Do Consumers Want Mandatory GMO Labeling?

The newest release of our monthly, nationwide food demand survey (FooDS) is now up.  The report contains data on trends in meat demand and awareness and concern over various food issues.  

Given the renewed interesting in mandatory labeling for genetically engineered food, we added two new questions to the July survey (if you're interested, you can see the results of a previous survey we conducted in California just before the Prop 37 vote); a version of that report is coming out in the Journal of Agricultural and Resource Economics).

The first question on GMO labeling asked in the most recent survey was worded:

Which of the following do you think the FDA or USDA should require to be labeled on food packaging?

Then, 10 items were listed, and respondents had to place four and only four items in a box indicating which items they though were most important to label.  Here are the results.

I must say that I am shocked by the results.  63.6% said they thought "added growth hormones and antibiotics" should be labeled followed by 55% who said "GMOs."  Oddly, those items which ARE currently required to be labeled, including fat content, total calories, and known allergens (e.g., nuts), fell further down the list.  At first I thought this might be a mistake, but after double and triple checking the data, this is apparently how consumers responded.  Perhaps they take currently mandated information (e.g., calorie content) for granted (or don't realize it is mandated).  Perhaps GMOs are just more in news these days drawing attention?  On a technical note, the order of the 10 items was randomized across respondents, so these findings cannot result from some sort of order effect.  All in all, I'm not sure what is driving the result but I welcome any insights if you have them.

Secondly, we asked consumers: 

Which of the following best describes your views on mandatory labeling of foods containing genetically modified (GMO) ingredients?

They could pick one (and only one) of the following responses: 

• I support mandatory labeling because consumers have a right to know regardless of the cost 
• I support mandatory labeling, but only if it doesn't significantly raise food prices or cause frivolous lawsuits
• I do not support mandatory labeling because voluntary labeling exists and will thrive if consumers really want to avoid GMOs
• I do not support mandatory labels because the scientific consensus suggests GMOs are safe to eat
• I don't know (5)

Here are the results

A majority (54%) said they wanted mandatory GMO labeling because they said they had a right to know regardless of the costs.  This result is surprisingly high and doesn't quite mesh with the actual voting outcome in California (or our previous survey which showed voting intentions influenced by cost and information).

As I articulated in several editorials in Sept-November last year, I do not think the economic arguments for mandatory GMO labeling are particularly strong (voluntary labeling is a different matter all together).  These survey results suggest little public support for that particular view.  However, there is also ample evidence to show that most consumers are woefully uninformed about biotechnology and that information can have big effects on attitudes (and as Prop 37 showed - voting outcomes).

Study shows GMO feed improves liver health in pigs!

Curiously, that it not the headline that is circling the web.  Rather, the headline in credible (but uncritical) news agencies is "Scientists say new study shows pig health hurt by GMO feed."

Wow!  Sounds scary.  I had to check it out.  The claim comes from a study published in the Journal of Organic Systems (you can find it on the author's web page).  My first thought was: A journal that essentially promotes organic is not exactly credibility inspiring.  But, the study should speak for itself and I read it.  

What you'll find is, by and large, a fishing expedition.  The authors fed one group of pigs a diet of GM corn and soy and another group of pigs a diet of non-GM corn and soy.  They then tested for differences between the two group.  Here's where the problem comes in.  The authors didn't set out with a specific causal hypothesis - they simply tested for differences in everything from liver size to body weight to the headline-grabbing stomach inflammation.  I counted more than 40 different p-values coming from tests in the paper.  Just by chance, the authors would expect to find one or two significant differences and that's exactly what they found.  Out of the 40+ tests conducted there were two p-values less than 0.05 (at 5% level of significance, you'd expect 1 test out of 20 (or 2 out of 40) to be significant just by chance).  One significant result showed higher proportion of pigs with "severe stomach inflation" in GM fed pigs than in non-GM fed.  The other showed a elevated levels of GGT (a signal of liver problems) in non-GM fed pigs relative to GM fed.   

