Trump's climate gift to Russia

Trump's recent announcement that the US would withdraw from the Paris Accords was hailed as a monumental political, environmental, and economic mistake.  But given all the theater surrounding the announcement, others also saw it as an effort to distract the public from the ongoing investigation of the Administration's ties to Russia.

It's hard to see how this latter claim could actually be evaluated.  But it got me thinking:  what are the benefits to Russia of the US withdrawing from the Paris accords?  Was the US withdrawal a climate gift to Russia?

Now, I'm guessing Trump has not read our paper showing that warming temperatures will have unequal economic effects around the world (unlike Obama, to repeat my shameless self promotion from last week).  In that paper, and consistent with a huge microeconomic literature, we see clear evidence in the historical data that cold high-latitude countries tend to experience higher GDP growth when temperatures warm, with the reverse being true in most of the rest of the world where average temperatures are already warmer (the US included).  Basically, if you're currently cold, you do better when it warms up;  if you're already warm, additional warming hurts you.  Pretty intuitive, and also shows up very clearly in the half century of data we have on economic growth from around the world.

Here's the original plot from our paper, with the figure on the left showing the historical relationship between temperature and GDP growth for all countries in the world.  If you're average temperature is below about 13C, historically your economy grows faster when annual temperatures warm.  If your at or above 13C, growth slows as temperatures warm.  The US has a population-weighted annual average temperature of just over 13C.  Russia has a population-weighted average temperature of just under 5C.  Russia is cold!

Figure 2 from Burke, Hsiang, Miguel 2015 Nature. Effect of annual average temperature on economic production. a, Global non-linear relationship between annual average temperature and change in log gross domestic product (GDP) per capita (thick black line, relative to optimum) during 1960–2010 with 90% confidence interval (blue, clustered by country, N= 6,584). Model includes country fixed effects, flexible trends, and precipitation controls. Vertical lines indicate average temperature for selected countries. Histograms show global distribution of temperature exposure (red), population (grey), and income (black). b, Comparing rich (above median, red) and poor (below median, blue) countries. Blue shaded region is 90% confidence interval for poor countries. Histograms show distribution of country–year observations. c, Same as b but for early (1960– 1989) and late (1990–2010) subsamples (all countries). d, Same as b but for agricultural income. e, Same as b but for non-agricultural income.

Last week we calculated the potential harm done to the economy of withdrawing from Paris.  The idea was this:  withdrawing from the Paris accords would make global temperatures rise relative to what they would have been if the US had met its Paris obligations (an additional +0.3C by 2100, according to these guys).  For reasons already stated, warming temperatures are bad for overall economic output in the US. So we can then calculate, what's the difference in output between now and 2100 that would occur in a withdrawal versus a non-withdrawal world?  For the US, the effects were pretty big:  I calculated that, in present value (i.e. discounting future losses at 3%), the US economy would lose about $8 trillion between now and 2100 due to the extra temperature increase induced by withdrawing from Paris.

What about Russia?  Again, Russia is cold, so extra warming is likely to help the Russian economy, all else equal.  You can actually see this really clearly in the Russian data.  Below is the plot of Russian GDP growth rates versus Russian temperatures, using data 1990-2010 (1990 being the first post-Soviet-collapse year that "Russia" shows up in the national accounts data).  Specifically, these are growth deviations from trend versus temperature deviations from trend;  we are detrending the data since you don't want to conflate trends in temperature that could be correlated with other trending factors that also affect growth.

This is just 20 data points, but the estimated effects are HUGE.  Basically, Russian GDP growth is multiple percentage points higher when temperatures warm by a degree C.  And the Russia-specific estimate is even higher than what we would predict the effect would be for Russia using the global response function pictured in blue above.

Anyway...  Basically what I did is to re-do the same calculation we did last week for the US, but now focusing on effects on the Russian economy and calculating what happens to Russian GDP in the scenario where the US withdraws from Paris versus the scenario where the US stays in.  To be conservative, I use estimates from the global response function, not the Russia-specific mega-response just noted.

