Assume we have a time machine and transport ourselves to the year 2100. Would we, with the benefit of decades of extra data, be able to use purely reduced-form econometric methods to estimate the overall economic damage from climate change? I say no.
The argument follows from what’s laid out in the JEL paper on climate damages with Catie and Derek – specifically the connection between the conceptual framework in that paper and the idealized experiment that a causal inference approach would like to approximate. Climate change is persistent, anticipatable, and affects the whole world, so the ideal (and impossible) experiment would involve replicating the planet, having one replicate experience a different climate over many years, and then comparing the economic conditions across the two worlds.
Even with an extra 75 years of data, I don’t see an empirical approach that would allow us to capture all of the important features of this ideal experiment. As we discuss in the paper, cross sectional comparisons of climate and economic output across locations fail to capture the widespread nature of climate change (they are only looking at local effects). Global time series approaches are the mirror image. To gain identification, they need to isolate temperature shocks which are not anticipatable and which have limited persistence.
With more data, one might consider using a global time series analysis that focuses on longer-run variation in temperature. To make things concrete, imagine regressing global output in a given year on global average temperature over the previous 30 years, the 30 years before that, and 30 years before that. A 90 year distributed lag model in long-difference form.
We can notice right away that even with many decades of additional data, this regression will have a tiny number of observations. Ok, so let’s take our time machine hundreds of years further in the future. Does that solve the problem? Again, not really, because we are inherently limited to comparing recent 30 year periods to historical 30 year periods – we don’t have replicate worlds, so we never have a contemporaneous control group. What else has changed between these 30 year periods aside from the climate?
Against epistemic nihilism
Even though I am pessimistic about the ability for purely reduced-form approaches to definitively determine the damages from climate change, that’s not a reason for nihilistically claiming that damages are unknowable. It makes me believe the way forward is to employ a mixture of approaches, taking the strengths from each. Indeed, I would say this is the central message of the JEL paper.
One way to do this would be to go back to the future of IAMs (integrated assessment models).1 Many IAMs have focused on assessing mitigation policy, with less attention paid to the damage function including both transitory and durable adaptation, especially endogenous technical change. With the growing power of dynamic IO and heterogeneous agent macro, I could see a world where careful applied micro studies provide portable statistics for macro models that feature rich characterization of individual (firm and household) and public adaptation (like a supercharged Fried (2022)). The recent spatially granular macro climate models (review) are also in line with this idea. Although the total package of climate change effects is not identifiable, different studies and methodologies get at different slices of the problem – direct effects of local weather, spillovers, effects of past weather on durable adaptation.
In principle, if we had a sufficiently rich and well-characterized model, we can use simulation to perform the world replication thought experiment I started the post with. This is sci fi, but less sci fi than a time machine, and it would actually enable us to understand total damages!2
Version history
2026-04-17: First version
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Time machines are apparently on the mind. ↩
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Credit to Cannon Cloud for helping me realize that my recent bullishness on a new wave of IAMs was indeed about their ability to break out of the climate damages trilemma we lay out in our review. ↩