Food Shortages, Climate Change, and GMOs

I filmed a spot on Fox Business Network this morning in response to this story about possible food shortages due to climate change.

I was glad one of the hosts asked me about GMOs - it wasn't a topic I had anticipated coming up.

By the way, if you want to see some good recent work on the effects of climate change on agriculture within the U.S., see this comment and reply in the American Economic Review by Fisher, Hanemann, and Roberts and by Deschênes and Greenstone.  Both sets of authors wind up at the conclusion that climate change is likely to have a negative effect on agricultural profits in the U.S., but the two sets of authors differ in their subjective views about whether the effects are "large".  Neither study considers the mitigating effects of trade on consumers (i.e., we could import food from other countries who benefit from warmer weather), neither considers the mitigating effects of technological development and adaptation (they assume we wake up tomorrow with 2100 temperatures and must live with today's technology), and neither considers the great deal of uncertainty in the predictions arising from climate change modeling.  These are good studies, but I'm saying there's still a lot we don't know, and probably a lot we can't know until it happens.  

Unintended Effects of the Precautionary Principle

This post by David Zaruk at the Risk-Monger blog gives a number of examples illustrating that precaution isn't always the best strategy.  Sometimes the precautionary principle is invoked as a reason to avoid taking an action.  In other instances it is invoked as a reason to take an action.  "We've got to do something" in the face of some problem, and "trying something" in the face of uncertainty is the taken as more cautious than doing nothing.   David writes:

“During the Great Plague of London (1665-66), the authorities were convinced that the outbreak of bubonic plague was being spread by cats. As cats had then been looked upon by religious leaders as symbols of evil and witchcraft, the crisis created the perfect opportunity for zealots to purge London of this feline scourge. The local authorities had no evidence that the cats were spreading the plague, but via the virtue of precaution, they could be seen to be acting in a time of panic. Resisting public pressure from vocal zealots was not politically expedient, and in any case, who would really care if a few thousand cats were tossed in the Thames. Well the rats thought this was just fine, and as the rat population exploded, so too, obviously, did the plague (spread via rat fleas).”

He goes on to point out the unintended effects of modern day precautionary actions.  These typically come about because of a failure to think on the margin and to consider behavioral response.  If pesticide X is banned, that doesn't mean farmers use no pesticide.  They switch to pesticide Y.  Thus, the better question is how X compares to Y.  Another questions rarely asked: if X is banned, then what new mix crops will farmers grow instead, and what environmental, health, or fiscal effects will that have?  

David concludes:

“Precaution is the perfect tool – it worked for the zealots in medieval times and it still works today.

Note that I am not equating the use of the precautionary principle with medieval mind-sets. Precaution is a basic human reaction (no one willingly wants to hurt themselves). But the use of precaution as a regulatory tool by eco-religious zealots to spread fear in order to promote some medieval-inspired conception of agriculture and a communal-based economy regardless of evidence or the negative consequences is not only irrational, but also morally indefensible.”

(Note: I don't necessarily agree with all the hyperbole in the piece, particularly the last sentence) 

AAEA Fellow Remarks

Its been a great week in San Francisco at the annual meetings of the Agricultural and Applied Economics Association (AAEA).  In addition to serving as president elect of the association, it was a great honor to be named fellow last night at the award's ceremony.  The fellow recipients didn't get a chance to share any remarks last night and I had a number of people ask me what I would have said.  So, I thought I'd share what I had written on the note card in my pocket.   

“I’m honored to receive this award.

Last year during his fellow’s acceptance speech, my colleague Wade Brorsen, gave great advice: be a chronic finisher. To that I’ll add the following: it’s a lot easier to finish when you work with people you enjoy and on topics you love.

I’m blessed to be a part of this profession. I’m truly proud to be an agricultural economist.

It would be impossible to adequately thank my many mentors, co-authors, and friends who helped make this happen. I’ve had the pleasure of being surrounded by ambitious, problem solving, go-getters. Over late night bar napkins, early morning coffees, and engaging lunches at Little Dooey’s, Harry’s, and Eskimo Joe’s, they taught the value of hard work and instilled a love for learning.

And, as you can see, I have a lovely family. I am eternally grateful for their love and support.

I am humbled to be a relatively young recipient of this award. But, I don’t want anyone to get the idea that this means I’m hanging up my hat. Rather in 10 or 20 years’ time, I hope you’ll look back on my career and say it was like a fine wine – one that continued to get better with age.

Thank you.
”

Big Fat Surprise

I just finished reading Nina Teicholz’s best selling book The Big Fat Surprise, which takes issue with our long-held belief that low-fat diets in general, and diets free of animal fat in particular, best promote good health.  

It’s been an enjoyable read, and the history of the development of our dietary beliefs and guidelines is both fascinating and eye opening.  There is a bit of a tendency in the book for the author to nit pick any study which doesn’t support her hypothesis without applying the same skepticism of those studies which do support it, but overall, she makes a compelling case.  I probably found chapter 10 on "Why Saturated Fat is Good For You" most interesting in that regard.  Teicholz lays bare the sad state of affairs associated with the science behind much of the nutritional advice we’re given.  One takeaway is that we really don’t know as much as is often presumed about what sorts of diets increase/decrease chances or heart attack or cancer.  

