Jobs are increasingly automated which presents challenges for low skilled workers and will redefine social structures, specially affecting the working class. In 1952 Kurt Vonnegut published Player Piano, this novel describes a Utopian world where machinery has taken over most of the tasks working class used to perform. Consequently, social class is determined by technology and divides society in two: the previous working class made redundant by machine replacement, and the class that controls that machinery. The ruling class of engineers live in high class neighbourhoods whereas the previous working class is pushed to live in the Homestead outside the city away from the upper class. This fictional story is for the first time in history becoming a more tangible reality (Ford, 2018).Throughout history technological advances have impacted the way we live, work and socialise. Not only do rapid technological advances generate shifts within all industries, it also creates new businesses (Erik. Brynjolfsson, 2014). Following the before mentioned trend, recent technological advances indicate a transition from the Third to the Fourth Industrial Revolution (Schwab, 2016).The impact of technology on workers in blue and white collar jobs is a topic which has been widely covered (Erick Brynjolfsson, Andre McAfee or Martin Ford). Existing research primarily focuses on salary and questions either capitalism or the post capitalist era. This leaves a gap on how this will affect class distinctions and physical segregation in our cities.
Impact of Automation in the western job markets
This current wave of automation is different in terms of impact on structure and labour distribution: In the XIX century automation focused on replacing tasks that were unsafe and precarious, in the XX century automation moved towards tasks that would free workers from repetitive work. However, in the current wave of automation (XXI century) machines are approaching intelligent systems that can make better and faster decisions than humans (Davenport and Kirby, 2015). Consequently, there will impact on social classes is and will likely affect the residential differentiation. A primary debate surrounding of automation effects in cities is the rate of ‘job loss’ and whether automation creates or destroys jobs (Frey and Osborne, 2017). However, there is still no conclusive evidence as to the exact effects of automation on the share of jobs (Winick, n.d.). The main studies on this topic report conflicting results: A) 47% of US jobs are at high risk of automation in the next few decades (Frey and Osborne, 2017), B) 9% of jobs in the organisation’s 21 member countries can be automated (The Risk of Automation for Jobs in OECD Countries, 2016), and C) 400-800 million jobs worldwide could be automated by 2030 (Bughin et al., n.d.). Brynjolfsson and McAfee (2014) describe the current scenario with an example of the photography industry: A comparison on the amount of photos taken and the jobs created by it. They argue that we have reached a peak in the number of digital photos taken. Comparing the number of employees Kodak used to have, (145,300) to the current number of employees of Instagram (4,600), highlights the Fourth Industrial revolution (Ford, 2018). It is relevant to point out that Kodak used to employ blue collar workers while companies like Instagram focus on white collar. Thus it becomes clear that there will be an impact, not only in the number of jobs that will fully disappear, but on the distribution of social classes and wealth. A recent study in the United Kingdom have pointed out how the North and South Divide will be deepened by the rise of automation (“Cities Outlook 2018,” n.d.). These studies conclude that approximately 30 percent of the current workforce, in cities like Mansfield, Sunderland, Wakefield and Stoke, is likely to shrink by 2030. However, the impact of automation will set up some cities likely to grow due to the high-skilled workforce. 1.4m existing jobs in British cities are in occupations predicted to grow by 2030 and these will affect between 5-10% of British cities. But, as the pattern follow the North and South Divide, the high skilled and high paying jobs of the future will be placed in the South (“Cities Outlook 2018,” n.d.).Consequently, the sum of jobs may not vary significantly, however the distribution and placement of jobs will vary (i.e. by placement social class) as automation transfers unskilled jobs into skilled jobs. Furthermore, the skilled jobs require individuals to access cultural capital, due to being more technical professions associated with technology, which is increasing cost *1. This is likely to affect cities differently depending on their demographics and leading industries. In the US, small cities seem to have been impacted more by automation (Frank et al. 2018) whereas the bigger cities tend to host the managerial and technical professions. This will change the landscape of cities by centralising highly qualified employees with high incomes in big cities and suburbs to host lower income workers. While this is not different to the present configuration it might deepen/emphasise these differences. Yet, while the before mentioned reports and studies show different impacts of automation there are some disagreements on how to measure or forecast automation.
*1 "Between 1962 and the 1990s higher education in Britain was effectively free, as the state paid students’ tuition fees and also offered maintenance grants to many. In 1998 university fees were reintroduced at £1000 per year. In 2004 they were raised to £3000, now converted into loans repayable on an income-contingent basis, but still regarded as ‘top-up’ fees supplementing the state’s direct grants to universities. Following the 2010 election, the basis of university finance was radically transformed, as student fees, now raised to £9000, largely replaced the teaching element in the state grants” Policy, “History & Policy.”(Anderson, 2018)
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