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The word robot can be defined as a machine that can navigate through and interact with the physical world of factories, warehouses, battlefields and offices (Brynjolfsson & McAfee, 2014). To famously quote Warren Bennis, The factory of the future will have two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment . Artificial intelligence and robotics are leading the way for a new industrial revolution (Berg, Buffie & Zanna, 2018). Robots can unload, load, product send and retrieve with little supervision from humans. Artificial intelligence is beginning to take over the jobs of bankers, accountants, and even teachers. Self-driving vehicles are on track to take millions of jobs from taxi, delivery and truck drivers along with Uber aiming to be a driverless company by the year 2030. According to Frey and Osborne (2017), rapid advances in automation are expected to threaten 45-57% of all United States jobs. With the astonishing advance in artificial intelligence and robotics it has sparked a keen interest from journalists, economists and technophiles regarding how this could impact the labor force across the world. This essay will review current literature regarding the rise of robots and their impact on labor forces internationally. More specifically the review will discuss the jobs at risk of automation, a possible robot taxation and the way in which robots may affect wage inequality.
Jobless Future
Robots abilities do not just sit with computing, but they can also learn, understand and react to external factors. Robots today can write newspaper articles, grade exams, play instruments and paint. In Boston and the Silicon Valley many new start-up firms have developed technologies that are able to replace the need for human labor entirely. Momentum Machine is a new start-up company that aims to fully automate the production of gourmet burgers. The founders have stated that it is designed to obliterate the need for human labor entirely (Virgillito, 2017). On the other hand, sectors like health care and medicine have been slower to introduce robots and machine learning algorithms, however their usage is believed to be complementary to human activity rather than labor replacing. For instance, venture capitalists financing start-up companies which are specialized in designing elder-care robots are increasingly seeking the use of artificial intelligence in medicine that could potentially reduce the cases of medication errors (Virgillito, 2017).
In September 2016, Mercedes-Benz, a German vehicle manufacturer formed a strategic partnership with Starship Technologies (Daimler, 2017). This is a start-up company based in Estonia which specializes in the development of automatic robots for last-mile deliveries. The specialized robots from Starship move along the sidewalks weighing less than 40 pounds when loaded, their use involves delivering parcels or groceries from shops or hubs directly. Consumers can track their delivery via their smartphone which also are used to open the sealed and locked cargo upon arrival of the robot. When the customer has received their parcel, the robot automatically returns to their designated base. These robots also comply with safety laws and are only allowed move at the speed of a pedestrian. The downfall of this is stores would need branches everywhere for efficient quick delivery or either customers would have to accept long delivery times. To avoid this issue the alliance has advocated an idea where the trucks will be used as a mobile launch platform for all the robots. The way this concept works is as follows. The truck will load the goods for the customers at a central depot. A certain area of the truck is left vacant to load the working robots on board. The truck then leaves to the centre of the city and once a drop off zone is reached the required number of robots are loaded with the goods to deliver to the desired customer. After the robot deliverers the customers purchase, they return to their depot. This process continues until all the shipments inside the truck are delivered by the robots. Following a full truck delivery, robots will be used to load more shipments for the following set of customers (Boysen, Schwerdfeger & Weidinger, 2018).
Robots in the Workplace
Research has found that on an individual level of job insecurity socioeconomic determinants are highly relevant. Workers in strong positions in the labor market have reported lower job insecurity as a result of two reasons. Firstly, labor contracts are more secure in these strong job positions resulting in a worker being able to retain their job more easily. Secondly, a worker in a strong position can find employment more easily if they become unemployed than a worker who is in a lower skilled job. Highly educated workers, full time workers and managers all report lower levels of job insecurity than non-managers, workers in a part time job and lower educated workers (Green, 2009; Mau, Mewes & Schöneck, 2012).
Most research on robots does not focus on the workplaces worldwide but rather several small observations along with cultural backgrounds having an influence towards robots (Bartneck, Suzuki, Kanda & Nomura, 2007). Although there are a vast number of subcultures in countries, Western economies tend to be less positive regarding robots in contrast to Eastern economies (Shaw-Garlock, 2009). Countries with low uncertainty are expected to adapt more easily to a change in their environment than countries with a high uncertainty. Robots provide a strong example for this and therefore countries with low uncertainty avoidance will see the introduction of robots in their workplace in a more optimistic light than those countries with high uncertainty. This outlook has been supported by Hofstede (1991) who suggests that countries with low uncertainty are more adaptable to change.
