Organisms can reduce risk exposure through short-term avoidance. However, this strategy may not be equally accessible throughout a population depending on the risk in question. We combine cellphone movements, satellite-based wildfire smoke plumes, and Census data to document substantial heterogeneity/inequity in communities' tendencies to out-migrate to avoid smoke. Higher-income and whiter populations travel out of their counties at significantly higher rates during smoke events. These results suggest that the same populations who face social and environmental injustice on many other measures are less able to avoid wildfire smoke---underscoring equity concerns for wildfire damages and climate adaptation.
Dynamic inconsistency in intertemporal choice has long been considered a hallmark of non-exponential discounting. Recent work has challenged this view from a variety of perspectives, including the view that time variance --shifting preferences between measurement dates-- can also explain apparent preference reversals. While a nascent literature identifies time-variance and demonstrates its role in explaining time-inconsistency, we lack both a model that allows time-variance to tractably interact with other properties of time preference, and a longitudinal study of sufficient depth to identify such a model. In this paper, we develop the ``nested exponential'' discount function which is general with respect to time-invariance, time-consistency, and stationarity. The function nests both exponential discounting and a version of present-biased discounting within its parameter space, enabling transparent model selection at both the aggregate and subject levels. We evaluate time-invariance and the performance of the nested exponential model in a 12-week longitudinal study featuring seven surveys. Our elicitations give us unprecedented precision in estimating dynamic inconsistency, non-stationarity, and time-variance. We find that subjects in our study exhibit significant decreasing patience over the course of the study, and that time-variance explains roughly 72% of time-inconsistent choices in our data. This does not mean our data are best-explained by exponential discounting plus preference drift: hyperbolicity is a key feature of our data, and it is well captured by the nested exponential function.
Works in Progress
Learning as a Source of Time-varying Time Preferences
Political Organizing with Limited Information