sou · H3B365 · 2015-08-04 00:00:00 — Because it only takes few workers to serve global markets in a dig- ital world, digital industries are unlikely to create many new jobs. in digital industries may, however, create jobs elsewhere in the According to existing estimates, one tech-job has in the past created roughly four new jobs locally.4 To take advantage of these technological advances, would do well in investing in skills development. Because Swedish adults are well equipped with technical, problem-solving, as well as digital skills, is in a good position to serve world markets with digital products and services, creating also new jobs beyond digital industries. At the same time, graduation rates in science and below the EU average are worrying. To lead the digital revolution in the 21st century, needs to further invest in skills development and also in maintaining its digital infrastructure. Computers at Work: “” and the Decline of Routine Computers are exceptional at performing routine tasks that follow explicit rule-based procedures, which a programmer can easily reduce to a set of steps for a piece of software to execute. Repetitive production tasks or clerical tasks that involve calculating or sorting are tasks that for decades have lent themselves to automation, while more complex or idiosyncratic tasks have remained more elusive to computerize. In particular, the spread of technologies such as automated inventory control, accounting and payroll systems, factory automation and industrial robots has led to marked employment declines in jobs such as cashiers, customer service and office clerks, as well as operators. Middle-skill jobs have disappeared as computer technology has spread in the workplace … A key characteristic of many such routine jobs is that they are con- centrated at the middle of the income distribution.5 As shown in Figure 1, all European countries have experienced a decline in middle-skill jobs over recent decades: in , for example, differences in the extent to which jobs consist of routine tasks can 4Goos et al. (2013). 5Autor et al. (2003). account for 44 percent of the observed job polarization between 1990 and 2005.6 Although automation has had effect on total hours worked, it has reduced the hours worked by those with mid- dling skills.7 … while employment has grown in high-skill and low-skill jobs The decline in routine employment has, however, been accompa- nied by substantial growth in high-skill analytical jobs, but also low-skill manual service occupations. A common denominator for both types of jobs is that they typically involve non-routine tasks, which have proven challenging to automate. For example, high- skill jobs such as health professionals or corporate managers often require creative and complex problem-solving skills. Because com- puters generally constitute a complement to workers with such skills, they tend to increase their productivity rather than lead to their displacement. This, in turn, helps explain why wages of edu- cated workers have pulled ahead relative to their less skilled peers over recent decades.8 at the lower end of the income distribution such as ele- mentary sales or service jobs, on the other hand, often involve workers performing non-routine manual tasks that require physical adaptability and social interaction. Because social interactions often involve an understanding of human heuristics or physical flexibil- ity, such work has proven notoriously difficult to automate.9 As a result, many low-skill service jobs have persisted despite the advancing capabilities of computers and robots. 6Adermon & (2014). 7Graetz & (2015). 8A large literature documents that computer technology raises the productivity and wages of skilled workers. For example, Krueger (1993) shows that using a computer on the job raises a worker’s wage by 10–15 percent, also explaining why wages have increased relatively more for educated workers over past decades. 9Autor (2014). Percentage point change in hours worked, 1993-2010 Source: Goos et al. (2014). Digital Disruption in the 21st Century10 In this section, we describe how recent and ongoing technological advances have redefined the scope of automation, bringing an increasing number of skills and jobs into the domains of computer- controlled equipment. We further discuss what human skills are most likely to be made obsolete and which jobs are thus most susceptible to computerization. The changing nature of work A growing literature suggests that recent advances in technology are profoundly transforming the nature of work.11 In the , for example, some 40 percent of workers report that their work- 10Parts of this section draw extensively on (2013), with some paragraphs quoted directly. 11Brynjolfsson and McAfee (2014). place has been restructured due to the implementation of new technologies in the past three years (Figure 2). Swedish workers report the highest rate of technological restructuring, suggesting that remains successful in adapting new technologies. reportthatworkersofSharenew technologiesorprocesses workplacetheinintroduced(%) KingdomUnited Estonia RepublicSlovak AverageOECD RepublicCzech Source: (2013). the forthcoming decades, rates of technological restructuring are likely to increase even further, as new technologies will radically transform entire industries: advances in 3D printing, big data, cloud technology, and industrial robotics all have the potential to pervasively impact the world of work. To get a sense of the potential impact of these technologies, take the example of 3D printing, or ‘additive manufacturing’: globally, there are some 320 million manufacturing workers, constituting roughly 12 percent of employment.12 Even if additive manufacturing would only make a dent in this market it would bring with it substantial disruption for workers. 