Job loss via automation

Search Entries[edit | edit source]

• Job Loss Automation 
• Job automation between countries
• Job “automation” impact
• Job automation industries
• Job automation substitution of labor
• Automation low skilled workers
• Automation job displacement
• Automation risk based on task content
• Task automation probability
• High task automation occupations
• Labor augmentation technological progress
• Robot tax
• Retraining workforce automation
• Universal Income
• Basic Universal income and inflation

Who is at risk?: work and automation in the time of COVID-19[edit | edit source]

Wallace-Stephens, F., & Morgante, E. (2020). "Who is at risk?: work and automation in the time of COVID-19."

• Pandemic rapidly accelerating rate of technological change as well as shining light on possible automated industries going forward
• Pre-pandemic industries at risk include agriculture, waste management, and simple manufacturing
• high furlough take up industries are at a high risk of automation

Automation: is it really different this time?.[edit | edit source]

Wajcman, J. (2017). Automation: is it really different this time?. The British journal of sociology, 68(1), 119-127.

• previously predicted job destruction is not what we thought it would be

Drivers of automation and consequences for jobs in engineering services: an agent-based modelling approach[edit | edit source]

Kyvik Nordås, H., & Klügl, F. (2020). Drivers of automation and consequences for jobs in engineering services: an agent-based modelling approach (No. 2020: 16).

• How profitable is it to automate a task or industry?
• Marketing automation and steps prior to automation
• automating decision making to increase production speed/output

The Substitution of Labor: From Technological Feasibility to Other Factors Influencing Job Automation[edit | edit source]

Teigland, R., van der Zande, J., Teigland, K., & Siri, S. (2018). The Substitution of Labor: From Technological Feasibility to Other Factors Influencing Job Automation. The Substitution of Labor from Technological Feasibility to Other Factors Influencing Job Automation (2018.

• Repetitive cognitive jobs have a high percentage of automation, such as cashiers, bank tellers, and telephone operators.
• Routine tasks are most of the time simple to automate, while nonroutine task are more difficult and while possible, often times still requires human oversight.

Exploring the Use of Robotic Process Automation in Local Government[edit | edit source]

Lindgren, I. Exploring the Use of Robotic Process Automation in Local Government. EGOV-CeDEM-ePart 2020, 249.

• Automating case handling; the process of collecting, managing and assessing information
• Automating case handling processes will increase efficiency in local government
• However automating case handling processes will require a restructuring of how case handling processes are run.

The Future of Employment: How Susceptible Are Jobs To Computerization?[edit | edit source]

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerization? Technological Forecasting and Social Change, 114, 254–280.

• The automation of results in the task only preforming to the ability of the programmer.
• "routine tasks are not sufficiently well understood to be specified in computer code."
• Jobs at risk include transportation and logistics occupations

Revisiting the risk of automation[edit | edit source]

Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157-160.

• Automation risk is low in occupations involved with programming, presenting, training, or influencing others.
• using the job level approach as opposed to the occupation level approach, overall risk of automation drops from 38% to 9 %
• Occupation level approach doesn't take non- automatable niches of broad spectrum jobs at a higher risk of automation into account.

The risk of automation for jobs in OECD countries: A comparative analysis.[edit | edit source]

Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis.

• Breaks down issues with the Frey and Osborne study, which uses occupation based approach to calculate automation risk
• Using the same data from the Frey and Osborne study with a singular task approach, this article re estimates future automation risk
• "Automatability is lower in countries which already invest a lot in ICT"

Job Transition: A Case of Mitigation Against Automation?[edit | edit source]

PANHALEUX, P. (2020). JOB TRANSITION: A CASE OF MITIGATION AGAINST AUTOMATION? PHILIPPE PANHALEUX, ARYAZ EGHBALI, ROGER WATTENHOFER. The Value of Work and Its Rules between Innovation and Tradition:‘Labour Is Not a Commodity’Today, 23.

• Uses the Gaussian process to determine risk of automation
• Provides a middle ground on automation probability
• Looks at possible transitions of high risk job types to other jobs based on demand for work in low risk job types.

