Publications on Google Scholar
Dr. Sears is an Assistant Professor fixed term in the Department of Agricultural, Food, and Resource Economics. Prior to this appointment, Dr. Sears received his Ph.D. from the University of California, Berkeley and his M.S. from Montana State University.
Dr. Sears’ research focuses on topics in environmental and consumer behavioral economics, and integrates both theoretical and empirical methods from public finance. Much of his work seeks to measure how consumers respond to shocks – whether environmental, informational, social, or public policy-driven – and use these insights to inform the design of more efficient, more equitable, and better-targeted policies. Dr. Sears’ work frequently combines administrative and spatial data with forefront econometric techniques for identifying causal treatment effects and exploring heterogeneous responses. Recent topics include residential water consumers’ responses to price and non-price behavioral drought policies, the interplay between local and federal nutrition assistance, and COVID-19 stay-at-home policies and travel behavior changes. Dr. Sears has published his research in a range of journals, including The American Journal of Health Economics, Transportation Research Part D: Transport and Environment, IEEE Access, and The Journal of Behavioral Data Science. He has presented his research at dozens of conferences and seminars and had his work featured on national news outlets such as The Wall Street Journal.
The bulk of Dr. Sears’ teaching is in the areas of data science, econometrics, and statistics as well as microeconomic theory and data for business decision-making. While at U.C. Berkeley, he helped pioneer the use of cloud-based, interactive resources for econometric instruction and produced remote instructional content for Haas School of Business, where he won multiple awards for his graduate-level teaching.
- Environmental and resource economics
- Consumer behavioral economics
- Public finance
- Applied microeconometrics
- AFRE 203: Data Analysis for the Agri-Food System (2022/Fall Semester)