Team: 4
School: Del Norte High
Area of Science: Behavioral and Social Sciences
Interim: The Relationship Between Unemployment and Automation
Team number: 4
Team members:
Ayvree Urrea ayvreeurrea@gmail.com
Kiara Onomoto kiaraonomoto@gmail.com
School name: Multi-School CottonwoodDel Norte
Sponsoring Teachers:
Karen Glennon
Patty Meyer
Area of science: Behavioral and Social Sciences
Project title: The Relationship Between Unemployment and Automation
Problem Definition:
New technological innovations can create an increase in unemployment rates. With the rapidly growing technology advancements, the use of humans for jobs like architect, health, composition, and more, are becoming less necessary. Technology changed the usual skill set of humans needed for certain jobs. These technological advances are also more likely to be used because they are able to create goods for a lesser amount of money compared to jobs taken by humans. This is a good thing for big corporations who would rather incorporate more automation and less human labor costs, but on the other hand, can hurt the employment of people. We want to see which one is more beneficial to the world as a whole. These huge corporations and business managers believe these innovations will actually increase the diversity of jobs, while people who are looking for jobs may think it is limiting the amount of jobs left for humans. In manufacturing companies jobs are easily given to robots and 82% of people in the United States said that within the next 30 years much of the work done by humans will probably be done by computers [2]. Jobs are changing rapidly and this can affect workers' lifestyles and possibly the economy, whether negative or positive. Automation also changes the types of workers in jobs because it makes it more difficult for workers who didn’t go to college or finish high school.
Plan to solve the problem:
Our plan to solve the problem is to look at data from different job industries within manufacturing, such as the automobile industry. We want to compare unemployment rates and the types of automation they use. We will use this as a tool that can make predictions for unemployment going forward. We plan to use percentages of rates of both automation and unemployment to parameterize predictions. This will apply a scenario going forward with the assumption that trends for automation and unemployment continue in some way.
Progress:
We have been in contact with Jonathan Wheeler and Todd Quinn for our research. Jonathan Wheeler is a data and curation librarian at UNM, and Todd Quinn is a business and economics librarian at UNM. They have helped guide us in our research to find data on unemployment rates and automation. We have also been working with Flora Coleman, who is a computer engineer at Sandia Labs. She has been helping us with our Python program. We have done research on the Python reference library to familiarize ourselves with commands that can be used in our program. We have started researching information about U.S. unemployment rates, several inventions that relate automation, and how Americans see automation in the workplace. We have looked at unemployment rates for every ten years since 1950, along with the U.S population that corresponds to the years. We have found that the year with the most unemployment was in 2010, with 9.6% [7]. We are planning to research more about the inventions that are used in manufacturing, such as the assembly line for the Ford Model T and the use of robots. We have also found that unemployment has declined in sectors such as agriculture and manufacturing due to automation but have also created jobs in sectors such as in trade. We have also found that the labor share for the U.S has decreased by 7 percentage points over the past few decades, specifically during the early 2000’s [3]. This loss of labor share also increased during the Great Recession and has not been able to be brought up in recent years.
Expected results:
We expect to see that unemployment rates rise as there is more capability for automation in certain jobs. In 2016 there were only 5.3% of all workers in tech-related jobs [1]. This would mean that a lot of workers would lose their jobs to robots before being able to get a job to make the technology that is used in place of workers. While unemployment rates themselves may not increase, the rates of workers in tech-related jobs would rise and those not in them would decrease. We predict that as more robots are used for human jobs, there could be a giant shift in the types of jobs available to humans and therefore could affect the pay people receive and the quality of life they have. Robots could also possibly affect productivity levels for the better which might help people get more jobs.
[1] GAO-19-257, WORKFORCE AUTOMATION: Better Data Needed to ... www.gao.gov/assets/700/697366.pdf.
[2] Geiger, A.W. “How Americans See Automation and the Workplace in 7 Charts.†Pew Research Center, Pew Research Center, 6 Aug. 2020, www.pewresearch.org/fact-tank/2019/04/08/how-americans-see-automation-and-the-workplace-in-7-charts/.
[3] Liu, Sylvain Leduc and Zheng. “Are Workers Losing to Robots?†Federal Reserve Bank of San Francisco, Federal Reserve Bank of San Francisco, 30 Sept. 2019, www.frbsf.org/economic-research/publications/economic-letter/2019/september/are-workers-losing-to-robots/.
[4] Lund, Susan, and James Manyika. “Five Lessons from History on AI, Automation, and Employment.†McKinsey & Company, McKinsey & Company, 10 Oct. 2019, www.mckinsey.com/featured-insights/future-of-work/five-lessons-from-history-on-ai-automation-and-employment.
[5] Manyika, James, et al. “Harnessing Automation for a Future That Works.†McKinsey & Company, McKinsey & Company, 20 Nov. 2019, www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works.
[6] Selyukh, Alina. “Will Your Job Still Exist In 2030?†NPR, NPR, 11 July 2019, www.npr.org/2019/07/11/740219271/will-your-job-still-exist-in-2030.
[7] “Unemployment Rate.†FRED, 6 Nov. 2020, fred.stlouisfed.org/series/UNRATE.
Todd Quinn, Associate Professor, Business & Economics Librarian, University Libraries, University of New Mexico
Jon Wheeler, Data Curation Librarian, University of New Mexico Libraries
Flora Coleman, Computer Engineer, Sandia Labs
Team Members:
Sponsoring Teacher: Karen Glennon