Are the curious trends in despair and diets related?

There’s a new working paper out by Anne Case and Angus Deaton on one of the most curious (and saddest) trends in America – mortality rates for whites have been rising. There have been various stories about these trends, such as this one that first turned me onto it. It’s clear that the proximate causes are an increase in “deaths of despair,” namely suicides, drugs, and alcohol. It’s also clear that self-reported mental health has been declining in this group. But why?

Any explanation has to account for the fact that the same trends aren’t seen in other racial groups, even though many of them have lower incomes (see figure below from Case and Deaton):

It should also account for the fact that the same trend isn’t seen in other predominantly white countries:

One explanation that seems to have gained most traction is that whites “lost the narrative of their lives.” That is, maybe rising economic inequality and other economic trends have affected all groups, but whites expected more. To me this seems plausible but not really convincing.

Ok, so let me offer another theory. I’ll first say that I think it’s a bit off the wall, but I don’t think I’m going crazy (despite Marshall’s frequent hinting that I am). The idea basically stems from another body of literature that I’ve recently been exploring, mainly because I was interested in allergies. Yeah, allergies. Specifically, a bunch of people I know have sworn that their allergies were fixed by eliminating certain foods, and given that some people in my family have bad seasonal allergies, I decided to look into it.

It turns out that wheat is thought by many to trigger inflammation and allergies. But what’s relevant here is that it’s also thought to affect mental health. More than that, there are actually clinical studies like this one showing that depression increases with gluten intake. There are only 22 subjects in that study, which seems low to me but obviously I don’t do that sort of work. A good summary of scientific and plenty of non-scientific views on the topic can be found in this article. Incredibly, there was even a study in the 1960s showing how hospital admission rates for schizophrenia varied up and down with gluten grain rations during World War 2.

So what’s the connection to the trends in deaths of despair? Well the striking thing to me is that wheat effects are generally only seen in white non-hispanics. Celiac disease, for instance, is much lower in other racial groups. Second, it’s apparently known that celiac has been rising over time, which is thought to indicate increased exposure (of all people) to gluten early in life. And the trends are most apparent in whites, such as seen in the figure below from this paper.

Just to be clear, I realize this is mostly speculation. Not only is this not my area of expertise, but I don’t have any data on the regional trends in gluten or wheat intake in the U.S. to compare to the regional trends in death. I’m not even sure that such data exist. It seems that studies like this one looking at trends in gluten consumption just assume the gluten content of foods is fixed, but it also seems a lot of products now have gluten added to make them rise quicker and better. (Some blame the obsession with whole grain foods, which don't rise as quickly.) If anyone knows of good data on trends in consumption, let me know. It would also be interesting to know if they add less gluten in other countries, where mortality rates haven’t risen.

(As an aside: there’s also a recent study looking at wheat and obesity in cross-section. Apparently country obesity rates are related to wheat availability, but not much else.)

Also to be clear, I still like wheat. Maybe having spent most of my career studying wheat producing systems has made me sympathetic. Or maybe it’s the fact that it has sustained civilization since the dawn of agriculture. But I think it’s possible that we recently have gone overboard in how much is eaten or, more specifically, in how much gluten is added to processed food in this country. And even if there’s only a small chance it’s partly behind the trends of despair (which aren’t just causing mortality, but all sorts of other damage), it’s worth looking into.

Testable predictions

As our blog wakes up from an apparent winter hibernation, I’ve been thinking about some predictions for the spring that came out last fall. One on my mind, of course, is that the Warriors will dominate the NBA. That one seems a pretty safe bet at this point.

A more daring bet was one put out last November on the corn season in 2016 by Weather Trends 360. It’s a pretty entertaining video, which I link to below. I'm not sure it's convincing, but I have to give them credit for making a very testable series of predictions. Specifically:

1) An unusually wet March and April, leading to delayed planting

2) A widespread freeze in early May

3) Very high temperatures and low rainfall in June and July, leading to low yields

4) Corn prices of $7 per bushel by July 2016 (or roughly double what they are right now)

So let’s see what happens. These are very specific and, in some cases, pretty bold predictions. If they turn out to be right, I’ll definitely be taking notice next year. Meanwhile, back to enjoying the Warriors.

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.

Breaking down the pause

The only redeeming thing about long plane rides is the chance to catch up on reading and movies. Alas, my flight to Japan had no movies, and it was only eight hours, so I didn’t have time to finish reading Sol’s last G-FEED post.

