Dylan Hogan

New York, New York

I am a PhD student in Sustainable Development at Columbia University SIPA. I’m broadly interested in issues related to climate change and economic development, with particular interest in the role of adaptation mechanisms in reducing impacts to humans and ecosystems. Most recently, I’m interested in topics related to agriculture and water availability.

Prior to starting my PhD, I worked for the Climate Impact Lab, an interdisciplinary team at University of Chicago estimating the social impacts of climate change, where I contributed to analyses of mortality, agricultural yield, and labor productivity. I received my undergraduate degree in Applied Mathematics and Economics from Brown University.

Please see a summary of my research below, and feel free to contact me to discuss my work.

Selected research

Empirical Approaches to Climate Change Impact Quantification

Dylan Hogan, and Wolfram Schlenker

Handbook of the Economics of Climate Change, 2024

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Robust empirical analysis of climate change impacts is essential for designing effective climate policies and facilitating adaptation to climate damages. However, identifying the causal impacts of climate change faces the fundamental challenge of endogeneity — we cannot observe the Earth under alternative climate futures, making it difficult to fully disentangle climate impacts from other potential drivers of key socioeconomic outcomes. In this chapter, we review the rapidly expanding set of empirical approaches for quantifying the causal impacts of climate change. We begin by outlining several core econometric methodologies that have been applied to climate-related questions across various geographies, sectors of the economy, and societal dimensions. We next discuss key lessons learned from several decades of climate impact studies, including methodological extensions to account for adaptation, allowing for non-linear effects, potential specification issues when using weather data for economic analysis, and frontier applications of climate impact methodologies and novel data sources.
Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Dylan Hogan, and Wolfram Schlenker

Nature Communications, 2024

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Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.