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My State is Better than Yours: Competition between State Food Branding Programs

The journal Agribusiness just released a new paper I co-authored with Clint Neill and Rodney Holcomb. The work was motivated by the observation that every state in the U.S. has an agricultural marketing program aimed at promoting foods from their state. Examples include the “Taste NY” and “Pride of New York” programs as well as “Go Texan” and “California Grown.”

Our questions were two fold: 1) How much do consumers value products labeled with their state’s logo relative to other states’ products, and 2) what are the implications for state marketing programs?

We surveyed 6,900 consumers in an eight‐state contiguous region. For our application, we chose milk, and asked people which of several milk products with different state logos (and a regional or national brand) they preferred at different prices.

Not surprisingly, we find that consumers prefer products with their own state’s logo. For example, Texans’ average willingness-to-pay (WTP) for Texas milk is $4.14/gallon, but Texans’ value for milk from bordering states, New Mexico, Oklahoma, and Arkansas only averaged $1.82, $2.65, and $2.72/gallon, respectively. There are a number of interesting patterns. Here’s an excerpt from the text:

While each state’s consumers tend to prefer their own label and have a distinct order of preference for other states, the asymmetry between states is less clear. For example, Oklahoma consumers are willing to pay $2.84 for the Texas label but Texas consumers are only willing to pay $2.65 for the Oklahoma label, so there is an asymmetry of $2.84−$2.65 = $0.19. Thus, Oklahomans value the Texas label $0.19 more than Texans value the Oklahoma label.

Table 5 shows this type of asymmetry for all combinations. Interestingly, every other state’s consumers value the Colorado label more than Colorado consumers value other states’ labels. Alternatively, New Mexico consumers value all other state brands more than the other states’ consumers value the New Mexico label.

While it is perhaps obvious that people in a state will tend to prefer their own products, it is also important to note that people have some value from agricultural products from other states (and, in fact, some small share of people prefer products from another state). The result is that state branding programs “steal” consumers from other states (the effect is a bit like the prisoner’s dilemma problem). The state branding program looks great if your the only state that has the program, but if all states have their own programs, the effects partially serve to cancel each other out. Here’s what we write about this so-called “beggar thy neighbor” effects:

In the case of market shares, we were able to illustrate the large decreases as a group of producers from one state starts with having no state branded competitors to competing against several other brands within a region. Producers, ideally, would have a higher return if they were the only ones with a state label, but the optimal strategy for all agents in the region is to utilize a state label. Thus, the potential beggar‐thy‐neighbor scenario is possibly a Nash equilibrium. Furthermore, states who market their brand outside their borders are shown to have increased total market share

For example, below is a graph showing what happens to demand for milk with a “Made in Oklahoma” label when no other states label their product (the green line with triangle markers) relative to what happens to demand for “Make in Oklahoma” milk when other states introduce their own labels (the red and blue lines). As the figure below shows, the market share more than halves when one state’s label has to compete with all the others in a region.

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One potential solution (at least from the producer’s perspective) we discuss is for groups of states to band together and use a regional label.

Consumer perceptions of "healthy" claims

Last week I wrote about a study I conducted on how consumers think about the word “natural.” As a part of the same project, I also delved into consumer’s perceptions of the word “healthy.”

“Healthy”, at least as a food package claim, has been defined by the FDA since 1993 by reference to total fat content, with changes made in 2016 to discriminate between different types of fat. Recently, however, the FDA has begun a process to potentially re-define the term, suggesting the need for more information on consumer’s current perceptions of the term and labeling claim.

One of the first questions on this topic I asked my sample of over 1,200 nationally representative food consumers was an open-ended question: “What does it mean to you for a food to be called ‘healthy’?” A word cloud constructed from the responses is below (the full report is available here). Words like good, fat, nutrition/nutrient/nutritional, natural, sugar, calorie, and organic were most commonly mentioned. Responses provided some support for current FDA definition as “fat” is one of the most commonly mentioned words (mentioned by 10.4% of respondents), although nearly as many (6.6%) mentioned sugar. More than a quarter of respondents provided imprecise or tautological-like definitions like “good ingredients,” “good for you,” or “healthy ingredients.”

