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Crop Yields and Taste

That modern agriculture is incredibly productive - much more than the past - is undeniable. These USDA data, for example, suggest we produce about 170% more agricultural output now than in the late 1940s. I have argued that these these increases in agricultural productivity are signals of improved sustainability. Some people believe the the productivity improvements have been accompanied with offsetting externalities or degredations in animal welfare. A different kind of critique is that modern crops - despite being more productive - aren’t as high “quality.” For example, this piece in Politico by Helena Bottemiller Evich, titled “The great nutrient collapse” discuses evidence that vitamin content of crops has fallen as yields have increased, and there is the often-heard complaint that tomatoes don’t taste as good as they once did.

There is some biological basis for these latter concerns. If a crop breeder selects plants for higher yields, they are selecting plants that are spending their energy and nutrients into producing bigger seeds and fruits, which is energy that could have gone (in lower yielding plants) to growing leaves or roots or other compounds that affect taste and vitamin content.

I had these thoughts in the back in my mind when I came across the Midwest Vegetable Trial Report put out by researchers at Purdue and other Midwestern universities. The report compares different vegetable varieties in terms of yield and other output characteristics. I noticed for a couple vegetables - green beans and sweet corn - there were also measures of taste for each variety. Granted, these were not full-on scientific sensory evaluations and they involved small numbers of tasters, but still I thought it would be useful to test the conjecture that higher yielding varieties taste worst.

Some researchers from University of Kentucky put together the green bean report. They compared the performance of 19 different varieties of green beans. The most productive variety (named “Furano”) yielded 785 bushels over six harvests, whereas the lowest yielding variety “Slenderette” only produced 233 bu/acre in six harvests. As the image below reveals, however, there was only a weak correlation between taste and yield. The correlation was negative (-0.26), but not particularly large. About 6.6% of the variation in yield is explained by taste. The best tasting variety “Opportune“ had a taste score of 4.1 (on a 1=poor to 5=excellent scale) and a yield of 557; the worst tasting variety “Bronco” had an average taste score of 2.3 and a yield of 543. So, the best tasting bean had better yield than the worst tasting bean. Overall, the results below provide some weak support for a yield, taste trade-off.

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The report also provided production and taste data on supersweet corn (this part was authored by Purdue researchers Elizabeth Maynard and Erin Bluhm). They compared 16 different types of bicolored supersweet corn (they also evaluated two varieties of white and two varieties of yellow, which I’m ignoring here). They had tasters rate “flavor” on a 1 to 5 scale. As the figure below shows, there is actually a positive correlation between flavor and yield, as measured by ton/acre. The correlation is 0.15, but the relationship is weak. The authors also report yield in a slightly different way, ears/acre, and by this measure the correlation is slightly negative (-0.09).

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These results don’t necessarily negate the idea that the taste of vegetables has declined over time as higher yielding varieties have been adopted, but they do suggest that in 2017, among the particular varieties tested and among the few tasters asked, there is only a very weak correlation between taste and yield for green beans and supersweet corn.

Arbitraging the Market for Food Fears

A couple weeks ago, the best selling author Michael Lewis was on campus, and I went to listen to him talk. I’ve read several of Lewis’ books, and it was interesting to hear him talk about some of the underlying themes that united them.

In his 2017 book, the Undoing Project, Lewis writes the history of Kahneman and Tversky and the development of behavioral economics, a field that posits people do not always make rational decisions. In an earlier book, Moneyball (published in 2004), a few stat/econ types realized baseball teams were leaving money on the table by ignoring data on what really drives team wins. One team manager, Billy Beane, attempted to arbitrage the market for players by buying “undervalued” players and putting them to higher-valued use. In another earlier book, the Big Short (published in 2010), Lewis talks about the people who made big bucks on the financial crisis by recognizing that markets were “mispricing” the risks of systemic mortgage failures. In some ways the books are out of order because Lewis’s earlier books described how various people made serious money from the sorts of behavioral biases that Kahneman, Tversky, and others uncovered.

What’s this got to do with food?

Many of the systematic biases that lead people to mis-price baseball players and mortgage-backed securities are likely leading people to mis-price foods made with new technologies. Take GMOs. A Pew study found 88% of scientists but only 37% of the public thought GMOs are safe to eat. Is it possible scientists are wrong and the public is right? Sure, but if you had to place a bet, where would you put your money?

Or, let’s take at a widely studied behavioral bias - the tendency for people to exaggerate the importance of low-probability risks. The propensity to overweight low probability events was one of the cornerstones of prospect theory, which was introduced by Kahneman and Tversky. This theory is sometimes credited as herding the birth of modern-day behavioral economics, and the paper was a key contributor to Kahneman later winning a Nobel Prize. If there is a 1% chance of an outcome occurring, when making decisions, people will often “irrationally” treat it as a 5% or 10% chance. There are many, many studies demonstrating this phenomenon.

