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No, Farm Policy Doesn't Have Much to Do with Obesity

Yesterday, David Ludwig and Kenneth Rogoff, prominent pediatrician and economist respectively, published an article in the New York Times about obesity.  The following is a passage from the piece.  

Farm policies have made low-nutritional commodities exceptionally cheap, providing the food industry with enormous incentive to market processed foods comprised mainly of refined grains and added sugars. In contrast, vegetables, whole fruits, legumes, nuts and high-quality proteins are much more expensive and, in “food deserts,” often unavailable.

The authors have already taken a bit of a beating about this on Twitter from the agriculturally-literate-intelligentsia. Why?  Because these sentences give the incorrect impression that farm policy is a major contributor to obesity.  That's not saying farm policies aren't inefficient, only that they do not have the effects many people claim they do.

Why would farmers support policies that would make commodities "exceptionally cheap" and thus lower their profits?  Yes, there are some policies that likely increase production beyond what would happen in an un-distorted market, but there are other policies that reduce production.  Take corn, for example, which is the largest agricultural crop in the U.S. in terms of value of production.  The existence of subsided crop insurance subsidies and commodity programs might increase the tendency to produce more than would otherwise be the case, but ethanol policies from the EPA re-direct much of that production to fuel rather than food. Moreover, there are countervailing policies such as the Conservation Reserve Program (CRP), which remove land from production.  In addition, sugar policies push the price of sugar up, not down.  

The authors also point to processed food as another big evil, but in so doing they (correctly) undercut the argument that farm policy is a major culprit.  How so?  Well, for every $1 we spend on food, only about $0.15 results because of the cost of the farm product.  The other 85% of the cost is from transportation, processing, packaging, marketing, retailing, etc.  As a result, changes in farm commodity prices have relatively small impacts on retail prices.  

Fruits and vegetables are indeed more expensive than many commodity crops, but that's because of biology not policy (see more on that here and here).  Here's what I wrote in one of those posts:

why do we grow so much corn, soy, and wheat in the U.S.? A primary answer is that these plants are incredibly efficient at converting solar energy and soil nutrients into calories (they’re the best, really the best). Moreover, these calories are packaged in a form (seeds) that are highly storeable and easily transportable - allowing the calories to be relatively easily transported to different times and to different geographic locations. Contrast these crops with directly-human-edible fruits/vegetables like kale, broccoli, or tomatoes. These plants are poor converters of solar energy to plant-stored energy (i.e., they’re not very calorie dense), and they are not easily storeable or transportable without processing (mainly canning or freezing), which requires energy.

If you don't believe me, there is a long literature by agricultural economists on this subject.  See this book by Julian Alston and Abby Okrent or these papers in American Journal of Agricultural Economics or Journal of Health Economics, the later of which was co-authored with Brad Rickard.  Other papers take entirely different approaches but arrive at the same conclusion.  See this paper in Food Policy by Corey Miller and Keith Coble or this one by Alston, Sumner an Vosti, also in Food Policy.

As for the efficacy of the other policies proposed by Ludwig and Rogoff, I'm skeptical of their efficacy in truly affecting obesity.  See this paper I recently published in Applied Economic Perspectives and Policy or my 2013 book, The Food Police.

Farmer's Share of the Retail Dollar - Enough Already

Every so often, the people seem to get excited about the farmer’s share of the retail dollar – particularly when USDA updates the figures or a news article mentions the issue.  A couple months ago, for example, the National Farmer’s Union issued a press release decrying the fact that farmers “only” receive 14.8 cents of every dollar consumers spend on food.  About the same time, the Food Tank put out this tweet.

The widespread implication seems to be that a lower share of the retail dollar is an unambiguous sign that farmers are worse off.  But one has very little to do with the other.  Let me try to illustrate with an example.   

Suppose there are two countries where the farmer’s share of the retail dollar differs dramatically.  In Country A, the share is only 10% and in Country B, the share is 90%.  So, when a consumer spends $1 on food, the farmer in Country A receives 10 cents and the farmer in Country B receives 90 cents.  On a dollar-spent-on-food basis, it thus looks like a farmer would much prefer to live in Country B than Country A.  But, let’s dig a little deeper.

Suppose the farmers in our two countries actually produce the same value of agricultural output.  To make the math easy, let’s say farmers in Country A produce $100 billion worth of ag output and farmers in Country B do the same. 

What are consumers in the two countries spending on food?  By definition, consumers in Country A are spending $100 billion/0.1 = $1,000 billion and consumers in country B are only spending $100 billion/0.9 = $111.11 billion. By definition, for a fixed value of ag output, a smaller value for the farmer's share of the retail dollar implies a larger total food economy. As I'll show in a minute, it matters a lot if you're selling into a $1 trillion market or a $111 billion market.