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In addition to the open-ended question on the meaning of “healthy”, respondents were provided with a list of 13 factors that consumers might use to judge whether a food is healthy. The figure below shows that about a quarter of respondents indicated sugar content and use of hormones or antibiotics, 19.2% pointed to fat content, and 18.4% pointed to pesticide residues. The top four answers included two nutrients (sugar and fat) and two food production processes/ingredients (hormones and pesticides), suggesting consumers consider healthiness to be more than just defined by nutrient content. However, it should be noted that hormones and pesticides were infrequently mentioned (both mentioned by less than half a percent of respondents) when unaided.

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To further explore how consumers define and think about healthiness, a couple binary choice questions were posed. Consumers were about evenly split on whether a food can be deemed healthy based solely on the foods’ nutritional content (52.1% believing as such) or whether there were other factors that affect whether a food is healthy (47.9% believing as such). Consumers were also evenly split on whether an individual food can be considered healthy (believed by 47.9%) or whether this healthiness is instead a characteristic of one’s overall diet and the combination of foods consumed (believed by 52.1%). These responses suggests difficulty in creating a definition of “healthy” on food packages that is broadly acceptable to consumers. Answers to these two questions are not determinative of each other, but rather there are four distinct consumer segments with regard to healthy food conceptions. The figure below indicating the percent of respondents who answered these two questions in the four possible manners.

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Respondents were also provided a list of 15 foods in random order and were asked to indicate whether each was healthy, unhealthy, or neither healthy nor unhealthy. For each item, a healthiness score was created by subtracting the percent of respondents who considered a process unhealthy from the percent of respondents who considered a process healthy. The figure below shows the results.

Almost all respondents (96.2%) considered fresh vegetables to be healthy, and almost none (0.9%) considered them unhealthy, yielding a net healthy score of 96.2-0.9=95.3% for fresh vegetables. Fresh fruit, fish, eggs, and chicken were likewise broadly considered healthier than not. Frozen vegetables/fruit were considered less healthy than fresh, and canned were considered less healthy than frozen, although even canned was considered, on net, more healthy than unhealthy. Only three of the 15 items listed were considered by more respondents to be unhealthy than healthy: vegetable oil, bakery and cereal items, and particularly candy.

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To explore how consumers conceptualized the healthiness of different foods, the questions used to create the figure above were further analyzed using factor analysis. The first factor, shown on the vertical axis of the following figure shows all animal products with high values and other non-animal products with lower values, suggesting consumers use animal origin as a primary factor in judging whether a food is healthy. Another factor illustrated on the vertical axis, indicates freshness or degree of processing is another dimension to healthiness evaluations. These results indicate that healthiness is not a single unifying construct, but rather consumers evaluate healthiness along a number of different dimensions or factors. A food, such as beef or fish, can be seen as scoring high in some dimensions of healthy but low in another.

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Respondents were asked to indicate their extent of agreement or disagreement with eight statements. The highest levels of agreement were with the statement, “Individual needs determine whether various foods are healthy for an individual.” Only 7.8% of respondents disagreed with this statement, whereas more than 70% agreed with it. There were also strong beliefs that healthy food is safe to eat and natural. There was only moderate agreement that healthier food is tastier. About 44% of respondents neither agreed nor disagreed with this statement. There was slightly more disagreement than agreement that healthy food is more convenient to eat. A majority of consumers (58%) disagreed that healthy is more affordable.

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There is a lot more in the full report.

What is "Natural"?

I recently completed a survey of over 1,200 U.S. consumers to find out exactly what they think “natural” means when evaluating different foods. The full report is available here and topline results for all questions asked are here (the survey also covered consumers’ perceptions of “healthy” claims, which I’ll blog on later).

Here is the motivation for the study:

While food companies are allowed to use a “natural” label or claim, the Food and Drug Administration (FDA) has refrained from defining the term. One consequence has been a large number of lawsuits in recent years in which plaintiffs claim to suffer harm from being misled about food product contents or ingredients when accompanied with a natural label (Creswell, 2018). In 2015, the FDA requested public comment on the use of the term natural in food labeling, signaling a potential move to define the term. Such events suggest the need for more information about how food consumers perceive and define the term natural.

One of the initial queries was an open-ended question which asked, “What does it mean to you for a food to be called ‘natural’?” Here is a word cloud constructed from the responses.

