Measuring Beef Demand

There has been a lot of negative publicity about the health and environmental impacts of meat eating lately.  Has this reduced consumers' demand for beef?  Commodity organizations like the Beef Board run ads like "Beef It's What's for Dinner."  Have these ads increased beef demand?  To answer these sorts of questions, one needs a measure of consumer demand for beef.  In my FooDS project, I try to measure this by using consumers' willingness-to-pay for meat cuts over time.  But, there are other ways.

I just ran across this fascinating report Glynn Tonsor and Ted Scroeder wrote on beef demand.  At the onset, they explain their overall approach.

One way to synthesize beef demand is through construction of an index that measures and tracks changes in demand over time. An index is appealing because it provides an easy to understand, single-measure indicator of beef demand change over time. A demand index can be created by inferring the price one would expect to observe if demand was unchanged with that experienced in a base year (Tonsor, 2010). The “inferred” constant-demand price is compared to the beef price actually transpiring in the marketplace to indicate changes in underlying demand. If the realized beef price is higher (lower) than what is expected if demand were constant, economists say demand has increased (decreased) by the percentage difference detected. Applying this approach to publically available annual USDA aggregate beef disappearance and BLS retail price data provides information such as contained in Figure 1 indicating notable demand growth between 2010 and 2015 based upon existing indices currently maintained at Kansas State University.

They then show the beef demand index that Glynn has been updating for several years now based on aggregate USDA data.

In their report, Tonsor and Schroeder show, however, that measures of beef demand depend greatly on: 1) the data source being used, 2) the cut of beef in question, and 3) consumers' region of residence.  For example, here is a different beef demand index based on data from restaurants (or the "food service sector") segmented into different types of beef.  You'll notice the pattern of results below differs quite a bit from the aggregate measure above.  And, whereas demand for steak fell during the recession, demand for ground beef rose.

Another interesting result from their study is that the commonly used retail beef price series reported by the Bureau of Labor Statistics doesn't always mesh well with what we learn from from retail scanner data (in their case, data from the compiled by the company IRI).  Not only are BLS prices a biased estimate of scanner data prices, the bias isn’t constant over time.  In the report, Tonsor and Schroeder speculate a bit on why this is the case.  

In the near future, Glynn and I aim to compare my demand measures from FooDS with these demand measures. 

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. 

Food Demand Survey (FooDS) - February 2017

The February 2017 edition of the Food Demand Survey (FooDS) is now out.  

From the regular tracking portion of the survey, we find that (compared to one month ago) willingness-to-pay (WTP) decreased for all food products, but most especially for chicken wings and the two non-meat products.  For some historical context, I thought I'd also show changes in WTP for steak and ground beef over time and show how they compare with changes in retail prices as reported by the Bureau of Labor Statistics (BLS).  

The above graphs reveals three things.  First, WTP is not the same thing as a price.  WTP is (at least in theory) a "pure" measure of demand, but prices can be affected by demand and by supply-side factors.  Second, despite the above statement, it appears there is some relationship between the two measures as the correlations between WTP and prices are 0.44 for ground beef and 0.55 for steak.  Third, WTP as measured by FooDS is much more volatile from month-to-month than are prices.  

You can read the whole report for the results from the other tracking portions of the survey.

Several new ad hoc questions were added this month to investigate how consumers respond to information about the herbicide glyphosate.  Working with one of my Ph.D. students, Trey Malone, we picked this topic because it is one we thought consumers were unlikely to have much knowledge about but for which there had been many news stories written.  We were interested, in particular, about forms of confirmation bias - where people seek out information that may confirm their prior beliefs, and by the research in cultural cognition, which suggests we choose information to believe based on our "tribe."  

We asked respondent’s willingness-to-pay for organic vs. non-organic apples and granola bars before and after receiving information about glyphosate at GMOs. Respondents were randomly
allocated to one of five treatments. Respondents in the first four treatments were provided an article to read from one of four sources: The Pulse of Natural Health Newsletter, Food Babe, National Review, or Science Magazine. So far this would be a pretty standard study on the effects of information.  Then, in the fifth treatment, respondents were allowed to pick which of the four sources of information they wanted to read (they were given the name of the source and the title of the article).

We will report the full results associated with the effects of information on willingness-to-pay later, however, I will note that the “negative” information about glyphosate from Natural Health and Food Babe had a much bigger effect than the “positive” information from National Review and Science Magazine.

We asked all respondents, “How trustworthy or untrustworthy do you consider each of the following news sources for information regarding food?” They responded on a scale from -5=very untrustworty to +5=very trustworthy. Science Magazine was the most trusted with a mean response of 1.8. Next was National Review at 1.33 followed by Natural Health at 1.28. Far behind (and statistically significantly lower) was the Food Babe at 0.55.

