Banning Soda Purchases Using Food Stamps - Good idea or bad?

According to Politico:

“The House Agriculture Committee this morning is delving into one of the most controversial topics surrounding the Supplemental Nutrition Assistance Program: whether to limit what the more than 40 million SNAP recipients can buy with their benefits. Banning SNAP recipients from being able to buy, say, sugary drinks has gotten some traction in certain public health and far-right circles, but it looks like the committee’s hearing will be decidedly open-minded on the debate. ”

I've written about this policy proposal several times in the past.  It's an example of good intentions getting ahead of good evidence.  Do SNAP (aka "food stamp") participants generally drink more soda than non-SNAP participants?  Yes.  Is excess soda consumption likely to lead to health problems?  Yes.  But, will banning soda purchases using SNAP funds reduce soda consumption.  Probably not much.  

In fact, I just received word that the journal Food Policy will publish a paper I wrote with my former Ph.D. student, Amanda Weaver, on this very topic.  First is the logical (or theoretical) argument:

“In public health discussions, however, the conceptual arguments related to the Southworth hypothesis have received scant attention (see Alston et al., 2009, for an exception). A soda consuming SNAP recipient who spends more money on food and drink than they receive in SNAP benefits can achieve the same consumption bundle regardless of whether SNAP dollars are prohibited from being used on soda by rearranging which items are bought with SNAP dollars and which are bought with other income. Thus, an extension of the Southworth hypothesis to this case would predict little or no effect of a soda restriction as long as the difference in total food spending and SNAP benefits does not exceed spending on sugar-sweetened beverages. ”

If that wasn't transparent, consider the example I gave in this paper I wrote for the International Journal of Obesity:

“To illustrate, consider a SNAP recipient who receives $130 in benefits each month and spends another $200 of their own income on food for total spending of $320. Suppose the individual takes one big shopping trip for the month and piles the cart with food, including a case of Coke costing $10. Suppose the cost of all the items in cart comes to $320. SNAP benefits cannot cover the entire amount, but the individual can place a plastic divider on the grocery conveyer belt, put $130 on one side (to be paid for with the SNAP benefits), and put $200 on the other side (to be paid for with cash). Now, suppose there is a ban on buying soda with SNAP. What happens? The individual can simply move the $10 case of Coke from the SNAP side of the barrier to the cash side and replace it with other items worth $10. The end result is the same regardless of whether the SNAP restriction is in place or not: spend $320 and Coke is purchased. ”

So, in theory, people can "get around" these sorts of SNAP restrictions very easily making the restriction ineffectual.  

Now, back to my Food Policy paper.  Our experiment results show the following: 

“As conjectured by H3, for the 65% of participants (78/120) who did not consume soda in T3, soda expenditures were unaffected soda restriction. H4 posited that consumers who had expenditures of more than $2 (including a soda purchase) in T3 would likewise be unaffected by the soda restriction as they moved to T4. However, this hypothesis was rejected (p<0.001). Soda expenditures fell from an average of $1.000 to $0.588, contrary to the theoretical prediction. We find that 58.8% (20/34) of the respondents to which the hypothesis applied behaved as the theory predicted (they did not change soda expenditures); however, the remaining 41.1% (14/34) reduced soda expenditures when moving from T3 to T4. ”

So, maybe restrictions on soda purchases by SNAP recipients will affect their soda consumption after all.  Here are our thoughts on that:

“Previous research has identified heterogeneity in cognitive abilities and in consistency with economic theories (Choi et al., 2014; Frederick, 2005), and future research might seek to explore the extent to which cogntive ability plays a role in the ability of extramarginal consumers to recognze that they can achieve the same consumption bundle despite the soda restriction. In addition, our experiment was a one-shot game. In a field environment, respondents can talk to friends, gain experience, and alter behavior over time as they learn that the same consumption bundle can be achieved despite the restriction. This learing conjecture could be tested in an experimental setting by conducting repeated trials with feedback. It could also be tested using field data (after a policy was passed) by investigating the change in soda purchases for inframarginal buyers over time. Another hypothesis that could explain the anomolous result is that the soda restriction could have non- pecuinary effects, providing information about realtive healthfulness of items or signaling what people “should” be doing. For example, Kaplan, Taylor, and Villas-Boas (2016) found that, following a widely publisized vote to tax sodas, Berkeley California residents reduced soda consumption before the tax was even put into place, illustrating significant information effects surrounding soda consumption policies. Future research could further explore this signaling effect by including a treatment that restricts purchases of food items not generally percieved as unhealthy or by including survey questions about percieved healhfulnes of an item before and after a restriction.”

Another thing to keep in mind is that such restrictions may limit people's willingness to participate in SNAP in the first place.  Even in our experimental context, we find that soda restrictions do indeed affect participation as measured by use of the "coupon" or "stamp" (both whether it is used at all and the amount of the coupon used).  

