Where are people most sensitive to changes in the price of bacon?

Whether trying to understand the impact of taxes, animal welfare regulations, or meat packing plant shutdowns, we need an elasticity of demand for pork. The elasticity of demand tell us how the quantity of pork consumers want to buy changes with the price of pork. Given the importance of such questions, it probably isn’t surprising to learn that there are many studies aiming to measure elasticties of demand. These studies typically focus on THE elasticity of demand for pork - a single aggregate number. However, these aggregate assessments likely mask a great deal of heterogeneity across markets and different products.

In some new research with Glynn Tonsor, done for the National Pork Board, we utilized grocery store scanner data from 51 U.S. retail markets for 6 different pork products to estimate 51*6 = 306 market- and product-specific own-price elasticity estimates. Our data also enables us to observe differences in consumer purchasing and spending patterns across the country.

There are so many interesting results, it’s hard to succinctly summarize. Here are a few highlights.

First, consider variation in bacon purchases across four markets over time. Of the four locations in the figure below, per-capita bacon purchases tend to be highest in Phoenix and lowest in LA (it is worth noting that bacon prices tend to be much higher in LA than Phoenix). The impact of the initial COVID-19 disruptions is also apparent in the data.

There is wide variation in price sensitivity across location and pork product. The figure below summarizes the distribution of price elasticities over the 51 markets for the six pork products

Want to know how your locale ranks in terms of consumption, prices, or elasticity? Check out the full report.

Two biases - one solution

I was listening to a recent episode of Planet Money that discussed the sunk cost fallacy (or sunk cost bias). The episode reminded of something I’ve long thought: one bias, taken out of context, might in-fact help solve another bias (which itself seems to be problematic when viewed in isolation).

Let me start by describing the two biases. First, the sunk cost bias. I remember well a moment in college when I realized this economics thing might be for me. I was skiing with a group of friends, one of whom was worn out by lunchtime, announcing they were heading back to hotel. Another friend, encouraging the deserter to stay, said something along the lines of: “Common, these lift tickets are expensive. You’ve got to keep going to get your money’s worth.” I remarked we should stop pestering the deserter: the lift ticket was a sunk cost.

Nothing, at this point, would refund the cost of the lift-ticket. So, the decision was not whether to buy a lift ticket or not (that cost was sunk), but, rather, the decision was which course of action, now at lunch, would make the individual happier: A) continue skiing or B) rest in the hotel room. I was pleased that I seemed to convince my friends this was the right way to think about it. Lessons to avoid the sunk cost fallacy (or bias) are probably taught in virtually every ECON 101 class, and yet, it seems to be a bias to which we all routinely fall prey.

Consider a second, seemingly unrelated bias: present bias (or time-inconsistent preferences). It isn’t irrational to care about the present more than the future. But, it is problematic if the rate at which we discount the future changes depending on when we are asked. Consider a simple example. Which would you prefer: A) $100 today or B) 101 tomorrow? Now, a second question. Which would you prefer: C) $100 one year from now or D) $101 one year and one day from now?

It is common for people to choose A over B (“give me the quick $100 bucks now!”) and then D over C (“I’ve already waited a year, what’s one more day to get a dollar?”). There is a problem with that choice pattern. Choice of A over B implies a person is unwilling to wait a day for a dollar but choice of D over C implies the opposite: a willingness to wait a day for a dollar. When people exhibit these sorts of time inconsistent preferences, they tend to want to start a diet tomorrow. But, when tomorrow becomes today, they’re no longer willing to start the diet, and again plan to do it … tomorrow.

These two biases, the sunk cost fallacy and time-inconsistent preferences, are widely discussed in economic research, but rarely together. However, it strikes me that, at least in some circumstances, the sunk cost fallacy can help solve time-inconsistent preferences.