Automation, workers’ skills and job satisfaction[edit | edit source]

Schwabe, H., & Castellacci, F. (2020). Automation, workers’ skills and job satisfaction. Plos one, 15(11), e0242929.

• says 40% risk of automation, but that was based on a poll where just individual workers were asked what they thought
• Occupation types are broken down into
  • Agriculture, forestry, and fishing
  • Industry (Highest risk)
  • Construction
  • Services

What Can Machines Learn and What Does It Mean for Occupations and the Economy?[edit | edit source]

Brynjolfsson, E., Mitchell, T., & Rock, D. (2018, May). What can machines learn, and what does it mean for occupations and the economy?. In AEA Papers and Proceedings (Vol. 108, pp. 43-47).

• Using O* Net data to calculate suitability for machine learning of given occupations.
• Top 5 highest suitability for machine learning occupations are as follows:
  1. Concierges
  2. Mechanical drafters
  3. Morticians, undertakers, and funeral directors
  4. Credit authorizers
  5. Brokerage clerks

How Computer Automation Affects Occupations: Technology, jobs, and skills[edit | edit source]

Bessen, J. E. (2016). How computer automation affects occupations: Technology, jobs, and skills. Boston Univ. school of law, law and economics research paper, (15-49).

• Partially automated jobs could actually create jobs by causing a higher demand

Adjusting Tax Policy to the Challenges of Digitalization, Inequality and Technological Unemployment[edit | edit source]

Berberov, A. B., & Milogolov, N. S. (2020). Adjusting Tax Policy to the Challenges of Digitalization, Inequality and Technological Unemployment.

• In the short term, we will see a rising unemployment due to automation
• tax revenues decrease as a result of low skill workers losing their jobs
• Possible tax competition between countries of different stages of development.

Automation, wage inequality and implications of a robot tax[edit | edit source]

Zhang, P. (2019). Automation, wage inequality and implications of a robot tax. International review of Economics & finance, 59, 500-509.

• Three sectors:
  1. Robot-Producing sector
  2. Robot-Using sector
  3. non-automatable sector
• "acceleration in automation will widen the wage gap" & "tax on robots will narrow down the gap unconditionally"
• Capital Relocation Effect: Capital in the robot-producing sector will flow into the robot-using sector
• Displacement Effect: Robots employed in the robot-using sector will reduce the unskilled wage rate

The Fourth Industrial Revolution and a Possible Robot Tax[edit | edit source]

Erdoğdu, M. M., & Karaca, C. (2017). The fourth industrial revolution and a possible robot tax. Institutions & economic policies: Effects on social justice, employment, environmental protection & growth, 103-122.

• Internet of Things is likely to produce as much as $14.4 trillion in economic benefits (Schwab, 2017)
• silicon valley companies produced similar revenue to 1990s car companies with ten times fewer employees (Manyika & Chui, 2014, August 13)
• Universal Basic Dividend (UBD) which is an equal distribution of all dividend capital raised by companies in a given country

Retraining and reskilling workers in the age of automation[edit | edit source]

Illanes, P., Lund, S., Mourshed, M., Rutherford, S., & Tyreman, M. (2018). Retraining and reskilling workers in the age of automation. McKinsey Global Institute.

• Portion of work force changing work categories, which will require retraining
• "Generation" Is a good retraining model, offers free training if skills required for existing jobs with high job retention

Robotic process automation as an emerging career opportunity[edit | edit source]

Schlegel, D., & Kraus, P. Robotic process automation as an emerging career opportunity: an analysis of required qualifications and skills.

• Looks at job loss and new job availability in Germany, as a result of automation
• Finance and accounting jobs were highly effected by robotic process automation
• A majority of new jobs require at least a background in IT

Jobs lost, jobs gained: Workforce transitions in a time of automation[edit | edit source]

Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., ... & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute, 150.

