Research Publication

Abstract:

The analysis of policy impacts in a dynamic and uncertain reality is vital to supporting informed economic policy design and implementation. Dynamic, stochastic economic models used in policy evaluation necessarily simplify the world as we know it. This motivates us to explore, refine, and extend tools aimed at producing marginal valuations that shed light on why some policies are optimal and how others, though suboptimal, can be improved. We present novel representations of these marginal valuations that embrace uncertainty and support robust implementation—even in environments characterized by “deep uncertainties.” These representations offer a more complete understanding of how interactions among multiple state variables, concerns about model misspecification, and uncertainties surrounding potentially long-term implications contribute to the cogent assessment of policies. We argue that these methods are particularly salient for evaluating the global cost of climate change and the global value of research and development with long-term prospects for success.

Appendix

Published research in the Proceedings of the National Academy of Sciences (PNAS).

Acknowledgments:

Fernando Alvarez, William Brock, Dennis Epple, Benjamin Hebert, Ken Miyahara Coello, Darrell Duffie, Kevin Murphy, Aleksei Oskolkov, Diana Petrova, Richard Romano, Tom Sargent, Grace Tsiang, Xunyyu Zhou, Bowen Dong, Jessie Liao, and Zhaoyang Xu provided helpful comments. Hansen was partially supported by the Climate Systems Engineering initiative (CSEi) faculty research grant. Souganidis was partially supported by the National Science Foundation Grant DMS-2153822, the Office for Naval Research Grant N000141712095 and the Air Force Office for Scientific Research Grant FA9550-18-1-0494. This research was also supported in part by the Research Computing Center at the University of Chicago for providing computational resources and the Climate Systems Engineering initiative (CSEi) at the University of Chicago.