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Impacts of Agricultural Research and Extension

About a month ago, I posted on some new research suggesting decline rates of productivity growth in agriculture.  Last week at a conference in Amsterdam, I ran into Wally Huffman from Iowa State University, and knowing he's done work in this area, I asked him if he had any thoughts on the issue.  As it turns out, along with Yu Jin he has a new paper forthcoming in the journal Agricultural Economics on agricultural productivity and the impacts of state and federal spending on agricultural research and extension.  

Jin and Huffman also find evidence of a slowdown in productivity growth, writing: 

“We find a strong impact of trended factors on state agricultural productivity of 1.1 percent per year. The most likely reason is continued strong growth in private agricultural R&D investments. The size and strength of this trend makes it unlikely for average annual TFP growth for the U.S. as a whole to become negative in the near future. However, for two-thirds of the states, the forecast of the mean ln(TFP) over 2004-2010 is less than trend. The primary reason is under-investment in public agricultural research and extension in the past. For public agricultural research where the lags are long, it will be impossible for these states to exceed the trend rate of growth for TFP in the near future.”

They also find large returns to spending on agricultural research, and even larger returns to spending on extension.  They find the following:

“For public agricultural research with a productivity focus the estimated real [internal rate of return] is 67%, and for narrowly defined agricultural and natural resource extension is over 100%. Stated another way, these public investment project could pay a very high interest rate (66% for agricultural research and 100% for extension) and still have a positive net present value. Hence, these [internal rate of return] estimates are quite large relative to alternative public investments in programs of education and health. In addition, there is no evidence of a low returns to public agricultural extension in the U.S., or that public funds should be shifted from public agricultural extension to agricultural research. In fact, if any shifting were to be recommended, it would be to shift some funds from public agricultural research to extension. ”

The paper includes a couple really interesting graphs on research spending and extension employment over time.  First, they show that for four major agricultural states, real spending on agricultural research peaked in the mid 1990s. 

And, while extension staff has declined in some states, it hasn't in others.  

The Behavioral and Neuroeconomics of Food and Brand Decisions

That's the title of a special issue I helped edit with John Crespi and Amanda Bruce in the latest issue of the Journal of Food and Agricultural Industrial Organization.  

Here's an excerpt from our summary:

“To economists interested in food decisions, progress seen in other fields ought to be exciting. In the articles for this special issue, we gathered information from a wide range of research related to food decisions from behavioral economics, psychology, and neuroscience. The articles, we hope, will provide a useful reference to researchers examining these techniques for the first time…The variety of papers in this special issue of JAFIO should provide readers with a broad introduction to newer methodological approaches to understanding food choices and human decision-making”

A complete listing of the authors and papers are below (all of which can be accessed here)

•       The Behavioral and Neuroeconomics of Food and Brand Decisions: Executive Summary
o   Bruce, Amanda / Crespi, John / Lusk, Jayson

•       Cognitive Neuroscience Perspectives on Food Decision-Making: A Brief Introduction
o   Lepping, Rebecca J. / Papa, Vlad B. / Martin, Laura E.

•       Marketing Placebo Effects – From Behavioral Effects to Behavior Change?
o   Enax, Laura / Weber, Bernd

•       The Role of Knowledge in Choice, Valuation, and Outcomes for Multi-Attribute Goods
o   Gustafson, Christopher R.

•       Brands and Food-Related Decision Making in the Laboratory: How Does Food Branding Affect Acute Consumer Choice, Preference, and Intake Behaviours? A Systematic Review of Recent Experimental Findings
o   Boyland, Emma J. / Christiansen, Paul

•       Modeling Eye Movements and Response Times in Consumer Choice
o   Krajbich, Ian / Smith, Stephanie M.

