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The Effects of Farm and Food Policy on Obesity in the United States

That's the title of a new book by Julian Alston and Abigail Okrent.  Right now it's only available as an ebook, but the hard copy should be out soon.  Here's the publisher's description.

This book uses an economic framework to examine the consequences of U.S. farm and food policies for obesity, its social costs, and the implications for government policy. Drawing on evidence from economics, public health, nutrition, and medicine, the authors evaluate past and potential future roles of policies such as farm subsidies, public agricultural R&D, food assistance programs, taxes on particular foods (such as sodas) or nutrients (such as fat), food labeling laws, and advertising controls. The findings are mostly negative—it is generally not economic to use farm and food policies as obesity policy—but some food policies that combine incentives and information have potential to make a worthwhile impact. This book is accessible to advanced undergraduate and graduate students across the sciences and social sciences, as well as to decision-makers in the public, private, and not-for-profit sectors.

 

I had the pleasure of seeing a pre-release copy of the book and provided the following blurb:

That obesity is a serious challenge in America is undeniable. Yet, appropriate policy responses are far less clear. The Effects of Farm and Food Policy and Obesity is a tour de force. Alston and Okrent provide a solid economic framework for thinking about obesity policies, bust myths about the causes of the problem, and offer nuanced solutions. The book is a must read for anyone seriously interested in role of food and agricultural policy in addressing obesity.

How Votes on GMO Labeling Change Concern for GMOs

At the annual meetings of the Agricultural and Applied Economics Association last week in Chicago, I saw an interesting presentation by Jane Kolodinsky from the University of Vermont.  She utilized some survey data collected in Vermont before and after mandatory labels on GMOs appeared on products in that state to determine whether consumers seeing GMO labels on the shelf led to greater or lower support for GMOs as measured by her surveys.  

I'm not sure if she's ready to make those results public yet, so I won't discuss her findings here (I will note I'm now working with her now to combine some of my survey data with hers to see whether the findings hold up in a larger sample).

Nonetheless, her presentation led me think about some of the survey data I collected over the years as a part of the Food Demand Survey (FooDS) project.  While I don't have enough data from consumers in Vermont to ask the same question Jane did, I do have quite a bit of data from the larger states of Oregon and Colorado, which held public votes on mandatory labeling for GMOs back in December 2014.  

In particular, I can ask the question: did the publicity surrounding the vote initiative on mandatory GMO labeling cause people to become more or less concerned about GMOs in general?

We have some strong anecdotal evidence to suggest that support for GMO labeling fell pretty dramatically in the months leading up to the vote.  For example, here are the results from several polls in California (including one data point my research with Brandon McFadden generated) on support/opposition to mandatory GMO labeling.  The figure below shows support for the policy was high but fell precipitously as the election campaigning began, and as we all know by now, the policy ultimately failed to garner majority support in California.

There is a similar pattern of support for mandatory GMO labeling in other states where the voter initiatives were held.  However, just because public support for a mandatory labeling policy fell as a result of campaign ads, this doesn't necessarily mean people thought GMOs were safer or more acceptable per se.  Indeed, many of the negative campaign ads focused on possible "paydays for lawyers" or inconsistencies in the ways the laws would be implemented, rather than focusing on the underlying technology itself.  

The Food Demand Survey has been conducted nationwide and monthly since May of 2013.  In November of 2014, two states - Colorado and Oregon - held widely publicized votes on mandatory GMO labeling.  These data can be used to calculate a difference-in-difference estimate of the effect of mandatory GMO labeling vote on awareness of GMOs in the news and concern about GMOs as a food safety risk.

The survey asks all respondents, every month, two questions of relevance here.  First, “Overall, how much have you heard or read about each of the following topics in the past two weeks” with response categories: 1=nothing; 2=a little; 3=a moderate amount; 4=quite a bit; 5=a great deal.  Second, we also ask, “How concerned are you that the following pose a health hazard in the food that you eat in the next two weeks” with response categories: 1=very unconcerned; 2= somewhat unconcerned; 3=neither concerned nor unconcerned; 4=somewhat concerned; 5=very concerned.  One of the 16 issues we ask about is "genetically modified food."

These data allow us to calculate a so-called difference-in-difference estimate.  That is - were people in CA and OR more concerned about GMOs than people in the rest of the country (this is the first difference) and how did this gap change during and after all the publicity surrounding the vote (this is the second and third difference)?  The "treated" group are the people in CA and OR while the "control" group consists of people in all other US states.