To me, the take home message is that there is no difference in GM and non-GM fed pigs that is not attributable to chance (the authors would need to correct their p-values for multiple comparisons to truly say this is non-random; they'd also need a causal theory for why one result is significant while 40+ are not).  Oddly, the only result that they find significant and gets played up is also one of the ones that is not an "objective" measure but is one in which veterinarians have to make a judgement call as to which stomachs are inflamed and which are not.  If you add together the severe and moderately inflamed, what you find is that 52% of non-GM feed pigs meet this condition and 56.9% of GM feed pigs meet this condition - a difference that is unlikely significant (moreover, a higher percent of GM fed pigs (11.1% ) had no stomach inflammation as compared to non-GM fed (5.4%).  Again, the authors need to do some kind of joint test of significance across all 4 inflammation categories. 

Unfortunately, this study, much like the previous French-rat study will be used uncritically by anti-GMO activists, and it wont be taken in the larger context of the hundreds of other studies showing no differences.   

Although the paper should stand (or fall) on its own merits (or demerits), it is sometimes useful to look at author connections.  And even though no conflicts of interests were declared, Mark Lynas points out on his blog that these are hardly disinterested parties. Among other issues,  the funding comes from a company promoting non-GMO "natural" food.  For other critical analysis see the Lynas blog as well as this one.

Economists, Fat Taxes, and the 3500kcal rule

Economists are often sought out to help determine the effects of fat and soda taxes.  We are generally well-equipped to estimate how much less of a particular type of food will be eaten when prices increase as a results of a tax.  However, we are much less well-equipped to go the next step and figure out how changes in the consumption of a food results in a change in weight - the key statistic of interest.  That last step requires some knowledge of nutrition, biology, and metabolism.  

Unfortunately, it turns out that one of the critical "thumb rules" that we economists have used from those literatures, that a change in 3500 kcal will equate to a change in 1 pound of body weight, is likely highly misleading and overstates the effects of the tax (not to mention that it says nothing of when the weight change will happen or how long it will take to happen).  

I've previously blogged about some of the issues with this thumb rule but I'm not sure how widely the problem is understood or recognized among economists.  For example, here are some quotes from some recent, otherwise well-done papers.     

Okrent and Alston in the American Journal of Agricultural Economics in 2012 (free version here) said:

One frequently used relationship in textbooks (e.g., Whitney, Cataldo, and Rolfes 1994) and academic articles that address the potential impacts of fiscal policies on weight (e.g., Chouinard et al. 2007; Smith, Lin and Lee 2010) is that a pound of fat tissue has about 3,500 calories. We used this multiplier to convert changes in annual calorie consumption into changes in body weight.

Dharmasena and Capps in the Health Economics in 2011 said:

Finally, using the conversion ratio of 3500 cal per pound of body weight, we calculate the induced change in the per capita body weight in pounds as a result of aforementioned change in the per capita caloric intake.

Kulcher et al in Applied Economic Perspectives and Policy (formerly the Review of Agricultural Economics) in 2005 said: 

Assuming that no food would be substituted, at 3,500 calories per pound of body weight (American Dietetic Association), the [estimated] reduction translates into less than a fourth of a pound.

To be fair, I didn't appreciate the problem till only recently.  My own paper with Schroeter and Tyner in the Journal of Health Economics in 2008 stated the following (although we used a different calculation to derive weight changes):

On average, in order to gain (lose) one pound, a person needs to consume (burn) 3500 calories in addition to the typical caloric intake (expenditure). Overall, a surplus (deficit) of 500 kcal a day brings about a gain (loss) of body fat at the rate of one pound per week and a surplus (deficit) of 1000 kcal a gain (loss) of two pounds per week (Whitney et al., 2002).