Here's the main finding:  Trump's decision to withdraw the US from Paris is a $2.2 trillion dollar gift to Russia (paid out over the next 85 years).  Below is the figure showing what happens to Russian GDP under a withdrawal versus a no-withdrawal scenario (left), and the annual gains in GDP in each year (to 2100).  By 2100, Russia is ~10% richer than it would have been otherwise, and the (discounted) sum of these GDP gains is about $2.2 trillion dollars.

Given that there's no evidence that Trump has read our paper, I don't think we can claim that this climatic gift to Russia was purposeful.  But it's sadly ironic that an announcement that might have been meant to distract us from Russian meddling was simultaneously a monumental economic gift to that country.

Warming makes people unhappy: evidence from a billion tweets (guest post by Patrick Baylis)

Everyone likes fresh air, sunshine, and pleasant temperatures. But how much do we like these things? And how much would we be willing to pay to gain more of them, or to prevent a decrease in the current amount that we get?

Clean air, sunny days, and moderate temperatures can all be thought of as environmental goods. If you're not an environmental economist, it may seem strange to think about different environmental conditions as "goods". But, if you believe that someone prefers more sunshine to less and would be willing to pay some cost for it, then a unit of sunshine really isn't conceptually much different from, say, a loaf of bread or a Playstation 4.

The tricky thing about environmental goods is that they're usually difficult to value. Most of them are what economists call nonmarket goods, meaning that we don't have an explicit market for them. So unlike a Playstation 4, I can't just go to the store and buy more sunshine or a nicer outdoor temperature (or maybe I can, but it's very, very expensive). This also makes it more challenging to study how much people value these goods. Still, there is a long tradition in economics of using various nonmarket valuation methods to study this kind of problem.

New data set: a billion tweets

Daily or monthly weather data?

We’ve had a few really hot days here in California. It won’t surprise readers of this blog to know the heat has made Marshall unusually violent and Sol unusually unproductive. They practice what they preach. Apart from that, it’s gotten me thinking back to a common issue in our line of work - getting “good” measures of heat exposure. It’s become quite popular to be as precise as possible in doing this – using daily or even hourly measures of temperature to construct things like ‘extreme degree days’ or ‘killing degree days’ (I don’t really like the latter term, but that’s beside the point for now).

I’m all for precision when it is possible, but the reality is that in many parts of the world we still don’t have good daily measures of temperature, at least not for many locations. But in many cases there are more reliable measures of monthly than daily temperatures. For example, the CRU has gridded time series of monthly average max and min temperature at 0.5 degree resolution.

It seems a common view is that you can’t expect to do too well with these “coarse” temporal aggregates. But I’m going to go out on a limb and say that sometimes you can. Or at least I think the difference has been overblown, probably because many of the comparisons between monthly and daily weather show the latter working much better. But I think it’s overlooked that most comparisons of regressions using monthly and daily measures of heat have not been a fair fight.

What do I mean? On the one hand, you typically have the daily or hourly measures of heat, such as extreme degree days (EDD) or temperature exposure in individual bins of temperature. Then they enter into some fancy pants model that fits a spline or some other flexible function that capture all sorts of nonlinearities and asymmetries. Then on the other hand, for comparison you have a model with a quadratic response to growing season average temperature. I’m not trying to belittle the fancy approaches (I bin just as much as the next guy), but we should at least give the monthly data a fighting chance. We often restrict it to growing season rather than monthly averages, often using average daily temperatures rather than average maximums and minimums, and, most importantly, we often impose symmetry by using a quadratic. Maybe this is just out of habit, or maybe it’s the soft bigotry of low expectations for those poor monthly data.

As an example, suppose, as we’ve discussed in various other posts, that the best predictor of corn yields in the U.S. is exposure to very high temperatures during July. In particular, suppose that degree days above 30°C (EDD) is the best. Below I show the correlation of this daily measure for a site in Iowa with various growing season and monthly averages. You can see that average season temperature isn’t so good, but July average is a bit better, and July average daily maximum even better. In other words, if a month has a lot of really hot days, then that month's average daily maximum is likely to be pretty high.

