Date of Award

Summer 8-1-2016

Degree Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

Paul Ferraro

Second Advisor

Glenn Harrison

Third Advisor

Carlianne Patrick

Fourth Advisor

Maximo Torero


Climate scientists have predicted an increase in weather variability in the last decades that will continue in the future. My dissertation focuses on understanding the behavioral factors that encourage household adaptation to climate change and testing the effectiveness of one popular policy recommendation taking into account behavioral factors.

Since climate change is fundamentally an intertemporal decision under risk, time and risk preferences will shape how individuals and groups adapt on their own and in response to public policies and programs. Applying recent developments in experimental designs and estimators, in my first chapter I conduct field experiments to characterize time and risk preferences in a rural population in the western, arid region of Costa Rica, targeted by policymakers for climate change adaptation investments. Decisions about these investments are often made at the household-level, rather than at the individual-level. Thus, I expand on previous experimental studies by characterizing time and risk preferences at both the levels of individuals and married couples. My results show that people are impatient and optimistic, that couples are more impatient than individuals and that the preferences of married couples are the ones that best described the household adaptation decision.

Being optimistic and impatient requires adaptation technologies that offer immediate benefits even when no drastic weather changes occur, like resource-conserving technologies. In my second chapter, I report the results from a randomized controlled trial to test the impact of water-efficient technology adoption on water use in the same area in Costa Rica. The adoption of such technologies has been highlighted in numerous government and multilateral plans as a key component of climate change adaptation strategies even though no rigorous studies have been implemented. My experimental estimate is 11%, which is economically and statistically lower than what engineers estimate (up to 35%). I explore the reasons why the experimental estimator differs from the predicted impacts using an engineering estimation approach. Finally, I perform a cost-benefit analysis, and compare the discounted expected utility of purchasing and not purchasing the technology.