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1. AI exposure predicts unemployment risk: A new approach to technology-driven job loss

   https://doi.org/10.1093/pnasnexus/pgaf107  

   Frank, Morgan R; Ahn, Yong-Yeol; Moro, Esteban (March 2025, PNAS Nexus)

   Kimbrough, Erik (Ed.)

   Abstract Is AI disrupting jobs and creating unemployment? This question has stirred public concern for job stability and motivated studies assessing occupations’ automation risk. These studies used readily available employment and wage statistics to quantify occupational changes for employed workers. However, they did not directly examine unemployment dynamics primarily due to the lack of data across occupations, geography, and time. Here, we overcome this barrier using monthly occupation-level unemployment data from each US state’s unemployment insurance office from 2010 to 2020 to assess AI exposure models, job separations, and unemployment through a new measure called unemployment risk. We demonstrate that standard employment statistics are inadequate proxies for occupations’ unemployment risk and find that individual AI exposure models are poor predictors of occupations’ unemployment risk states’ total unemployment rates, and states’ total job separation rates. However, an ensemble approach exhibits substantial predictive power, accounting for an additional 18% of variation in unemployment risk across occupations, states, and time compared to a baseline model that controls for education, occupations’ skills, seasonality, and regional effects. These results suggest that competing models may capture different aspects of AI exposure and that automation shapes US unemployment. Our results demonstrate the power of occupation-specific job disruption data and that efforts using only one AI exposure score will misrepresent AI’s impact on the future of work. 

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2. Identifying Alternative Occupations for Truck Drivers Displaced Due to Autonomous Vehicles by Leveraging the O*NET Database

   https://doi.org/10.1177/00027642221127239  

   Van Fossen, Jenna A.; Chang, Chu-Hsiang; Ford, J. Kevin; Mack, Elizabeth A.; R. Cotten, Shelia (September 2022, American Behavioral Scientist)

   Automation continues to be a disruptive force in the workforce. In particular, new automated technologies are projected to replace many mid-skill jobs, potentially displacing millions of workers. Career planning agencies and other organizations can help support workers if they are able to effectively identify optimal transition occupations for displaced workers. We drew upon the 24.2 Occupational Information Network (O*NET) Database to conduct two related studies that identify alternate occupations for truck drivers, who are at risk of job loss due to the adoption of autonomous vehicles. In Study 1, we statistically compared the jobs that we identified based on different search methods using O*NET classifications based on their similarity to the knowledge, skills, values, and interests held by truck drivers. In Study 2, we conducted a survey of truck drivers to evaluate their perceptions of the occupations identified as objectively similar to their occupation. Results indicate that optimal transition occupations may be identified by searching for occupations that share skills as well as the same work activities/industry as a given occupation. These findings hold further implications for career planning organizations and policymakers to ease workforce disruption due to automation. 

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3. A network analysis of cross-occupational skill transferability for the hospitality industry

   https://doi.org/10.1108/IJCHM-01-2021-0073  

   Huang, Arthur Yan; Fisher, Tyler; Ding, Huiling; Guo, Zhishan (September 2021, International Journal of Contemporary Hospitality Management)

   Purpose This paper aims to examine transferable skills and viable career transition pathways for hospitality and tourism workers. Future career prospects are discussed, along with the importance of reskilling for low-wage hospitality workers. Design/methodology/approach A network analysis is conducted to model skill relationships between the hospitality industry and other industries such as health-care and information technology. Multiple data are used in the analysis, including data from the US Department of Labor Occupational Information Network (O*NET), wage data from the Bureau of Labor Statistics and job computerization data (Frey and Osborne, 2017). Findings Although hospitality workers have lower than average skills scores when compared to workers from other career clusters included in the analysis, they possess essential soft skills that are valuable in other industries. Therefore, improving hospitality workers’ existing soft skills may help them enhance their cross-sector mobility, which may allow them to obtain jobs with a lower likelihood of computerization. Practical implications The findings shed light on workforce development theories and practice in the hospitality industry by quantitatively analyzing cross-sector skill correlations. Sharpening transferable soft skills will be essential to enhancing hospitality workers’ career development opportunities. Originality/value To the best of the authors’ knowledge, this is the first study that specifically examines the skill taxonomy for the hospitality industry and identifies its connection with other in-demand career clusters. 

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4. Race/Ethnicity and Employment Insecurity: Impacts of COVID-19 on the US Labor Force and Beyond

   https://doi.org/10.1007/s11113-024-09911-5  

   Wang, Qingfang; Kang, Wei (August 2024, Population Research and Policy Review)

   Abstract Using the Household Pulse Survey and American Community Survey, this study examines employment insecurity experienced across different racial/ethnic groups of the U.S. labor force under the pandemic disruptions. It highlights significant employment security disparities based on race, ethnicity, and income during the pandemic. However, there are no significant gender and racial disparities within the lowest income group when controlling for other conditions. In contrast, gender and racial disparities in EI are much more pronounced among mid-to-high income groups. Non-White individuals were disproportionately affected by job loss due to health and COVID-related employment issues, unlike Whites who faced unemployment more due to other factors. This pattern was more evident among lower-income groups. The trends shifted in later stages, with high-income Black and Hispanic workers becoming more likely to be unemployed due to non-health and non-employment reasons. Middle-income workers across all races were least likely to stop working for reasons other than COVID-related health or employment issues. In addition, regardless race or ethnicity, women more likely to be unemployed due to health reasons and less so due to employment issues compared to men, and the gender disparities increased with higher household incomes. We propose that the apparent immediate effects of the pandemic are actually indicative of deeper, systemic issues within the U.S. labor market, specifically the occupational segregation tied to race/ethnicity, gender, and class. Recovery efforts must take a holistic approach and integrate economic development policies, workforce development strategies, and social policies targeting poverty alleviation, health disparities, and people of color. 

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5. Simulating regional workforce impacts of decarbonizing integrated steelmaking

   https://doi.org/10.1073/pnas.2419294122  

   Miles, Jillian; Combemale, Christophe; Karplus, Valerie J; Pistorius, P Chris (May 2025, Proceedings of the National Academy of Sciences)

   Global efforts to mitigate climate change are increasing pressure on heavy manufacturing industries to decarbonize production. The iron and steel industry is responsible for 7% of CO2 emissions globally (2% in the United States) and is often a major employer in the regions where iron and steel is produced. Understanding the future prospects for workers in regions with high CO2 emitting industries—including impacts of phasing out or evolving such industries—will be critical for informing regional economic and clean energy strategies. We simulate the impact of an “in-place” transition that replaces today’s integrated production with direct reduced iron (DRI) used in electric arc furnaces (EAFs), using Southwest Pennsylvania as an application of our generalizable approach. Our results suggest that the integrated steelmaking workforce today has the skills, knowledge, and abilities (SKAs) to fill over 95% of all jobs required by DRI/EAF facilities, but the number of jobs is only 25% of those at integrated plants. We also find that some occupational groups have greater general transferability into the broader job market, while other groups, such as production workers, are ill-equipped today based on current SKAs to transition out of the iron and steel industry. Our methodology further suggests factors that limit transitions: Around 85% of occupations are more limited by missing skills, while 15% are more limited by insufficient wages. These results may help to improve the design of social policy and the targeting of retraining programs, while the simulation approach can be readily adapted for other regions and industries. 

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