Why might consumers in Country A spend so much more on food than consumers in Country B despite the same volume of ag output in both countries?  Well, it could be there is more market power with greedy agribusinesses and retailers siphoning off profits in Country A than B (that seems to be the common layman’s interpretation).  But, it could also be that consumers in Country A have the preferences or ability to pay more for better packaging, increased food safety, better working conditions in food processing, more convenience (they pay the processor or a restaurant to do more of the preparation for them), etc.

So, what happens if there is a 10% increase in consumer demand for food in both Country A and Country B?  This could happen, for examples, if the populations increase in each country or if the respective food industries run advertisements or there are post-farm innovations that increase quality. 

Now, let’s construct a very simple economic model (such as the one we use in this paper), where, in both countries, the elasticity of demand is -0.8 and the elasticity of supply is 0.2, and the farm product is supplied to the retail sector in fixed proportions. 

In this situation, a 10% increase in consumer demand in country A (with only a 10% farmer’s share of the retail dollar) will increase farmers' profits by $29 billion.  However, in country B, where farmers “get” a full 90% of the retail dollar, that same 10% increase in consumer demand only increases farmers' profits by $8.8 billion.  So, for the same percentage increase in consumer demand, farmers in country A are more than 3x better off than farmers in country B despite the fact that their share of the retail dollar is only 10% instead of 90%. 

So, here’s a fundamental lesson: a small share of a big number can be much higher than a larger share of a smaller number.

Now, none of this means that one cannot construct scenarios in which producers are worse off when the farmer’s share of the retail dollar falls.  That’s easy to do too.  But, as I’ve shown here, I can easily do the opposite. 

The point?  Changes in the farmer’s share of the retail dollar are meaningless insofar as telling us whether farmers are better or worse off. 

Don't believe me?  Listen to other agricultural economists.  Here is Gary Brester, John Marsh, and Joseph Atwood and colleagues writing in a 2009 journal article:

We have empirically demonstrated that [the farmer’s share of the retail dollar] statistics and, by construction, farm-to-retail marketing margins, are not reliable measures of changes in producer surplus (welfare) given exogenous shocks to various economic factors … In fact, little or no accurate information is conveyed by [farmer’s share of the retail dollar] statistics … Consequently, these data should not be used for policy purposes.

GMO labels - not as bad as I thought

Science Advances (the open-access version of Science Magazine) just published a paper I co-authored with Jane Kolodinsky from the University of Vermont.  I suspect the paper's findings may raise a few eyebrows, as we find that opposition to GMOs in Vermont fell relative to that in the rest of the U.S. after mandatory labeling was adopted in that state.

Some background context might be useful here.  Several years go, I was decidedly in the camp that thought imposition of mandatory labels would cause people to be more concerned about GMOs because it would signal that something was unsafe about the technology.  Prominent scholars such as Cass Sunstein have argued the same.  A few years ago, Marco Costanigro and I put this hypothesis to the test in a paper published by Food Policy, and we found little evidence (in a series of survey-based experiments) that the label per se neither increased or decreased aversion to GMOs.  So, I was less convinced that this particular argument against mandatory GMO labeling was valid, but I was still unsure.  

Then, last summer at the annual meetings of the Agricultural and Applied Economics Association (AAEA), I saw Jane present a paper based on survey data she collected in Vermont before and after mandatory labels went into place there.  Her data suggested opposition to GMOs fell at faster rate after mandatory labels were in place.  Despite my findings in Food Policy, I remained dubious and Jane and I went back and forth a bit on the robustness of her findings. 

I'd been in enough conversations with Jane to know that we had different philosophical leanings about the desirability of GMOs, but this was an empirical question, so we put our differences aside and decided to join our data and put the hypothesis to the test.  Through the Food Demand Survey (FooDS), I had been collecting nationwide data on consumer's concerns about GMOs, and I suggested we combine our two sets of data and do a true "difference-in-difference" test: Did the difference in concern among consumers in VT and the result of the US increase or decrease after mandatory labeling was adopted in VT?

Our article in Science Advances has the result:

This research aims to help resolve this issue using a data set containing more than 7800 observations that measures levels of opposition in a national control group compared to levels in Vermont, the only U.S. state to have implemented mandatory labeling of GE foods. Difference-in-difference estimates of opposition to GE food before and after mandatory labeling show that the labeling policy led to a 19% reduction in opposition to GE food. The findings help provide insights into the psychology of consumers’ risk perceptions that can be used in communicating the benefits and risks of genetic engineering technology to the public.

One important caveat should be mentioned here.  Our result does NOT suggest people will suddenly support GMOs once mandatory labels are in place.  Rather, our findings suggest that people will be somewhat less opposed than they were prior to labels.  I mention this because in the wake of my paper with Marco in Food Policy some of the media's interpretation of our results (such as that of the New York Times editorial board), could have been construed as suggesting that imposition of mandatory labels would not cause economic harm.  That may or may not be true.  But, this new study suggest that labels per se may in fact reduce opposition.

It was great to work with Jane on this project, and for me it was a good lesson to test your beliefs, particularly when there are theoretical reasons that could support the opposing point of view.