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Words like artificial, additive, chemical, and organic were most commonly mentioned. More than 10% of respondents specifically mentioned the word artificial. A non-trivial share of respondents suggested the word was meaningless, marketing hype, or that they did not know what the word meant.

Respondents were also provided a list of possible words/definitions and asked which best fit their definition of natural. No preservatives and no antibiotics/hormones topped the list.

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Despite associating preservatives with lack of naturalness, when asked about specific preservatives, responses are more nuanced. Preservation by canning and with sugar/salt/vinegar were perceived by more people as natural than not-natural, whereas preservation with benzoates/nitrites/sulphites was not.

To hone in on which processes/foods people consider natural vs. not natural, they were shown the following figure. Respondents were asked “Which of the following foods or processes do you consider to be natural? (click up to 5 items on the image that you believe are natural).” The question was repeated except “natural” was replaced with “NOT natural.”

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You can find some colorful heat-maps of the resulting clicks in the full report. Here, I’ll just note that about half of respondents (47.1%) clicked on the image of the raw commodities as being natural. The next most commonly clicked areas, chosen by between 20% and 30% of respondents, was grits/oatmeal, wash/clean, and wash/grind/slice. Even after showing the processes involved, 19.8% clicked vegetable oil as natural and 13.3% clicked flour as natural. By contrast, “Bleach” was most most frequently clicked (by 33.8% of respondents) as not natural, followed by “Crystalize”, and then alcohol, syrup, and sugar.

A curious result revealed is that, in many case, final foods are often considered more natural than the processes which make them. For example, more people clicked alcohol as natural than clicked fermentation as natural. Vegetable oil was perceived as more natural than pressing or bleaching, both processes which are used to create this final product. Similarly, sugar is perceived as more natural than crystallization, but of course, the latter is necessary to produce the former. These findings suggest that it is possible for a final product to be considered natural even if a process used to make the product is not.

I also asked questions about crop production processes and perceptions of naturalness.

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About 80% more respondents said organically grown crops were natural as said such crops were not natural. Crops grown indoors and that are hydroponically grown were, on net, seen as more natural than not. All other crop production practices were rated as not natural by more respondents than were rated as natural. Thus, the results suggest consumers are skeptical of the naturalness of most modern crop production practices. Curiously, this is true for use of hybrid seeds. Crops produced with biotechnology were much more likely to be considered not natural than natural. Consumers perceived organic as natural, but not the pesticides used in organic agriculture or the methods (i.e., mutagenesis) used to create many organic seeds. Again, these findings suggest that it is possible for a final product to be considered natural even if a process used to make the product is not; in this case, the finding is likely to result from a lack of knowledge about organic production practices.

On the topic of misperceptions, just because a federal definition of natural exists does not mean consumers know or understand the definition. The USDA currently defines “natural” for meat products, and it is primarily defined as “minimally processed.” However, only about a quarter of respondents in this survey (26.6%) correctly picked this definition when asked how the USDA defines the term. More than 30% of respondents incorrectly believed the USDA definition of natural implies “no hormones” and 23.8% thought a natural label implies “no antibiotics.” These data suggest more than half of respondents are misled by the USDA definition of natural, a result supported by the other recent academic research.

There is a lot more in the detailed report, including more information on question wording and methods of analysis. For example, analysis of correlations between responses (via factor analysis), suggests “natural” is not a single monolithic construct in consumer’s minds, but rather is multidimensional. A food or process can be considered natural on one dimension but not another, as shown in the following figure.

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Thanks to the Corn Refiners Association, who funded this survey. They gave me free reign to ask the questions and analyze the data as I wanted. You can see their interpretation of the results and their policy recommendations here.


Market Impacts of GMO Labeling

Readers might recall the result from the study Jane Kolodinsky and I published in Science Advances earlier this year. We found that the provision of mandatory labels in Vermont appears to have reduced opposition to GMOs in that state. However, as I noted at the time,

Our result does NOT suggest people will suddenly support GMOs once mandatory labels are in place.

Indeed, the data suggest consumers will still want to avoid products with GMO labels, which provides incentives for food retailers and manufacturers to find ways to avoid GMO ingredients.