You can also see that the relationship isn’t exactly linear. So a model with yields vs. any of these monthly or growing season averages likely wouldn’t do as well as EDD if the monthly data entered in as a linear or quadratic response. But as I described in an old post that I’m pretty sure no one has ever read, one can instead define simple assymetric hinge functions based on monthly temperature and rainfall. In the case of U.S. corn, I suggested these three based on a model fit to simulated data:

This is now what I’d consider more of a fair fight between daily and monthly data. The table below is from what I posted before. It compares the out-of-sample skill of a model using two daily-based measures (GDD and EDD), to a model using the three monthly-based hinge functions above. Both models include county fixed effects and quadratic time trends. In this particular case, the monthly model (3) even works slightly better than the daily model (2). I suspect the fact it’s even better relates less to temperature terms than to the fact that model (2) uses a quadratic in growing season rainfall, which is probably less appropriate than the more assymetric hinge function – which says yields respond up to 450mm of rain and are flat afterwards.

Model	Calibration R2	Average root mean square error for calibration	Average root mean square error for out-of-sample data  (for 500 runs)	% reduction in out-of-sample error
1	0.59	0.270	.285	--
2	0.66	0.241	.259	8.9
3*	0.68	0.235	.254	10.7

Overall, the point is that monthly data may not be so much worse than daily for many applications. I’m sure we can find some examples where it is, but in many important examples it won’t be. I think this is good news given how often we can’t get good daily data. Of course, there’s a chance the heat is making me crazy and I’m wrong about all this. Hopefully at least I've provoked the others to post some counter-examples. There's nothing like a good old fashioned conflict on a hot day.

Effect of warming temperatures on US wheat yields (Guest post by Jesse Tack)

This post discusses research from a paper coauthored with Andrew Barkley and Lanier Nalley in the Proceedings of the National Academy of Sciences. The paper can be found here. We utilize Kansas field-trial data for dryland winter wheat yields. A major strength of this data is that we were able to match yield data with daily temperature observations across eleven locations for the years 1985-2013.

So, there is a lot of variation in the data, and we can accurately measure local temperature exposure. Max, Sol, Wolfram, and Adam Sobel have a nice paper on the importance of such accuracy here, and Wolfram has blogged on the importance of daily versus more aggregate (e.g. monthly) measures here.

Although not the main focus of our paper, we find that the frequency at which temperature exposures are measured has a large impact on simulated warming impacts (see the supplementary information here). Any stats geek – myself included – will tell you that accurate identification requires sufficient variation, and the more variation the better! Mike and Wolfram have some great posts on constructing temperature measures here and here.

We follow their prescribed method for interpolating temperature exposures and constructing degree days. However, it is still common in many empirical analyses to use minimum and maximum temperatures to construct a measure of average temperature and call it a day. Don’t do this! You are missing so much important variation in temperature exposure that can be measured using the interpolation approach outlined by Mike and Wolfram.

Another consideration not often taken into account in climate change impact studies is that warming temperatures can have both positive and negative yield impacts. Extreme temperatures on both the low (cold) and high (heat) end of the temperature distribution are typically bad for crops. So if we think of warming as a shifting of the distribution to the right, the result is fewer of the former (positive effect) and more of the latter (negative effect).

So what? Well, we find that the net warming impact is negative for winter wheat in Kansas (more heat trumps less freeze), but omitting the beneficial effects of freeze reduction leads to vastly overestimated impacts (Figure 1).

Figure 1. Predicted warming impacts under alternative uniform temperature changes across the entire Fall-Winter-Spring growing season. Impacts are reported as the percentage change in yield relative to historical climate. The preferred model includes the effects from a reduction in freezing temperatures, while the alternative holds freeze effects at zero. Bars show 95% confidence intervals using standard errors clustered by year and variety.

The upshot here is that an accurate identification of warming impacts for winter wheat requires accounting for both ends of the temperature distribution. It would be interesting to know if this finding applies to other crops as well.

