Climate inequality (Job Market Paper)

This paper characterizes the relationship between anthropogenic climate change and global inequality, a subject intersecting two of the defining challenges of the 21st century but which remains virtually unstudied at subnational levels. To make overdue progress addressing this research gap, I improve upon frontier methods for estimating climate impacts to overcome outstanding methodological limitations and apply these refinements to fine-scaled data on globally representative income distributions. Next, I document new evidence that temperature shocks significantly and persistently impact distributions of income within countries, an effect driven by concentrations of harm onto the lowest income-earners in warm climates as well as a surprising vulnerability of the top 1% in these countries to environmental shocks. Integrating these inequality effects over observed distributions of income and the spatial incidence of global warming, I find that climate change between 1981 and 2016 regressively redistributed global shares in a reduced stock of income both between and within countries largely by depriving the world’s poorest of economic opportunity that would otherwise have been available. Altogether, these results constitute the most comprehensive evidence yet of the regressive impact of climate change on global inequality.

  • Latest draft
  • Early version (2019) supervised by Elizabeth Baldwin and David F. Hendry named Oxford’s first ‘exceptional’-class (mark of 80+) graduate economics thesis in at least four years
  • Revised version (2023) supervised by Suresh Naidu awarded Columbia’s Wueller Prize for runner-up best pre-dissertation proposal

Other work in progress

Economic disruption, cultural anti-persistence, and intimate-partner violence (with Tanushree Goyal)

Political transition under environmental stress

Elite capture and identity politics: A formal model of substantive representation

Large potential reduction in economic damages under UN mitigation targets

Marshall Burke $\cdot$ Matthew Alampay Davis $\cdot$ Noah S. Diffenbaugh (2018)

Nature, 557, 549-553

We present a probabilistic framework for assessing aggregate economic impacts of anthropogenic warming. Our construction decomposes uncertainty associated with mid-century and end-of-century economic projections into distinct sources of uncertainty associated with i) econometric estimation of the economic effects of environmental change, ii) climate models of the spatial distribution of anthropogenic warming, iii) the projected schedule of greenhouse gas concentrations associated with a radiative forcing, and iv) the social discounting regime of choice. We apply this framework to characterize the economic benefits of climate policy, emphasizing how achieving the most ambitious mitigation targets of the 2015 Paris Agreement would obviate essentially certain economic calamity that will otherwise concentrate in developing countries.

Figure 4: The impact of global warming on global GDP per capita, relative to a world without warming, for different forcing levels.

Press

Nature $\cdot$ Stanford $\cdot$ Bloomberg $\cdot$ CBS (TV) $\cdot$ The Guardian $\cdot$ Reuters $\cdot$ The Hill $\cdot$ Yahoo $\cdot$ Axios $\cdot$ The New Yorker $\cdot$ Business Insider $\cdot$ Rolling Stone $\cdot$ The Daily Show (TV)

Other citations

New York Times $\cdot$ The Guardian $\cdot$ Governors of New York, California, and Washington $\cdot$ IPCC Special Report on Global Warming of 1.5°C (SR15) $\cdot$ MSNBC (TV) $\cdot$ “The Uninhabitable Earth” by David Wallace-Wells $\cdot$ Rezo $\cdot$ Bernie Sanders $\cdot$ US House Committee on Financial Services $\cdot$ IPCC Sixth Assessment Report (AR6-WGII)

Combining satellite imagery and machine learning to predict poverty

Neal Jean $\cdot$ Marshall Burke $\cdot$ Michael Xie $\cdot$ Matthew Alampay Davis $\cdot$ David B. Lobell $\cdot$ Stefano Ermon (2016)
Science, 353 (6301), 790-794

Efforts to study and design policy addressing the challenges of global poverty and inequality are hampered by the infrequency and prohibitive expense of reliable measurement of welfare, particularly in the developing world. Here we demonstrate a scalable method for overcoming this data scarcity which works by extracting economic information from an unconventional but inexpensive source of data with increasingly frequent and essentially global coverage: high-resolution daytime satellite imagery.

Our “transfer learning” pipeline proceeds by first assigning a convolutional neural network model pre-trained for generic image classification the task of identifying features in high-resolution daytime satellite imagery predictive of night-time luminosity, a crude proxy for economic activity. In effect, the CNN learns to produce a nonlinear mapping from unstructured satellite images to a low-dimensional vector representation of its most economically relevant features. Ridge regression models are then optimized to produce out-of-sample estimates of economic outcomes of interest. In an initial application to five diverse sub-Saharan African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—our entirely open-source models are found to be able to explain up to 75% of the variation in village-level average economic outcomes as measured by gold-standard household surveys, demonstrating potential to improve state capacity to administer social programs by reducing misallocation risks and search costs.

Press

Science $\cdot$ Stanford $\cdot$ The Washington Post $\cdot$ BBC $\cdot$ Scientific American $\cdot$ The Atlantic $\cdot$ The Onion $\cdot$ Bill Gates $\cdot$ Center for Global Development