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.

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.