But I was able to catch up on various papers I’ve been meaning to read, including a recent collection of short papers that Nature Climate Change put together related to the recent hiatus, or pause, in global warming since 1998. As readers of this blog will surely know, a lot of attention has been given to the lack of significant warming trend since 1998, aka “the pause”. (Not to be confused with an "awkward pause.”) I’ve normally viewed the pause as much ado about nothing, or at least very little, since you’d expect variation around the long-term trend and models have consistently shown a non-small probability for flat trends over a 10 or even 15 year period, even as longer-term trends are positive. So having not followed the conversation too closely, I was interested to catch up with these papers. Here are a couple of interesting lessons:

First, Gavin Schmidt and co-authors explain how a lot of the disparity between expected and observed trends is not necessarily due to natural variability, but instead to the fact that short-term forcings since 2000 are not what models had assumed. As they say “the influence of volcanic eruptions, aerosols in the atmosphere and solar activity all took unexpected turns over the 2000s. The climate model simulations, effectively, were run with the assumption that conditions were broadly going to continue along established trajectories.” But instead, all of these factors deviated in a way that caused climate to be cooler. In other words, the models weren’t the problem, but the assumptions used to force them were. They also emphasize that none of these factors should be expected to continue to cool climate much, and predict that “ENSO will eventually move back into a positive phase and the simultaneous coincidence of multiple cooling effects will cease. Further warming is very likely to be the result.“

Second, an interesting piece led by Sonia Seneviratne shows that trends in daytime extreme temperatures over land, arguably a more relevant measure in terms of climate impacts, haven’t slowed down at all (red line in figure below). Essentially, they object to the entire notion of a “pause.” 

All of these papers on the pause also reminded me of a great book I recently finished – “Thinking Fast and Slow” by Daniel Kahneman. He talks a lot about loss aversion - how humans tend to dislike losses about twice as much as they like gains -  and how even small losses can be very annoying. One consequence is that for an identical putt, professional golfers will tend to try harder and make it more often if it’s for par (to avoid loss) than if for birdie (to get a gain). I’m sure that explains why I always miss birdie putts! Another consequence is that people are usually risk averse when it comes to gains (i.e. prefer to take $100 than flip a coin for a 50/50 chance of winning $200), but risk seeking when it comes to losses (i.e. prefer to flip a coin for a 50/50 chance of losing $200 rather than give $100, because $100 hurts almost as much as $200). This is compounded by another tendency humans have - to way overweight events that have low probability in their decisions. So even if there’s a very small chance of a “no loss” outcome, people will tend to take a chance hoping that outcome will happen, because it will be so much more pleasant than even a small loss, and because they think it’s more likely than it actually is.

All this seems like a pretty good description of many people's reaction to climate change. It might be a small deal or it could be really bad. We could maybe guarantee a small loss if we paid for a lot of mitigation and adaptation. Or we can roll the dice and hope for the best. After reading his book, it hardly seems surprising that people would opt for the latter. They do in all walks of life, and are worse off because of it. It is also hardly surprising that people will latch onto anything that seemingly justifies overweighting the probability of no loss, such as the pause. I'm not saying that either are the appropriate response to the problem, especially by institutions that should be less prone to these behavioral quirks, but it may help to partly explain people's fascination with the pause.

The pause has also highlighted for me how wide the full distribution of potential trends over a 10 year period is, and that includes the potential for very rapid warming. What will happen if warming rates in the next 10 years are at the other end of the distribution – other than, of course, people asking climate scientists why their models are too conservative? It’s a question I’ve been looking at with Claudia Tebaldi in terms of crop implications, which hopefully will be a paper in the not-so-distant future to blog about.

Commodity Speculation or Market Power

After seeing how much Goldman profited from selling MBS that they knew were junk, it's hard to feel sorry for Goldman receiving so much grief for its commodity storage and trading activities.  The worry seems to be that because Goldman has become increasingly involved in commodities markets that they must be manipulating prices for profit, and in the process pushing prices away from their fundamental values---ie., supply and demand.

Do we actually know whether there is a problem here? It's possible that Wall Street is trying to manipulate the market.  But this is a hard thing to do, even for a really big company, especially one that doesn't produce the stuff it's trying to monopolize.  Also bear in mind that anyone can buy and store commodities, so it's not like there are huge barriers to entry.  Those who have tried to corner commodity markets in the past haven't fared well.

My sense is that cornering a commodity market via hoarding is basically impossible once the market realizes what the major player(s) is doing.  And if they're having senate hearings about Goldman's storage and trading activities, I think it's fair to say the cat's out of the bag.

So, what is Goldman doing? If it's not a market power story I'd guess they're trying to buy low and sell high, just like everybody else. They probably believe they have a better handle on market fundamentals than other commodity speculators.  Perhaps they do.  But if this is all they are doing, then they are effectively reducing price volatility and helping to make the market work more efficiently.

On public radio this morning a reporter (sorry, I forget who), asked Omarova whether Goldman's profits just meant that consumers were paying higher prices.  Omarova said "that's absolutely right." But it's absolutely wrong if Goldman's just speculating.  Goldman's profits are coming out of the pockets of speculators who bet prices would fall when they rose, and vice versa.  In fact, that's probably the case if it's a market power issue too.

Anyway, if this is about Goldman trying to corner the storage market, that's a problem and Goldman deserves the grief they're receiving. But that strikes me as unlikely as it would be foolhardy.  My guess is that this is just speculation, which means Goldman's profits translate directly to better allocation of commodities over time, less commodity price volatility, and basically zero influence on average prices.