How does climate volatility alter citizen demands, change voting behavior, and affect the long-term reputation of elected (and unelected) officials? Does this effect come primarily through the economic damages caused by climate volatility or through alternative channels? Are they persistent or transitory?

As climate volatility becomes more extreme, so too will its destabilizing impact on politics. Yet we know relatively little about its effects on voting behavior, particularly in the developing world, and even less about downstream effects on the reputation of candidates and political institutions. Exploring the mechanisms behind these effects is also difficult due to a lack of data with the spatial and temporal resolution necessary for credible subnational analysis.

Here, we provide some of the first large-scale evidence on climate volatility’s effect on several measures of political accountability by combining several sources of survey data with high-resolution meteorological and climatic data. We also utilize a novel source of subnational economic data generated by combining remote sensing data with a convolutional neural network to generate annual, high-resolution estimates of growth at the 1x1km level for all of Africa. This ML-generated measure is a considerable improvement over nightlights-based alternatives and permits credible mediation analysis linking negative political outcomes to climate volatility through reductions in economic growth. We supplement our focus on Africa with companion estimates from Latin America, exploiting variation in national-level institutions to examine whether they can explain the substantial effect heterogeneity we observe in our reduced-form results.

ABOUT THE SPEAKERS

Amanda Kennard is an Assistant Professor of Political Science at Stanford University. She studies the politics of decarbonization and the impacts of climate change on political systems. She holds a Ph.D. from Princeton University, an M.S. from Georgetown University, and a B.A. from New York University.

Brandon de la Cuesta is currently a Postdoctoral Fellow at the Stanford Center on Democracy, Development and the Rule of Law (CDDRL) and the Center on Food Security and the Environment (FSE), working primarily with Marshall Burke and other members of the Environmental Change and Human Outcomes (ECHO) lab to estimate the impact of climate change on various measures of political accountability. Brandon specializes in comparative political economy and causal inference with a strong regional focus on sub-Saharan Africa. Many of his current projects involve the use of remote sensing data and machine learning algorithms, particularly convolutional neural nets, to create global, high-resolution data that can be used for downstream inference tasks. A development economics application of this data was recently featured as the cover article in Nature.

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