What Do We Learn from the Weather? The New Climate-Economy Literature

Abstract

A rapidly growing body of research applies panel methods to examine how temperature, precipitation, and windstorms influence economic outcomes. These studies focus on changes in weather realizations over time within a given spatial area and demonstrate impacts on agricultural output, industrial output, labor productivity, energy demand, health, conflict, and economic growth, among other outcomes. By harnessing exogenous variation over time within a given spatial unit, these studies help credibly identify (i) the breadth of channels linking weather and the economy, (ii) heterogeneous treatment effects across different types of locations, and (iii) nonlinear effects of weather variables. This paper reviews the new literature with two purposes. First, we summarize recent work, providing a guide to its methodologies, datasets, and findings. Second, we consider applications of the new literature, including insights for the “damage function” within models that seek to assess the potential economic effects of future climate change. (JEL C51, D72, O13, Q51, Q54)

Climate Change, Agriculture and Migration: A Survey

“…In IAMs, this is usually summarized by a so-called "climate damage function", specifying how temperatures or other climatic variables affect economic activities, such as agricultural production and food consumption [20]. In other words, it is important to emphasize that to estimate the impact of weather-driven agricultural shocks on migration exploiting an agro-economic model, we still need to know some (statistically estimated) parameters, linking people mobility to climate change.…”

“…Specifically, IAMs used for economic policy analysis are based on general equilibrium models, considering the overall economy, not just the agricultural sector. Typically, they include four broad components: "(1) a model predicting the growth of greenhouse gas (GHG) emissions; (2) a model mapping GHG emissions into climatic change; (3) a damage function that calculates the economic costs of climatic change; and (4) a social welfare function for aggregating damages over time and potentially across space" [20] (p. 783).…”

“…The former were the first to make their appearance in the literature. Yet, in recent times, panel data studies have become progressively more relevant [20].…”

“…More recently, with the aim of capturing adaptation, some papers have introduced the so-called long-difference approach, exploiting a mixture between the cross-sectional and the time-series identification strategies [20,43]. The general idea is based on the fact that, since changes in climate are gradual, averaging across long time-spans should offer the possibility to pick up both direct and belief effects (i.e., adaptation) of climate change, because people only adjust their beliefs when environmental changes are expected to be persistent [32].…”

“…The general idea is based on the fact that, since changes in climate are gradual, averaging across long time-spans should offer the possibility to pick up both direct and belief effects (i.e., adaptation) of climate change, because people only adjust their beliefs when environmental changes are expected to be persistent [32]. However, studies comparing climate-change effects with short-and long-run data frequency have often found that the magnitude of the estimated impacts of climate change on agricultural productivity are not so different [20,43]. These results, implying no or little adaptation effects in agriculture, are compatible with two different interpretations.…”