frey and Osborne (2013, 2017) framework of engineering bottlenecks used in this paper. Indeed, with the exception of some relatively low-skilled jobs – notably, personal care workers – the findings here suggest a rather monotonic decrease in the risk of automation as a function of educational attainment and hourly wages. • A striking novel finding is that the risk of automation is the highest among teenage jobs. The relationship between automation and age is U-shaped, but the peak in automatability among youth jobs is far more pronounced than the peak among senior workers. In this sense, automation is much more likely to result in youth unemployment, than in early retirements. To some extent, this higher risk of automation may be countered by smoother transitions between jobs for youth people compared to older individuals. In most countries, young people are better skilled than their older counterparts so they may find it easier to find new jobs, including those created as a result of the introduction of new technologies. Furthermore, as high-risk jobs are, in many countries, associated with student jobs, schemes that facilitate internships in areas related to each student field of study may allow practicing job-specific skills as well as facilitate the acquisition of generic skills once achieved through low-skilled summer jobs. • This study also looks at the relationship between skills, education and information and communication technologies (ICT) at the individual level. The relationship between skills and ICT use at the individual level suggests that ICT behave like typical skills-augmenting technologies (i.e. higher ICT use is associated with higher skill proficiency). On the other hand, at the aggregate level, ICT adoption could be labour substituting. For instance, computer adoption in one occupation (e.g. managers) may reduce employment in other occupations (e.g. assistants). Furthermore, the variance in computer use across occupations is rather large at a medium risk of automation: occupations that are at low risk are almost without exception intense in computer use; at very high levels of risk, one tends to see mainly low users; however, a wide range of occupations in terms of computer use are bunched up between the 40 and 60% probability of automation. This is an interesting group of occupations and further analysis of the machines and tools that they employ, in addition to computers, would better reveal what technologies – e.g. robotics – are likely to be labour-substituting. In other words, there is a large share of unexplained variance in the risk of automation when only focusing on the use of computers (or lack of) as potentially labour-substituting technolog Workers at risk of automation receive less (not more) training than workers at high risk of automation. This is unchanged when controlling for age, educational attainment and country fixed-effects
- Explain the way in which the national estimates are calculated (probably already in Glenda’s presentation remind the audience that the calculations are based on tasks per occupation) Based on estimated levels of automatability by occupation at the national level. We choose to look at local labour markets using local employment per occupation category Probability of automatability is simply mapped onto the employment make-up of each local economy. Simply put, this means that if we say that accountants have a 70% risk of being automated, t
- On average across the sample of 32 OECD countries, 14% of jobs have a probability of being automated that is higher than 70% (alredy mentioned in Glenda’s presentation). There is a large variation in the degree of automatability across countries (alredy mentioned in Glenda’s presentation). Differences range from values around 7% in Nordic countries, up to values over 20% in Spain and Greece. Pay special attention to the UK average at 11.7%, since this is the country we will focus on for the rest of the presentation.
- Jobs at risk of automation varies substantially across UK sub-regions, and that just comparing countries provides a partial view to the problem. Examples that illustrate this include North & North-East Lincolnshire (max), with an impact of automation similar to the Italian average; and the west of London (min), where the impact of automation is more akin to the one faced by the average Norwegian.
- Unsurprisingly, we find that these differences partly depend on the presence of manufacturing as the main source of jobs in certain local economies. While places with a ser Indeed, local areas with a higher share of employment in services have fewer jobs at a high risk of automation. Not all types of services are associated with a lower risk of automation, however. The financial and insurance sector does seem to play a role in staving off automation (see London on the bottom-right corner). So do the hotel and restaurant industry, transportation and communications, and other services. In contrast, energy, construction, and health, education and public administration show no association with the share of jobs at high risk automation at the local level. vice-based economy tend to fare better.
- However, population density appears to have nothing to do with the share of jobs at risk of automation this is one thing in which cities will not have the upper hand.
- No way to stop the automation wheel, nor would that help productivity. But being able to estimate automation at the local level can help to understand in which areas, occupations and sectors skills will become obsolete. This gives policymakers more information with which to develop the smart specilization of their region. Once they know which skills already exist in the local area and in which sectors and occupations it is or isn’t likely that there will be employment, training and skills strategies can be designed accordingly. Outmigration from places affected by automation?
- List of countries could include any with a LFS Current methodology only maps national estimates onto local employment per occupation category. However, a more precise methodology could be developed, which would allow for the calculation of estimates of automation at the regional level. This methodology would follow ELS’s approach, additionally including variables that define the main characteristics of any regional economy. Automation isn’t just a threat for people. It is definitely a challenge to be addressed with adequate policies, but it also represents an opportunity.