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The Year in Review

21 for 21: A Collection of Publications from the Past Year

Browse our year-end recap of working papers and journal articles from Stanford Digital Economy Lab researchers and affiliates.

December 18, 2021
2-minute read

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During the past year, Stanford Digital Economy Lab researchers and affiliates published working papers and journal articles covering a range of topics related to the digital economy. From digital resilience to predictive analytics to racial segregation, these publications demonstrate how AI, machine learning, and other brilliant technologies are shaping society and the future of work.

Scroll down to find a collection of these remarkable working papers and articles from 2021. Looking for publications from previous years? Go here.

The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance

Researchers Erik Brynjolfsson, Wang Jin, and Kristina McElheran surveyed more than 30,000 manufacturers and discovered a sizable increase in productivity among plants that use tools to automate prediction. In “The Power of Prediction,” the research team outlines their findings and offers reasons why some companies using predictive analytics aren’t seeing such gains. Recognized for excellence by the Strategic Management Society and the National Association of Business Economists.

Digital Resilience: How Work-From-Home Feasibility Affects Firm Performance

Digital technologies are making some tasks, jobs, and firms more resilient to unanticipated shocks. S-DEL researchers extracted data from more than 200 million US job postings to construct an index that measure a firms’ resilience to the COVID-19 pandemic by assessing the work-from-home (WFH) feasibility of their labor demand.

Using Language Models to Understand Wage Premia

Does the text content of a job posting predict the salary offered for the role? There is ample evidence that even within an occupation, a job’s skills and tasks affect the job’s salary. Using a dataset of salary information linked to posting data. postdoctoral fellow Sarah Bana is applying natural language processing (NLP) techniques to build a model that predicts salaries from job posting text.

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