Consider a gym membership. If I exhibit time-inconsistent preferences, I won’t work out as much as I should. I will always imagine my future self being more disciplined and exercise-ready than my present-self ever will be. Yet, many of us pay large up-front gym membership fees. One economics study suggests people significantly over-pay for gym memberships and concludes we’d be financially better off choosing a “pay as you go” plan. But, what if paying a large-up front fee induces the sunk cost fallacy? “I’d better go to the gym to ‘get my money’s worth’”? If so, fretting over our sunk costs would lead us to exercise more than we might otherwise, helping offset the problem of time-inconsistent preferences, which, in isolation, would tend to lead us to exercise less than we otherwise might.

A commonly suggested solution for time-inconsistent preferences is to create commitment contracts. Commitment contracts occur when my present self undertakes actions (or commitments) to bind my future self, or at least makes it more costly for my future self to reverse course. An example is a Christmas Club savings account, a savings account where withdrawals are only allowed (without penalty) around the Holiday season. If people were perfectly rational, a Christmas Club account would be unnecessary; we’d just use our “regular” savings accounts that have more flexibility and, in all likelihood, pays higher interest rates. Yet, some people choose to use Christmas Club savings accounts as a type of commitment device (I’m binding my future self to not spend the money till the Holidays).

It strikes me that the psychological feelings we have around sunk costs act as a sort of commitment device. Although ECON 101 tells us to ignore sunk costs, the fact that we often fret over them suggests that, at least in certain circumstances, they may be binding us to a course of action our previous self wanted us to pursue.

Comparing the views of the Italian general public and scientists on GMOs

You might recall the widely discussed 2015 study from the Pew Foundation comparing attitudes of the general public to that of scientists. The headline finding that captured public attention was the following:

“A majority of the general public (57%) says that genetically modified (GM) foods are generally unsafe to eat, while 37% says such foods are safe; by contrast, 88% of AAAS scientists say GM foods are generally safe. The gap between citizens and scientists in seeing GM foods as safe is 51 percentage points. This is the largest opinion difference between the public and scientists [out of more than a dozen issues].”

In a new paper with Gioacchino Pappalardo and Mario D’Amico, we were curious about the extent to which this finding extrapolated to other countries that have purportedly been even more averse to GMOs. Gioacchino and Mario are with the University of Catania in Italy, so naturally, we extended this question to Italians. Our work was recently accepted for publication in the International Journal of Food Science and Technology. Here is the abstract:

“The gap between statements from scientific organizations about the safety of genetically modified (or GMO) food and public concerns about the technology is puzzling, raising questions about the extent to which expert opinion and scientific consensus can sway public opinion and whether scientific progress might be hindered by public opposition. This study sought to determine whether beliefs about GMO safety are high among experts in countries where there has been significant public opposition to the technology. Surveys conducted among 1,006 members of the Italian general public and 258 members of the Italian Association of the Agricultural Science Societies (AISSA) reveal that whereas 54% of our sample of the Italian general public believes GMOs are generally safe to eat, 81% of sample of the Italian agricultural scientists believe the same. Despite the gap in lay‐expert safety beliefs, results reveal greater similarity between scientists and the general public on topics related to beliefs about the impact of GM food on food prices, the developing world, and concentration in the agricultural supply chain.”

Market Shares and Substitution Toward Plant Based Meat

In the past, I’ve discussed research we’ve conducted on consumer demand for emerging plant-based meat alternatives vs. traditional meat (e.g., see here or here). Today, I’m happy to link to a new, extensive study on the topic conducted with Glynn Tonsor and Ted Schroeder at Kansas State for the Cattlemen’s Beef Promotion and Research Board.

There are a lot of interesting results stemming from four different experiments and multiple questions asked, but I’ll hit just a few highlights. Firs, along a variety of dimensions, consumers’ perceptions of beef are favorable relative to consumers’ perceptions of plant-based alternatives. For example, here is one series of questions.

Second, if given a pair-wise choice between a beef burger and a Beyond Meat burger at the same price, roughly a quarter of consumers choose the Beyond Meat option. Interestingly, the choice wasn’t much affected by whether we provided nutrition facts panels, ingredient lists, or whether the beef burger was organic, as shown below.

