Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2409.14936

Temperature Variability and Natural Disasters

Aatishya Mohanty·Nattavudh Powdthavee·Cheng Keat Tang·Andrew J. Oswald
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper studies natural disasters and the psychological costs of climate change. It presents what we believe to be the first evidence that higher temperature variability and not a higher level of temperature is what predicts natural disasters. This conclusion holds whether or not we control for the (incorrectly signed) impact of temperature. The analysis draws upon long-differences regression equations using GDIS data from 1960-2018 for 176 countries and the contiguous states of the USA. Results are checked on FEMA data. Wellbeing impact losses are calculated. To our knowledge, the paper's results are unknown to natural and social scientists.

Explore related subjects

econ.GNq-fin.EC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Aatishya Mohanty, Nattavudh Powdthavee, Cheng Keat Tang, Andrew J. Oswald. 2024-09-23. Temperature Variability and Natural Disasters. https://arxiv.org/abs/2409.14936

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Computing Endogenous Transformations in Processing Networks: A Dynamic Calibration Approach

Understanding how supply chains endogenously transform requires a parametric model of processing networks with non-neutral substitution elasticities. While the Cascaded CES production function provides a rigorous framework, dynamically calibrating its structural parameters from time-series data constitutes a highly non-convex inverse optimization problem. Since enforcing strict microeconomic concavity renders standard monolithic approaches computationally intractable, we propose a novel structure-exploiting algorithm to bypass this limitation. By leveraging the physical upstreamness topology of the network, our hybrid heuristic effectively breaks the curse of dimensionality inherent in economywide processing networks. Applying this framework to U.S. time-series data, we provide a scalable computational engine to fully endogenize complex supply-chain transformations, ultimately uncovering the elastic origins of asymmetric macroeconomic tail risks.

econ.GN↗
arXiv2026

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN↗
arXiv2026

Screening Out the Needy: The Effects of SNAP Work Requirements

We examine the effectiveness of work requirements as a screening device in the Supplemental Nutrition Assistance Program (SNAP). Work requirements for "able-bodied adults without dependents" were suspended after the Great Recession and gradually reinstated across counties and states in the 2010s. Using linked administrative SNAP and employment data from five states and a triple-differences design, we find that work requirements reduce SNAP participation by seven percent without increasing labor supply and disproportionately screen out low-income individuals. We develop a welfare framework to interpret these results and find that the social costs of work requirements exceed budget savings.

econ.GN↗