12 MGI (2013). Computers are now capable of performing complex tasks As technology races ahead, many complex tasks that were recently thought impossible for computers to perform have been success- fully transformed into well-defined and automatable problems, with strides being made in Learning, improving the capa- bilities of computers in areas such as pattern recognition, natural language processing, and perception. What is perhaps most striking are the many fronts at which technological advances are making complex tasks increasingly susceptible to automation, bringing a wide range of jobs into the scope of computerization.13 In the remainder of this section, we highlight some of these develop- ments. Algorithms and robots are reshaping health care jobs Improved pattern recognition and information processing is allowing health care jobs to become increasingly computerized. , ’s com- puter has been fed with more than 600,000 evidence reports, 1.5 million patient records, and two million pages of text from journals. Using information on a patient’s symptoms and genetics, this data is used to develop treatment plans with the highest probability of success.14 Cheap sequencing will further improve diagnostics and provide treatments tailored for a patient’s genetic setup: while the cost billion over a 13-year period, an individual’s genome can today be sequenced in a few hours for less than ,000.15 Lastly, improve- ments in tactile sensors are enabling surgical robots to perform less invasive procedures – some of which lie outside the capabilities of human surgeons – reducing postsurgical complications.16 13See, for example, Brynjolfsson & McAfee (2014). 14MGI (2013). Two years ago, released ’s programming interface to software developers, allowing them to use it as the basis for developing a wider range of digital services; last year they announced a 10-year initiative to apply ’s capabilities in developing countries to address problems in education and healthcare. 15MGI (2013). 16MGI (2013). Big data and Learning algorithms mean that a wide range of complex work is now automatable Big data is increasingly available due to an increasing amount of information becoming digitized. Learning algorithms can be coupled with such data to aid the computerization of non-rou- tine tasks for which simple rule-based queries are impossible to formulate, or to identify patterns or relationships in data that may be impossible to spot for a human observer.17 Above all, Learning allows computers to “learn” without explicit programmed instructions, allowing them to improve over time. One example of this technology at work is ’s Clear- well system, which uses language analysis to identify concepts in text. In two days, the system is able to analyze and sort almost 600,000 documents, transforming the tedious and time-consuming pre-trial research work of paralegals and patent lawyers.18 ’s, a department store chain, similarly uses big data to adjust the real- time pricing of some 73 million items available online, based on changes in demand and inventories. Lastly, PredPol exploits big data and complex algorithms to predict future crimes down to 500- square-foot geographical areas. Following its introduction in , burglaries dropped by some 25 percent due to more effi- cient deployment of law enforcement officers. Computers powered with Artificial Intelligence can now write crime, finance, and sports news stories Crafting a narrative or telling a story is a uniquely human capabil- ity. In 2010, however, Narrative Science launched Quill, an auto- mated narrative generator that uses Artificial Intelligence to generate easy-to-read narratives in conversational language from structured data. For example, the program StatsMonkey uses Quill’s capabilities to produce computer-generated news stories from available base- ball game statistics. In a similar fashion, and The announced last year that they would use automated technol- ogy to generate their corporate earnings stories and the 17Brynjolfsson and McAfee (2014). 18MGI (2013). Times regularly publishes news stories about earthquakes as well as homicides written by algorithms. Astonishingly, such computer- generated texts are nearly indistinguishable from those produced by a human writer, which will arguably render human journalists obsolete in text production that does not require human-level creativity, context, or judgment. Navigating a car in rush-hour traffic was long thought to require a wide range of uniquely human skills, such as anticipating another driver or pedestrian’s actions. As late as 2004, and argued that executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behaviour, leading most to believe that autonomous vehicles were impossible. Six years later, however, Google had shown that auto- mated vehicles were capable of navigating on American highways without any input from human drivers. Within five years Google expects to have a commercial version of their self-driving car on the market; several major car manufacturers similarly have near or fully autonomous vehicles in their production pipelines. Self-driv- ing cars may serve to transform both how we and commute, but also fundamentally change a wide range of jobs in transportation and logistics. Smart grid technology is reshaping the management of cities The cost-effectiveness and capabilities of digital sensors are rapidly improving, promising to revolutionize the management of urban infrastructure and delivery of public services. Sensors can, for example, monitor traffic patterns, with automatic adjustment of congestion charges or traffic lights, a technology that have reduced commuting times in and by 10–20 percent.19 In the same way, sensors can be used to monitor water infrastructure: and both use sensors, cutting water leaks by about half.20 As sensors spread to a wider range of areas, the 19MGI (2013). 