All in all, I think the above discussion shows that despite the intuitive appeal of a simple policy restricting SNAP purchases, the actual consequences are likely to be much more complicated. 

Economics and Obesity Policy

The International Journal of Obesity just released a a short review paper I was invited to write, which discusses the economics of policies aimed at reducing obesity. In the paper, I touch on the economic approach for thinking about government intervention in this space and whether there are market failures that would justify intervention.  I then move on to discuss a variety of specific issues that are often discussed in relation to obesity such as farm policy, soda taxes, healthy food subsidies, food assistance programs (and proposed restrictions on them), and information policies. 

Here is the conclusion:

“This article presented a somewhat pessimistic view on the ability of government policy to substantively influence obesity prevalence. Obesity is a complicated and multifaceted issue. So too are the effects of anti-obesity policies. One response is to argue for an all-out ‘war’ on obesity. It probably is true that government policy mandating what farms grow, restricting the
supply and type of food to consumers, and controlling prices, offerings and advertisements by food manufacturers could reduce obesity prevalence. But, is this the type of coercive
society in which we would like to live? Society faces very real tradeoffs between economic freedom, technological progress, and obesity prevalence. These sorts of tradeoffs are unfortunate, but they reflect very real constraints to effective economic policy making.”

My paper joins several others that critically evaluate anti-obesity policies.

Country of Origin Labeling and Cattle Imports

My post from back in November about the (lack of a) relationship between the repeal of mandatory country of origin labeling (MCOOL) and cattle prices seems to have been receiving a lot of attention lately.  A main driver seems to be that Tomi Lahren, a conservative journalist with a large social media following, again promoted the idea that MCOOL was a cause of declining cattle prices in a video interview with R-CALF's CEO.  For a summary of the controversy see this article by Carrie Stadheim in the Tri-State Livestock News. 

I won't re-adjudicate my original arguments as you can read them for yourself.  However, I do want to bring some data to bear on an additional claim that has been made in relation to MCOOL and cattle prices.  The article in the Tri-State Livestock News contains a quote that seems to be attributed to me, but I said nothing of the sort.  I presume, instead, the "he" in quote below is the R-CALF CEO.  Here's the quote:

““Without COOL…meatpackers can reach out and source live cattle and beef from 20 countries, bring it into the US, sell it to unsusepecting consumers with a US inspection sticker on it, even though it comes from a foreign source and consumers don’t know the difference,” he said.”

So, let's take a look at the implication of this argument.  We repeal MCOOL, and now meatpackers turn to the 20 countries and import more meat.  And, presumably, this caused the decline in cattle prices?

Well, here is USDA data on meat and veal imports to the US and on live cattle imports to the US.  The solid black line is the date of the repeal of MCOOL.

There was an uptick in live cattle imports right after repeal of MCOOL but then an even more dramatic decline.  Overall the above figure suggests no discernible impact of MCOOL on US imports of beef or cattle.  If I look at the total imports the first 11 months of 2015 prior to repeal of MOOL and compare it to the first 11 months of 2016 after the repeal of MCOOL (I use the first 11 months because the December 2016 data is not yet out), I find that, if anything, US imports of beef and cattle are, in fact, down after the repeal of MCOOL by 369 million pounds and by 297,290 head, respectively.   

Here's the thing.  Yes, it is true that: "meatpackers can reach out and source live cattle and beef from 20 countries, bring it into the US".  But, all those countries selling meat to the US can sell it instead to dozens and dozens of other countries instead.  And, why would these countries try to sell more meat to the US when prices are down in this country?  They wouldn't and the didn't.  

In any event, the point of all this isn't to argue for or against MCOOL.  Rather, I'm simply trying to make sure the claims being made about MCOOL mesh with the best evidence we have, and that evidence suggests that repealing MCOOL seems to have had very little effect on cattle prices.  Attention would be better focused on other issues to help ranchers and cattle producers who are currently experiencing financial hardship.  

What do school children want to eat?

In the past I have, at times, been somewhat critical of the National School Lunch Program (NSLP) guidelines destined to make school lunches healthier by reducing calories, sodium content, saturated fat, etc.  It's not not that I'm against healthy kids!  Rather, I bristled at the idea of a bunch of nutritionists, policy makers, etc. setting rules and guidelines for how they think kids should eat without considering how the children would respond to the rules.  Nutritional content is but one of the components we care about when eating - don't we also care about how the food tastes, how much it costs, whether it leaves feeling full, whether it is safe to eat, etc. etc.  In short, the guidelines were established with limited understanding of what children want to eat, and as such we knew very little about whether the rules might increase food waste, increase the frequency of home lunches, cause unintended substitution patterns, and so on.  