Robot Tax
As automation is on the rise and more jobs are at risk many ask the question should robots be taxed? Bill Gates has recently reignited the robot tax debate whereby he believes they should be taxed. The European Parliament have discussed policies that address the impact of automation on the labor market and South Korea have already acted to prevent a jobless economy. There are two types of workers, routine and non-routine. A routine worker performs a task that can be automated which we refer to as a robot. If there is partial automation a robot tax is optimal. This taxation increases routine workers wages and reduces non-routine workers wages. This gives the Government greater means to reduce inequality. On the other side if there is full automation it is not optimal to tax robots as there are no longer routine workers and taxation would simply distort production decisions along with not reducing income inequality which would be the main reason for the introduction of a robot tax in the first place (Guerreiro, Rebelo & Teles, 2017).
The introduction of a robot tax would result in an improvement in wage inequality between skilled and unskilled workers. A robot tax would result in a reduction in after-tax revenue of a firm in the sector producing robots leading to fewer robots produced than the previous production. As a result of this, the price of robots will increase due to a lower supply in the economy. The robot-using sector will use robots as a substitute for unskilled labor, resulting in an increase in the wage rate of those workers. The productivity of skilled workers in this sector will decrease, therefore the skilled wage rate will also decrease. To conclude on this, the wage differential between these workers will narrow (Zhang, 2019).
Robots can be either subsidized or taxed. General equilibrium to compress the wage spreading would be exploited following a robot tax. A compression on wages would make it less distortionary for an income tax which would allow for greater redistribution along with raising welfare. If a robot substitutes primarily for routine labor at middle incomes, a robot tax therefore would decrease the wage inequality at the upper end of the wage distribution however inequality would be raised at the bottom end (Thuemmel, 2018).
The reasoning behind the decline in skilled labors productivity is due to a capital outflow. The demand for robots falls as the robot-using sector can use unskilled workers to substitute robots. As a result, the increment of robot pricing cannot exceed the tax rate. This results in a decrease of effective robot pricing therefore the marginal product value of skilled workers declines, and skilled workers wages cannot increase through the pricing channel (Zhang, 2019).
Wage Inequality
The rapid growth in automation has raised concerns for future issues with unskilled workers finding jobs that pay a fair wage (Tirole, 2017). According to Brynjolfsson and Mcafee, there has never been a worse time to be a worker with only ordinary skills and abilities to offer . Foxconn, an iPhone manufacturer employing 1.2 million workers has reported that they would automate the entire factories with the use of robots (The Verge, 2016). Research backed by empirical evidence shows that the substitution of devices or robots regarding artificial intelligence for labor overpowers the inexpert wage rate which is usually referred to as the displacement effect (Acemoglu & Restrepo, 2017). Does the presence of the displacement effect suggest that automation will broaden wage inequality between the skilled and unskilled workers? Research has created a model based around three sectors. A robot-manufacturing sector, a robot-using sector and a non-automatable sector. The robot manufacturing uses skilled employment and traditional capital to manufacture robots which is understood as automated capital. The work involved in the robot-using sector can be also automated employing unskilled workers, robots and capital which is tradition to manufacture final goods. We assume that unskilled workers can be fully replaced by robots in the robot-using sector (Gasteiger & Prettner, 2017). Any exertion carried out in the non-automatable area cannot be automatic (Murnane, 2013) and this area produces its final goods using unskilled workers along with traditional capital. The presence of this non-automatable area allows these unskilled workers to be employed in this sector where their jobs are being replaced by robots in other fields of work. This basic model produces two effects of automation on wage inequality. The unskilled wage rate is suppressed by the displacement effect and this occurs by the fact that automation increases robots that replace unskilled workers in the robot-using sector. Secondly, the effect of capital reallocation with strength to suppress the wage rate of skilled workers comes from the fact that the construction and application of robots require traditional capital along with an acceleration in automation which boosts demand for capital in the robot manufacturing sector and robot using sector.
Conclusion
Although automation will not result in a complete elimination of many jobs in the future, it will have an impact on all future jobs to an extent. There is the potential for automation to move beyond routine and repetitive activities in manufacturing and we could soon see it in a much wider range of activities than we do in todays economy, with the potential to redefine workplaces and labor in many sectors.
References
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