Size and Vulnerability in Meat Packing

Today, I participated (virtually) in the WSJ Global Food Forum, and there continues to be lively discussion about COVID19 disruptions to the food supply chain, and there were ample questions put to food industry executives about how to improve resiliency. The shutdowns that happened in the beef and pork packing sectors in April and May continue to raise questions about size and concentration. I’ve written about this issue repeatedly and have run simulations, revealing my somewhat contrarian view that a smaller, more distributed packing sector wouldn’t have necessarily performed better in response to this pandemic.

A key challenge is that we do not have good data relating packing plant size to likelihood of shutdown or disruption. However, the USDA does publish a monthly report revealing hog and cattle slaughter and red meat production by state, which might provide some indirect clues about size and vulnerability.

For some states (even some of the most important packing states), data on slaughter volumes are not release due to confidentiality rules, but there is better coverage for total red meat production. So, let’s start there. I focus in on data in April and May, when the worst of the COVID-related shutdowns occurred, and compared total April+May production in 2020 to production in April+May in 2019 across all 50 U.S. states.

The median change was -1%, however there was wide dispersion across states ranging from -49% in South Dakota to +130% in West Virginia. How does this change relate to the total volume of red meat production in each state in the prior year (a proxy for the processing capacity of the state)? Did states that have more red meat production capacity (presumably because of larger, more concentrated plants) experience larger year-over-year declines during the COVID19 shutdowns?

The figure below plots the relationship. Among the states with the smallest levels of production in 2019 (i.e., the smallest capacity), there was a tendency to see an increase in production during the 2020 shutdown periods compared to the same time last year, and moreover, as state’s total production capacity increases, the declines initially seem to get worse. This would seem to confirm the prevailing narrative that places with less concentration were less affected (and generally benefitted) from the COVID19-related packing plant shutdowns.

However, after about 300 million lbs of production (during April and May), there is essentially no relationship between these variables. For example, Nebraska produced 1340 million lbs of red meat in April and May 2019, and they experienced a year-over-year decline of 25% in 2020. However, Colorado produced “only” 361.8 million lbs of red meat in April and May 2019, and they experienced a -32% year-over-year decline in 2020. A states total capacity didn’t seem to matter much after a certain level.

Here is a map of the changes in red meat production in April and May 2020 compared to April and May 2019, showing how each state faired during the worst of the COVID19 plant shutdown period. The biggest red meat packing states are: 1) Iowa, 2) Nebraska, 3) Kansas, 4) Texas, 5) Illinois, and 6) Minnesota. Three of those states are in red but three aren’t. Moreover, there are states like Washington that experienced one of the largest declines, despite being middle of the pack in terms of total production.

Some of the states with the largest percent increases (dark green) are near states with the largest decreases (red), a phenomenon likely resulting from producers trying to find nearby processing facilities when their “typical” plant shutdown.

Here is the same analysis but focusing in on number of hogs slaughtered in April and May 2020 relative to April and May 2019. Again, states with a smaller number of hog slaughtered, while all over the map, tended to be more likely to experience gains than losses; however, once one moves beyond about 500,000 head slaughtered (in April and May 2019), there is essentially no relationship between the size of a state’s processing capacity and the extent of it’s shutdown.

And, here is the change in number of hogs slaughtered in April and May 2020 compared to April and May 2019 by state.

The relationship between a state’s cattle slaughter in 2019 and it’s change during COVID is below, and a similar phenomenon is observed as was the case for pork and red meat production. States with minimal slaughter capacity tended to see a strong uptick in processing during COVID, but once one moves beyond the smallest of processing states, there is essentially no relationship between COVID19 processing changes and a state’s processing capacity.

And, finally, here is the geographic distribution of the change in cattle slaughter in April and May 2020 relative to April and May 2019.

Effects of a Ban on Junk Food Advertising

About a month ago, Tamar Haspel re-opened a debate on the merits (or, rather, demerits) of junk food advertising to children in her regular Washington Post column. My intent is not to take issue with anything written there per se, but rather to bring up a dimension to this debate she didn’t address.

Even if accepts the premise that “advertising works”, and increases the rate at which people buy junk food, that knowledge is insufficient to understand the impacts of an advertising ban for at least two reasons. First, what will people consume instead once ads are banned, and what is the cost and healthfulness of the newly purchased items? Second, how will food manufacturers and consumers respond to the ban?

In a paper back in 2014, Vincent Réquillart and Louis-Georges Soler, while very much in favor of policies aimed at promoting healthy eating, do a good job describing the various ways that food companies might respond to advertising bans or taxes. Companies don’t just “sit still.” For example, if a firm can no longer advertise, what happens to the money the previously spend in this way? Perhaps they invest in cost savings technologies that allow them to lower the price of the food, which would encourage additional consumption. Or, unable to compete by advertising, firms may engage in more price competition, again driving down prices and bringing more consumers into the market - presumably the opposite of the intended effect of the policy.

