For the next installment of the DINS Seminar Series, we welcome Dr. Sarah Bana, Assistant Professor of Management Science at Chapman University and a Digital Fellow at the Stanford Digital Economy Lab! Join the Department on Wednesday, April 5, at 11 am for Dr. Bana's presentation on "work2vec," in which she discusses using vector representations of the content of job postings to identify the value of various IT skills and how firms adjust their workforce composition after adopting new technology.
work2vec
Sarah Bana, Assistant Professor of Management Science, Chapman University, and Digital Fellow, Stanford Digital Economy Lab
April 5, 2023, 11:00 am to 12 noon
in-person -- Room 538-539 Conference Room, 130 North Bellefield Avenue (across from the IS Building). Refreshments will be served.
To attend virtually: Topic: DINS Seminar Series -- Sarah Bana
Time: Apr 5, 2023 11:00 AM Eastern Time (US and Canada)
Join Zoom Meeting: https://pitt.zoom.us/j/94544266139
Meeting ID: 945 4426 6139
Abstract: How valuable are various IT skills? How do firms adjust their workforce composition after adopting new technology? Addressing these questions and others on the role of technology in the labor market has been hindered by data limitations. This project explores an innovative approach by transforming the text content of job postings into vector representations, referred to as work2vec.
In the first paper, I apply natural language processing (NLP) techniques to build a model that predicts salaries from the job posting text. This follows the rich tradition in the economics literature of estimating wage premia for various job characteristics by applying hedonic regression. Using an attribution method called integrated gradients, I find that Cloud Computing and System Design and Implementation skills are amongst the most valued IT skills in 2019, while Database Administration and Data Management have more moderate effects.
In the second paper, my co-authors Erik Brynjolfsson, Daniel Rock and Sebastian Steffen and I develop a spatial representation of work using a variational autoencoder (VAE). We use this representation to measure how occupations and the job space as a whole are evolving over time, along with the specific question of machine learning (ML) adoption. We find that firms adopting ML tools significantly alter the composition of job postings towards occupations that appear more complementary to ML tools.
Bio: Sarah Bana is an Assistant Professor of Management Science at Chapman University and a Digital Fellow at the Stanford Digital Economy Lab. Dr. Bana's research focuses on the effect of technology on the labor market. This involves combining novel sources of text data, like job postings and syllabi, with natural language processing methods to develop innovative insights. Her research has been published in outlets such as the Journal of Econometrics, the Journal of Policy Analysis and Management, and Sloan Management Review, and featured by the BBC Business Daily. Dr. Bana's research has been funded by the Russell Sage Foundation. She received her Ph.D. in economics at the University of California, Santa Barbara. Her dissertation received Honorable Mention for the Upjohn Institute’s best Ph.D. dissertation on employment-related issues.