•       Visual Attention and Choice: A Behavioral Economics Perspective on Food Decisions
o   Grebitus, Carola / Roosen, Jutta / Seitz, Carolin Claudia

•       Towards Alternative Ways to Measure Attitudes Related to Consumption: Introducing Startle Reflex Modulation
o   Koller, Monika / Walla, Peter

•       I Can’t Wait: Methods for Measuring and Moderating Individual Differences in Impulsive Choice
o   Peterson, Jennifer R. / Hill, Catherine C. / Marshall, Andrew T. / Stuebing, Sarah L. / Kirkpatrick, Kimberly

•       A Cup Today or a Pot Later: On the Discounting of Delayed Caffeinated Beverages
o   Jarmolowicz, David P. / Lemley, Shea M. / Cruse, Dylan / Sofis, Michael J.

•       Are Consumers as Constrained as Hens are Confined? Brain Activations and Behavioral Choices after Informational Influence
o   Francisco, Alex J. / Bruce, Amanda S. / Crespi, John M. / Lusk, Jayson L. / McFadden, Brandon / Bruce, Jared M. / Aupperle, Robin L. / Lim, Seung-Lark

Food Demand Survey (FooDS) - November 2015

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

A few highlights from the tracking portion of the survey:

  • After a dip last month, WTPs for all products are back up in November.
  • There was a BIG increase in awareness of E. Coli in the news in the past two weeks, perhaps due to the discussion surrounding the Chipotle. 
  • Despite the increase in awareness of E. Coli in the news, there was not a big change in concern about the issue.
  • Consumers expect higher meat prices and expect to consume less meat in the next two weeks as compared to October.

Three ad hoc questions were added in response to the news a couple weeks ago that the International Agency for Research on Cancer, IARC) for the World Health Organization classified red meat as probably carcinogenic.  

First, were a few questions meant to determine the tradeoffs people make between the taste of food, how long they live, cost, and food safety.   

This was done by posing the following scenario: “Imagine you could live anywhere in the world. Suppose there were eight different locations you could choose from that were similar in all respects except for the types of food available. For each of the following eight locations, please rank how desirable it would be to live there.”

Then, eight options were presented (in random order) that varied food cost (at 10% to 20% of after tax income), taste (either better or worse tasting than you're used to), chance of foodborne illness (either 1 or 3 foodborne illnesses per lifetime), and life expectancy (either 75 or 85 years old) of different hypothetical locations.  There are 2^4=16 possible different locations, and I showed people 8 of these such that none of the characteristics were correlated with the others.

Statistical analysis indicates the following formula (all coefficients except the intercept are statistically significant at the 0.01 level) implied by respondents' rankings:

(9-ranking)=desirability of location=0.10-0.035*Cost+1.31*BetterTaste-0.52*#Sicknesses+0.066*Age.

So people dislike higher food costs and foodborne illnesses, and they like better tasting food and living to older ages.  No surprise there.  The interesting questions relate to the magnitudes.

The results reveal people would be willing to pay about 38% higher food prices for better vs. worst tasting food (1.31/.035) and would give up 20 years of life expectancy to live with better tasting food (1.31/0.066). An extra case of foodborne illness is equivalent to about 15% higher food prices in terms of satisfaction with a location (0.52/0.035).

Looking at how rankings can change by moving from the lowest to the highest level of each characteristic suggests taste is the most important issue followed by safety, then life expectancy, then cost.  The results reinforce what we already know: some people will continue to eat bacon even if the IARC says it increases the risk of cancer because they like how it tastes.

The next set of questions focused more specifically on whether people thought different issues could cause cancer, and then how many cancers the issues caused.  In short, I tried to separate out (as the IARC does) the certainty with which we know whether something causes cancer from the size of the effect: that is, how much does a substance increase your cancer risk (see my previous discussion on this).

More humorously, Ted Underwood put it this way on Twitter: 

In any event, I asked “How much evidence do scientists have that each of the following items causes cancer?” Using the classification scheme used by the International Agency for Research on Cancer (IARC), respondents were asked whether they believed each item to be carcinogenic to humans (5), probably carcinogenic to humans (4), possibly carcinogenic to humans (3), carcinogenicity not classifiable (2),  or probably not carcinogenic (1).

