Spaces for Agreement: A Theory of Time-Stochastic Dominance and an Application to Climate Change

Abstract

Many investments involve both a long time horizon and risky returns. Making investment decisions thus requires assumptions about time and risk preferences. Such assumptions are frequently contested, particularly in the public sector, and there is no immediate prospect of universal agreement. Motivated by these observations, we develop a theory and method of finding "spaces for agreement." These are combinations of classes of discount and utility function, for which one investment dominates another (or "almost" does so), so that all those whose preferences can be represented by such combinations would agree on the option to choose. The theory combines the insights of stochastic dominance and time dominance and offers a nonparametric approach to intertemporal, risky choice. We then apply the theory to climate change and show using a popular simulation model that even tough carbon emissions targets would be chosen by almost everyone, barring those with arguably "extreme" preferences.JEL Codes: D61, H43, Q54

Hurricanes, climate change, and social welfare: evidence from the Caribbean

“…In order to conduct our welfare analysis we adopt the approach pioneered by Dietz and Matei (2016). More specifically, the authors extended the stochastic and almost stochastic dominance methods of preference ranking within a dynamic framework.…”

“…More specifically, the authors extended the stochastic and almost stochastic dominance methods of preference ranking within a dynamic framework. Here we simply outline the application of this approach within our hurricane damage context, and refer the reader for full details and proofs to Dietz and Matei (2016).…”

“…The net present value of this scenario can then be described analogously to (1). In this context one can define the preference relation between scenario X and scenario Y in terms of what Dietz and Matei (2016) coin as X time stochastic first-order dominating Y . More specifically, scenario X Time Stochastic First-order Dominates (T SF D) scenario Y iff :…”

“…We then generate hypothetical distributions of annual series of damages over time, where we allow the climate signal to emerge in the storm distributions gradually. With this data at hand, we employ the time-stochastic dominance preference ranking framework developed by Dietz and Matei (2016) to explore what sets of utility functions and discount rates allow one to potentially rank hurricane damages under future relative to current climate for the Caribbean region. We extend the class of applications of this framework to examine whether climate change leaves societies worse off rather than comparing different policy scenarios as was done in its original application.…”

“…The remainder of the paper is organized as follows. In the next section, we outline the time-stochastic dominance preference ordering framework by Dietz and Matei (2016), as well as our construction of damages from tropical storms. Section 3 describes the various data sets employed in the analysis.…”