Beef and Pork Marketing Margins and Price Spreads during COVID-19

That’s the title of a new working paper co-authored with Glynn Tonsor at Kansas State University and Lee Schulz at Iowa State University. As I’ve previously written, there has been a lot of interest in price movements of livestock relative to wholesale meat during the pandemic. Just last week, there was another call by the U.S. Congress for research into the issue. Recently, there have been several good discussions of this issue including a piece by Cortney Cowley with the Kansas City Federal Reserve Bank and a report by the USDA Agricultural Marketing Service.

Here is the abstract of our paper:

“The controversy surrounding wholesale and farm-level price movements following a packing plant fire in Kansas was but mere prelude to the unprecedented COVID-19-related disruptions and historic rise in the spread between livestock and wholesale meat prices. Concerns about concentration and allegations of anticompetitive behavior have led to several civil suits and inquiries by the U.S. Department of Agriculture and the U.S. Department of Justice, with increases in price differentials serving as a focal point. This article notes the difference between price spreads and marketing margins, outlines corresponding economic theory, and describes the empirical evidence on wholesale meat and livestock price dynamics in the wake of COVID-19 disruptions. At one point during the pandemic, beef and pork packers were both operating at 60% of the previous year’s processing volume. We explore how such a massive supply shock would be expected to affect marketing margins even in absence of anti-competitive behavior. Moreover, we document how margin measurements are critically sensitive to selection of data and information utilized. Finally, we conclude with some discussion around policy proposals that would pit industry concentration against industry coordination and economies of scale.”

You can read the whole thing here.

Time for Food Resilience

That’s the title of a piece I wrote for the City Journal about food system resiliency in the face of COVID-19. A few excerpts are below.

“Food production is not the problem. Farmers’ markets this summer, for example, have struggled because consumers have been reluctant to congregate with others, not because farmers couldn’t grow enough food. In some cases, farmers have dumped tons of milk and produce because anticipated demand for these commodities suddenly disappeared. Unlike other manufacturing systems, plant and animal growth can’t be stopped with the flip of a switch, nor can food-processing chains be quickly reoriented from wholesale to retail production.

While demand for food eaten away from home was falling, demand for food purchased at grocery stores spiked, leading to some empty shelves. Grocery stores can anticipate and plan for peaks in demand, such as the days around Thanksgiving and Christmas, or even regional disruptions related to natural disasters such as hurricanes. Global shocks that occur once a century are impossible to predict or plan for. Pressured to reduce food waste and cost, groceries operate on nearly just-in-time delivery systems. Holding excess inventory is costly, and in the case of fresh produce, wasteful. We can ask grocery stores to store more inventory, but with associated costs.”

Some thoughts on possible solutions …

“To create a more robust food-supply chain, we need to take a thorough look at the legal and regulatory impediments that prevented food from flowing to areas of falling demand to areas of rising demand. In the pandemic’s early days, many locales not only shut down restaurants but also prevented restaurants from selling inventory to consumers because they lacked grocery licenses. Food and Drug Administration rules prevented farms that delivered eggs and egg products to restaurants from diverting supply to grocery stores, for example. Many of these rules were ultimately relaxed, but not until after the worst effects had been felt.

Facilitating markets that utilize prices to signal where food is most needed is vital to ensuring that food supply is not interrupted. While extensive public markets trade in agricultural commodities, trade is less expansive for retail foodstuffs, where supply is often centralized by large food distributors or grocers. Lessons can be learned from food banks that use the power of markets to aggregate information and get food to where it is most desired. Such markets can benefit large and small farms alike. One of my colleagues developed an online market platform for local farmers to connect with consumers facing Covid-19 related closures of farmers’ markets.

More innovation and automation in food distribution and retailing will also limit contagion while facilitating efficient markets. We have become accustomed to self-checkouts at the grocery; robots are already doing a good deal of cow-milking. Driverless cars and trucks could ensure the movement of food while minimizing risk of contagion. Online sales of food for delivery or in-store-pickup will continue to rise; centralized warehouses that stock and deliver directly to our doorsteps can go further to help prevent disease spread. The supermarket of the future may be much smaller and focused on fresh items like meat and vegetables that we want to pick by hand, with processed items coming directly from distribution centers. Developments that improve the shelf life of food will facilitate the development of emergency stockpiles—and reduce food waste.”