Report with Recommendations to the Commission on Civil Law Rules on Robotics[edit | edit source]

European Parliament, Committee on Legal Affairs (2017), Report with Recommendations to

the Commission on Civil Law Rules on Robotics (A8-0005/2017)

The Fourth Industrial Revolution: what it means, how to respond[edit | edit source]

Schwab, K. (2017). The fourth industrial revolution. Currency.

Technological Adaptation, Cities, and New Work[edit | edit source]

Lin, J. (2011). Technological adaptation, cities, and new work. Review of Economics and Statistics, 93(2), 554-574.

Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion[edit | edit source]

Bradley, J., Barbier, J., & Handler, D. (2013). Embracing the Internet of Everything to Capture Your Share of $14.4 Trillion, White Paper

The Future of Jobs Report 2020[edit | edit source]

World Economic Forum. (2020). The Future of Jobs Report 2020. World Economic Forum, Geneva, Switzerland.

Telehealth and patient satisfaction: a systematic review and narrative analysis[edit | edit source]

Kruse, C. S., Krowski, N., Rodriguez, B., Tran, L., Vela, J., & Brooks, M. (2017). Telehealth and patient satisfaction: A systematic review and narrative analysis.

Opening the Frey/Osborne black box: Which tasks of a job are susceptible to computerization?[edit | edit source]

Brandes, P., & Wattenhofer, R. (2016). Opening the Frey/Osborne black box: Which tasks of a job are susceptible to computerization?. arXiv preprint arXiv:1604.08823.

Industrial Growth and Industrial Revolutions[edit | edit source]

Coleman, D. C. (1956). Industrial growth and industrial revolutions. Economica, 23(89), 1-22.

The second industrial revolution, 1870-1914[edit | edit source]

Mokyr, J., & Strotz, R. H. (1998). The second industrial revolution, 1870-1914. Storia dell’economia Mondiale, 21945.

Origins and pathways of innovation in the third industrial revolution[edit | edit source]

Taalbi, J. (2019). Origins and pathways of innovation in the third industrial revolution. Industrial and corporate change, 28(5), 1125-1148.

Accelerating Workforce Reskilling for the Fourth Industrial Revolution[edit | edit source]

World Economic Forum. (2017). Accelerating workforce reskilling for the fourth industrial revolution: An agenda for leaders to shape the future of education, gender and work. Geneva, Switzerland: World Economic Forum.

The Fourth Industrial Revolution and Higher Education[edit | edit source]

Penprase, B. E. (2018). The fourth industrial revolution and higher education. Higher education in the era of the fourth industrial revolution, 207.

The future of employment.[edit | edit source]

Frey, C. B., & Osborne, M. (2013). The future of employment.

Why Are There Still So Many Jobs? The History and Future of Workplace Automation[edit | edit source]

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of economic perspectives, 29(3), 3-30.

Skills, education, and the rise of earnings inequality among the “other 99 percent”[edit | edit source]

Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among the “other 99 percent”. Science, 344(6186), 843-851.

COVID-19 educational disruption and response[edit | edit source]

UNESCO (2020a). COVID-19 educational disruption and response. Retrieved April 7, 2021, from https://en.unesco.org/covid19/educationresponse.

The importance of self‐service kiosks in developing consumers' retail patronage intentions[edit | edit source]

Lee, H. J., Fairhurst, A. E., & Lee, M. Y. (2009). The importance of self‐service kiosks in developing consumers' retail patronage intentions. Managing Service Quality: An International Journal.

Implementing successful self-service technologies.[edit | edit source]

Bitner, M. J., Ostrom, A. L., & Meuter, M. L. (2002). Implementing successful self-service technologies. Academy of management perspectives, 16(4), 96-108.

Open Textbook Proof-of-Concept via Connexions. The International Review of Research in Open and Distributed Learning[edit | edit source]

Baker, J., Thierstein, J., Fletcher, K., Kaur, M., & Emmons, J. (2009). Open Textbook Proof-of-Concept via Connexions. The International Review of Research in Open and Distributed Learning, 10(5). https://doi.org/10.19173/irrodl.v10i5.633