Focus areas: environmental economics, labor economics

Jeffrey Shrader is an Assistant Professor at SIPA. His research areas include environmental and labor economics. His work focuses on the role of expectations and forecasts in helping individuals prepare for changing environmental and economic conditions. This work helps policymakers understand the benefits and limitations of information-based policy interventions and sheds light on the total economic costs of environmental changes. Shrader also studies how individuals choose to use their time and the implications that time use decisions have for economic productivity.

Prior to joining SIPA, he was the 2017–2018 Economic Fellow at the Institute for Policy Integrity at New York University School of Law. At the Institute, he worked to improve federal and state decision-making related to climate, environmental, and energy policies. He holds a Ph.D. in economics from the University of California, San Diego and a B.A. in economics and mathematics from Columbia University.

Research & Publications

January 2019|Review of Economics and Statistics|Jeffrey Shrader, Matthew Gibson

Time Use and Labor Productivity: The Returns to Sleep (forthcoming)

May 2016|The European Physical Journal: Special Topics|Jeffrey Shrader, Roy Allen, Joshua Graff Zivin

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model–it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.