You can find the whole thing here.

Concentration and Resilience

We’ve fortunately worked our way through the worst of the COVID-19 related packing plant shutdowns that caused massive disruptions in beef and pork sectors, and packing plants are now operating at levels on par with last year.

One of the features of the beef and pork packing sectors that has served as a focal point is the concentrated nature of production, with a relatively small number of plants accounting for a large share of total industry output. Many people have begun to advocate for a less concentrated packing sector under the premise that this market structure would be less prone to the sorts of disruptions we witnessed during the pandemic (see my previous discussion on the matter here).

Ultimately this is an empirical question amenable to analysis, provided one is willing to make some assumptions. To explore this question a bit, I set up a hypothetical simulation that allows us to investigate how total output in an industry varies with the number of packing plants, each of which faces a known probability of closure.

I consider a case in which maximum total output for the entire industry is 100,000 units, but consider different “worlds” where there are a different number of packing plants responsible for producing this total. In one “world” that is highly concentrated, there are a mere 5 plants, and each plant is the same size, implying each plant produces 100,000/5= 20,000 “units” when operating. If a plant is open, it produces 20,000 units, but if it gets a bad luck of the draw, it closes and produces 0. By contrast, another “world” is highly diffuse and there are 100 plants, and each plant is the same size, implying the each plant produces 100,000/100 = 1,000 units when operating. In this world, if a plant is open, it produces 1,000 units, but if it gets a bad luck of the draw, it closes and produces 0. I explore expected outcomes where each plant in each world faces an independent probability of closure.

First, let’s consider a case where the likelihood of any individual plant closing is 0.1 (i.e., 10% of the time a given plant will close). The figure below shows the likelihood that total industry output falls below a given level of production for different “worlds” with different levels of concentration. So, for example, in the highly concentrated world (5 plants producing all the output), there is only a 42% chance that total industry output falls below the a maximum capacity of 100,000. By contrast, in the highly diffuse world (100 plants producing all the output), there is 100% chance that total industry output falls below the maximum. Why? Because with so many plants, there is a very high chance at least one of them goes down.

For a given level of output, a world that has a smaller probability of falling below the output level is “better” in the sense that it ensures greater production for both consumers and producers. So, while the most concentrated world is “best” at ensuring the highest level of output, it is also the case that more diffuse worlds are better at ensuring output doesn’t fall below very low levels.

So, for example, in the figure below, the most concentrated world (5 plants) has a roughly 8% chance of falling below 70,000 units of production, where the most diffuse world (100 plants) has a 0% chance of falling below 70,000 units of production. The intuition is that in a world with 100 plants, and each plant only faces a 10% chance of closure, it is practically certain enough plants will remain open to produced at least 70,000 units.

How do these results hold up if the probability of a plant closure changes? The three figures below consider that question for closure probabilities of 0.2, 0.3, and 0.5.

What does the pattern of results reveal?

• First, the obvious. As the probability of plant closure increases, there is a greater likelihood of falling below a given level of total industry output for any particular level of industry concentration.

• Generally, concentrated “worlds” are better at ensuring high levels of output while less concentrated “worlds” are better at ensuring output doesn’t fall below “low” levels.

  • Still, the differences in probability of ensuring a “minimum threshold” of output are not particularly large across different levels of concentration.

• As the probability of plant closure increases, it is more likely that a more concentrated “world” is preferable to a more diffuse “world” insofar as ensuring a given level of total industry output.

The results of this exercise confirm most people’s basic intuition that a more diffuse packing sector would be less prone to the worst possible outcomes than a more concentrated sector. However, the results also reveal some important nuance. Namely “how much” less prone to the worst outcomes is a more diffuse sector? The figures above suggest “not much” as there are not big differences in the lines at the far left-hand corner of the charts unless plants face a really high chance of closure (e.g., a 50% chance). The figures also reveal a less intuitive result - namely that when facing a given probability of closure, a more concentrated sector has a higher likelihood of hitting high levels of industry output. Thus, we have a trade-off between the likelihoods of producing the maximum vs. preventing the worst possible outcomes.

