The Political Polarization of Meat Demand

There is growing criticism of meat production industries in popular culture and mainstream media. Examples include the recent release of the EAT-Lancet report, the World Health Organization pronouncement on red meat and cancer, the proposed Green New Deal and “farting cows,” and much more. The result is an increasing number of news stories linking beef consumption with climate change and other adverse environmental impacts. As shown in this report (co-authored by Glynn Tonsor, Ted Schroeder, and myself), the number of news stories mentioning beef and climate change increased almost 800% since the early 2000s.

Here’s the thing. We know climate change is a politically polarized issue. Might linking beef and meat consumption to a politically polarized issue in turn cause meat consumption itself to become politically polarized? As I’ve shown in previous posts (e.g., see here or here), self defined political ideology (on a scale of very liberal to very conservative) is one of the strongest predictions of whether someone says they are a vegetarian or vegan.

To investigate this issue, I turned to the body of work that referred to as the Cultural Cognition Project that is most associated with Dan Kahan at Yale. The basic idea is that individuals conform their beliefs about disputed matters of fact to values that define their cultural identities (or match their tribe). In one of the most interesting demonstrations of this concept, Kahan shows that the likelihood of agreeing with the statement “There is solid evidence of recent global warming due mostly to human activity such as burning fossil fuels” is increasing in a person’s measured scientific intelligence (essentially a score on a science quiz) but only for people who identify as liberal democrats. For people who identify as conservative republicans, higher scientific intelligence is associated with a reduced likelihood of agreeing with the above sentence. The result is that (unlike what we’d expect if “more education” was the answer), the greatest disagreements are among the most scientifically literate but of opposite political parties. One take home message from these sorts of findings is that the smarter you are, the easier it is to fool yourself.

Ok, back to meat. As readers of this blog likely know, I ran the Food Demand Survey (FooDS), which surveyed 1,000 consumers every month (different samples of consumers were drawn every month) for five years. On the survey, we asked every respondent to answer 9 simulated shopping questions in which they choose between two beef, two pork, two chicken, and two vegetarian meal options at different prices (or a “I wouldn’t buy any of these” option). These data can be used to construct a very simple measure of demand, in which we simply count the number of times (across the nine choices) beef or any meat product was chosen (see this post for some discussion on these data). For reference beef (either ground beef or steak) was chosen about 2.2 times on average across the nine choices and any meat option was chosen a bit less than 7 times on average across the nine choices. (One important note is that despite all the negative news about beef alluded to at the beginning of this post, we do not find overall downward trends in beef demand in recent years; this is also consistent with Tonsor’s demand indices).

The question is how these measures of demand relate to political ideology and education (I use education because, unlike Kahan, I did not ask a scientific intelligence quiz on my surveys). I estimated equations that relate beef or overall meat demand to an extensive set of demographics (age, income, gender, region of residence, household size, etc.), political ideology (I asked both a party affiliation question and a very liberal to very conservative scale from which I create two groups: liberal democrats and conservative republicans), education, a time trend, and interactions between the last three sets of variables. The sample size is about 60,000 observations.

Here’s a graphical illustration of the results for beef. Beef demand is higher for conservative republicans than liberal democrats (holding constant all other demographic factors), and this demand gap grows with education. Liberal democrats reduce their demand for beef as their education increases, but for conservative republicans, beef demand is essentially flat across education levels. The other interesting result, shown in the bottom panel, is that beef demand is becoming increasingly politically polarized over time. The beef demand gap between the average conservative republican and liberal democrat is increasing over time.

Here is the same analysis for overall meat demand (beef + pork + chicken). The results here are even stronger. There is very little partisan gap among lower educated liberals and conservatives, but a large gap in meat demand among liberal democrats and conservative republicans who have a graduate degree. The gap results mainly from liberal democrats reducing meat demand as education increases. Again, the partisan gap is growing over time.

What does all this mean? Unfortunately, I suspect it implies conversations about the meat consumption will become more difficult and tumultuous in the coming years. It may also mean that disagreements about the impacts of meat consumption on the environment and health are less likely to be “settled” by science because they are becoming wrapped up in people’s cultural values and tribe identities. Fortunately, there are a number of resources provided via the Cultural Cognition Project that provide insights about effective communication in this polarized world..

