Spending on Beef over Time

Meat demand has been a frequent topic on this blog (e.g., see here, here, here, or here). As some of the previous posts indicate, “demand” is a hard thing to measure. A slightly easier thing to measure is spending. As it turns out, the Bureau of Labor Statistics (BLS) has been tracking consumer spending at the household level on food at home in a number of categories, including beef, pork, and poultry, in their annual Consumer Expenditure Survey.

Using these data, I constructed the following animation showing the relationship between spending on beef and total household expenditures by quintiles of income.

The figure shows, at any point in time, higher income households (or those with greater total spending) spend more on beef than lower income households (or those with less total spending). In econ-speak, beef is a “normal” good. However, for any given income (or total spending) level, spending on beef has fallen precipitously since 1984 (all figures are in inflation-adjusted 2017 dollars). The change over time is most dramatic for the higher income/spending households. In 1984, the lowest and highest quintile income households spent $265 and $681 per year (in 2017 dollars), respectively, on beef for consumption at home. By 2017, these figures had fallen to $158 and 352, respectively.

These data could be reflective of downward demand shift - i.e., consumers willing to pay less for each pound of beef than they were in the past. Other possible explanations for the downward decline in spending include changing beef prices over this period, changing household demographics (the average number of people in today’s households is slightly smaller today than in the mid 1980s; fewer people normally means less spending), other protein sources, such as poultry, becoming relatively less expensive or more attractive, a shift toward more food spending away from home (the BLS only tracks spending for individual food categories for food eaten at home), and more.

How do the data in the above figure square with measures of demand (such as these constructed by Glynn Tonsor), which show no clear trend in beef demand since the early 1990s? Well, as I mentioned above, spending isn’t “demand” because while the figure controls for income, it doesn’t control for prices. Another possible explanation is that the data in the figure above are for households, while aggregate demand statistics like those created by Tonsor are calculated nationally. It is possible for total aggregate demand to rise even if each individual household’s demand is falling if population is increasing and more households are being added. That is, in fact, what has happened. There were about 86 million households in the US in 1984. Today there are about 128 million households.

Food Affordability Over Time

On a number of occasions, I’ve written about the Engel Curve, which relates the share of consumer spending on food to the consumer’s income (or total expenditures on all goods). Whether we compare consumers within a country or compare spending across countries, a common relationship holds: the higher a consumer (or country’s) income, the smaller the share of their income they tend to spend on food.*

This relationship indicates that as consumers and countries get richer, we’d expect food expenditure shares to fall, a phenomenon generally thought to be associated with higher consumer well-being. While this relationship is widely known among economists, there is another fact that is not as widely known. In particular, the entire Engel Curve has been shifting downward over time. That is, for any given level of income, consumers today are spending less on food than they were in the past.

To illustrate this phenomenon, I pulled data from the Consumer Expenditure Survey that has been collected annually since 1984 by the Bureau of Labor Statistics (BLS). The BLS report food expenditures and total expenditures by quintiles of income. These data were used to create the following animation.

The video shows that, despite the year-to-year variation, there is a fairly steady shift in the Engel Curve over time downward and to the right. That is, consumers are getting richer over time (i.e., their total expenditures are rising), and for any given level of total expenditures, the share being spent on food is generally falling. There are several possible drivers of this phenomenon, but one likely culprit is technological progress. For any given level of income or total expenditure, innovation and technological change has brought down the price of food such that consumers are able to eat what they want while being able to spend more of their income on other, non-food items. That is, food today is more affordable (at least by this metric) for households of all incomes (or total expenditure categories).


*Note: just because the food share falls, it doesn’t mean total spending on food falls as income increases. In general, richer consumers spend more on food than poorer consumers. However, spending on non-food items tends to increase at a faster rate than spending on food as income rises, leading to a smaller share of income being spent on food.

Reducing Food Waste - Where's the Incentive?

In recent years, there has been a lot attention being focused on reducing food waste. While I have argued for more nuance than one often sees in popular exhortations to reduce waste, the issue is important: it would be nice to find ways to save all the resources that go into producing food that ultimately winds up in the garbage.

A number of discussions over the past couple months have led to an aspect of this problem that hasn’t received much attention. Namely, what is the incentive of food producers and manufacturers to reduce waste? Or, what are the most effective mechanisms to reduce waste?

One way of reducing waste is what we might call the “demand side” strategy. Try to convince consumers to consume all of what they buy and throw out less. Our stomachs and pantries are only so large, and as a result, this presumably means consumers would ultimately buy less food. In economic terms, this leads to a downward shift in demand, which results in lower prices and less food sold. For producers, this is certainly a bad outcome: selling less food at lower prices means lower revenues and profits. From the perspective of a food producer, all they care about is whether the product sells. What you do with it after you buy it is of little consequence to the seller. As such, one might wonder how much incentive food producers and sellers have to reduce waste, at least via this demand side strategy. To boot - we don’t know for sure whether consumers are better or worse off. They pay lower prices but also buy less food, and as a result the impacts on consumers is ambiguous.

A different way to try to reduce food waste might be called a “supply side” strategy. One challenge with popular conceptions of food waste is that they seems to imply there are large inefficiencies in food supply chains. That some people seem to indirectly imply that farms, food manufacturers, and grocers are losing or throwing out food that they could profitably sell. To be sure, there are likely some inefficiencies in the food supply chain, but food and ag are tend to be competitive, low margin businesses which makes it hard to believe they’re leaving dollar bills lying around that they could easily pick up. To incentivize these firms to reduce waste, loss, and spoilage, something has to change to reduce the cost of preservation. That “something” is likely investment in research and the creation of technologies that enable farms and food manufacturers to affordably make use of food that might otherwise have been unsalable. An old example might be the advent of canning or refrigerated rail cars. More modern examples might include better grain storage bins or storage management practices, vacuum packaging, high pressure pasteurization, etc.

In economic terms, these technologies can be conceptualized as shifting the supply curve downward shift - i.e., lowering the marginal cost of delivering a given quantity of food to the market. Such a shift would lower the price of food while enabling more more food to be sold. Consumers are definitely better off: they get to have more food at lower prices. Whether producers as a group are better off from the supply shift depends on how sensitive producers and consumers are to price changes, but producers who are early adopters of the new technology are almost certainly better off.

Whether the demand-side or supply-side strategy leaves “society” better off (at least as defined by producer profits and consumers’ economic well-being) is not completely predictable because it depends on relative elasticities of supply and demand for the foods in question, among other factors. Ignoring any externalities from food that is thrown out, I would generally expect the “supply side” strategy to be better: we know it makes consumers better off and likely makes producers better off too (though not always). But, it ultimately results in more food being sold and potential (and perhaps ironically) more consumer waste. So, the big unanswered question is the nature and size of the “externality” of food thrown away.

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.”