To analyze these question, I split the data into three time periods - "before" the vote (the months prior to September 2014), during the vote (Sep, Oct, Nov, Dec of 2014 and Jan of 2015) and after the vote (all the months after January 2015).  There were 485 "treated" people in CO and OR before the vote, 172 in these locations during, and 908 in these locations after (out of a total sample size of almost 49,000). 

In terms of awareness, here's what I found. 

Compared to people other parts of the U.S., people in CO and OR indeed reported hearing more about GMOs in the news during the ballot initiative vote than they did before and after (the increase in news awareness during the months surround the vote was statistically significant at the 0.01 level).

But, here's the key question.  Did the vote increase or decrease concern about GMOs as a food safety risk?  Apparently there was no effect.  The graph below shows, as compared to people in other states where there were no votes, there was actually a small increase in concern for GMOs in CO and OR in the months during the vote (however, the increase was not statistically significant, p=0.36), which then fell back down to pre-vote levels after the vote.  

So, despite evidence that the vote initiative on mandatory labeling led to an increase in awareness of GMOs in the news, it did not substantively affect concern about GMOs one way or the other.

Benefits and Costs of Local Food Policies

I've been critical of many of the local foods policies that have been touted as solutions to economic, environment, or health problems (e.g., see here or here).  Much of my criticism is rooted in the fact that advocates have failed to meaningfully and accurately articulate how policies to, say, require local schools or hospitals to source food within a certain radius or to subsidize farmers markets will improve the environment or increase a region's economic growth.

In the debate about local foods, proponents and opponents have largely talked past one another, and one of the hindrances to more fruitful dialog is the lack of a formal mathematical model from with people can illustrate the effects they believe to disseminate from promotion of local foods.  While surely not everyone will agree with the details of any particular model and the conclusions coming from it, a model at least provides a starting-point from which one can articulate what they believe the model is missing which would justify or condemn local food policies.

Enter this new paper by Jason Winfree and Philip Watson in the American Journal of Agricultural Economics.  The authors present just such a mathematical economic model in which one can talk about the benefits and costs of local food policies.  They generally show that local food policies are more costly than beneficial.  However, they do show that in certain conditions (if there is a lot of market power and extensive externalities), it is possible (though not necessarily likely), that local foods policies can produce more benefits than costs. 

In a blog post at Oxford University Press discussing their paper, they summarize their findings as follows.   

The formal model generally concludes that the traditional case for comparative advantage remains largely unaffected by these concerns [about the environment, food security, and economic growth]. In fact, in many instances, the buy local movement harms the local economy. One of the basic tenets of economics is that two regions can be made better-off through trade. Buying local generates inefficiencies that reduce social welfare. The policies intended to support the “buy local” movement results in a region producing a good where they do not have a comparative advantage. The costs of policies increase because the locally produced good forgoes the benefits of specialization and the division of labor.

Consider the case of negative externalities generated by foods brought in from distant locales. Proponents claim that pollution generated from transporting non-local goods to local markets justifies their claim. However, if the externalities require some kind of public response, a Pigovian tax makes more economic sense than encouraging “buy local.” The tax addresses the source of the externality. Buying local leaves the externality in place and does not address the inefficiency associated with deviating from comparative advantage.

Trends in Animal Welfare Concerns and Meat Demand

I'm preparing a talk at next week's annual meeting of the Agricultural and Applied Economics Association (AAEA) on trends in consumer concerns about animal welfare, and I thought while I'm at it I'd share a few of the results here.  All the results below come from the Food Demand Survey (FooDS), a monthly survey of over 1,000 consumers that has been ongoing for over four years (each of the graphs below contains information obtained from more than 48,000 survey responses).

One of the first things we ask in the FooDS relates to "food values".  A list of 12 items is presented to respondents and they are asked which are most/least important when buying food.  Respondents have to click and drag four of the items into a "most important" box and also put four in a "least important" box, leaving four in neither box.  The nice thing about this questioning approach is that it requires a tradeoff - respondents can't say all issues are important and they have to indicate some as least important.  To create a scale of importance, I simply calculate the percent of times an issue is placed in the most important box and subtract it from the percent of times it is in the least important box, creating a measure that ranges from 100% to -100%.  