Why don't we vote like we shop?

Readers of this blog will know I’ve been interested in the divergence of voting and food shopping behavior for some time (e.g., see here, here, or here). Much of this interested was prompted by the stark example provided by Proposition 2 in California back in 2008, when about 64% of Californian’s went to the polls and outlawed the production of a good they routinely bought (caged eggs) resulting in a so-called vote-buy gap. I think it was Glynn Tonsor who I first heard refer to the outcome as an unfunded mandate: voters required producers to adopt costlier production practices for which shoppers had already revealed they were unwilling to provide sufficient compensation in the marketplace.

While we know this phenomenon exists, it isn’t clear why. Along with Glynn Tonsor, Bailey Norwood, and Andrew Paul, we set out to tackle this question by conducting some real-food, real-money experiments. The resulting paper is now forthcoming in the Journal of Behavioral and Experimental Economics.

What did we do? We recruited people to participate in a series of decision making exercise, where they first made a shopping choice. The shopping choice options were: A) a more expensive cookie made with cage free eggs, B) a less extensive cookie made with conventional eggs, C) a snack without animal products, or D) refrain from buying anything. Then, in a second step, people were placed into groups (of small or large size), where they voted on whether to ban option B (the cookie made from conventional eggs) for the people in their group. Finally, if the vote passed, people re-chose from the constrained set of options. This basic set-up was repeated in several conditions that varied information, group size, and more to test different reasons for vote-buy gap.

The first result is that we can replicate the vote-buy gap in our experimental setting. We note:

“approximately 80% of the individuals who chose snacks with caged eggs when shopping subsequently voted to ban snacks with caged eggs. The finding rules out the suggestion that the vote-buy gap is an illusion or statistical artifact, as it can be re-created in an experimental lab setting at an individual level. ”

As the above results suggests, we are immediately able to rule out one explanation for the vote-buy gap - something we call the non-buyer hypothesis. The non-buyer hypothesis suggests the vote-buy gap is something of an illusion because, proverbially speaking, apples (voters) are being compared to oranges (shoppers). In the “real world”, many consumers of eggs are voters; however, not all voters are egg consumers. For example, individuals who are vegan do not buy eggs, but they may vote in favor of initiatives similar to that of Proposition 2. But, in our study we can compare each individual’s shopping choice to their own vote, and as the foregoing quote indicates, 80% of people switch.

We find a bit of support for the following, which is tested by giving one group more salient information about which options used cage and which used cage free eggs:

“Knowledge Hypothesis: The vote-buy gap is caused by the fact that consumers themselves believe, or perceive that other consumers believe, that they are buying cage-free eggs when in fact they are buying cage eggs; better, more salient information about housing practices when shopping will reduce the vote-buy gap.”

But, even in the condition where clear-transparent information was given about the types of eggs used to make each cookie, the share of people who voted to ban cage eggs was 15 to 20 percentage points higher than was the market share of purchased snacks made with cage free eggs.

I was personally most excited to test two hypotheses that related to group size (people voted in groups of roughly five or fifty). Here were those hypotheses, both of which suggest greater likelihood of voting “yes” to ban snacks with cage eggs in larger groups as compared to smaller groups.

“Public Good Hypothesis: The vote-buy gap is caused by the fact that more animals and people are impacted by a ban than the impact of a single shopping choice for cage-free eggs; as the size of the group affected by a vote increases, individuals are more likely to vote in favor of the initiative if it has a desirable public good component, thereby increasing the vote-buy gap.

Expressive Voter Hypothesis: The vote-buy gap is caused by the fact that in large groups, an individual’s vote is unlikely to be a deciding factor, privileging expressive preferences over instrumental preferences; as a group size increases, and the likelihood that an individual’s vote is consequential and decisive falls, individuals are more likely to vote in favor of the initiative, thereby increasing the vote-buy gap.”