An additional strength of our data is that we observe 268 wheat varieties in-sample, which allows us to estimate heterogeneous heat resistance. As with other crops, winter wheat has experienced a steady increase in yields over time due to successful breeding efforts. Much of this increase is driven by a lengthened grain-filling stage, which increases yield potential under ideal weather conditions but introduces additional susceptibility to high temperature exposure during this critical period. David has some great posts on evolving weather sensitivities here, here, and here.

Essentially, if this line of reasoning holds we should expect to see a tradeoff between average yields and heat resistance across varieties. We group varieties by the year in which they were released to the public and allow the effect of extreme heat to vary across this grouping. [Aside: there are practical reasons why we group by release year that are discussed in the paper, we are experimenting with other grouping schemes in on-going projects].

We find that there does indeed exist a tradeoff between heat resistance and average yield, with higher yielding varieties less able to resist temperatures above 34°C (Figure 2). If the least resistant variety is switched to the most resistant variety, average yield is reduced by 6.6% and heat resistance is increased by 17.1%. We also find that newer varieties are less heat resistant than older varieties. Linear regressions using estimates for the 268 varieties indicate that these relationships are statistically significant (P-values < 0.05).

Figure 2. Mean (average) yields and heat resistance are summarized by release year. Heat resistance is measured as the percentage impact on mean yield from an additional degree day above 34°C. The smaller the number in absolute value the more heat resistant the variety is.

These findings point to a need for future breeding efforts to focus on heat resistance, and there is currently much work being done in this area. Check out the Kansas State University Wheat Genetics Resource Center (WGRC) and the International Maize and Wheat Improvement Center (CIMMYT) here and here.

From a historical perspective, our results indicate that such advancements will likely come at the expense of higher average yields. However, there is potentially a huge upside to developing a new variety that combines high yields with improved heat resistance. Under such a scenario, reduced freeze exposure could outweigh increased heat, leading to a net positive warming effect.

In the absence of such a silver bullet variety, the average-yield/heat-resistance tradeoff presents an interesting challenge for producer adaptation, which will ultimately be driven by some economic decision-making process. Producers are individuals, or families, and as such they have a certain tolerance for exposing themselves to risk. Much work has been done showing that farmers enjoy smoothing their consumption over time, which is akin to reducing profit variation. Farrell Jensen and Rulon Pope have a nice paper on this here.

So from a climate change adaptation perspective, it is important ask whether producers prefer a variety that offers high average yield but low heat resistance, or a variety with lower average yields coupled with high resistance? Are there important risk preference differences across producers, or are they a fairly homogeneous group? Currently, we don’t have a firm answer for these pertinent questions.

There has been much work in the agricultural economics literature on risk preference heterogeneity and the extent to which producers will trade off average yield for a reduction in yield variance. However, yield variance captures deviations both above and below the average, which might not be the relevant measure of risk under a warming climate since we are largely concerned with negative (i.e. downside) yield effects.

Martin Weitzman refers to this as fat-tailed uncertainty, and has done some really interesting work in this area (e.g. here). Jean Paul Chavas and John Antle are agricultural economists that seem to be working in this direction using the partial moments framework that John developed, see here, here, and here.

Knowledge about the willingness of producers to trade off yield for risk reduction should clearly be an important focus of future breeding efforts. Historically, plant physiologists and geneticists have worked independent of agricultural economists, but this should change as climate change presents a clear need for well-conceived interdisciplinary research.

In closing, it is worth pointing out that public policy will also likely have a strong effect on the welfare implications for producers under warming. Direct funding support for research provides one linkage, but another often overlooked linkage arrives in the form of subsidized agricultural production. For example, do policies that protect producers against large-scale crop losses provide a disincentive to adopt heat resistant varieties? Wolfram and Francis Annan have looked at this issue here and find that U.S. corn and soybean producers’ adaptation potential is skewed by government programs, in turn implying that producers will choose subsidized yield guarantees over costly adaptation measures.