In this context, I was pleased to see this recent article in the International Journal of Obesity, which we economists can use to derive better weight effects.  Here is the abstract

Despite theoretical evidence that the model commonly referred to as the 3500-kcal rule grossly overestimates actual weight loss, widespread application of the 3500-kcal formula continues to appear in textbooks, on respected government- and health-related websites, and scientific research publications. Here we demonstrate the risk of applying the 3500-kcal rule even as a convenient estimate by comparing predicted against actual weight loss in seven weight loss experiments conducted in confinement under total supervision or objectively measured energy intake. We offer three newly developed, downloadable applications housed in Microsoft Excel and Java, which simulates a rigorously validated, dynamic model of weight change. The first two tools available at http://www.pbrc.edu/sswcp, provide a convenient alternative method for providing patients with projected weight loss/gain estimates in response to changes in dietary intake. The second tool, which can be downloaded from the URL http://www.pbrc.edu/mswcp, projects estimated weight loss simultaneously for multiple subjects. This tool was developed to inform weight change experimental design and analysis. While complex dynamic models may not be directly tractable, the newly developed tools offer the opportunity to deliver dynamic model predictions as a convenient and significantly more accurate alternative to the 3500-kcal rule.

Finally, I will end by noting that there are many papers that use economic models to project how a tax/subsidy will change the consumption of certain nutrients, and similar thumb rules are used to translate to changes in heart attacks, diabetes, etc.  Although I don't know for sure, I suspect many of the exact same sorts of problems exist with these thumb rule extrapolations as exists with the 3500kcal=1lb rule, not to mention the larger difficulty of ascribing causation in those models.   

Food Sector Linkages

Parke Wilde at Tufts University mentioned a new project he spearheaded in a recent blog post.  Parke and colleagues have crated a tool that lets the user visualize the input-output data provided by the Bureau of Economic Analysis.   

I've looked at these sorts of tables before, but I've always found it is a bit hard to distill insights from them.  This tool provides an easy way to ​visualize the flows between different food sectors.  Great idea!  

Below is a video of Parke describing the tool:​

The Food Demand Survey (FooDS)

For a number of years, I've thought about creating a monthly survey to track consumer knowledge, concerns, and preferences for various food-related issues.  After no small amount of effort, and thanks to the funding from the Willard Sparks Endowment and DASNR and the assistance of Susan Murray, that vision has now become a reality.  

The inaugural issue is now up online, and we will to follow with regular monthly releases.

Of course, this initial issue can't report changes , but that information will come.

For those who might be interested, the purpose of the project is to provide timely information on:

• Indices of consumer sentiments on (or beliefs about) the safety, quality, and price of food consumed at home and away from home.
• Indices of consumers' anticipated demand for various meat products consumed at home and away from home.
• Awareness of food-related issues or events that could affect demand.
• Emerging policy or marketing issues.

It is envisioned that such data could be used by analysts to:

• Construct and analyze trends in beliefs, demand, and awareness
• Forecast changes in consumption
• Compliment (i.e., merge with) existing sources of secondary data (e.g., USDA disappearance or scanner data) in food demand analysis

Some of the motivations for starting the project include the following.

• Although scanner data is available to analyze immediate past behaviors, it is inherently backward-looking.  A consumer survey can be devised to be forward looking, potentially providing better forecasts.  Moreover, analyzing demand using scanner data is tricky due to issues of supply shifts, endogeneity, unobserved quality variation, promotions, etc that can be overcome with a well-designed survey.
• Current meat demand indices are aggregate, quarterly, assume a constant demand elasticity, and attribute all price/quantity changes to shifts in demand; a survey is more rapid and can better isolate demand-side issues.
• Existing surveys of consumers (i.e., panel diaries or home scanning data) only focuses on at-home food consumption; away from home food consumption now accounts for just under half of all food expenditures.
• Although some marketing companies routinely track eating intentions and awareness of food issues, the data is proprietary and is not publically released in any uniform fashion.  Moreover, their survey questions are not always designed using state-of-the-art techniques in consumer research.