Job level abilities and skills proxies

To capture the abilities and skills required by occupations at the three-digit occupation level we draw on O*NET version 15. O*NET is an occupational database by the US Department of Labor that narrowly defines occupations with respect to the tasks and activities and the skills and abilities required on the job. This database has been used frequently in the automation literature both in the US and the UK [2, 3, 5, 16]. The difference in task content of occupations in the US versus the UK has been shown to be small, further justifying using this resource to classify occupation attributes using UK data [3, 21].

Specifically, O*NET offers 80 distinct items in the abilities classification. The first column of Table 2 lists each of these items and column (2) provides a brief description of the item. The third column of Table 2 documents the secondary category a specific ability is in, while column (4) provides an overall category. In addition to abilities, O*NET offers 40 distinct items in the skills classification. Again the first column of Table 3 lists each of these items and column (2) provides a brief description each item. The third column documents the secondary category a specific ability is in, while column (4) provides an overall category. Given the large number of abilities (80) and skills (40) items, the secondary and overall category in columns (3) and (4) from Tables 2 and 3 will be utilized respectively for the interpretation of the analysis.

EU LFS and Lordan and Josten

Our analysis relies on data from Lordan and Josten [10] who match data from the European Labour Force Survey (EU-LFS) between 2013–2016 to their automation classification. The EU-LFS is conducted across all Member States of the European Union, Iceland, Norway, Switzerland and the United Kingdom and consists of quarterly collected household data on employment. We include all countries that have data in the years we analyze, in addition to 3-digit occupation codes. A full list of the countries we include can be found in Table 1.

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Table 1. Lordan and Josten (2020) shares of employment in 2013–2016 by country.

https://doi.org/10.1371/journal.pone.0266326.t001

Lordan and Josten [10] match their definition of recently automatable jobs based on patents at the 3-digit code occupation level to the EU-LFS using crosswalks provided by Lordan and Pischke [17]. To derive the automation classification, Lordan and Josten [10] revise 216 occupations that have been classified as non-automatable by Autor and Dorn [6] and search for the occupation name together with either the term “robot”, “automation”, or “artificial intelligence” in Google patents and then also in Google News. Depending on the number of patents and/or news articles, an occupation is then classified as either fully automatable, polarized automatable (i.e. partially automatable) or non-automatable.

Table 1 shows the shares of automatable employment (i.e. polarized and fully automatable) by EU-LFS country. We first note that the Lordan and Josten [10] classifications suggest that a large share of jobs in every country is recently automatable. Specifically, Finland is the country that has the lowest share of jobs that are classified as recently automatable (approximately 21%). For the remaining Scandinavian countries (Norway, Sweden and Denmark), Table 1 suggests that about 37% of the jobs are automatable, similar to the shares for the Netherlands and Iceland. In contrast, for Croatia, Czech Republic, Italy and Latvia more than 50% of the jobs are recently automatable. Overall, Table 1 highlights that there is large variation in the share of occupations that can be automated across EU-LFS countries.

Abilities

The results from the pooled country analysis for abilities are documented in Table 2. We first note that the regression considering abilities explains 97% of the variation in the Lordan and Josten [10] automation indicator. That is, we can explain almost all of the variation in the indicator variable with these measures of ability. Second, if we take column (3) and column (4) there is no single secondary or overall ability category that persistently has negative or positive signs. Rather, within each of these categories there are abilities that are becoming less important and others that are becoming more important. For example, arm-hand steadiness is a fine manipulative psychomotor ability (e.g. the ability to keep your hand and arm steady while moving your arm) that is highly unlikely to be automated given the estimates. In contrast, finger dexterity which is also a fine manipulative psychomotor ability (i.e. the ability to make precisely coordinated movements of the fingers of one or both hands to grasp, manipulate, or assemble very small objects) is likely to be automated. Similarly, fluency of ideas is an idea generation and reasoning ability which relates to the ability to come up with a number of ideas about a topic (i.e. the number of ideas is important, not their quality, correctness, or creativity) which is highly unlikely to be automated. In contrast, problem sensitivity (i.e. recognizing that there is a problem but not solving the problem) in the same category is highly likely to be automated.