In an interesting paper in the most recent edition of the American Journal of Agricultural Economics, a team of six researchers sought to do what should have been done prior to implementing nutritional guidelines.  In particular, the authors studied almost 280,000 school lunch choices of about 5,500 elementary age children in a suburban South Carolina school district.  The authors know the precise foods available at each lunch offering, the nutritional characteristics of the foods, which foods the child selected (or whether the child brought a lunch from home - note that lunch menus were published well in advance), and some of the characteristics of the child who made the choice such as their grade, gender, race, and whether they received free or reduced price lunch.

The authors are able to take all this data to estimate demand curves associated with different food offerings.  Their demand models let them answer questions like the following:

• If the sodium content of a pizza offering were lowered, how would that change the number of children who select it?  
• If a low fat pizza is paired with a peanut butter sandwich, which would most people choose?
• If the caloric content were unilaterally lowered on all offerings, how many more children would bring their lunches from home?       

Here's what the authors find:

“If the protein content of Entrée 1 is increased by 3.2grams (one standard deviation of all entree offerings over the course of study), students are, on average, 2.8 percentage points more likely to select that offering. Increasing the fat content of Entrée 1 by one standard deviation (3.9grams) has a similar effect, though smaller in magnitude; students are only 0.2 percentage points more likely to select Entrée 1 because of this increase in its fat content. Increasing the carbohydrate content has the opposite effect; the average probability of choosing Entrée 1 over the alternatives decreases by 3 percentage points if the carbohydrate content increased by 6.8grams (one standard deviation). Thus, the first row of table 3 reveals that students prefer more fat and protein but dislike additional carbohydrates. While the results for sodium are positive, the effect is not statistically significant.”

There are important differences across children:

“While an increase in the fat content of Entrée 1 increases the average probability that a student receiving free lunch will select it, the same increase in fat reduces the likelihood a student who pays full-price will select Entrée 1. The results also suggest that students who pay full-price are more likely to select offerings with more protein than students receiving free or reduced-price lunches (Bonferroni p-value <0.0001), and those who received free lunches are more likely to reject entrées with additional sodium relative to students who pay full-price or students who received reduced-price lunches (Bonferroni p-value =0.0044).”

The authors use their results to suggest how "schools can increase the healthfulness of their students’ meals by replacing unhealthy options with relatively healthy options that are already popular amongst the students."  One things the authors didn't do (but which is possible given their estimates) is to ask: are the children better or worse off (at least as measured by their own preferences revealed by their short run choice behavior) with the new nutritional standards?  Which types of children are now happier or sadder?  Because there is no price variation in the dataset, the authors can't provide a monetary measure of the loss (or gain) in student happiness, but they could covert it to some other unit they measure - such as grams of protein or calories.  

Nonetheless, this is a really interesting study, and it has a number of important findings.  Here's some from the conclusions:

“Nationwide between school year 2010–11 and 2012–13, the number of students receiving free lunches increased while the number of students purchasing full-price lunches decreased, leading to an overall reduction in participation by 3.7% (Government Accounting Office 2014). The results of our analyses suggest that the underlying preferences for offerings with higher levels of fat and lower levels of carbohydrates may be driving the decline in NSLP participation. Full-price participants are most likely to respond to changes in the nutritional content of the offered entrées by opting out of purchasing a school lunch altogether. Our findings have particularly important implications for the NSLP’s stated goal of reducing childhood obesity as they indicate that children are likely to reject those entrées that are most compatible with this particular aim. However, our results do suggest that the future guidelines reducing sodium levels may not trigger additional participation declines.”

Food and Ag Related Election Results

Donald Trumps surprising electoral win is likely to dominate the headlines for weeks.  But, across the nation there were a variety of less-well-publicized votes on issues related to food and agriculture.  Here are a few of those results. 

Massachusetts Ballot Question 3 appears to have passed with a whopping 78% of the vote.  This state ballot initiative bans farmers from producing eggs from hens in so-called battery cage systems and it bars grocery stores from selling eggs from such systems.  Here are results from my research papers on the effects of this type of regulation in California. 

Oklahoma State Question 777 appears to have failed garnering only about 40% of the vote.  This was a so-called "right to farm" amendment to the state constitution (details here).  Also in Oklahoma, state question 792 passed, allowing (among other things) grocery stores to sell wine and liquor stores to sell cold beer.

Soda taxes appear to have passed in San Francisco and Oakland with roughly 60% of the vote and also appears to have passed in Boulder, CO with about 55% voting in favor.   These cities now join Berkeley, Chicago, and Philadelphia in instituting soda taxes on the premise that they will fight obesity.

Marijuana legalization was on the ballot in several states, While perhaps not considered an agricultural issue, somebody has to grow the stuff!  A majority of California, Nevada, and Massachusetts citizens voted in favor of legalizing recreational marijuana use, but the issue failed to garner majority support in Arizona.