Thus, even if we come to know what the optimal adaptation path is, it is not clear how we will get there. Economists love to talk of the unintended consequences of public policy. Sometimes it seems that every good policy has a dark side. It’s called the dismal science for a reason ;-)   

People are not so different from crops, turns out

A decade of strong work by my senior colleagues here at G-FEED have taught us that crops don’t like it hot: 

• Wolfram and Mike have the original go-to paper on US ag [ungated copy here], showing that yields for the main US field crops respond very negatively to extreme heat exposure
• David, Wolfram, Mike + coauthors have a nice update in Science using even fancier data for the US, showing that while average corn yields have continued to increase in the US, the sensitivity of corn to high temperatures and moisture deficits has not diminished. 
• And Max et al have a series of nice papers looking at rice in Asia, showing that hot nighttime temperatures are particularly bad for yields.

The results matter a lot for our understanding of the potential impacts of climate change, suggesting that in the absence of substantial adaptation we should expect climate change to exert significant downward pressure on future growth in agricultural productivity.

But we also know that for many countries of the world, agriculture makes up a small share of the economy.  So if we want to say something meaningful about overall effects of climate change on the economies of these countries (and of the world as a whole), we're also going to need to know something about how non-agricultural sectors of the economy might respond to a warmer climate. 

Thankfully there is a growing body of research on non-agricultural effects of climate -- and there is a very nice summary of some of this research (as well as the ag research) just out in this month's Journal of Economic Literature, by heavyweights Dell, Jones, and Olken. [earlier ungated version here].

I thought it would be useful to highlight some of this research here -- some of it already published (and mentioned elsewhere on this blog), but some of it quite new.  The overall take-home from these papers is that non-agricultural sectors are often also surprisingly sensitive to hot temperatures

First here are three papers that are already published:

1. Sol's 2010 PNAS paper was one of the first to look carefully at an array of non-agricultural outcomes (always ahead of the game, Sol...), using a panel of Caribbean countries from 1970-2006. Below is the money plot, showing strong negative responses of a range of non-ag sectors to temperature.  Point estimate for non-ag sectors as a whole was -2.4% per +1C, which was higher than the comparable estimate for the ag sector (-0.1% per 1C).

From Hsiang (2010)

2. Using a country-level panel, Dell Jones and Olken's instaclassic 2012 paper [ungated here] shows that both ag and non-ag output responds negatively to warmer average temperatures -- but only in poor countries. They find, for instance, that growth in industrial output in poor countries falls 2% for every 1C increase in temperature, which is only slightly lower than the -2.7% per 1C decline they find for ag. They find no effects in rich countries. 

3. Graff Zivin and Neidell (2014) use national time use surveys in the US to show that people work a lot less on hot days.  Below is their money fig:  on really hot days (>90F), people in "exposed" industries (which as they define it includes everything from ag to construction to manufacturing) work almost an hour less (left panel).  The right panels show leisure time.  So instead of working, people sit in their air conditioning and watch TV. 

from Graff Zivin and Neidell 2014.  Left panel is labor supply, right two panels are outdoor and indoor leisure time. 

And here are three papers on the topic you might not have seen, all of which are current working papers:

4.  Cachon, Gallino, and Olivares (2012 working paper) show, somewhat surprisingly, that US car manufacturing is substantially affected by the weather.  Using plant-level data from 64 plants, They show that days above 90F reduce output on that day by about 1%, and that production does not catch up in the week following a hot spell (i.e. hot days did not simply displace production).

5. Adhvaryu, Kala, and Nyshadham (2014 working paper)  use very detailed production data from garment manufacturing plants to show that hotter temperatures reduce production efficiency (defined as how much a particular production line produces on a given day, relative to how much engineering estimates say they should have produced given the complexity of the garment they were producing that day).  Not sure if I have the units right, but I think they find about a 0.5% decrease in production efficiency on a day that's +1C hotter.

6. Finally, in a related study, Somanathan et al (2014 working paper) use a nation-wide panel of Indian manufacturing firms and show that output decreases by 2.8% per +1C increase in annual average temperature.  They show that this is almost all coming from increased exposure above 25C, again pointing to a non-linear response of output to temperature.  For a subset of firms, they also collect detailed worker-level daily output data, and show that individual-level productivity suffers when temperatures are high -- but that this link is broken when plants are air conditioned.

