Impacts of Coronavirus on Food Markets

Last week was a whirlwind of trip and event cancellations, movement of courses online, and the dusting off of emergency and contingency plans. This week is likely to bring more social-distancing and quarantining measures. The ultimate toll and impacts of the coronavirus are highly uncertain at present.  Nonetheless, it might be useful to speculate a bit about impacts of coronavirus and the events surrounding it on food markets. 

1. Grocery buying behavior. It has been fascinating to watch online, and in my own local grocery stores, which items consumers are choosing to stock-up on.  The run on toilet paper, for example, seems on the surface of it, downright irrational.  After all, COVID-19 does not cause digestive issues.  As irrational as the initial movement to toilet paper may seem, it isn’t crazy for subsequent consumers to then stock up too.  After all, it doesn’t take much for a reasonable person to see that if all other consumers are buying up all the toilet paper, that they’d better off getting theirs before none is left.  There is a long and interesting economics literature on information cascades and herding behavior, which shows that even if you disagree with what other people are doing, it is sometimes sensible to go along with the crowd.      

Much of the information we have at this point on which items are stocking-out is anecdotal, but there do seem to be some common trends in what I see in my own local stores and commentary online.  For example, it seems many of the new plant-based burgers are being left behind while the rest of the meat case is being cleared (see here or here).  I was surprised to see in my own local store, that virtually all the beef was gone (except for a bit of ground beef), about half the pork was gone, and chicken was plentiful.  This must say something about people’s psychology to go for the highest-price, perishable produce in this time of panic; that or differences in supply chain issues, but more on that later.  In other aisles, rice and pasta went quickly, presumably for issues related to the long shelf life, should quarantining result.  Still, I noticed what was left in those aisles were the gluten-free options and the lesser-known brands or unusual flavors, suggesting stock-outs are related to item popularity.  I hope we can learn more about this behavior after the fact. Unfortunately, it’s difficult to study stocking-out phenomenon because stores are usually well stocked, and because grocery store scanner data only shows us what people bought, but we can’t see what people didn’t buy because it wasn’t available.

When we go into survival mode, it anecdotally seems we don’t choose #plantbased meat first (or rather, we stick with the original) #covid19 https://t.co/ZyWfBY1fLt

— Sara Place (@drsplace) March 14, 2020

2.       Stock-outs and supply chains.  The New York Times ran a story yesterday with the heading “There Is Plenty of Food in the Country.” I largely agree.  The stock-outs we are seeing now are likely temporary disruptions resulting from consumers pulling forward buying behavior in anticipation of future reduce mobility.  But, it’s unlikely people will eat more in aggregate because of the coronavirus.  Thus, this is largely a temporal adjustment in buying behavior with smaller effects on aggregate food demand. 

However, there could be more serious food market disruptions. Some of the stock-outs and slowdowns in grocery check-out lines are because employees are staying at home and practicing social-distancing.  This problem is likely to grow if more people become ill. So, while we might have the food supply available, will we have the workers to get it to us?  

Now, take a step back in the supply chain, and this is where worker issues could have serious issues.  Remember all the fervor over the beef packing-plant fire back in August?  While the impacts was counter-intuitive to many producers, the economics were straightforward: an unexpected disruption in supply depressed cattle prices and boosted wholesale beef prices.  It isn’t far-fetched to imagine worker illnesses getting to the point that plants have to temporarily shut down on a scale that is at least as large as the August-fire, which removed about 5% of the nation’s beef processing capacity.  One difference is that destroying a plant via fire is not the same as temporarily closing plants due to lack of healthy workers; one resulted in a long-term price adjustment while the latter is more likely a temporary price fluctuation.

One thing that makes me nervous even about temporary closures, if large scale, is the animals that have been placed to be market-weight in the next few weeks.  While feedlot cattle can likely remain on feed a few weeks longer with relatively small changes in profitability, that is less true for hogs, and particularly chickens.  Meat supply chains are optimized for efficiency and low-cost production, not necessarily for flexibility and resiliency.    

A signal to keep an eye on is the amount of meat in cold storage (the data currently available are lagged by at least a month).  The buying behavior we’re seeing now is likely to pull meat out of storage and onto our dinner plates.  However, that boost in domestic demand is likely to be offset by reductions in foreign demand, and the coronavirus has hit hard some of our biggest export markets.

The flip-side of this is that we rely on imports from China for a variety of consumer goods, and this trade is likely to be disrupted by coronavirus. I’ve often been critical of the local foods movement, but it’s times like these that highlight some of the benefits of localization and heterogeneity in the food supply chain.

3. Recession. Given the reaction of the stock-market and the disruption to normal business and spending activity, the chances of a recession are high.  The “Great Recession” in 2007-09 had significant impacts on food spending, particularly spending on food away from home.  Here are data from the Bureau of Labor Statistics Consumer Expenditure Survey.  These data show food spending at home only declined slightly after the recession, but the share of spending that occurred outside the home (at restaurants, etc.)  fell from 0.44 to 0.41. 

It is also interesting to look at how spending on different types of food changed during the Great Recession.  The figure below shows spending on food eaten at home (plus total alcohol spending).  All at-home food spending increased in 2008 before falling in 2009, but the increase was smaller for beef and pork, which implies the share of food spending on these items fell over this period.  Spending on alcohol took the biggest hit.  By contrast, spending on fruits and vegetables, cereal and bakery, and dairy, fared pretty well during the last recession.