Most issues, including red and processed meat, were most rated as “possibly carcinogenic” (The FooDS report has the % in each category; here's a nice figure of how the IARC actually classifies different substances).  Here how the different issues I asked about lined up in terms of respondents' perceptions.

While the above was meant to measure certainty of evidence (i.e., the p-value), the last question was meant to measure effect size.  Respondents told: “According to the American Cancer Society, there will be about 1.6 million new cancer cases in the United States this year. What percentage of those new cancer cases do you believe are caused by the following items?”

On average, respondents stated that approximately 30% of new cancer cases each year are due to smoking. Respondents stated that 14% of new cancer cases were due to other factors not listed. Processed meat was thought to be the cause of 6.5% of new cancer cases, while red meat was stated to be the cause of 5.8% of new cancer cases and tea ranked the lowest for causes of new cancer cases at 1.7%. 

Despite the fact that the two questions are meant to measure different things, people probably conflate the two things in their minds (which is why this piece in the Atlantic said the IARC's classification is “confusogenic to humans.”).  Here are the mean answers from the 1st question plotted against the mean answers to the 2nd question.

The correlation between the two is 0.62.  Even removing smoking, the correlation is 0.33.  That is, people tend to conflate the certainty of the evidence with the size of the effect.   

Fads in Fad Diets

Over at Vox.com Jullia Belluz had a post last week showing trends in google searches for different diets from 2005 to 2015.

Scroll through the following graphic.  I knew the gluten-free diet was trendy, but I hadn't appreciated the extent to which it had fully and completely overtaken all the other fad diets.  Back in 2006 and 2007, organic diets seemed to be most popular in many locations, by 2009 it seemed to be gone and replaced by a rise in vegan and gluten-free diets.  We're all gluten free now.  What's next?

The making of hybrid corn

After giving a talk at University of Nebraska a couple weeks ago, Cory Walters suggested the book The Hybrid Corn Makers: Prophets of Plenty written by Richard Crabb in 1947 (the whole book can be downloaded here).  I’m a couple chapters in and it is already fascinating.  The introduction (which explains hybrid corn)  was written by HD Hughes, a professor who was at Iowa State College at the time.    

Hughes writes:

“One of the greatest advantages of the technique of breeding hybrid corn is the opportunity afforded to develop strains especially well fitted to particular conditions of weather, soil, disease, and insects. By bringing together the right combination of inbreds, hybrids are “custom built” for particular needs. . . . From this we can see how important it is to find the particular hybrids best adaptetd to conditions likely to prevail in a given location. Many hybrids, especially those used in the northern corn-growing areas, are so closely adapted to particular conditions that they are superior to other hybrids only in an area no more than fifty or one hundred miles north or south”

I share this passage because there seems to be a common, modern view that "monoculture" cropping agriculture has led to a dramatic reduction in genetic diversity.  I gave a talk last week to a large intro to food science class and talked a bit about biotechnology.  One student asked me precisely this question: Don't GMOs reduce genetic diversity and thus make the entire system more vulnerable to  disease, etc.  But, as the above quote shows, even in the 1940s, there are many different types of corn in different locations, and that's true still today.  I also pointed this out when responding to Nassim Taleb's claims about GMOs: 

“Moreover, what he doesn’t seem to get with regard to modern GMOs is that a GMO isn’t a variety. A particular trait - say herbicide resistance - is introduced into many, many varieties in different parts of the country and the world. ”

In any event, the first chapter of the book is an interesting discussion on the history of corn and how it spread across South and North America.  Crabb writes:

“Scouts sent by Columbus to explore what is now the Island of Cuba became the first white men of record to see corn. On November 5, 1492, the first corn fields they encountered stretched across the Caribbean countryside continuously for eighteen miles.”

and

“Columbus returned to Spain in early in 1493, carrying with him the first maize ever seen in Europe. That year corn grew in the royal gardens of Spain and withing two generations was growing as a food crop in every country of sixteenth-century Europe. In less than a century, Indian corn had moved completely around the world.”