This paper measures the economic impact of climate change on US agricultural land by estimating the effect of random year-to-year variation in temperature and precipitation on agricultural profits. The preferred estimates indicate that climate change will increase annual profits by $1.3 billion in 2002 dollars (2002$) or 4 percent. This estimate is robust to numerous specification checks and relatively precise, so large negative or positive effects are unlikely. We also find the hedonic approach—which is the standard in the previous literature—to be unreliable be- cause it produces estimates that are extremely sensitive to seemingly minor choices about control variables, sample, and weighting.
Conceptually, DG are correct in noting that omitted variables can in principle cause bias in a hedonic regression and that fixed effects can control for time-invariant idiosyncratic features of the unit of observation, in this case the county. However, it is also possible that fixed effects can increase the bias due to omitted variables if time-varying omitted variables (or data errors) are more strongly correlated with the treatment than time-invariant omitted variables that have been removed via the fixed effects. These fixed effects increase bias stemming from both endogeneity and measurement error. We have identified some important data errors and time-varying omitted variables, like storage, that are strongly correlated with both weather (the treatment variable) and DG's dependent variable, reported sales minus reported expenditures. These data errors and omitted variables bias toward zero results obtained by regressions that use sales as a proxy for production value.
Fisher et al. (2012) (hereafter, FHRS) have uncovered coding and data errors in our paper, Deschênes and Greenstone (2007) (hereafter, DG). We acknowledge and are embarrassed by these mistakes. We are grateful to FHRS for uncovering them. We hope that this Reply will also contribute to advancing the literature on the vital ques- tion of the impact of climate change on the US agricultural sector.