20MGI (2013). efficiency and reliability of electricity grids, urban transportation, street lightning, and water infrastructure may improve dramatically as such processes become increasingly automated. Advanced industrial robots is further limiting the need for manual labor Industrial robots are becoming increasingly flexible, intelligent, and mobile, due to advances in AI, vision, mobile robotics, and machine-to-machine communication which will make it feasible to automate an even wider range of tasks than in the past.21 Rethink Robotics, a start-up company, has developed , a flexible robot that can easily be trained to perform nearly any man- ual task working for as as an hour.22 To program , a worker simply guides the robot’s arms to perform a set of motions, which the robot then remembers. is able to work in chaotic conditions alongside human workers and can also easily be retrained to perform alternative tasks simply by replacing the attachments on its arms. Similarly, , a Japanese technology firm, recently released Pepper, a robot that uses a cloud-based AI system, allowing it to learn from human interactions making it suitable for tasks such as baby-sitting or caring for people of old age. Lastly, strides were being made in replicating human social intelligence last year when a chat bot for the first time passed the ‘test’, which entails convincing a human judge that she is interacting with another human rather than a machine.23 Robots may thus soon be able to interact in natural language with humans, opening up a wide range of applications in service professions that demand social interaction. 21MGI (2013). 22Brynjolfsson and McAfee (2014). 23The test was set out in ’s 1950 paper Computing Machinery and Intelligence and involves human judges interacting in text conversations with a . To pass the test, a should be able to converse in a way that is indistinguishable from a human. In 2014, a Russian chat bot managed to convince a third of the judges at a test competition, leading the organizers to proclaim that the test had been passed for the first time in history. Three Types of Tasks Remain Challenging to Automate Notwithstanding the technological advances of recent years, some tasks remain elusive to computerize. In particular, tasks that require skills such as creative intelligence, perception and manipulation, or social intelligence are still challenging to condense into computer code, making jobs that involve such tasks less susceptible to computerization. Level of originality required in job Notes: This figure shows how the probability of computerization varies with the level of originality required (according to the definition maintained by O*NET, originality entails “the ability to come up with unusual or clever ideas about a given topic or situation, or to develop creative ways to solve a problem”) across 702 US occupations. Source: (2013). The psychological processes underlying human creativity are difficult to specify. Thus, the principal obstacle to computerizing creativity is stating our creative values sufficiently clearly that they can be encoded in a program, making it challenging to design robots that mimic human capabilities in this area. In particular, re- ducing creativity to a set of rules or guidelines that can be expressed in computer code remains a daunting challenge for computer scientists.24 While algorithms can now create texts following clearly 24 (2003). defined templates – as the example of Quill above makes clear – writing a novel with unique characters and plot, or designing an aesthetically pleasing piece of furniture, require skills that (so far) remain uniquely human. Consequently, jobs that require creative skills, such as originality, are less likely to be computerized in the near future (Figure 3). Replicating the complexities of human perception has long been a central challenge. Although basic geometric identification of objects in controlled environments is relatively mature, identifying un- familiar objects in unstructured environments, such as a messy kitchen, remains a daunting challenge for . Interestingly, humans perform such identification and manipulation tasks effort- , summarized in what computer scientists refer to as Moravec’s paradox: it is comparatively easy to make computers exhibit adult level per- formance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.25 Thus, basic tasks such as cooking, folding laundry, or washing dishes that require sensimotoric skills are still challenging for robots to perform in unstructured environments, making jobs that require such skills less susceptible to computerization. that revolve around care, negotiation, and persuasion typically demand substantial interaction with other humans, requiring social intelligence skills. Yet, computers have had trouble to replicate even basic human social and emotional skills. Although robots are increasingly being able to replicate human heuristics, their range remains limited. complex tasks, such as adapting to social cues or understanding the nuances of a person’s facial expressions 25 Moravec (1988, p. are still out of reach for computers, again reflecting Moravec’s par- adox at work. Therefore, jobs that involve, for example, assisting or caring for others remain outside of the domains of computers. Which to Disappear? the next decades, workers in production, transportation and logistics, services and sales, as well as a wide range of administrative support and office occupations are likely to experience job dis- placement due to computerization (Table 1).26 While the automa- tion of production jobs reflects a continuation of a trend from the late 20th century, cheaper and more sophisticated robots will make it feasible to substitute for workers in construction, manufacturing and services. Many simpler service tasks, such as lawn mowing, mopping, and vacuuming, are already being automated: sales of household robots are currently growing by 20 percent per year.27 Meanwhile, the computerization of jobs in transportation and logistics reflect recent breakthroughs in autonomous vehicles, such as more cost-effective sensors. Similarly, as voice recognition and natural user interfaces allow unstructured verbal interactions between humans and , call centers, customer service, and telemarketing jobs will also become increasingly computerized. Yet, while automation of knowledge work is becoming increasingly feasible, low-skill workers face the largest threats from ongoing technological advances. 