Curious trend in sales of plant-based meat alternatives

I came across a report from IRI showing changes in sales of plant-based meat alternatives during March, April, and May relative to the same time last year. The figure below shows some dramatic sales growth during the early part of the pandemic. Of course, the large percentage growth is partly explained by the fact that plant-based sales were starting from a low base (i.e., if you go from 1lb to 2lbs, that’s 100% growth, whereas if you go from 100 to 101lbs, that’s only 1% growth).  The rest of the report shows the enormous difference in relative magnitudes as plant-based sales are only about 0.66% of total dollar sales (or 0.33% of total lbs of meat sales) .

What I want to focus on here though isn’t the spike in sales in March, but rather what happened later. Why wasn’t there a similar spike in sales in April and May? It was in late April and early May that beef and pork production were being most adversely affected from plant shutdowns. During this time, wholesale meat prices were skyrocketing and there were stories in every major media outlet about the possibilities of meat shortages. Data from USDA and the Bureau of Labor Statistics shows retail ground beef prices in grocery stores, for example, jumped more than 10% from April to May (after rising about 5% from March to April).

So, at time when beef prices rose dramatically, retailers were limiting how much beef consumers could buy, and there was overall limited availability of beef, the growth in sales of plant based meats began to fall from almost 80% at the end of April to 57% at the end of May.

At the time the economic environment was most opportune for consumers to switch from beef and pork to plant-based alternatives, it seems that few made that substitution. The figure below shows the trends in volume (or lbs) market share. The share of plant-based sales did indeed rise over the period in question from 0.22% of all lbs purchased to 0.33% of lbs purchased. I’m a bit surprised it wasn’t more.

One of the inferences we can draw from these data, which is also consistent with the research we recently conducted, is that a lot of the purchases of plant-based alternatives are coming from consumers who wouldn’t have bought much beef or pork to begin with.

I look forward to seeing how these trends continue to evolve.

A couple new papers

In the past couple days, I’ve had a couple papers published with some recent Purdue MS graduates.

The first paper is with Lacey Wilson in Food Policy (I blogged about a previous version of the paper back in February). Here’s the abstract:

“Previous studies, as well as market sales data, show some consumers are willing to pay a premium for redundant or superfluous food labels that carry no additional information for the informed consumer. Some advocacy groups have argued that the use of such redundant labels is misleading or unethical. To determine whether premiums for redundant labels stem from misunderstanding or other factors, this study seeks to determine whether greater knowledge of the claims - in the form of expertise in food production and scientific literacy - decreases willingness to pay for redundant labels. We also explore whether de-biasing information influences consumers’ valuations of redundant labels. An online survey of 1122 U.S. consumers elicited preferences for three redundantly labeled products: non-GMO sea salt, gluten-free orange juice, and no-hormone-added chicken breast. Respondents with farm experience report lower premiums for non-GMO salt and no-hormone-added chicken. Those with higher scientific literacy state lower premiums for gluten-free orange juice. However, after providing information about the redundancy of the claims, less than half of respondents who were initially willing to pay extra for the label are convinced otherwise. Over 30% of respondents counter-intuitively increase their premiums, behavior that is associated with less a priori scientific knowledge. The likelihood of “overpricing” redundant labels is associated with willingness-to-pay premiums for organic food, suggesting at least some of the premium for organic is a result of misinformation.”

Lacey is currently employed as a data scientist at 8451.

The second paper, led by Aaron Staples, and co-authored with Carson Reeling and Nicole Widmar just appeared in Agribusiness. Here’s the abstract:

“Commercial and regional brewers are increasingly investing in environmental sustainability equipment that reduces input use, operating costs, and environmental impacts. These technologies often require prohibitively high upfront costs. One potential solution for these brewers is to market their product as sustainable and charge a premium to offset some of the costs. We undertake a stated preference choice experiment targeting a nationally representative sample of beer buyers and elicit preferences for multiple attributes related to environmental sustainability in beer. We find that, on average, beer buyers are willing to pay $0.70/six‐pack for beer produced using water and wastewater reduction technologies, $0.85 for carbon reduction practices, and $0.98 for landfill diversion practices, though water sustainability practices appeal to a largest share of beer buyers. We also find that preferences for sustainability attributes are widely distributed among beer drinkers, largely irrespective of sociodemographic characteristics. The positive price premiums across sustainability attributes suggest beer buyers value sustainable brewing, and brewers could attract new consumers by simultaneously communicating their commitment to sustainability and differentiating their product”

Aaron is in the PhD program in agricultural economics at Michigan State.