How will people adapt to rising temperatures and extreme weather events, how costly will these adaptations be, and will they be undertaken before it is too late? In this work, I test the hypothesis that large flood events shock nearby residents and affect their future real estate purchase habits. I leverage the natural experiment created by flood disasters in the United States and focus on real estate markets, where transactions data is readily available, stakes are high, and choices differ significantly in terms of risk exposure. Drawing on data from over four million Florida real estate transactions between 2005 and 2017, I use negative binomial count models and find evidence that scarred buyers who experienced disasters in their previous homes alter their behavior when they enter Florida housing markets, shifting purchases away from coastal properties and retreating to larger properties inland. The effects are sharp but transient, disappearing and even reversing in the years after a shock. I then develop a residential sorting model that can estimate the quantitative effect of disaster shock on individual demand for housing near the water, using a genetic matching procedure to find a group of non-scarred buyers that serves as a control group. I employ this model to measure the behavioral welfare implications of transient shifts in preferences or beliefs, and find suggestive evidence that the informational content of storm exposure is worth several hundred dollars to shocked buyers. Finally, I estimate hedonic models which show that a large influx of scarred buyers to a given Florida market is powerful enough to decrease the amenity value of water proximity in equilibrium by up to 5% of total home value, with potentially huge implications for coastal housing markets.
Record-breaking heat days disproportionately influence heat perceptions (with Dolores Albarracín) [link] (Nature: Scientific Reports 2023)
From heat waves to hurricanes, tangible weather experiences have been shown to strengthen personal belief in climate change. We ask whether a high temperature day that breaks local heat records – which is a mathematical construct not directly accessible to the senses – has additional impacts on perceptions of worsening heat, above and beyond that of the absolute temperatures. Matching historical heat records to survey data from the United States, we find that each record heat day in a county in 2022 increases perceptions that excessive heat is getting worse, even when controlling for average temperatures, the number of extreme heat days, and demographic factors. Our estimates suggest that exposure to sixteen record heat days predicts roughly the same difference in excessive heat perceptions as between the average Democrat respondent and a political independent.
Prior research has documented a tendency for insurance demand to rise abruptly in the aftermath of natural disasters. This may be attributable in part to the information shock that a disaster represents. Understanding how beliefs about risk evolve is of first-order importance for projecting climate adaptation in coming decades. Using a three-wave survey of 959 residents of flood-prone coastal counties in the southeastern United States, including 430 for whom we have longitudinal data, we study how individuals’ beliefs evolve after two types of informational shocks about future local flooding risk: viewing flood maps, and experiencing a local flooding disaster. In the first wave of the survey, we randomly expose a portion of the respondents to U.S. government flood risk maps covering their neighborhoods. We find that, on average, exposure to maps causes respondents to update their beliefs of their homes’ flood risk downward substantially (40%, 95% CI 23%-54%) and reduces their reported level of worry. We also find evidence that map exposure differentially affects respondents based on their risk factors, suggesting that maps help to refine beliefs. We then leverage the natural experiment created by differential flood exposure during the hurricane seasons of 2017 and 2018, the latter of which fell between the initial wave of our survey and two follow-up waves. We find that flood disasters systematically increase flood concern in affected counties, as expected, although the finding is not robust to the inclusion of extreme outliers. More surprisingly, we find that those who experienced a flood before Wave 1 saw greater increases in concern between waves than those who did not. This latter finding does not match a simple theory of belief upating and suggest an involved updating process that may complicate climate adaptation.
Partition at your own risk: Evidence on risk-taking prevalence and motives from the field (with Saurabh Bhargava) [link]
Economists have long sought to understand the prevalence of and motives for risk aversion in the field. In practice, this inquiry faces several potential confounds: biased beliefs (e.g., gambling, investing), imperfect understanding (e.g., insurance), or limited generalizability (e.g., game shows). We overcome these challenges with rare data detailing the choices, productivity, and beliefs of 20,133 employees across 18 large North American firms who participated in a simple, all-or-nothing, goal-rewards program with $9.4 million in incentives. We estimate nearly one-half of employees selected a goal lower than the EV-maximizing benchmark, assuming rational expectations, resulting in a 46 percent average loss of potential rewards. This conservative goal choice persisted across diverse financial stakes ($69 to $4,500) and employee experience. We additionally show that conservative choice cannot be explained by a standard expected utility (EU) model with plausible risk preferences or through common departures from EU such as biased-beliefs (employees exhibit substantial overconfidence about productivity), non-linear decision weights, or gain-loss utility. We replicate the pattern of conservative choice, corroborate limits of EU-based explanations, and rule out potential confounds through an incentive-compatible online goal-reward paradigm. We propose—and experimentally validate—a novel decision-heuristic in which risk averse choice emerges from an inferential bias due to contingency neglect in the context of pairwise comparisons. We conclude with experimental evidence suggesting that this heuristic offers a potential explanation for empirical puzzles in other risky-choice settings of economic interest such as deductible-based insurance and portfolio allocation.