No, really, climate change will be bad!

I would be remiss to purport to blog about the economics of energy, the environment, and the developing world if I failed to highlight a new (important) study that came out in Nature this week.

The all-star team of Marshall Burke, Sol Hsiang (who has a fancy new website), and Ted Miguel is at it again, with a paper on the effects of temperature on GDP around the world. Before they even get to the empirics, they provide some really nice insight as to why when there are sharp non-linearities in micro temperature response functions, we shouldn't expect to see these same kinks in macro response functions. The idea is basically the following: a micro response function tells us the marginal effect of having an additional (hour, day) in a given temperature range. Imagine, as with US maize and lots of other things, temperature is increasing up to a point and then has a sharp decrease beyond that point. The macro response function will aggregate these days or hours up to a longer time period (a year, say), meaning that the overall effect of annual temperature on annual output will be a weighted average of the two slopes of the micro response, weighted by the number of days in each temperature range. Was that confusing? Check out Figure 1, panels d, e, and f (the math to derive this is all in the supplement to the paper as well):

This key insight is really important in allowing us to understand how we should expect micro responses to differ from macro ones. Cool. 

The authors then go on to empirically estimate the global macro temperature response function, settling on (after many robustness checks) a quadratic in temperature. What they come up with is a strong inverted-u shaped relationship, with an optimum around 55F (that might seem low, but remember that we're talking about annual average temperature here). This suggests that some (colder) countries might benefit from global warming, and hotter countries have a lot to lose. They tackle several points that are often brought up in this literature, and end up unable to reject that the rich and poor country responses are the same (though the confidence intervals are quite large as well. Minor gripe: 90% confidence intervals are shown in the paper. Yes, I know that 95% is arbitrary too, but it is the empirical economics standard...); they show that agriculture takes a big hit in both poor and rich countries, and that non-ag GDP seems to take slightly less of one in richer countries, but the relationship between temperature and non-ag GDP is still downward sloping; and finally, that the response functions in 1960-1989 look almost identical to the 1990-2010 response functions, suggesting that there hasn't been a ton of adaptation during the time period of their data.

Using these estimates, they go on to make some beautiful figures showing climate damage projections out to 2100 (IMHO, as much as I know that they like Figure 3, I think it's aesthetically pleasing but not the most legible). They find that, using fairly reasonable assumptions about growth and emissions paths, global GDP is projected to be approximately 25% lower in 2100 with climate change than without -- a much larger effect than all three current IAMs used in US policy (DICE, FUND, and PAGE) would suggest . There are wide confidence intervals around this estimate to be sure - but it's also worth noting that the majority of the uncertainty here comes from Europe and North America. These are large economies, and so have a large effect on GDP per capita overall, but are also close to the estimated global optimum, meaning that if the optimum is off by a little bit, the effects for these countries could even flip in sign.

I think this paper is a really important contribution to the climate-economics space. The effects are huge, and the paper (and supplemental information, and stuff that got left out of the supplemental information but was in an earlier non-circulating working paper version) is very thorough.

A few small comments: it is worth noting that there's a ton of statistical uncertainty floating around here.  Panel C of the first extended data figure shows the estimated marginal effects with lags included - and in every estimate that includes lags, the confidence interval bounds zero (and I think these are still the 90% CI's?). The confidence intervals on Figure 5a, the main estimate, also sit squarely on top of zero. And, as with every projection exercise, we should take this one with a giant brick of salt. These guys do a good job, but remember that they're also using short-run fluctuations in temperatures to trace out this response function. This is nice because, conditional  on the right fixed effects, we generally think that it's as good as randomly assigned, but does make plugging the estimates into a projection a little tricky to interpret. It's standard in this literature to do this kind of thing - and the fact that they find no evidence of adaptation in the 50+ year period they're looking at helps shore up the argument for doing so - but it's worth keeping in mind that that's what's being done.

It's also really important to think carefully (in all of these papers - not just BHM) about what's actually being used for identification. We know from Wolfram and Craig McIntosh that using higher-order polynomials in fixed effects models re-introduces cross sectional variation (and any omitted variable bias that comes with it!). I think in an earlier version of the paper, I saw a binned model floating around, which removes this concern, and had similar point estimates, but this general point is something that's under-appreciated, I think. (And, even with binned models, we need to be really careful when presenting something as the aggregate temperature response function, if there are only a few countries that ever end up in the really hot bins. That's a soapbox for another day.)

