The rise of technological unemployment and its implications on the future macroeconomic landscape

• Published in: Futures : the journal of policy, planning and futures studies planning and futures studies, 2017-03, Vol.87, p.1-9
• Main Authors: Kim, Young Joon, Kim, Kyungsoo, Lee, SuKyoung
• Format: Article
• Language: English
• Subjects: Computer simulation; Creativity; Data management; Employment; Future macroeconomic landscape; Machine learning; Markov chains; Occupations; Rate of computerization; Technical education; Technological unemployment
• ISSN: 0016-3287; 1873-6378
• DOI: 10.1016/j.futures.2017.01.003

•Two-state and three-state Markov chain models are employed in this study.•They show that external factors can be used to control the rate of computerization.•Factors include government-led, educational initiatives and work incentives.•Such factors can be manipulated to counter unregulated technological progress.•Revised education will prepare the transition into a computer-driven world. Recent technological advances in big data, machine learning, and robotics, have begun to have a negative influence on existing employment opportunities for human beings. Numerous studies have demonstrated a worrisome decline in low- and medium-income employment resulting from the replacement of human workforce with machines. A seminal study by Frey and Osborne in 2013 predicted that 47% of the 702 examined occupations in the United States faced a high risk of decreased employment rate within the next 10–25 years as a result of computerization. Despite the seemingly dystopian future foreshadowed by these numbers, the wholesale replacement of labor by machines will most likely not become a reality in the foreseeable future for an array of reasons, including the creativity required by many occupations and interventions by governments. However, despite the barriers, computerization is likely to have a significant effect on the current market. In this paper, we aim to track the relative quantities of jobs that are either susceptible or non-susceptible to computerization in the future, by developing and utilizing an analytical model using Markov chains. Various simulations performed using this model demonstrate the importance of intervention policies, such as improved technical education of the public, in controlling the rate of computerization. Moreover, the simulations identify the probable creation of new jobs that would facilitate new human employment. Although radical changes in technology and economy await humanity, adequate preparation will help to facilitate a smoother transition into the age of computers.