There is one nit I want to pick with a phrase in Teicholz’s book.  It is a technical one, but because it is the sort of thing I expect my students to fully understand, I'll delve into it.  On page 167 of the paperback version she writes (about an epidemiological study finding no relationship between breast cancer and consumption of dietary fat), “These conclusions were all associations.  But although epidemiology cannot demonstrate causation, it can be used to reliably show the absence of a connection.” (the emphasis is hers)

That claim is patently false (I'm presuming by "connection" she means "causation").  The trouble with the sort of correlation analysis used in many epidemiology studies is that of missing variables.  We can't observe everything about people's behaviors or about the effects of dietary changes, and that results in "omitted variable bias."  That bias can inflate or reduce the size of a measured effect.   In fact, contrary to Teicholz's claims, omitted variable bias can make a "real" effect look like nothing.

Wikipedia describes the problem, but similar treatments can be found in almost any introductory econometrics textbook.     

Suppose we have the following true relationship:

y=b0 + b1*x + b2*z + e

where y is the chance of breast cancer among women, x is amount of fat consumed, and z is a personality trait reflecting the person's overall health conscientiousness.  The "true" relationship we want to know is given by b1.  

But, suppose we only observe y and x and we don't observe z.  Also suppose that z is related to x in the following way: z = a0 + a1*x + u.  Substituting this equation into the first means that when the epidemiologist runs their analysis they calculate:

y = b0 + b1*x +b2*(a0 + a1*x + u)+ e

or, re-writing:

y = b0 + b2*a0+ (b1+b2*a1)*x + b2*u+e.

So, the researcher looks at the relationship between x and y, and thinks they're estimating the "true" effect b1, but in reality, they're estimating the effect (b1+b2*a1), which could be larger or smaller than b1.  

Suppose b2 takes the positive value of +1.5 (more conscientious women are less likely to develop breast cancer) and a1 is also positive and takes the value of +2 (more conscientious women pay more attention to all that health advice and eat less fat).  This means the effect b2*a1 is positive at the value of +3.  But b1 could be negative (more fat = more breast cancer).  Say, b1=-3.  If the positive effect of b2*a1=+3 outweights the negative effect of b1=-3, so the estimated effect is 3-3=0.  It will look like there is no effect even though there really is one.  Even if the effects don't precisely outweigh each other, the estimated effect could be small enough that it the research concludes it isn't statistically different from zero.

Now, I'm not saying that there is a relationship between fat consumption and breast cancer - rather, I'm just making a conceptual point that omitted variables can result in upward or downward bias.  What I can more confidently say is that only the last part of Teicholz's claim is right:  "epidemiology cannot demonstrate causation."  

Now, there are regression methods that can get us much closer to the truth, but I don't often see these used in epidemiology studies.  In economics, the so-called "credibility revolution" has led to more specification testing and attention to causal-identification using instrumental variables, discontinuity designs, differences-in-differences, and others.  A good introduction to the topics and methods is given in Mostly Harmless Econometrics.

Food Demand Survey (FooDS) - July 2015

The July 2015 edition of the Food Demand Survey (FooDS) is now out.  

Overall, there seemed to be a slight reduction in demand for most meat products this month compared to June as indicated by a reduction in WTP, a reduction in expectation of price increases, and a reduction in planned purchases.  Some of this might have to do with the fact that there was an uptick in planned expenditures away from home, perhaps due to vacations.

Awareness of and concern for bird flu fell this month compared to last.  In July, there was an increase in awareness and concern among those issues that tend to fall at the bottom of the scale of concern.

Several ad-hoc questions were added this month.  

Overall, respondents were generally satisfied with their lives.  They were asked:  “All things considered, how satisfied are you with your life as a whole these days? Using the scale below, where 1 means you are “completely dissatisfied’ and 10 means you are “completely satisfied”, where would you put your satisfaction with your life as a whole?”

Similar to last month, the most popular response was an 8. 

Despite that, there seemed to be some pessimism with regard to the future in general and food and agriculture in specific.  

We asked,  “If you could be born at any time when would it be?” Participants stating they would choose to be born “in the past, 50 years ago” ranked the highest of the groups
at 31.21%. This may correspond to the category which most closely matched the lag in time from which participants were actually born (i.e., they preferred to be born when they were actually born).  Only 18.1% of participants stated they would choose to be born now.  Less than 20% said they would want to be born in the future.

Participants were also asked: “Overall, when you think about the state of food and agriculture in this country, do you think . . .” About 32% of respondents stated that “things are getting a little worse” for food and agriculture in this country, while only 19% of respondents agreed that “things are getting a little better”. About 27% of respondents stated that “things are about the same as they have been”.

Finally, Brandon McFadden from the University of Florida suggested a question that is a riff off a popular internet infographic showing the number of genes affected by different plant breeding techniques.  

Participants were asked: “For each of the following plant breeding techniques, how many genes are typically altered in the process?” Consistent with the comments in my recent Washington Post interview, the vast majority most consumers do not know how many genes are affected by any plant breeding techniques. Among those who stated an opinion,” selection” ranked the highest, at 7.65%, for not having any genes altered. For selection, having 1 to 4 genes altered ranked highest amongst participants at 11.8%. Hybridization was ranked highest by 11.8% of participants for having 5 to 9 genes altered. About 9% of participants stated that 10 to 19 genes were altered using genetic modification. Genetic modification was the highest of the group of 20 more genes affected at 7.14%.