I am an economist working at the intersection of climate, development, and political economics. Much of my research to date has focused on understanding the social consequences of environmental change in order to inform the design of equitable and effective climate policy.

My approach to these broad and active areas of research is distinguished by its emphasis on distributional considerations. A recurring theme in my work is that while environmental change is diffuse and governed by complex biophysical systems, its social effects are channeled through and allocated by economic and political systems which may encode patterns of social inequality. By necessity, I draw from diverse literatures within economics and collaborations across the social and environmental sciences, adapting and repurposing theory and methods to the demands of each research question.

WP: Climate inequality

This paper explores the relationship between anthropogenic climate change and global inequality, a subject bridging two of the defining challenges of the 21st century but which remains virtually unstudied at subnational levels. I apply recently developed methods for estimating dynamic responses to identified shocks to newly available fine-scaled data on global distributions of income. First, 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.

Next, I conduct a counterfactual analysis by integrating these inequality dynamics over observed distributions of income and the spatial incidence of systematic warming between 1980 and 2016. I show that climate change has regressively redistributed global income largely by depriving the world’s poorest of economic opportunity that would otherwise have been available: For the poorest percentile of income-earners in our data, we find that income levels absent systematic warming since 1980 would be 29% [18, 41] higher than realized. Further, I find that climate change over this period has reduced between-country inequality by 8.7% [4.9, 13.3] and within-country inequality by 2.6% [0.0, 5.6]. Altogether, these results constitute the most comprehensive evidence yet of the regressive impact of climate change.

WP: Temperature, institutions, and the political climate

Despite widespread regard of climate change as a threat to global stability, the nature of the relationship between environmental change, political preferences, and regime change is undertheorized in environmental political economy. I consider two possible frameworks by repurposing influential existing theories of regime change. Under a “mercury uprising” hypothesis, adverse environmental shocks diminish the opportunity cost of contesting autocratic rule thereby improving prospects for democratic reform. On the other hand, a “state of exception” hypothesis would suggest that the public responds to these shocks with a willingness to compromise civil liberties for authoritarian features of governance which may more credibly ensure security thereby leading to a diminution of democratic institutions.

I empirically test the relative merits of these theories first by measuring the impacts of identified temperature shocks on survey-based proxies for the demand for democracy. Next, I use multi-valued and dichotomous measures of democracy to investigate the dynamic effects of these shocks on the democratic quality of institutions. The results from these analyses consistently support the latter theory. In particular, I find that a positive 0.5$^\circ$C temperature shock increases the propensity for autocracies in temperate geographies to democratize by between 1.5-3.7 percentage points depending on the operating definition of democracy but reduces this propensity for their tropical counterparts by 1.6-2.4 percentage points. The same shocks also increase the propensity for warm-climate democracies to break down by 0.6-2.0 percentage points. In these cases, effects are substantial and significant but exhibit very limited persistence beyond the year of the shock after accounting for autocorrelation in the shock itself. Finally, we find that the same shocks have null effects on the propensity for temperate democracies to undergo reversal, suggesting these institutions have historically proven impervious to environmental disruption.

WP: Evidence of a drought effect on the hazard into spousal violence

Matthew Alampay Davis $\cdot$ Tanushree Goyal

In this work in progress, we explore how income shocks may impact the probability that a previously non-violent marriage turns violent. We combine high-resolution weather data with comprehensive duration data on the experiences with domestic violence in India to estimate how drought-induced shocks may affect gendered outcomes. Early findings suggest that negative income shocks substantially reduce the hazard into marital violence. Specifically, a negative precipitation shock of a magnitude expected once every 4-5 years reduces the risk of a never-violent relationship becoming violent by 0.037 percentage points, representing a 70% reduction in the baseline hazard during the formative years of a marriage.

We also replicate a recently published study relating these weather shocks to the risk of child marriage using updated methods and much-improved data. I am able to corroborate the original finding of drought effects in India which reduce the hazard into child marriage though estimates are smaller and more persistent than characterized in the original study. Critically, I find this phenomenon is reproduced in Sub-Saharan African contexts, contrary to the original finding of opposite-signed effects, which suggests that consumption-smoothing incentives may not be as pertinent a determinant of child marriage as previously suggested. Instead, secondary results suggest that child marriage seems may be less economically motivated than marriages occurring later in life.

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