A couple years ago, Pierre Dubois, Rachel Griffith, and Martin O’Connell published a very careful and through paper in the Review of Economic Studies on this very topic by studying advertising on potato chips in the U.K. They found that an advertising ban would lower the share of consumers buying potato chips by about 5.3 percentage points; however, they also estimated that in response to the ban, firms would lower chip prices, which would bring more consumers back to the chip market, making the net effect of the advertising ban only a 4 percentage point reduction on the share of shoppers buying chips.

So far so good if the goal is an overall reduction in chip buying. However, they also showed that the advertising ban (after all the anticipated price changes) would increase consumption of other unhealthy products by about 2.7 percentage points. The problem, as they point out, is that “these alternative snacks are, on average, less healthy than potato chips (their mean nutrient score is 20 compared to around 14 for potato chips).”

They offer a solution to this problem: a broader ban on advertising to include all “junk food,” however it is unclear which foods would be deemed “junk.” And, the broader point remains: there will likely be offsetting price effects, albeit perhaps not large enough to completely offset the impacts of the lack of advertising.

Ultimately, Tamar ends her piece making a moral argument, and insofar as advertisements aimed at kids, she raises some good points. Still, it is important to recognize policies often have unintended effects. Neither companies nor consumers are passive bystanders in the face of policy changes. They respond, and if not in ways that completely offset the intended effects, at least in ways that can significantly dampen the intended effects.

Economics of Household Food Waste

That’s the title of a new “Policy Short” article I’ve co-authored with Brenna Ellison for the Canadian Journal of Agricultural Economics. Here is the abstract:

“Food waste has drawn increasing public attention, and the high levels of estimated waste are largely considered to be a failure of our current food system. Recently, economists have begun to weigh in, showing food waste can emerge as the result of a complex equilibrium affected by consumers’ preferences for convenience; expectations about future food prices and availability; food safety concerns; producers’ costs of holding inventory, transportation, and storage; government regulation; and technology. If food waste is a form of inefficiency, there are either strong economic motivations to reduce waste, or unmeasured costs or preferences affecting waste decisions. If consumers have behavioral biases, suffer from information asymmetries, or do not pay the full cost of their waste, there may be a role for government intervention to reduce waste, but most empirical models in the literature have not articulated or quantified the extent of the deadweight loss from the market failures in relation to food waste. In some cases, waste reduction efforts could harm producers if overall demand for food is reduced or harm consumers if overconsumption is encouraged, quality or safety degrades, or supply disruptions occur. Technological innovations, which lower the cost of storage or extend shelf life have the potential to improve both consumer and producer welfare.”

We end with the following:

“Advocates for food waste reduction often proceed on the premise that any intervention that reduces waste is a desirable outcome. However, as this paper illustrates, waste reduction strategies have benefits and costs, and there are likely a number of trade‐offs that must be made to reduce waste. Additional understanding of the economic drivers of waste decisions can help ensure that efforts to reduce food waste are not a waste of time and energy.”

You can read the whole thing here.

The non-price effects of soda taxes and bans

The American Journal of Agricultural Economics just published a paper I co-authored with Sunjin Ahn, who is a post-doc at Mississippi State University entitled “Non‐Pecuniary Effects of Sugar‐Sweetened Beverage Policies.” (for the non-economists out there, “non-pecuniary” just means non-price).

Here was our motivation for the study:

“There is some market evidence that passage of SSB [sugar sweetened beverage] taxes might generate outcomes beyond that predicted by price elasticities (or the pecuniary effects). Non‐pecuniary effects could amplify the effects of a tax, increasing the intended effects of the policy. In particular, the tax (and the debate and publicity surrounding it) could send information to consumers about the relative healthfulness of beverage options and send cues as to which choices are “socially acceptable”
...
Signaling and information effects associated with SSB taxes are only one potential non‐pecuniary effect, and it is possible that some non‐pecuniary factors, such as reactance, could dampen the effects of a tax, and in the extreme could result in outcomes opposite that intended by the policy. ... Reactance is thought to arise from perceptions of threats to individual freedom, among other factors (Brehm 1966). Thus, although it seems clear that non‐pecuniary effects might exist, the size and the direction of the effect is ambiguous. ”

We tackled this issue by conducting a series of experiments through surveys with consumers. We asked consumers to participate in a series of simulated grocery shopping exercises. Consumers first made choices between beverage options at a given set of prices, and then they were randomly allocated to different treatments where either:

• A) prices of SSB increased but respondents were not told why,

• B) prices of SSB increased and respondents were told it was a result of a soda tax,

• C) prices of SSB increased and respondents were told it was a result of a shortage of sugar beets and sugar cane,

• D) the size of SSB was reduced but respondents were not told why,

• E) the size of SSB was reduced and respondents were told the reduction was due to a government ban on large sized sugared sodas, or

• F) the size of of SSB was reduced and respondents were told the reduction was due to a plastic shortage.

