I am a political economist working at the intersection of environmental, development, and welfare economics. A recurring theme in my work is that while the climate is governed by complex biophysical systems, its welfare impacts are distributed through social systems that enforce, exacerbate, and reify patterns of inequality and deprivation. This agenda seeks to refine the economic analysis of climate change and inform the design of equitable climate and development policy.

By necessity, my work draws on interdisciplinary approaches and collaborations across the social and natural sciences. I also maintain a secondary interest in historical political economy as it pertains to the determination and persistence of social inequalities.

Works in progress

Global income distributions and social welfare under climate change

  • Major revision in progress
  • JMP version previously circulated with the title “Climate inequality”

Temperature, institutions, and the political climate

  • Major revision in progress

Evidence of a drought effect on the hazard into spousal violence

with Tanushree Goyal

Publications

Large potential reduction in economic damages under UN mitigation targets

Marshall Burke, W. Matthew Alampay Davis, and Noah S. Diffenbaugh (2018)
Nature 557(7706): 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.

Select 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)

Select 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)

Combining satellite imagery and machine learning to predict poverty

Neal Jean, Marshall Burke, Michael Xie, W. Matthew Alampay Davis, David B. Lobell, and Stefano Ermon (2016)
Science 353: 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 the daytime imagery predictive of night-time luminosity, a crude proxy for economic activity. In effect, the CNN learns to produce a nonlinear mapping from the unstructured images to a low-dimensional vector representation of its most economically informative features. Ridge regression models are then optimized to produce out-of-sample estimates of consumption expenditures and asset wealth. In an initial application to five diverse sub-Saharan African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—our entirely open-source models are able to explain up to 75% of the variation in village-level outcomes as measured by household surveys, demonstrating potential to reduce misallocation costs in the administration of targeted social programs.

Select 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