So, where does animal welfare fall in importance?  As the graph shows, it is 7th in the middle of the pack (this graph combines all the data from the last four years).  Animal welfare is much less important than taste, safety, nutrition and price but more important than origin, fairness, or novelty.  About 18% of consumers place animal welfare in the most important box and 31% place it in the least important box, creating a score of 18%-31%=-14%

The importance of animal welfare has increased a bit over time.  Here are the month-by-month averages going back more than four years.  Animal welfare importance has remained fairly stable for the past year, hovering around -10%, but this is higher than in 2013, when it was as low as -20%.

One question that might arise is "so what"?  Do these statements of importance on animal welfare and other food values have any relation to meat demand?  The answer is "yes" - there are some strong correlations.  In FooDS, we also ask people to make nine choices between different cuts of meat (and two non-meat items) at different prices.  A crude index of demand can be calculated as the number of times (out of nine) a meat product, say beef steak, is selected minus the number of times (out of nine) a non-meat item is selected (this produces a measure that ranges from -9 to +9).  Here are estimated relationships between food values and demand for steak and ground beef (controlling for demographics and other factors). 

Relationship between food values and steak demand

Relationship between food values and steak demand

Relationship between food values and ground beef

Relationship between food values and ground beef

The above graphs show that people who have higher concern for animal welfare have lower demand for steak and ground beef (recall the vertical axis is a demand index that ranges from -9 to +9; for reference the mean demand index for steak is 0.9 and the mean for ground beef is 1.32).

Results indicate that if an individual who indicated animal welfare as the most important food value (a score of +1) instead indicated animal welfare as a least important food value (a score of -1), steak would be chosen -0.42 fewer times on average. Similarly for nutrition, results indicate that if an individual who indicated nutrition as the most important food value (a score of +1) instead indicated nutrition as a least important food value (a score of -1), steak would be chosen -0.33 fewer times on average.  Conversely, people who think taste and appearance are relatively important food values have higher demand for steak and ground beef.  Not surprisingly, importance on price is a positive contributor for ground beef demand but a negative contributor for steak demand.   If an individual with the four most favorable food values for steak demand were replaced with an individual with the four least favorable food values, then steak demand would increase by 2.49 (given that the mean is 0.9, this is a very large change). The take-home: to the extent animal welfare increases in importance over time, these results suggest demand for beef will fall (I find similar results for pork and chicken products too).  

By, the way, I can place these food values in the context of other correlates with demand.  Here is a comparison of different determinants of steak demand (the upper left-hand image is the food values graph that was already shown but rescaled so comparisons are made to the lowest impact).  Next to food values, household income, political ideology, and gender have the biggest impacts on steak demand.  Steak demand is higher for higher income and more conservative individuals and for males.  

Correlates with Steak Demand

Correlates with Steak Demand

In FooDS, we also ask, for more than 16 different issues,  “Overall, how much have you heard or read about each of the following topics in the past two weeks” and we classify responses as 1=nothing; 2=a little; 3=a moderate amount; 4=quite a bit; 5=a great deal.  Below are the results pertaining to animal welfare related issues.

Awareness of issues in the news over time

Awareness of issues in the news over time

Result seem to suggest an up-tick in awareness of animal-welfare related issues during 2016, which subsequently declined.  However, this increase in awareness also occurred for ALL the issues we track (the solid black line), many of which (like E. Coli, pink slime, etc) have nothing to do with animal welfare.  

A similar pattern emerges in relation to "concern" for the same set of 16 or so issues over time.  We ask, “How concerned are you that the following pose a health hazard in the food that you eat in the next two weeks”, where 1=very unconcerned; 2= somewhat unconcerned; 3=neither concerned nor unconcerned; 4=somewhat concerned; 5=very concerned.  (Yes, I realize, asking whether animal welfare is a "health hazard" is strange, but that's what data I have).  The graph below slows a slight uptick in concern for animal welfare related issues, but this is also true for ALL the issues we track (the solid black line).  In other words, people don't seem to be discriminating much between animal welfare and other food issues.  

Concern for various issues over time

Concern for various issues over time

Finally, one of the questions we ask every month is whether respondents are vegetarian or vegan.  There has been an increase in this self-reported measure over time (see here or here for my previous discussions of these data).  In early 2014, the figure was between 3% and 4% of respondents.  This has roughly doubled and we now routinely see values between 7% and 8% of respondents self-identifying as vegetarian or vegan.  

