ANALYZING THE IMPACT OF AUTOMATION ON EMPLOYMENT IN DIFFERENT US REGIONS: A DATA-DRIVEN APPROACH
Date of Award
Master of Science in Information Systems and Technology
Information and Decision Sciences
First Reader/Committee Chair
Automation is transforming the US workforce with the increasing prevalence of technologies like robotics, artificial intelligence, and machine learning. As a result, it is essential to understand how this shift will impact the labor market and prepare for its effects. This culminating experience project aimed to examine the influence of computerization on jobs in the United States and answer the following research questions: Q1. What factors affect how likely different jobs will be automated? Q2. What are the possible effects of automation on the US workforce across states and industries? Q3. What are the meaningful predictors of the likelihood of automation for certain jobs or groups? Q4. How can governments and businesses best prepare for the effects of automation on the workforce? Q5. What are the most effective ways to reduce the negative effects of automation on workers and communities? The findings are as follows: Q1: Jobs that do not require higher education are more likely to be automated. Additionally, income and automation are negatively related. The study concludes that job occupations with higher education are less likely to be automated. In contrast, jobs in lower-skilled industries, such as manufacturing and retail, are more vulnerable to automation. Q2: As automation technology advances, it will create new jobs in emerging fields and lead to job losses in certain industries. The effects of automation on employment reductions will vary across states and industries, with certain ones being more susceptible than others. Q3: Income and education are significant factors that impact the likelihood of automation. Jobs that pay lower wages and do not require higher education are more likely to be automated. Q4: State governments and businesses should invest in (1) learning and guidance programs to benefit workers and obtain additional expertise and knowledge to prepare them for the changing job market. (2) Promoting innovation and entrepreneurship is another recommendation to create new job opportunities in emerging fields like robotics, AI, and data analysis. Implementing policies that support workers affected by automation, such as unemployment insurance or retraining programs, can also provide a protection net for workers who have dropped their occupations due to computerization. (3) Lastly, understanding the specific industries and job types that are most vulnerable to displacement is a key recommendation. Q5: Effective ways to reduce the negative effects of automation on workers and communities include (1) providing financial and educational support for affected workers, (2) creating policies that incentivize businesses to invest in training and reskilling programs, and (3) fostering a culture of innovation and creativity that encourages the development of new technologies and industries. Possible areas of further study related to the research questions addressed in this paper include exploring the impact of automation on different demographic groups (Research Question 3), investigating the potential implications of automation on the future of work (Research Questions 2 and 5), and examining the role of public policies in addressing the challenges posed by automation (Research Questions 4 and 5). In conclusion, automation has the potential to transform the US workforce and bring both benefits and challenges. The findings suggest that education and income are significant factors that impact the likelihood of automation. The effects of automation on employment reductions will vary across states and industries. State governments and businesses should prepare for the effects of automation by investing in learning and guidance programs, promoting innovation and entrepreneurship, and implementing policies that support workers affected by automation. Understanding the specific industries and job types that are most vulnerable to displacement is also crucial for mitigating the negative effects of automation.
Balasubramanian, Thejaas, "ANALYZING THE IMPACT OF AUTOMATION ON EMPLOYMENT IN DIFFERENT US REGIONS: A DATA-DRIVEN APPROACH" (2023). Electronic Theses, Projects, and Dissertations. 1738.
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