Oddly, I have never seen a behavioral economists use this insights to argue that fears over growth hormones, GMOs, pesticides, preservatives, etc. are overblown. However, there are many food and agricultural scientists who argue that many of our food fears are, in fact, irrational in the sense that public perceptions of risk exceed the scientific consensus.

Now, getting back to Michael Lewis’s books on the people who figured out how to profit from behavioral biases in fields as divergent as baseball players and mortgage-backed securities, if we really think people are irrationally afraid of new food technologies, is it possible to put our money where our mouth is? Or, buy fears low and sell them high?

Here are a few half-baked thoughts:

  • If people are worried about the safety of food ingredients and technologies, shouldn’t they be willing to buy insurance to protect against the perceived harms? And if consumers are overly worried, they should be willing to pay more for insurance than it actually costs to protect against such harms. If we believe this is the case, then creating insurance markets for highly unlikely outcomes should be a money-making opportunity. On the plus side, such markets might also take some of the fear out of buying foods with such technologies since people can hedge their perceived risks.

  • Let’s say your Monsanto (now Bayer), Syngenta, BASF, or another seed/chemical company. What can you do to assuage consumers’ fears of your technologies, particularly if you believe the perceive risks are exaggerated? Why not offer up a widely publicized bond that will be held in trust in case some adverse event happens within a certain period of time? (This is like when contractors or other service suppliers attempt to gain trust by being bonded). If it is really true that consumers’ fears are exaggerated, the bond won’t be paid out (at least not in full), and will revert back to the company.

  • Did you know that it is possible to invest in lawsuits? Investors, whose money is used to front the legal bills, earn a portion of the payout if a plaintiff wins a settlement against a corporation or other entity responsible for some harm. The “price” of such investments is likely to rise the greater the public’s perceived odds of winning the case, which presumably related to perceptions of underlying risks. I can imagine institutions or markets arising that would enable investors to short such investments - to make money if the plaintiff losses the case. The current Monsanto-glyphosate verdict not withstanding, shouldn’t it be the case that one could profitability short lawsuits surrounding the safety of food and farm technologies if the fears around them are indeed overblown?

Other ideas?

Look at Me, I'm Buying Organic

That’s the title of a new paper I co-authored with Seon-Woong Kim and Wade Brorsen, which was just published in the Journal of Agricultural and Resource Economics.

We know consumers have a number of motivations for buying organic food - from perceptions about health, taste, safety, and environment to perceptions about impacts on smaller farmers. Whether these perceptions are accurate is debatable. In this paper, we were interested in an all together different motivation: the extent to which consumers feel social pressure to buy organic.

In our study, people made simulated choices between apples or milk cartons, where one of the characteristics was the presence or absence of the organic label. People were divided into one of four groups:

1) The control (CTRL): no manipulation.

2) The eye (EYE) treatment. This is going to sound crazy, but following some previous research, we showed an image of a person’s eyes on the screen as people were making their apple and milk choices. Prior research suggest that exposure to an image of eyes creates the aura of being watched, which increases reputational concerns and cooperative behavior.

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3) The name (NAME) treatment. Just prior to the apple/milk choices, people in this group were asked to type in their first name, and we ask them to confirm if they lived in the location associated with their IP address. The idea was to remove the perception of anonymity and increase social pressure.

4) The friend (FRND) treatment. Here we used a vignette approach. Jut prior to the apple/milk choices, people were told, “Now, imagine you are in the specific situation described below. Your good friends have family visiting. They’ve asked you to help out by taking their sibling, whom you’ve never met, to the grocery store. While you’re there with your friend’s sibling, you also need to do some shopping for yourself.”

If social pressure is a driver of organic food purchases, willingness-to-pay for the organic label would be expected to be higher in the EYE, NAME, and FRND treatments relative to the control.

Here are some summary statistics showing the percent of choices made by consumers in each treatment group in which a product with the organic label was chosen.

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We see some support for the idea that organic is more likely to be chosen in the social pressure treatments (EYE, NAME, FRND) than the control. The effects are statistically different, but that aren’t huge for every treatment considered. However, the above table doesn’t consider price differences. When we convert the choices into a measure of willingness-to-pay, we find the biggest effect is for the vignette (FRND). For this treatment, we find willingness-to-pay for organic is about 88% higher for apples and 82% higher for milk than the control.

For all groups, we found that education levels moderate the relationship between the social pressure treatment variables and willingness-to-pay for organic. In particular, social pressure is higher for more highly educated consumers. The effect was particularly large in the EYE treatment, where more highly educated consumers valued the organic label between 150% and 200% than less educated consumers when exposed to eyes.