Are you a vegetarian of vegan?  (% saying "yes")

Are you a vegetarian of vegan?  (% saying "yes")

Redefining Agricultural Yields

I saw some recent discussion on Twittter of this post by Emily Cassidy in which she discusses her 2013 paper in Environmental Research Letters coauthored with Paul West, James Gerber, and Jonathan Foley.  The subtitle of her post and paper is: "from tonnes to people nourished per hectare."

It's an interesting and thought provoking piece, and at the heart of it are figures like this one Cassidy posted on her blog:

She writes:

And as you can see from the map above, a lot of farmland in the United States is not used to grow food, it is used to grow animal feed and biofuels. Over two-thirds of the calories grown in the U.S. are fed to livestock. And for every eight calories of corn and soybean fed to livestock, only one of those calories ends up on our plates.

In the published paper, the authors argue they, "illustrate where tremendous inefficiencies in the global food system exist today" and reach the normative judgement that, "shifting the use of crops as animal feed and biofuels would have tremendous benefits to global food security and the environment."  

There are some methodological issues that I think are important in this discussion, some of which the authors themselves acknowledge and discuss, but I'll get to those in a minute.  

First, I want to make the case that this state of affairs is not as "inefficient" or "irrational" as is often portrayed.  

For one, take a look at the above figure.  Is there some commonality between the locations with more green (more production for "food" - supposedly the "good" outcome)?  These locations tend to be the spots that are relatively poorer, hungrier, and more malnourished.  That ought to give us pause - that the locations with the supposedly "good" farming practices have some of the biggest challenges with under-nourishment.  

Now, we shouldn't mistake correlation with causation (i.e., production for "food" probably isn't causing food security problems), rather I suspect this pattern is largely explained by income effects.  What we're probably seeing in the above graphs relates not to production practices per se but to preferences of relatively rich people vs. relatively poor people.  Our production practices are constrained by what people want to buy.  In the same way one can argue it's "inefficient" for a relatively wealthy person to have a bigger car or bigger house or private jet, one can also point out that this sort of person has the means to pay for enjoyable things that are somewhat less efficient.  If all we cared about was caloric/protein efficiency, we humans should eaten a spartan, undiversified diet of beans and rice. So, that's the first answer: people in relatively richer countries eat more meat because they like it and they can afford it.  Maybe we shouldn't like or want to eat animal products, but as economists are fond of saying, de gustibus non est disputandum.

Beyond "preferences", 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.

This gets to some of the methodological issues in these sorts of calculations.  As I've discussed before using various analogies, there are two ways to view livestock.  One is that they are inefficient - using up a lot of energy to make food.  Another is that they are good at converting one form of energy that is highly storeable/transportable but untasty (field corn, soy, sorghum) to another form (eggs, meat, dairy) that we like to eat.  Rarely do these sorts of research papers include the the calories (or energy) used in food processing.  It is a mistake to compare the calories in steak to the calories in a wheat kernel.  The wheat kernel requires energy/processing to convert to flour and then more energy to get pasta or bread.  In the developing world (largely the green countries in the above graph), I suspect a lot of this processing isn't measured because it occurs in the household.  The cowpeas, cassava, or beans require grinding and cooking to be human-edible, and the energy used to accomplish this isn't measured.  The historian Rachel Laudan has written eloquently on this in a number of places (see her blog or book), and it is a feature of our modern food system that is vastly under-appreciated.

The other two issues the authors mention in their journal article as worthy of additional research are food waste and the ability of livestock like cattle to convert human-inedible calories from grasses into human-edible meat/dairy.  On that last topic, there is a nice report by the Council for Science and Technology written Jude Capper and others.  To those issues, I'd also add that we need to think about water use (the corn/soy/wheat in the U.S. is largely un-irrigated whereas fruits/veggies require comparatively large amounts of water often supplied by irrigation; of course, livestock consume water too) along with use of other inputs like pesticides and fertilizer (again, fruits/veggies can be relatively heavy users of pesticides).

Where does that leave us?  I'm not going to say it's perfectly rational for the U.S. to devote the majority of it's cropland to corn/soy/wheat, but I think this discussion suggests it's not irrational either.  

P.S.  In terms of tonnes of production USDA data suggest in the 2016-2017 marketing year, 40% of the corn/sorghum/barley/oats produced and imported in the U.S. went to "food, alcohol, and industrial use", 32% went to "feed and residual use",  14% was in "ending stocks" (i.e., it was stored for future use), 14% was exported, and the small remaining amount was "seed use".