Does everybody prefer organic?

A few years ago I was giving a talk at a conference in Europe, and I showed the following figure illustrating demand curves for organic milk.  The curves were created based on an analysis of grocery store scanner data (the underlying estimates and analysis are in this paper in the journal Food Quality and Preference).  

I showed the graph to illustrate to the group how demand for organic milk was lower for people that placed a higher relative importance on food safety than it was for people who placed a lower relative importance on food safety.  But, almost in passing, I told the audience that they might take the figure with a grain of salt because it shows that even if organic was the same price as conventional (i.e., the organic premium was $0), the demand curves predict market shares for organic of only about 8% and 14% (depending on the importance of food safety), which I thought was implausibly low.  

After my presentation, an individual who worked for a European food retailer asked why I thought the figures were implausibly low.  I said that I presumed most people would choose organic if it were priced the same as conventional. He said, however, that his retail experience was fully consistent with the graph I showed - even when he substantially lowered the price premium for organic, the market share remained relatively low.   

I've had those anecdotal thoughts in my mind for a while and recently was able to test them out in a more controlled, survey setting where we could vary product price in a way that there aren't confounds.  One of the "confounds" with the European's observation was likely the fact that the organic attribute was likely to appear on less-well-known brands, so we don't know if it was the lesser-known brand or the organic attribute causing the low market share.  Our attempt to remove these confounds is this new paper in the journal Applied Economics Letters co-authored with Seon-Woong Kim and Wade Brorsen.  

We conducted studies with apples and with milk.  In the studies, people made choices between different apples that varied by color (red or green), condition (bruised or not bruised), price, and production method (organic or conventional).  Alternatively, people made choices between milk that differed by fat content (skim, 1%, 2%, or whole), package type (cardboard or plastic), price, and production method (organic or conventional).  

We used the choices to infer the demand curves for organic vs. conventional, allowing for the fact that different consumers are likely to have different preferences for organic and other milk/apple attributes.  Here's what we found.

Even in these controlled studies, we find that if organic were priced the same as conventional (a price premium of 0%), not everyone would buy organic.  Priced evenly with conventional, organic would pick up only about 60% of the apple market (the remaining 40% going to conventional), and organic would pick up only about 68% of the milk market (the remaining 32% going to conventional).  

Given differences in yield and production costs, organic is almost surely going to be routinely higher priced than conventional. But, even if this weren't the case and organic could be competitively priced, these survey results show us that not every prefers organic food.

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. 

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.  

TASTE TRUMPS HEALTH AND SAFETY: INCORPORATING CONSUMER PERCEPTIONS INTO A DISCRETE CHOICE EXPERIMENT FOR MEAT

That is the title of a paper I just published with Trey Malone in the Journal of Agricultural and Applied Economics.  

Here are some of the key results:

“Our participants also indicate that they perceive chicken breast to be the healthiest option in our sample. Both beef products would generate substantial changes in WTP by increasing their perceived healthiness to that of chicken. For example, if hamburger had the same average health perceptions as chicken breast, WTP for hamburger would increase by $0.69. Deli ham, however, would experience an $0.83 increase in WTP if consumers were to believe it was as healthy as chicken breast. Even chicken wings would experience a $0.52 increase in WTP through a perception change.”

and

“The nonmeat options are actually perceived as safer than the meat options. As such, if the average participant perceived hamburger to be as safe as beans and rice,WTP would increase $0.34. Of all products, deli ham would benefit the most by an increase in perceived safety to the level of beans and rice. In fact, our sample indicates that pork products are not very highly appreciated. As noted, deli ham is perceived to be the worst tasting, least healthy, and least safe alternative in the choice set. Those negative perceptions are costly. If participants were to perceive deli ham as equal to chicken breast in taste and health, and equal to the perceived
safety of beans and rice, WTP for deli ham would increase by more than $2.”

You can read the whole thing here.

Where do people eat the most meat?

It seems a fairly simple question: In which U.S. states do people eat the most meat?  Yet, there is surprisingly little good, publicly available data on this question.  Yes, there are fun maps like this one at Slate, but they are far from scientific or data driven.  

