Research

Works in progress

Global income distributions and social welfare under climate change

• Revision in progress

Climate change is increasingly understood not only as a drag on economic growth but also as a driver of inequality, yet existing evidence overwhelmingly focuses on aggregate national outcomes, leaving its distributional incidence within countries largely unexplored. This paper develops a unified empirical framework for estimating how temperature shocks affect income growth across the global income distribution, combining newly available cross-country distributional income data with a state-dependent local projections approach that identifies heterogeneous and persistent climate damages while allowing persistence to be estimated rather than imposed. I find that temperature shocks disproportionately reduce income growth among lower-income populations, particularly in warmer economies, implying that climate change has systematically increased both within-country and between-country inequality. Using these estimates, I construct counterfactual global income distributions in the absence of post-1980 warming and evaluate welfare using inequality-sensitive measures. Accounting for distributional impacts substantially increases estimated global climate damages relative to conventional aggregate assessments and highlights the importance of integrating inequality, persistence uncertainty, and climate justice considerations into empirical climate damage analysis.

Temperature, institutions, and the political climate

• Revision in progress

Although climate change is widely recognized as a threat to global stability, its consequences for political preferences and institutions remain undertheorized. This paper frames the question around two competing hypotheses drawn from theories of demand-led political transition: one in which adverse environmental shocks lower the cost of contesting autocratic rule and raise pressure for reform, and one in which they act as crisis events that lead citizens to trade political liberties for security. I test them by estimating the dynamic effects of identified temperature shocks on survey-based measures of democratic demand and on institutional quality, using both continuous and binary measures of democracy and comparing across local climates and levels of development.

Representation as capital: A theory of inclusive and extractive identity politics

Though descriptive and substantive political representation are often assumed to move together, the evidence for such a relationship is mixed. This paper rationalizes this conditionality as the outcome of a contingent coalition between a political party’s elite and activist factions. A candidate from an underrepresented group generates surplus for the party to the extent that voters value descriptive representation. How this identity-based political capital is instrumentalized is then subject to intra-party bargaining: the elite wish to conserve the electoral advantage produced by the surplus, while the activists recognize an opportunity to platform substantively ambitious policy. A threshold in relative bargaining power thus separates two regimes: when activists are sufficiently empowered, descriptive and substantive representation are complements, giving rise to an inclusive identity politics; conversely, a party sufficiently captured by its elite will substitute descriptive representation for substantive policy concessions to preserve the electoral advantage, effecting an extractive identity politics. I derive mild sufficient conditions under which the threshold is preserved. Endogenizing bargaining power through a repeated activist abstention threat determines which regime a party can sustain and I show these results hold whether the factions bargain over candidate type or over credible policy commitments. Finally, I derive conditions under which party quotas are analogously inclusive or extractive, showing that a party elite strong enough to repeal an extractive institutional feature will choose to maintain it.

Evidence of a drought effect on the hazard into spousal violence

with Tanushree Goyal

This project studies how drought-driven income shocks affect the timing of intimate-partner violence within marriage. Combining high-resolution precipitation data with duration data from household surveys in India, we estimate how negative income shocks shift the hazard of first-time spousal violence over the course of a marriage, and, in a complementary analysis, revisit the relationship between drought and the timing of early marriage across dowry and bride-price contexts.

Publications

Large potential reduction in economic damages under UN mitigation targets

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.

Combining satellite imagery and machine learning to predict poverty

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.