26Frey & (2013). 27MGI (2013). Title Examiners Abstractors and Cargo and Freight Agents Photographic Process Workers and Timing Device Assemblers and Insurance Claims and Policy Insurance Appraisers Auto Damage Umpires Referees and Other Sports Receiving and Traffic Clerks Operators and Tenders Metal and Claims Adjusters Examiners and Source: (2013) Mental Health and Substance Oral and Maxillofacial Surgeons Fighting and Prevention Workers Clinical Counseling and School Computerization will mainly affect low-income workers Technological advances will mainly threaten workers with low edu- cational attainment in low-paid jobs: an occupation’s susceptibility to computerization exhibits a negative relationship with both aver- age income and education (Figure 3). As workers at the bottom end of the skill and income distribution are most likely to be dis- placed, this constitutes a break with the distributional impact of technology over past decades that mainly have affected middle-skill workers (Figure 1). Thus, as the impacts of computerization shifts downward in the income distribution, already growing income inequality is likely to be exacerbated. Furthermore, as the pace of technological progress accelerates in the next decades a substantial share of jobs that have previously constituted safe havens for low- skilled workers are likely to disappear. Average income (Thousands $) Share of workers with a Bachelor's Degree (%) Notes: These figures shows how the probability that a job will be feasible to computerize within the next two decades varies with the average income and educational attainment of workers across 702 US occupations. Source: (2013). Between a third and half of Swedish jobs are likely to disappear in the next two decades the next one or two decades, estimates suggest that 35 to 53 percent of Swedish employment is at high risk of computerization, meaning that as many as 2,5 million workers may be displaced.28 Across other European countries, the range of jobs at risk typically lies between 45 and 60 percent, placing ’s exposure in the mid-range.29 Although cross-country comparisons are made diffi- cult by different levels of detail in occupational classifications, crude but consistent estimates suggest that poorer countries in periphery have a higher share of jobs at risk, reflecting that a larger share of low-skilled jobs in those countries. Never- theless, even for relatively skilled countries, such as , the employment impact is likely to be substantial. New Work in the 21st Century As workers are displaced due to computerization, they will need to shift into jobs that are less susceptible to automation. In particular, this will require that they obtain skills that allow them to transition into entirely new types of work. In this section, we discuss new and emerging jobs that have appeared in the 21st century and the skills that they are demanding. 28Estimates by Unionen using 3-digit occupational classifications suggest that around 35 percent of all jobs are automatable, while estimates by (2014), that use the 1-digit level, suggest 53 percent of jobs are at risk. Because the 3-digit level analysis provides more detail, the number is likely to be closer to 35 percent. It shall be pointed out that while all of these estimates build on the work of (2013), showing that 47 percent of US jobs are susceptible to automation, estimates for other countries are not directly comparable to the US analysis due to the use of less detailed occupational classifications. 29Bruegel (2014). Technological advances may bring substantial disruption to 21st- century labor markets In times of rapid technological advances, disruptive innovations have always both created and destroyed jobs in a process of “crea- tive destruction”. the past century, technological advances have indeed led to vast shifts in employment in all developed countries; first from manual agricultural work to the mechanized factory, then from industrial production to complex and know- ledge-intensive services. Historically, technological progress has, if anything, created more jobs than it has destroyed: over the course of the 20th century the employment-to-population ratio increased, although mainly due to women entering the labor force. Yet, while the average labor force participation has increased somewhat across Europe, the labor force participation among male workers has steadily declined since the 1970s (Figure 5). Although there are clearly several varia- bles at work, the automation of male-dominated manufacturing work, provides one possible explanation. ratio (%) Notes: This figure shows the employment-to-population ratio for males and the total population aged 15 to 64 respectively for European countries. Source: (2014). If history will repeat itself remains to be seen, but there is un- doubtedly growing concern that the potential scope of automation expands, more workers will find it difficult to find meaningful employment, echoing ’s bold prediction at the height of the Great Depression that our discovery of means of economising the use of labour [is] out- running the pace at which we can find new uses for labour.30