I thought I'd try to partially fill this void by turning to data from my Food Demand Survey (FooDS) that has been running now for almost four years.  Because I've surveyed over 1,000 people in the U.S. for about 44 months, that means I have responses from over 44,000 people spread all across the country that I can use to help look for geographic differences.  

In FooDS, each person is told "Imagine you are at the grocery store buying the ingredients to prepare a meal for you or your household.  For each of the following nine questions that follow, please indicate which meal you would be most likely to buy."  Then, they are presented with nine questions that look like the one below.  The only differences across the questions are the prices assigned to each item and the order of the items.      

For sake of simplicity, I counted the number of times each person chose steak, how many times they chose chicken breast, etc.  Thus, the maximum possible "score" a person could have for each item is 9 and the lowest is 0.  To be clear, this isn't a measure of consumption, but rather it is an index of demand.  It is a measure of how much people "like" each of the choice options relative to all the other choice options.  For point of reference, across all the people in my sample, the most frequently chosen option was chicken breast (chosen on average 2.43 times out of 9) followed by ground beef/hamburger (chosen on average 1.33 times out of 9).  The least popular meat items were pork chop and ham, chosen on average 0.80 and 0.68 times, respectively, out of 9.

I won't go into all the hairy details here (email if you want to know more), but I then estimated some statistical models to infer how often, on average, consumers in each state chose each of the meat options.  Then, I calculated how different (in percentage terms) each state was from the mean number of choices, and I created maps.

I'll start with one that has a very obvious regional pattern: chicken wings.

Chicken Wing Demand by State

Demand for chicken wings is highest in the southeast US, where people chose this option 15% to 44% more often than in the average person in the US.  Consumers in western states like Oregon, Idaho, and Arizona chose wings 15% to 27% less often than the average consumer nationwide.

For other products, there is less of a regional pattern.  Below is the map for beef steak.  Demand for steak is highest in California, Nevada, Washington, Oklahoma, Minnesota, Illinois, Florida, and New York.  Steak demand is lowest in Idaho, Utah, Missouri,  and the Appalachian regions, Tennessee, Kentucky, and West Virginia.

Beef Steak Demand by State

While we are on beef, here is the map for hamburger/ground beef.  For ground beef, demand is generally highest in the upper midwest and is lower on the coasts.

Demand for Ground Beef by State

A somewhat similar pattern emerges for deli ham (shown below), although the location of heaviest demand moves a bit south and east relative to that for hamburger.  

Deli Ham Demand by State

Below is the map for pork chops.  This map is interesting in the sense that there are several instances where some of the highest demand states are situated adjacent to some of the lowest demand states (e.g., Oregon next to California; Oklahoma next to Texas; etc.)  However, one thing to note in the case of pork chops is the scaling: there isn't much difference across any of the states.  Consumers in Missouri have the highest pork chop demand, but only chose pork chops 2.7% more than the average consumer.  Consumers in California have the lowest pork chop demand, but only chose pork chops 3.3% less than the average consumer nationwide.  

Pork Chop Demand by State

The last individual meat product is chicken breast.  As shown in the map below, chicken breast demand is generally highest in the west and the northeast.  I'm not at all surprised to learn that chicken breast demand is near the lowest in my home state of Oklahoma (at -4.5%), trailing only North Carolina, Missouri, and Mississippi.  

Chicken Breast Demand by State

Finally, to round things out, here is a map associated with overall meat demand.  This figure was calculated by determining how many times a person chose any of the six aforementioned meat products (recall there were nine total options, three of which were non-meat).  On average people chose a meat option 7.03 times out of 9 total choices.  However, as the map below shows, there is some heterogeneity across states.  Overall meat demand is highest in the Midwest: consumers in Illinois, Indiana, and Iowa chose any meat option 1%+ more often than the average consumer.  Lowest overall meat demand was in places like California, Arizona, Maryland, Utah, New Jersey, and Massachusetts, where consumers chose a meat options at least 1% less often than the average consumer.  

Overall Meat Demand by State