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About
Sarah Bana is an Assistant Professor in the Department of Management (Information Systems and Innovation) at the London School of Economics and Political Science and a Digital Fellow at the Stanford Digital Economy Lab.
Her research examines how AI and digitisation reshape the skills and activities of workers and firms — including algorithmic hiring systems, AI-mediated job matching, and the labour market effects of generative AI. She works with large-scale digital-trace and archival data — job postings, university syllabi, payroll records, open-source contribution histories, and records from algorithmic hiring systems — building new measurement and descriptive evidence for parts of the labour market that are difficult to observe and largely unstudied, and drawing on quasi-experimental designs and field experiments, to characterise the heterogeneity of tasks across the labour market and detect how those tasks are changing.
Her work has been published in MIS Quarterly, the Journal of Econometrics, the Journal of Policy Analysis and Management, and the proceedings of NeurIPS and ACM FAccT, and has been funded by the Russell Sage Foundation and the Upjohn Institute. It has been featured by BBC Business Daily, the New York Times, Marketplace, The Atlantic, and MIT Sloan Management Review.
Before joining LSE, she was an Assistant Professor of Management Science at Chapman University and a postdoctoral fellow at the Stanford Institute for Human-Centered Artificial Intelligence and MIT's Initiative on the Digital Economy. She holds a PhD in Economics from the University of California, Santa Barbara.
Dr Sarah Bana is member of Information Systems and Innovation Faculty Research Group.
Research
Dr Bana studies how artificial intelligence and digitisation are changing what workers do, how firms hire, and who gets access to opportunity.
Her research builds new measurement from large-scale archival and digital-trace data — job postings, syllabi, payroll records, open-source activity, and algorithmic hiring records — and pairs descriptive evidence with quasi-experimental designs and field experiments.
Algorithmic hiring and AI-mediated matching.
Analysing 4 million algorithmically screened job applications, she and co-authors document racial disparities and "algorithmic monoculture" — applicants systematically rejected everywhere at once. A field experiment with 4,562 jobseekers found participation roughly one-quarter lower when AI matching was disclosed.
Generative AI and worker productivity.
Using Italy's temporary ChatGPT ban as a natural experiment, she and co-authors show losing access reduced open-source code development by 6.4% and skill acquisition by 8.4%.
Skills, education, and labour market adjustment.
She co-built Course-Skill Atlas, a dataset of skills from over three million US university syllabi, connecting what universities teach to graduates' earnings — and showing unemployment risk in AI-exposed occupations rose months before ChatGPT's release.
With ADP Research and the Stanford Digital Economy Lab, she works on the job unbundling project, measuring the changing value of the tasks that make up jobs in an AI economy.
Teaching
Engagement and impact
Dr Bana's research speaks to the measurement of technology and its labour market effects, with a focus on new technology and variation across workers and firms, often complementing traditional data collection.
She has given a keynote to the U.S. Census Bureau's Local Employment Dynamics programme, and has presented to US policy audiences including the Council of Economic Advisers, the Bureau of Labor Statistics, and the National Association of State Workforce Agencies. She has served as a panellist at the California State Assembly Rising Tide Policy Summit, the Stanford AI & Society Workshop, and an AI Forward Alliance side event at the UN Commission on the Status of Women.
Her work is regularly covered in the press, including BBC Business Daily, the New York Times, Marketplace, The Atlantic, the Wall Street Journal's Real Time Economics, and MIT Sloan's Ideas Made to Matter. She organises the AI Challenges Education Workshop (2025, 2026) and founded and ran @Econ_RA, a widely used account posting economics research assistant openings. She sits on the programme committee for the Conference on Information Systems and Technology (CIST) 2026 and reviews for the National Science Foundation and for journals across information systems and economics, among them Management Science, Information Systems Research, MIS Quarterly, the American Economic Review, and the Quarterly Journal of Economics.