Both hypotheses conjecture that the vote-buy gap will be larger in large groups than small groups. However, our data shows that when moving from small to large groups, the vote-buy gap is actually larger in the small groups than the large group, exactly the opposite of what is predicted.

In the end, we can be fairly confident the vote-buy gap is real and replicatable, but alas we still don’t have a good answer as for why. Here’s how we leave it in the paper:

“A residual hypothesis that remains if all others fail to explain the gap is inconsistent preferences. That is, people may have different preferences when shopping as compared to voting. This sort of preference inconsistency is at the heart of the so-called consumer vs. citizen phenomenon. The thought is that people adopt more public-minded preferences when in the voting booth but rely on more selfish motives when shopping privately. ... While this explanation is perhaps not intellectually satisfying, it is perhaps consistent with one long strain of economic thought, De gustibus non est disputandum, while contradicting another, fixed and stable preferences. ”

But, make no mistake: just because we don’t know the answer to the “why” doesn’t imply the vote-buy gap is inconsequential. Recall the unfunded mandate? Here’s what happened in our experiment.

“many individuals who originally chose to purchase a cookie decided, after the ban, to select “none” in their second selection when only higher-priced cookies were available. Depending on the treatment, anywhere from 21.74% to 43.38% of consumers who purchased cookies prior to the ban no longer did so after the ban. These lost purchases are precisely the worry of egg producers.”

The Future of Meat

Several weeks ago, I was interviewed by Stephen Dubner for Freakonomics radio and their associated podcast. The topic was the future of meat, and they just released the episode today. It’s not uncommon to do an hour long interview only to have the producers pull out a half minute clip to include in the show, so I was surprised to see how much of our interview they used. The other voice that gets a lot of air time is Pat Brown, a Stanford biomedical researcher who is the CEO and Founder of Impossible Foods - a company aiming to replace meat by using genetically engineered yeast to produce animal-like proteins via a fermentation process (I’ve reviewed the Impossible burger in a previous post). The transcript of the Freakonomics show is here. Or, download the episode from your favorite streaming service.

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.”

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.

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.

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.

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.

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.

There is a lot more in the full report.

The Superbowl and Chicken Wing Demand

With the Superbowl coming up this Sunday, I thought I’d take a quick look at whether this annual event has much effect on the market for a food it has come to be closely associated with: chicken wings.

I turned to USDA data compiled by the Livestock Marketing Information Center, which reports weekly prices on whole wings going back to 1992. Here is the price trend in nominal terms. There has been a strong upward trend in chicken wing prices over this time period, but of course some of that is due to inflation. However, even after adjusting for inflation, wings were about $0.90/lb in the early 1990s ($0.50/lb in nominal terms), and they averaged about $1.50/lb in 2018; during the latter part of 2018, prices were above $2.00/lb.

In the graph above, it’s hard to make out when, exactly, the Superbowl occurred. Looking at the history of the event over this time period, the Superbowl occurred in late January or early February every year since 1992. With that knowledge, I added orange lines to the graph to indicate the periods surrounding the Superbowl.

It sure looks like there is a price spike right around the time of the Superbowl each year, with a price decline immediately following. Indeed, if I look at the most recent decade, prices rise about 7% from early January to the Superbowl period (late January, early February), and then fall about 5% going in to mid- to late-February.

The price spikes are indicative of increasing demand over this time period. This is also consistent with data we collected in the monthly Food Demand Survey, were we often found a spike in consumer willingness-to-pay around the event.

Too bad I don’t have data on napkins and antacids …

P.S. One might wonder why this price phenomenon is different than that for turkeys. As I discussed back in November, turkey prices tend to fall around Thanksgiving when demand is peaking, perhaps due to strategic pricing behavior by retailers or from producers planning ahead and increasing supply around this time. A key difference with turkeys and wings, is that one is a whole and the other is a part. If there isn’t an overall demand increase for chicken around the Superbowl, then the wings will be in relatively short supply. It might make sense for a turkey producer to grown a whole bird in anticipation of the holidays, but it’s not possible for a producer to only grown wings in anticipation of the Superbowl.