To what extent can long-differencing capture climate adaptation?

2026-07-24

AI Summary

This study addresses a critical methodological flaw in existing approaches to measuring climate adaptation, which conflate short-term weather responses with long-term adaptive behavior, thereby biasing economic estimates of climate damages. The authors formally demonstrate—through econometric theory, numerical simulations, and empirical calibration—that both long-difference and fixed-effects regression models fundamentally fail to consistently identify true adaptation parameters due to this conflation. Their analysis reveals that conventional methods substantially underestimate the degree of adaptation by 30% to 80% and exhibit severely limited statistical power. These findings provide a crucial methodological warning for climate economics and point toward necessary refinements in empirical practice to yield more accurate assessments of human responses to climate change.

Abstract

Understanding the degree to which we are able to adapt to climate change is central to economic assessments of future climate damages. Economists increasingly use comparisons between long differences and fixed effects estimators to measure climate adaptation. We show that such comparisons can be misleading. Neither estimator is consistent for its intended parameter, as both the long-difference (LD) and fixed effects (FE) estimands are weighted averages of the long- and short-run responses to climate and weather. As a result, the difference between the two understates the true extent of adaptation, and the standard test based on this difference --while controlling size -- tends to be substantially underpowered in the settings researchers typically encounter. An empirically-calibrated simulation shows this difference understates adaptation by about 30--80%, depending on the averaging window.

Problem

• climate adaptation
• long-differencing
• fixed effects
• estimation bias
• climate damages

Innovation

• long-difference estimator
• fixed effects estimator
• climate adaptation
• estimation bias
• statistical power