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Table 2. Abilities estimates.

https://doi.org/10.1371/journal.pone.0266326.t002

Although it may seem counter-intuitive that within the same ability category there are pairs of abilities that are both automatable and non-automatable, the description of each item (see column (2) Table 2) highlights a logical theme. In general, an ability is automatable if it entails an underlying action that is repeatable or follows some logic. For example, control precision relies on adjusting controls quickly and repeatedly. The repetitive nature of this ability implies it is easily codifiable and machines have a comparative advantage in speed of execution. In contrast, speech recognition, which involves understanding spoken language, is unlikely to be automated. This finding is intuitive as it is difficult to predict what someone will say unless it entails common conversations such as those in telephone customer care. For example, customer care in banking now frequently utilize artificial intelligence to direct calls or provide bank balances but it is human operators that deal with complaints and other issues.

The overall conclusion from studying Table 2 is that jobs that are high on ‘brains’ (i.e. involve abstract thinking) are far less likely to be automated. In this case, ‘brains’ is short-hand for thinking and can involve reacting to other individuals (e.g. in caring or teaching professions), performing a service (e.g. as mechanic or fine dining waiter) or engaging in agile or creative thinking (e.g. in a leadership or knowledge worker role). Occupations that are predicted to be automated are low on ‘brains’ and high in routine contents. The abilities analysis is hence largely in line with the work by Author and Dorn [6] who predict that jobs that involve routine tasks will be automated. However, given that in this analysis we only focus on the occupations classified as non-automatable by Author and Dorn [6] it further reiterates that automation has continued to make progress automating jobs that are high in routine content.

Skills

The results from the pooled country analysis for skills are documented in Table 3. This regression explains 84% of the variation in the Lordan and Josten [10] automation indicator. Analyzing columns (3) and (4) of Table 3, the results are consistent with the analysis of abilities in that there is again no secondary or overall skills category that has a persistent negative or positive effect on the automation indicator. In addition and also consistent with the abilities analysis, there is a pattern which suggests that ‘brains’ (i.e. thinking and reacting) is becoming more important while on the job as compared to routine work.

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Table 3. Skills estimates.

https://doi.org/10.1371/journal.pone.0266326.t003

For example, within the overall category of basic skills (see column 4), the O*NET item active learning is a skill that is more likely to be automated. This is consistent with machines being able to process a large amount of information quickly. However, the O*NET items critical thinking and monitoring of performance, which essentially involve using information that is available to pass judgement and make decisions, are less likely to be automated. Within cross functional skills, skills that center around identifying potential problems, setting rules and gathering information are most likely to be automated (for example the O*NET items of time management, operation monitoring, system evaluation and management of human resources). In contrast, those skills that require manual actions (e.g. equipment maintenance), communicating information (for example instructing), leadership (e.g. persuasion) and knowledge work (e.g. systems analysis and technology design) are less likely to be automated.

A second notable pattern emerges if we reconsider Table 3. In general, the skills that require active interactions with people (i.e. implying that there is at least a two-way dialogue where an employee is reacting to the other person(s)) are not automatable. In essence, these jobs are an interaction between ‘brains’ and ‘people’ skills. In Table 3, these include coordination, instructing, monitoring and persuasion. This observation seems to suggest that jobs that involve ‘people’ interacted with ‘brains’ skills are also less likely to be automated.

Conclusion from the skills and abilities analysis

From the estimates documented in Tables 2 and 3 we make the following three conclusions:

1. We can explain almost all of the variation in the jobs that are newly defined as automatable by Lordan and Josten [10] using the O*NET items of abilities or skills.
2. Jobs that require ‘brains’ (i.e. abstract and non-linear thinking) are far less likely to be automated as compared to jobs which require linear and codifiable thinking skills and abilities. At the top of the income distribution, jobs that require non-linear thinking may need critical thinking, decision-making and creativity. Elsewhere in the income distribution these jobs require skills that have been traditionally delivered in apprenticeships, from mechanics and carpenters to florists and hairdressers.
3. Jobs that require ‘people’ engagement interacted with ‘brains’ are also less likely to be automated. These jobs include management across all levels, coordinators of all types, teachers, carer and medical practitioners (including nursing). The skills and abilities that are required for these jobs include soft skills. The value of these skills in terms of adult outcomes has become a topic of recent writings in economics (for example Heckman and Kautz (2013) [22]; Kautz et al., (2014) [23] and Lordan and McGuire (2019) [24]) and has been recently noted specifically as skills that will be needed in the advent of the Fourth Industrial Revolution [12, 25].

Activities

To summarize further the conclusions described in conclusions 2. and 3. from the skills and abilities analysis (i.e. that jobs which require ‘brains’ and ‘people’ interacted with ‘brains’ are those that are the least likely to be automated) we consider a third analysis on work activities and context of a person’s occupation. This third analysis is a replication of Table 3 in Lordan and Josten [10]. We later go further than Lordan and Josten [10] and present these estimates separately for each country in our dataset to allow for cross country comparisons.