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These results provide evidence that at least a portion of organic consumption is likely driven by a form of conspicuous consumption. Some might call it a form of conspicuous conservation, but that’s a whole other can of worms.

Are Consumers Eating Out Less Frequently?

According to this Grub Street article, the answer is yes.

The average American’s restaurant visits reached a 28-year low this year, falling from an average of 215 a year in 2000 to 186 a year in 2018. Data gathered by the NPD Group shows a particular and precipitous decline since 2008. Today, 82 percent of meals in America are made at home.

And, they speculate on causes in the slow-down on restaurant spending:

Going out to restaurants doesn’t seem like such a good idea when you’re saddled with student debt and contending with wage stagnation. (In fact, many Americans saw their wages decline over the last year.)

Meanwhile, restaurants are becoming increasingly more expensive compared to eating in.

I’m a little skeptical of these data? Why? Well, here’s data from the Bureau of Economic Analysis (BEA) data on personal consumption expenditures (these are the data that feed into calculation of GDP) for food at home and away from home.

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In inflation-adjusted terms, all consumer spending is up about 36% since 2001. Spending on food at home only rose about 24% over this time periods, but spending away from home increased 54%. (I’ve also shown spending on clothing for reference). Spending on food away from home fell during the Great Recession, but it has significantly rebounded since.

Now, it’s not impossible for both of these statistics to be simultaneously true - one can eat out less frequently but spend more money on each trip, and total expenditures could still rise.

But, the BEA also reports quantity indices, which provide an estimate of the volume of food sold away from home. Here are those data. These data suggest little evidence for a slowdown in the amount of food consumed away from home.

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The headline on the Grub Street article asks “Should Restaurants be Worried?” The BEA data suggest the answer is “no.”

Dealing with Lazy Survey Takers

A tweet by @thefarmbabe earlier this week has renewed interest in my survey result from back in January 2015, where we found more than 80% of survey respondents said they wanted mandatory labels on foods containing DNA. For interested readers, see this discussion on the result, a follow-up survey where the question was asked in a different way with essentially the same result, or this peer-reviewed journal article with Brandon McFadden where we found basically the same result in yet another survey sample. No matter how we asked this question, it seems 80% of survey respondents say they want to label foods because they have DNA.

All this is probably good motivation for this recent study that Trey Malone and I just published in the journal Economic Inquiry. While there are many possible reasons for the DNA-label results (as I discussed here), one possibility is that survey takers aren’t paying very close attention to the questions being asked.

One method that’s been around a while to control for this problem is to use a “trap question” in a survey. The idea is to “trap” inattentive respondents by making it appear one question is being asked, when in fact - if you read closely - a different question is asked. Here are two of the trap questions we studied.

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About 22% missed the first trap question (they did not click “high” to the last question in figure 2A) and about 25% missed the second question (the respondent clicked an emotion rather than “none of the above” in question 2B). So far, this isn’t all that new.

Trey’s idea was to prompt people who missed the trap question. Participants who incorrectly responded were given the following prompt, “You appear to have misunderstood the previous question. Please be sure to read all directions clearly before you respond.” The respondent then had the chance to revise their answers to the trap question they missed before proceeding to the rest of the survey. Among the “trapped” respondents, about 44% went back and correctly answered the first question, whereas about 67% went back and correctly answered the second question. Thus, this “nudge” led to an increase in attentiveness among a non-trivial number of respondents.

After the trap questions and potential prompts, respondents subsequently answered several discrete choice questions about which beer brands they’d prefer at different prices. Here are the key findings:

We find that individuals who miss trap questions and do not correctly revise their responses have significantly different choice patterns as compared to individuals who correctly answer the trap question. Adjusting for these inattentive responses has a substantive impact on policy impacts. Results, based on attentive participant responses, indicate that a minimum beer price would have to be substantial to substantially reduce beer demand.

In our policy simulations, we find a counter-intuitive result - a minimum beer price (as implemented in some parts of the UK) might actually increase alcohol consumption as it leads to a substitution from lower to higher alcohol content beers.

In another paper in the European Review of Agricultural Economics that was published back in July, Trey and I proposed a different, yet easy-to-interpret measure of (and way to fix) inattention bias in discrete choice statistical models.

Taken together, these papers show that inattention is a significant problem in surveys, and that adjusting results for inattention can substantively alter one’s results.

We haven’t yet done a study of whether people who say they want DNA labels are more or less likely to miss trap question or exhibit other forms of inattention bias, but that seems a natural question to ask. Still, inattention can’t be the full explanation for absurd label preferences. We’ve never found inattention bias as high as the level of support for mandatory labels on foods indicating the presence/absence of DNA.