So apparently it's not just crops that do badly when it's hot.  Most of the studies just mentioned cite the human physiological effects of heat stress as the likely explanation for why non-agricultural output also falls with increased heat exposure, and this seems both intuitive and plausible -- particularly given how similar the effect sizes are across these different settings.  But what we don't yet know is how these mostly micro-level results might aggregate up to the macro level. Do they matter for the projected overall effect of climate change on economies?  This is something Sol and I have been working on and hope to be able to share results on soon.  In the meantime, I will be setting my thermostat to 68F. 

Daily weather data: original vs knock-off

Any study that focuses on nonlinear temperature effects requires precise estimates of the exact temperature distribution.  Unfortunately,  most gridded weather data sets only give monthly estimates (e.g., CRU, University of Delaware, and up until recently PRISM).  Monthly averages can hide extremes - both hot and cold. Monthly means don't capture how often and by how much temperatures pass a certain threshold.

At the time Michael Roberts and I wrote our article on nonlinear temperature effects in agriculture, the PRISM climate group only made its monthly aggregates publicly available for download, but not the underlying daily data.  In the end we hence reverse-engineered the PRISM interpolation algorithm, i.e., we regressed monthly averages at each PRISM grid on monthly averages at the (7 or 10, depends on the version) closest weather stations that are publicly available.  Once we had the regression estimates linking monthly PRISM averages to weather stations, we bravely applied them to the daily weather data at the stations to get daily data at the PRISM cells (for more detail, see the paper).  Cross-validation suggested we weren't that far off, but then again, we only could do cross-validation tests in areas that have weather stations.

Recently, the PRISM climate group made their daily data available from the 1980s onwards.  I finally got a chance to download them and compare them to the daily data we previously had constructed from monthly averages.  This was quiet a nerve-wrecking exercise: how far were we off and does it change the results - or in the worst case, did I screw up the code and got garbage for our previous paper?

Below is a table that summarizes PRISM's daily data for the growing season (April-September) in all counties east of the 100 degree meridian except Florida that either grow corn or soybeans, basically the set of counties we had used in our study (small change: our study used 1980-2005, but since PRISM's daily data is only available from 1981 onwards, the tables below use 1981-2012). The summary statistics are:

First sigh of relieve! It looks like the numbers are rather close (strangely enough, the biggest deviations seems to be for precipitation, yet we used PRISM's monthly aggregates to derive season-totals and did not rely on any interpolation, so the new daily PRISM data is a bit different from the old PRISM data). Also, recall from a recent post that looked at the NARR data that degrees above 29C can differ a lot between data sets, as small differences in the daily maximum temperature will give vastly different results.

Next, I plugged both data sets into a panel of corn and soybean yields to see which one explains those yields better (i) in sample; and (ii) out of sample.  I used models using only temperature variables (columns a and b) as well as models using the same four weather variables we used before (columns c and d). PRISM's daily data is used in columns a and c, our re-engineered data are in columns b and d:

Second sigh of relief: It seems to be rather close again. In all four comparisons (1b) to (1a), (1d) to (1c), (2b) to (2a), and (2d) to (2c), our reconstruction for some strange reason has a larger in-sample R-square.  The reduction in RMSE is given in the second row of the footer: it is the reduction in out-of sample prediction error compared to a model with no weather variables. I take 1000 times 80% of the data as estimation sample and derive the prediction error for the remaining 20%. The given number is the average of the 1000 draws. For RMSE reductions, the picture is mixed: for the corn models that only include the two degree days variables, the PRISM daily data does slightly better, while the reverse is true for soybeans.  In models that also include precipitation, the construction of season-total precipitation seems to do better when I added the monthly PRISM totals (columns d) rather than adding the new daily PRISM precipitation totals (columns c).

Finally, since the data we constructed is a knock-off, how can it do better than the original in some cases?  My wild guess (and this is really only speculation) is that we took great care in filling in missing data for weather stations to get a balanced panel.  That way we insured that year-to-year fluctuations are not due to fact that one averages over a different set of stations.  I am not aware how exactly PRISM deals with missing weather station data.