Despite the fact that the Food Babe was the least trusted source of information, in the treatment where individuals could chose which information they wanted to read, 25.4% chose to read the article from the Food Babe. The only source chosen more often was Science Magazine (picked by 40.5% of respondents). Natural Health was picked by 19% and National Review by 15.1%.

When behavioral biases meet the market

Have you ever gone shopping, only to be overwhelmed by the number of options available to choose from?  You're not alone.  In fact, psychologists have created a name for the phenomenon: the "excessive choice effect."  In one of the more famous studies on the topic, aptly titled "When choice is demotivating", the authors found that when consumers were offered the opportunity to buy an exotic jam, 30% bought when only 6 varieties were presented.  However, only 3% of consumers bought when 24 variety were presented.  On the face of it, this seems to violate basic economic logic: when there are more varieties available, there is a greater likelihood of finding one you like, and thus there should be a higher likelihood of purchase.  

These sorts of findings have led to popular books (like this one titled The Paradox of Choice) and some bold claims that we'd all be happier and our society would have less depression if we (or namely the government) restricted our choice and freedom.  

Well, as it turns out, subsequent studies found that the "excessive choice effect" doesn't always exist, and the phenomena is much more nuanced than first suggested.  

Now, enter of of my Ph.D. students, Trey Malone (who is on his way to an assistant professor position at Michigan State University).  Our co-authored paper on this topic was just released by the Journal of Behavioral and Experimental Economics.  Trey's insight was this: if the "excessive choice effect" (or ECE) exists, surely companies will want to do something about it.  It's bad business to present consumers with so many options that they don't make a purchase.  Yet, in many markets (and in particular in the market for craft beer which was the focus of our study), there is an apparent explosion of variety of choice.  What's going on?  

From the paper:

In a competitive market, the choice architecture is endogenous, and sellers compete to provide environments that consumers find appealing, thereby increasing profits. In such cases, the market, at least partially, provides incentives to ameliorate the ECE by, for example, reducing search costs for consumers (e.g., see Kamenica, 2008; Kuksov and Villas-Boas, 2010; Norwood, 2006). This raises the possibility that ECE may arise in laboratory contexts or oneshot field experiments while at the same time having limited relevance in day-to-day business decisions. Whereas prior research mainly focus on the identification of an ECE, we show that sellers have access to market-specific mechanisms (or informational nudges) that narrow its influence. We demonstrate that if the ECE exists, sellers can mitigate or exasperate its negative effects through targeted interventions.

The interventions (or private nudges) that we consider were beer sellers providing consumers more information about the varieties either through a "special" or the provision of beer advocacy scores.  

Trey worked with a local wine bar in town to run field experiments. Unbeknownst to the patrons, we strategically varied the number of options on the beer menu over time.  The menu either presented 6 or 12 options (note that the menu of 12 included all 6 of the varieties on the smaller menu).  And, we also varied information about the beers as previously indicated, sometimes there was no extra information (the control) and other times we tried to reduce search costs by labeling one of the options a "special" or by providing beer advocacy scores for each option (these are akin to a quality rating by a reliable third party).  

The results are summed up in the following graph:

Thus, we found that the excessive choice effect was alive and well in a real-life purchase setting (people were more likely to NOT buy a beer when there were 12 options as compared to 6), but only when no extra information was provided.  The effect reversed itself when the menu included beer advocate scores. These results show how the excessive choice effect might be turned on and off by companies manipulating search costs.  

One of the main lessens here is that it would be a mistake to take a finding of a supposed "behavioral bias" (like the excessive choice effect) in a laboratory experiment to make grand claims for large government interventions without also considering how consumers and businesses themselves might react to those very same biases in the course of everyday life.  

How much will that organic, gluten free, vegetarian diet cost you?

I recently ran across this interesting website and online tool put out by the lender  According to their website:

The total cost of a grocery bill is majorly influenced by consumer shopping habits. According to the U.S. Department of Agriculture (USDA), the national average weekly grocery bill for individuals from ages 19 to 71 is $61.85. By referencing the USDA recommended balanced food plate to create a healthy grocery list and the national average for an individual food budget into consideration, we’ve uncovered how changing one’s diet to reflect a gluten free, organic, vegetarian or vegan diet can significantly affect the cost of their grocery bill.

Here is one of several graphics at the site.

On that note, I'll also link to a paper I recently published with Bailey Norwood where we compare the food expenditures of self-identified vegetarians and vegans to non-vegetarians.