I'll end with a key graph from the paper.

gmo_labels.JPG

The New GMO Labeling Law

Last week, the USDA finally released its proposed rule outlining the ways in which it may implement the National Bioengineered Food Disclosure Standard (NBFDS) (i.e., the a mandatory labeling law for GMOs) that was passed by the US Congress and signed into law back in the summer of 2016.  At the point, this is still a proposed rule: public comments are still being accepted until July 3, 2018.  

As I wrote at the time of its passage, the mandatory labeling bill was not particularly popular with the "anti" or "pro" GMO crowds.  I won't rehash all the issues involved or re-cover all the arguments for and against mandatory labeling (as an aside, I am amazed at how often I still see people citing my result on consumer preferences for DNA labels; I suppose that's a least one mark of success when people unknowingly cite your own research results to you as something you need to know!).  Here, I want to point out a few things that were news (at least to me) in the proposed rule.

  • One of the controversial facets of the original bill was that it allowed for disclosure of genetically engineered ingredients via a QR code (this is an issue we have researched - e.g., see here).  In addition to the QR code or a text disclosure, it appears companies might be able to also use one of several different types of labels (I am not aware of any publicly available research on consumer perception of these labels).  Here are some of the examples proposed:
newgmolabel.JPG
  • It also appears that a food may only have to be labeled if it actually contains genetically engineered (or shall i now say "bioengineered") ingredients that contain recombinant DNA.  Why does this matter?
    • Sugar and oil don't contain DNA.  Tests for recominant DNA are likely to come back negative even if applied to oil from derived from bioengineered corn or soy or if applied to sugar from bioengineered sugar beets.  As such, foods using oil or sugar derived from GE crops  may not ultimately be subject to the mandatory disclosure.
    • Other biotechnologies, such as gene editing, don't utilize recombinant DNA, and as such may not ultimately fall under this mandatory labeling law.
  • What will be the tolerances or thresholds that would trigger mandatory labeling?  The proposed rule didn't say for sure but offered several options:
    • A) disclosure is required if more than 5% of any ingredient (by weight) is bioengineered; 
    • B) disclosure is required if more than 0.9% of any ingredient (by weight) is bioengineered; 
    • C) disclosure is required if more than 5% of the entire product (by weight) is bioengineered.
    • It should be noted that these different thresholds are likely to imply VERY different costs of compliance; a 0.9% threshold is likely to be more than 5x more costly than a 5% threshold, and individual ingredient thresholds will be much more costly than total product thresholds.  
  • There are many exceptions, for examples for small manufacturer, for certain enzymes,  and for animal products derived from animals fed bioengineered feed.

Impacts of health information on perceived taste and affordability

The journal Food Quality and Preference just released a new paper I co-authored with Jisung Jo, a former student who now works at the Korea Maritime Institute.

Here is the motivation for the work:

One of the key mechanisms policy makers have utilized to encourage healthier eating is the provision of information via nutritional labels. However, research has shown that the provision of health information does not necessarily increase consumption of healthy foods ... A possible reason for the largely ineffectual impact of nutritional labeling might be because health information not only updates consumers’ health perceptions but also affects other perceptions, such as taste and affordability, which are the primary drivers of consumer purchase behavior

In other words, if you see a new labeling indicating a food is healthier than you previously thought, do you now think it will be less tasty?  Or more expensive?  

To explore this issue, we surveyed consumers in three different countries (US, China, and Korea).  We showed consumers a picture of a food item and asked them to rate the item, on simple scales, in terms of perceived taste, health, affordability, and purchase intention.  We did this for 60 diverse food items. Then, the ratings of all 60 foods was repeated after the subjects had received information about each food item’s healthiness, which was conveyed via a "traffic light" labeling system (green=healthy, yellow=medium healthiness, red=unhealthy).   Here's an example of one of the questions asked before and after the information:

jisungFQP1.JPG

Unsurprisingly, the provision of "green" labels tended to increased perceived healthiness and the provision of "red" labels tended to reduce perceived healthiness.  Of more interest is how these labels affected perceptions of taste, affordability, and ultimately purchase intentions.  

Unexpectedly, we found that providing information that a food was healthier than people previously thought tended to increase perceived taste.  People also tended to think items that are less healthy than previously thought will ultimately be less expensive.

We created the following graph to look at how projected changes in purchase intentions (after provision of health information) would change if one ignores the fact that health information also affects perceived taste and affordability.

jisungFQP2.JPG
Across all scenarios and in all three countries, we find that negative health information has the biggest effects on purchase intention changes. Intriguingly, the average purchase intention in scenario B is larger than that in scenario A. The values for scenario D are the same as the actual average of purchase intention (since they are just the model evaluated at the mean effect changes of all variables included in the model). Comparing the purchase intention changes as one moves from scenario A to D shows the effect of ignoring integrated health-taste-affordability perceptions.

Overall, this research underscores the need to understand how labels which convey health information might also alter other perceptions related to taste and affordability.