To replicate their work we first follow Lordan and Pischke [17] and create three factors that represent the ‘people’ ‘brains’ and ‘brawn’ content in each three-digit code occupation. That is:

1. ‘People’ is a variable, which distils the information from many domains in activities and context that relate to having interactions with people on a day-to-day basis.
2. ‘Brains’ is a variable which distils the information from many domains in activities and context that relate to abstract thinking.
3. ‘Brawn’ is a variable which distils the information from many domains in activities and context that relate to interacting physically with objects, including making them on a daily basis.

We then regress our dummy representing whether a job is automatable on the ‘people’, ‘brains’ and ‘brawn’ variables (using ordinary least squares (OLS)). Consistent with Lordan and Josten [10]) we also interact the three variables with each other, implying we also include in our regression people*brains, people*brawn and brains*brawn. We again control for fixed country differences and yearly differences in the regression. The results from this analysis are documented in Table 4.

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Table 4. ‘People’, ‘brains’ and ‘brawn’ estimates.

https://doi.org/10.1371/journal.pone.0266326.t004

Turning to Table 4, we can explain 42% of the variation in our automatable employment indicator with the three latent factors. The size and significance of the negative coefficient on the ‘brain’ factor strongly implies that jobs which require thinking are those that are safe from the most recent wave of automation. In addition, the interaction between the ‘people’ and ‘brains’ factors is negative and significant. This highlights that jobs which require thinking and social interactions are safer in the most recent wave of automation as compared to those that simply involve people interactions (as evidenced by the positive and significant coefficient on the ‘people’ factor). We note that the estimates in Table 4 imply that jobs that are high on ‘brawn’ content are those that are most likely to be automated in the most recent wave of automation. These jobs include making objects and physically lifting items. Overall, these conclusions align well with those that came from the abilities and skills analyses in Tables 2 and 3 respectively as they highlight the importance of abstract thinking and the combination of abstract thinking and people skills.

Country-level analysis

The S1 Appendix contains the estimates from Table 2 through 4 by country including all EU-LFS countries as depicted in Table 1. The differences across these estimates is driven only by differences in the occupation distribution for each country, while the classification of whether a job is recently automatable is fixed across time and country. We note that the separate analyses of skills and abilities for each country allows us to draw similar conclusions to the pooled country estimates in Tables 2 and 3. That is, the abilities and skills that are becoming more important relate to the ability and skill to use ‘brains’ for abstract, strategic and creative thinking and the ability and skill to use ‘brains’ interacted with ‘people’.

The ‘people’, ‘brains’ and ‘brawn’ categories allow us to summarize the differences and commonalities across countries most succinctly. These are:

1. For all countries, jobs that are high on ‘brains’ are least likely to be automated in the most recent wave of automation.
2. For many countries the ‘people’ coefficient is also negative and significant, implying that jobs that are ‘people’ facing are relatively safe from automation, regardless of their interaction with ‘brains’. These countries are: Austria, Belgium, Cyprus, Czech Republic, Denmark, Germany, Estonia, Spain, France, Ireland, the Netherlands, Norway, Sweden.
3. For almost all countries there is a negative and significant relationship between the potential for automation and the ‘people’ and ‘brains’ interaction. The exceptions are Cyprus, Estonia, Ireland, Norway, Sweden and the UK. We note that for Estonia, Ireland, Norway and Sweden both the ‘people’ and ‘brains’ effects are independently negative and significant, highlighting that ‘people’ jobs in general are unlikely to disappear significantly. For Cyprus and the UK, the estimates suggest that ‘brains’ are the most important skills and abilities to develop given the current distribution of jobs.
4. In general, the brawn effect is positive and significant, implying that jobs that are high on ‘brawn’ content are at risk from automation. The exception of countries where this effect is negative are Cyprus, Czech Republic and Estonia.
5. The interaction effect between ‘people’ and ‘brawn’ is in general positive, significant but small in magnitude for just over half the countries in our study. In contrast, it is negative and significant but small in magnitude for: Finland, France, Hungary, Ireland, Iceland, Latvia, Norway, Sweden, Slovakia and the UK. The difference seems to be driven by a relatively large number of jobs in these countries in the low end of the income distribution that require physical lifting and people (for example, cleaning and caring).
6. The interaction effect between ‘brains’ and ‘brawn’ is in general negative, significant but small in magnitude. There are only three exceptions. These are: Cyprus, Czech Republic and Ireland.