Accumulating evidence indicates that environmental temperature substantially affects economic outcomes and violence, but the reasons for this linkage are not well understood. We systematically evaluate the effect of thermal stress on multiple dimensions of economic decisionmaking, judgment, and...

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of...

Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and...

Until recently, neither climate nor conflict have been core areas of inquiry within economics, but there has been an explosion of research on both topics in the past decade, with a particularly large body of research emerging at their intersection. In this review, we survey this literature on...

Organized intergroup violence is almost universally modeled as a calculated act motivated by economic factors. In contrast, it is generally assumed that non-economic factors, such as an individual's emotional state, play a role in many types of interpersonal violence, such as "crimes of...

We survey the quantitative research literature linking climate and conflict, a body of research that spans multiple academic disciplines and has roughly doubled in size in the last decade. It makes three main contributions. First, we carry out a meta-analysis - updating Hsiang et al (2013) and...

Identifying the effect of climate on societies is central to understanding historical economic development, designing modern policies that react to climatic events, and managing future global climate change. Here, I review, synthesize, and interpret recent advances in methods used to measure...

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of...