���˴�Ƭ

Automation is on the rise. The nature of work is rapidly changing. And businesses and California policymakers are dealing with a growing set of challenges and opportunities presented by the state’s evolving workforce and the job market.

The ���˴�Ƭ Digital Economy Lab (S-DEL) and the ���˴�Ƭ Institute for Economic Policy Research (SIEPR) are embarking on research that will help evaluate how artificial intelligence and machine learning will impact the future of work in California for the next century. The project begins this summer and will be led by S-DEL Director Erik Brynjolfsson and SIEPR Director Mark Duggan. 

The work is happening in collaboration with , an initiative to envision and shape the long-term success of the state. Incubated at the University of California and ���˴�Ƭ, the California 100 initiative will focus on creating policy recommendations to ensure the state’s sustainability, innovation, and equity for the next century.

“The vision of the California 100 initiative aligns perfectly with the Lab’s vision of building a technology-driven economy that benefits everyone,” said Brynjolfsson, who is also the Ralph Landau Senior Fellow in Economic Growth at SIEPR. “We look forward to being a part of a project that helps companies and workers in California take on the challenges and opportunities posed by digitization and automation.”

���˴�Ƭ researchers will develop a Future of Work Dashboard that draws on S-DEL’s data and insights to illustrate the transformation of jobs throughout California. The dashboard will sample a range of occupations across different regions, wage levels, education levels, and skill bundles to assess the resilience of each job to automation. The data will also highlight the most valuable skills in each occupation, suggest adjacent lines of work, and provide a comprehensive outlook for each position. 

The Future of Work Dashboard

The Future of Work Dashboard will utilize data from ongoing ���˴�Ƭ Digital Economy Lab research, including:

·&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;

S-DEL researchers evaluated every job task in the ONET database for its suitability for machine learning, or SML, using a rubric that scores each task on 23 different criteria. The Suitability for Machine Learning Rubric project offers a theoretical framework for how occupations will change and predict which occupations specifically are most exposed to advances in machine learning and robotics methods as they propagate through the network of job tasks.

·&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;

Using data from 200 million online job postings, S-DEL is training a natural language processing model to learn the language of jobs. The Job2Vec project analyzes how jobs have changed in the past decade and demonstrates how different words in postings denote different occupations. In using this approach, researchers will create novel indexes of jobs, such as work-from-home ability.

·&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;&�Բ�����;

In a short span of time, the COVID-19 pandemic drastically disrupted the way we live and work—and will have profound effects on the economy and productivity for years to come. S-DEL’s research team is examining how businesses and workers are adapting to measures, such as lockdowns and remote working, brought on by the pandemic. The Lab offers insights, models, and analysis to help business leaders, lawmakers, and the public solve challenges posed by COVID-19.

Researchers will address issues tied to tax policy and minimum wage and their impact on innovation and automation. “Rigorous, data-driven research is the foundation for creating good economic policy,” Duggan said. “Our work at SIEPR has long informed policy decisions at the local, state, and federal levels, and this is an opportunity for us to make important contributions to California’s economic future.”

���˴�Ƭ’s research and insights will inform a broad set of policy recommendations that will be developed in conjunction with research from other universities and research institutions. The research will be completed in December 2021.