Colin Carter and Aleks Schaefer just published an interesting new study in the American Journal of Agricultural Economics, which powerfully shows that mandatory GMO labels are already having significant market impacts. They found a creative way to explore this issue by focusing on the market for sugar. They provide the following background:

In the United States, sugar is produced from both sugarcane and sugarbeets. Sugarcane stalks are milled to produce raw sugar. Raw cane sugar is then sent to a refining facility to be transformed into refined sugar. Sugbarbeets, in contrast, have no raw stage; they are processed from beet to refined sugar in one continuous process. The U.S. market share for beet (cane) sugar is approximately 58% (42%). Almost all U.S. sugarbeet production is GE, while cane sugar is GE-free. However, sugar derived from beets is chemically identical to sugar derived from cane.

This summary data they provide on prices of sugar from cane and beet sources suggests “something” change around the same time as the Vermont mandatory GMO labeling law.

Source: Carter and Schaefer, American Journal of Agricultural Economics

Source: Carter and Schaefer, American Journal of Agricultural Economics

Here are the main findings.

Our analysis supports the explanation that the divergence in U.S. prices for refined cane and beet sugar was the result of Vermont’s mandatory GE labeling. The divergence occurred on or around July 2016— the month the Vermont Act took effect.

Counterfactual price estimates generated by a regression model suggest that GE food labeling initiatives generated a small premium for cane sugar and a price discount for beet sugar of approximately 13% relative to what prices would have been in the absence of such legislation.

These changes in raw ingredient prices will ultimately have impacts on retail food prices. All this is suggests that mandatory labels aren’t a free lunch.

Dealing with Lazy Survey Takers

A tweet by @thefarmbabe earlier this week has renewed interest in my survey result from back in January 2015, where we found more than 80% of survey respondents said they wanted mandatory labels on foods containing DNA. For interested readers, see this discussion on the result, a follow-up survey where the question was asked in a different way with essentially the same result, or this peer-reviewed journal article with Brandon McFadden where we found basically the same result in yet another survey sample. No matter how we asked this question, it seems 80% of survey respondents say they want to label foods because they have DNA.

All this is probably good motivation for this recent study that Trey Malone and I just published in the journal Economic Inquiry. While there are many possible reasons for the DNA-label results (as I discussed here), one possibility is that survey takers aren’t paying very close attention to the questions being asked.

One method that’s been around a while to control for this problem is to use a “trap question” in a survey. The idea is to “trap” inattentive respondents by making it appear one question is being asked, when in fact - if you read closely - a different question is asked. Here are two of the trap questions we studied.

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About 22% missed the first trap question (they did not click “high” to the last question in figure 2A) and about 25% missed the second question (the respondent clicked an emotion rather than “none of the above” in question 2B). So far, this isn’t all that new.

Trey’s idea was to prompt people who missed the trap question. Participants who incorrectly responded were given the following prompt, “You appear to have misunderstood the previous question. Please be sure to read all directions clearly before you respond.” The respondent then had the chance to revise their answers to the trap question they missed before proceeding to the rest of the survey. Among the “trapped” respondents, about 44% went back and correctly answered the first question, whereas about 67% went back and correctly answered the second question. Thus, this “nudge” led to an increase in attentiveness among a non-trivial number of respondents.

After the trap questions and potential prompts, respondents subsequently answered several discrete choice questions about which beer brands they’d prefer at different prices. Here are the key findings:

We find that individuals who miss trap questions and do not correctly revise their responses have significantly different choice patterns as compared to individuals who correctly answer the trap question. Adjusting for these inattentive responses has a substantive impact on policy impacts. Results, based on attentive participant responses, indicate that a minimum beer price would have to be substantial to substantially reduce beer demand.

In our policy simulations, we find a counter-intuitive result - a minimum beer price (as implemented in some parts of the UK) might actually increase alcohol consumption as it leads to a substitution from lower to higher alcohol content beers.

In another paper in the European Review of Agricultural Economics that was published back in July, Trey and I proposed a different, yet easy-to-interpret measure of (and way to fix) inattention bias in discrete choice statistical models.

Taken together, these papers show that inattention is a significant problem in surveys, and that adjusting results for inattention can substantively alter one’s results.

We haven’t yet done a study of whether people who say they want DNA labels are more or less likely to miss trap question or exhibit other forms of inattention bias, but that seems a natural question to ask. Still, inattention can’t be the full explanation for absurd label preferences. We’ve never found inattention bias as high as the level of support for mandatory labels on foods indicating the presence/absence of DNA.