Also, as I mentioned above a little bit, while it's true that these guys aren't able to statistically reject that the poor and rich country responses are different, that doesn't mean that the true responses aren't different - it could be that there's not enough statistical power to address these questions in the data. That's going to be especially true at the colder end of the distribution - there are so few poor countries there that it's really hard to say anything concrete. 

All that said, I think this is a super interesting and important paper, and I'm glad that it's out in time for Paris. I've already learned a lot from these guys, and I continue to do so - they're some of the most careful, thorough, and productive researchers out there working on really policy-relevant topics. Plus, they make beautiful figures. This is a paper that's really worth diving into - I highly recommend actually reading the paper, the extended data, and the supplemental information (which is something I won't say very often)!

One last thing before I close: Marshall, Sol, and Ted have put up a really good companion site to their paper, that makes the results accessible and digestible. Plus, they've put up replication code - very important when you're working on such hot (ha) issues as climate and GDP. Take a look!

Edited to add: Marshall just posted a response to some frequent criticism on his blog. Worth a read.

WWP: An oldie but a goodie

I always appreciate papers when they teach me something about methodology as well as about their particular research question. A lot of the papers that I really like that have done this have already been published (David McKenzie at the World Bank has a bunch of papers that fall into this category - and excellent blog posts as well.) 

This week's WWP isn't particularly new, but is definitely both interesting and useful methodologically (and it is still a working paper!). Many readers of this blog (ha, as if this blog has many readers) have probably read this paper before, or at least skimmed it, or at least seen the talk. But if you haven't read it carefully, I urge you to go back and give it another look. Yes, I'm talking about Sol Hsiang and Amir Jina's hurricanes paper (the actual title is: The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence from 6,700 Cyclones). Aside from being interesting and cool (and having scarily large estimates), it also provides really clear discussions of how to do a bunch of things that applied microeconomists might want to know how to do.

It describes in a good bit of detail how to map environmental events to economic observations (don't miss the page-long footnote 13...). It also discusses how to estimate a distributed lag model, and then explains how to recover the cumulative effect from this model (something that I never saw in an econometrics class). It provides really clear visualizations of the main results (we should expect no less from Sol at this point). A lot of the methodological meat is also contained in the battery of robustness checks, including a randomization inference procedure, a variety of cuts of the data, more discussion of distributed lag and spatial lag models, modeling potential adaptation, etc etc etc. Finally, they do an interesting exercise where they use their model to simulate what growth would have looked like in the absence of cyclones, and (of course) do a climate projection - but also add a NPV calculation on top of it.

All in all, I think I'll use this paper as a great reference for how to implement different techniques for a while - and I look forward to reading the eventual published version. I'll let the authors describe their results themselves. Their abstract:

Does the environment have a causal effect on economic development? Using meteorological data, we reconstruct every country’s exposure to the universe of tropical cyclones during 1950-2008. We exploit random within-country year-to-year variation in cyclone strikes to identify the causal effect of environmental disasters on long-run growth. We compare each country’s growth rate to itself in the years immediately before and after exposure, accounting for the distribution of cyclones in preceding years. The data reject hypotheses that disasters stimulate growth or that short-run losses disappear following migrations or transfers of wealth. Instead, we find robust evidence that national incomes decline, relative to their pre-disaster trend, and do not recover within twenty years. Both rich and poor countries exhibit this response, with losses magnified in countries with less historical cyclone experience. Income losses arise from a small but persistent suppression of annual growth rates spread across the fifteen years following disaster, generating large and significant cumulative effects: a 90th percentile event reduces per capita incomes by 7.4% two decades later, effectively undoing 3.7 years of average development. The gradual nature of these losses render them inconspicuous to a casual observer, however simulations indicate that they have dramatic influence over the long-run development of countries that are endowed with regular or continuous exposure to disaster. Linking these results to projections of future cyclone activity, we estimate that under conservative discounting assumptions the present discounted cost of “business as usual” climate change is roughly $9.7 trillion larger than previously thought.

Edited to add: This turned out to be especially timely due to the record number of hurricanes in the Pacific at the moment. (Luckily, none of them are threatening landfall as of August 31st.)