By comparing how choices of SSBs change when people were told prices or size changes were a result of a policy vs. other non-policy factors, we can get a sense of the size and direction of the non-pecuniary effects.

When conducted our first study in 2016, we found significant results related to the SSB taxes. In particular, our results suggested people who were told price changes were a result of a tax were more likely to choose SSB than people who were not given a reason for the price change. We certainly weren’t the first to find such an effect. Here is a bit about previous research on this topic:

“Just and Hanks (2015) argued that consumers might respond with resistance when a new policy obstructs their ability to obtain their preferred option. They argued that the phenomenon arises because consumers are emotionally attached to consumption goods, resulting in reactance. Policies perceived as paternalistic might cause consumers to “double down” on purchases of forbidden or restricted goods (Lusk, Marette, and Norwood 2013). Just and Hanks (2015) constructed a model in which controversial policies such as a sin tax could lead to an increase in the marginal utility for a good, potentially leading to increased consumption even if prices rise. In addition, Hanks et al. (2013) found that demand for unhealthy foods under a tax frame increased while the demand for subsidized healthy foods fell. Similarly, Muller et al. (2017) found that almost 40% of low‐income individuals increased their share of expenditures on unhealthy food after an unhealthy food tax.”

When we sent the paper off for review, we received a number of valuable comments, which caused us to make a number of changes to our experiment, and repeat the study with some extensions in 2019. What did we find with these newer data? On average: nothing, nada, zilch. There was no significant difference in the average market share of SSBs across the various information treatments. However, we did find significant variability in the treatment effects, meaning some people choose more SSBs when they knew it was a tax/ban and others chose less; however, these variations were only partly explained by demographic effects. In summary, our results didn’t provide a clear answer on the question we sought out to address: non-pecuniary effects, to the extent they exist, seem to work in different ways for different people, making the net effect small and hard to identify, at least in our experimental setting.

A note on the publication process is worthwhile. Normally, it is very hard to publish null results. This is problematic for the advancement of science because it results in publication biases like the file draw problem. To the credit of Tim Richards, the journal editor, and the three anonymous reviewers at the American Journal of Agricultural Economics, we received a positive reaction and ultimately, after a more changes, acceptance for publication even though we failed to replicate our previous result and found null effects. This is really an example of peer-review working at it’s best.

Unscrambling COVID-19 Food Supply Chains

That is the tile of a new paper with Trey Malone and Aleks Schaefer, both at Michigan State University. Here is the abstract:

“This article uses evidence from the egg industry to investigate how the shift from food-away-from-home and towards food-at-home affected the U.S. food supply chain. We find that the onset of the COVID-19 pandemic increased retail and farm-gate prices for table eggs by approximately 141% and 182%, respectively. In contrast, prices for breaking stock eggs-which are primarily used in foodservice and restaurants-fell by 67%. On April 3, 2020, the FDA responded by issuing temporary exemptions from certain food safety standards for breaking stock egg producers seeking to sell into the retail table egg market. We find that this regulatory change rapidly pushed retail, farm-gate, and breaking stock prices towards their long-run pre-pandemic equilibrium dynamics. The pandemic reduced premiums for credence attributes, including cage-free, vegetarian-fed, and organic eggs, by as much as 34%. These premiums did not fully recover following the return to more “normal” price dynamics, possibly signaling that willingness-to-pay for animal welfare and environmental sustainability have fallen as consumers seek to meet basic needs during the pandemic. Finally, in spite of widespread claims of price gouging, we do not find that the pandemic (or the subsequent FDA regulatory changes) had a meaningful impact on the marketing margin for table eggs sold at grocery stores.”

We tried to tease out the effect of the pandemic itself on egg prices from the impact of FDA rules that barred eggs from easily moving from the restaurant to the grocery market. Here’s what we find on that latter point.

“These results suggest that had the FDA not suspended Egg Safety Rules for breaker producers seeking to sell into the table eggs market - farm-gate and retail table egg prices would have been approximately 53% and 56% higher than those observed in the last week of May. On the other hand, breaking prices in the same week would have been about 50% lower.”

The key results as they related to impacts on commodity egg prices are shown in the following graphs (the dashed lines are our forecasts of what would have happened had COVID19 not occurred).

You can read the whole paper here.