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Marriott Marquis, Grand Ballroom 10
American Economic Association
Empirical Research on Automation and “Smart” Technologies
Friday, Jan. 3, 2020 8:00 AM - 10:00 AM (PDT)
- Chair: James Bessen, Boston University
Automatic Reaction – What Happens to Workers at Firms that Automate?
AbstractWe provide the first estimate of the impacts of automation on individual workers by combining Dutch micro-data with a direct measure of automation expenditures covering firms in all private non-financial industries over 2000-2016. Using an event study differences-in-differences design, we find that automation at the firm increases the probability of workers separating from their employers and decreases days worked, leading to a 5-year cumulative wage income loss of about 8% of one year's earnings for incumbent workers. We find little change in wage rates. Further, lost wage earnings are only partially offset by various benefits systems and are disproportionately borne by older workers and workers with longer firm tenure. Compared to findings from a literature on mass layoffs, the effects of automation are more gradual and automation displaces far fewer workers, both at the individual firms and in the workforce overall.
Technological Change in Occupational Attribute Space
AbstractAnalysis of technological change from robotization to AI and digitalization of work has focused on what job tasks and activities are likely to be impacted by new software and hardware. Most analyses believe the new technologies will target routine cognitive and non-cognitive activities so that jobs with a lot of routine work will see declines in demand for human labor, with resultant loss of employment or falls in wages. Our study uses the U.S. Department of Labor's Occupational Information Network (O*NET) database to examine the stability and change in the attributes of occupations defined by the skills, knowledge, abilities, education, work context, activities, and style. We develop a new landscape of occupations based on O*NET's over 300 measures of occupational attributes from the early 2000s to the 2010s linked to the Current Population Survey to examine the relationship between changes in these occupational attributes, wages and employment.
Machine Learning in Healthcare? Evidence from online job postings
AbstractThis paper documents a puzzle. Despite the numerous popular press discussions of machine learning and artificial intelligence in healthcare, there has been relatively little adoption. Using data from Burning Glass Technologies on the skills listed in millions of online job postings over ten years, we find that AI adoption in healthcare remains substantially lower than in most other industries. Roughly 1 in 1,250 hospital jobs required AI-related skills in 2015-2018 compared to approximately 1 in 174 in finance & insurance, 1 in 88 in professional, scientific, and technical services, and 1 in 72 in information. Combining the job posting data with data on US hospitals, we document that under 3% of the hospitals in our data had posted any jobs requiring AI skills from 2015-2018. The low adoption rates mean any statistical analysis is limited. Nevertheless, the adoption we do see in the data shows that larger hospitals, larger counties, and integrated salary model hospitals are more likely to adopt.
Susan R. Helper,
Case Western Reserve University
New York University
Massachusetts Institute of Technology
- O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
- J2 - Demand and Supply of Labor