Another experiment, framed in a foodservice environment, explored how choices for beef burgers were affected by a Beyond Meat alternative vs. a Chicken Wrap. Short story: Introducing a Beyond Meat alternative has about the same impact as introducing a Chicken Wrap.

Finally, we conducted some simulated shopping choices (in both food service and grocery framings) to estimate own- and cross-price elasticities of demand for plant-based alternatives and traditional meat options.

As it turns out this sort of analysis is quite timely. On February 2, Impossible announced a 20% price reduction. Here is our estimated demand elasticities for all consumers and segmented by people we classify as regular meat consumers vs. those wo do not regularly consume meat (those who classified their diet as vegetarian, vegan, flexitarian, or other).

As the table above shows, a 1% drop in Impossible Burger’s price would lead to a 0.14% reduction in purchases of Store-Brand ground beef at retail grocery (across all consumers). If we extrapolate that to a 20% decrease, that suggests the recently announced price change will lead to a a 2.8% decline in Store Brand ground beef.

This reduction comes almost entirely from consumers who are not regular meat eaters (cross-price elasticity of +0.26) vs regular meat consumers (cross-price elasticity of +0.05). In fact, within the regular meat consuming segment we would project the price drop in Impossible would result in nearly 3x the impact on Beyond Beef as Store Brand ground beef.

There is much, much more in the report. You can read the whole thing here.

Bacon Causes Cancer: Do Consumers Care?

That’s the title of a new working paper I’ve co-authored with Purdue PhD student, Xiaoyang He. The answer to the question is: “yes,” retail bacon prices and sales fell following the pronouncement that processed meat was classified as a carcinogen; however, we did not find the same for other processed meat categories, ham and sausage. Maybe all those headlines like “The great bacon freak-out” and “Eating just one slice of bacon a day linked to higher risk…” really served to focus people’s attention. Here is the abstract:

“In October 2015, the International Agency for Research on Cancer (IARC) released a report classifying processed meat as a type 1 carcinogen. The report prompted headlines and attracted immediate public attention, but the economic impacts remain unknown. In this paper, we investigate the impacts of the IARC report on processed meat prices and purchases using retail scanner data from U.S. grocery stores. We compare changes in prices and sales of processed meat products to a constructed synthetic control group (using a convex combination of non-meat food products). We find a significant decrease in bacon prices and revenues in the wake of the IARC report release, but we find no evidence of a demand reduction in ham and sausage. At the same time, we find beef sales and revenue increased significantly after the report, while beef price significantly fell.”

That bacon prices fell alongside the volume sold is a clear signal that consumer demand for bacon fell as a result of the IARC report.

As we discuss in the paper, a key challenge with identifying the effects of the IARC report rests in constructing a counter-factual prediction of what would have happened to prices and sales of processed meat products had the IARC report not been released. We cannot use data from an unaffected location because the media reports were widely distributed across the U.S. Instead, we use statistical methods (the so-called synthetic control method) to identify alternative food products as controls. We describe the approach as follows:

“The synthetic control method sidesteps this problem and uses a combination of candidate controls instead. We Nielsen retail scanner data to determine the effect of the IARC report on processed meat markets. This data contains weekly information regarding sales, price, and revenue for processed meat categories as well as categories that are included in the synthetic control group. We use the data from 2014 to 2016, which includes approximately one year of data before and one year of data after IARC report released date. The post-IARC time period is long enough to determine, if any impact exists, how long it lasts.”

In essence we use the the estimated relationship among dozens of possible grocery item prices and bacon prices prior to IARC report release to predict what bacon prices would have been had the report release not occurred. Here is the calculation of actual and counter-factual bacon prices ($/oz) before and after the report release:

After a few weeks of bacon prices remaining above their predicted values, bacon prices ultimately averaged 6.5% lower than what we predict would have occurred had the IARC report not been released.

You can read the whole thing here.