There is an old saying that “generals are always fighting the last war.”  Likewise, it is probably wise not to focus too much on the past recession to predict how consumers might respond to one potentially caused by the coronavirus.  Nonetheless, the pattern of reduced spending on food away from home is already occurring, and meat demand is typically thought to respond significantly to income, which suggests, at least in these two cases, the pattern may re-emerge. 

During the past recession, rates of food insecurity spiked. There are concerns about impacts of school closures on childhood food security, and the USDA is considering policies that will allow delivery of free school lunch and breakfast to low income children even in instances where schools are closed.

4. Population. A couple months ago, I discussed the role of population in affecting food demand.  I was writing then about the fact that birth rates have been falling, and indicated a smaller population would put downward pressure on food prices and farm incomes.  Unfortunately, a global pandemic like the coronavirus has the potential to reduce the world’s population (or at least slow the increase). For example, estimates suggest the flu pandemic in 1918 sickened about 27% of the world’s population and killed about 2 to 3% of the world’s population at the time. Estimates of the potential number of deaths from the coronavirus are all over the board, but the greater the number of “excess” deaths, the greater the reduction in aggregate food demand. On the up-side, all this social-distancing and self-quarantining means many more couples will be home together. We may need to hang on to all those hospital beds for the new babies that will arrive in nine months.

Holy Chicken!

Morgan Spurlock’s newest documentary, available on Amazon, is Super Size Me 2: Holy Chicken! I wasn’t a big fan of the 1st edition of Super Size me, and I was expecting this one to fall into the sensationalized, muckraking, one-sided story telling I tend to associate with many food and agricultural documentaries. Yes, there was some of that, but overall I was surprised how much I liked about the film.

The documentary is mainly about Spurlock’s efforts to create a healthy-seeming fast food restaurant selling chicken sandwiches. Oh, and he decides to source and raise the chickens himself, using mainly the conventional production methods employed throughout the industry. One could quibble with some of the claims and comparisons, but I thought the documentary did a reasonably good job accurately showing how modern broilers are raised.

The best aspect of the documentary was the exposure of the ridiculousness of many food marketing claims and the health halos that such claims create. It was fascinating to see the ways fast food companies position themselves to create the aura of healthiness and wellness. To be sure, many fast food companies are indeed offering healthier items than in the past and have made efforts to improve healthiness of kids meals, etc., but it is also the case that a lot of effort has also gone into creating the illusion that a lot of what we’ve been eating all along is today somehow healthier.

From an economic standpoint, there is one bone to pick with the documentary. Spurlock is quite critical of the so-called tournament system that is used to pay the broiler producers (or chicken farmers as Spurlock calls them) who feed out birds for Tyson, Pilgrim’s, Perdue, Sanderson, and the other major chicken companies. Under this system, the performance of one producer is compared to a group of others, and pay is determined by how one’s performance compares to their peers. It is impossible not to feel sympathy for the farmers Spurlock interviews, and I have no intimate knowledge of the veracity of claims about whether the tournament system is used to retaliate against producers who do not toe the party line. As the documentary notes, there are ongoing lawsuits related to the use of the tournament system, and I presume the courts can sort out whether there is legitimate evidence of wrongdoing.

However, the documentary portrayed the tournament system in a one-sided way that made it seem a contrivance only to benefit the big chicken companies. What was missing is a discussion of a key motivation for the use of the tournament system, which rests on very solid economic principles. In particular, the tournament is a potential solution to something called the principal-agent problem. The “principal” (e.g., a company like Tyson) owns the birds and supplies the feed and other inputs to the producer (the “agent”). The problem is that, without high cost, Tyson can’t directly observe how much effort the producer puts into caring for the chickens Tyson owns; it is also difficult for Tyson to observe managerial talent and acumen, which (contrary what the documentary suggests) can have big effects on productivity. When such effort or talent can’t be easily observed or directly rewarded, there is little incentive for producers to put forth high effort. A tournament has the potential to better solve this problem of information asymmetry and induce high effort and high productivity, and thus lower chicken prices for the final consumer. I touched on this a while back when discussing The Meat Racket (see also Aleks Schaefer’s excellent review), which was highly critical of the tournament system.

I wonder if there won’t be other ways that evolve to help solve this principal-agent, asymmetric information problem? With all the sensors, tracking, and data analytics that are being created for agricultural applications, the cost of effort and quality monitoring might eventually fall, albeit in ways that might be perceived as encroaching on privacy. Profit-sharing is another mechanism that is sometimes used to help solve principal-agent problems that might have some application here. If the concern is that the tournament is being manipulated in some way, that seems like a problem that could be solved with a third-party verification system. All this is a way of saying that a lawsuit or legislation might do away with the tournament system, but it won’t remove the underlying problem of asymmetric information.

Who are you calling food insecure?

Every year, the USDA Economic Research Service (ERS) reports rates of food security in the United States. In 2018, 11.1% of U.S. households were estimated to be food insecure, down from a recent-history high of 14.9% in 2011.

These official statistics on food security are often interpreted in the media and by lay audiences as a measure of hunger. But, that’s not exactly what the USDA-ERS measures. A new paper by Sunjin Ahn, Travis Smith, and Bailey Norwood in Applied Economics Perspectives and Policy does a great job de-mystifying how official government measures of food insecurity are actually calculated. They also ably explain and articulate what other survey researchers must do to produce results that approximate the official measures.

Food insecurity is measured by the US Census Bureau asking a large sample of nationally-representative U.S. households a series of 10 questions (plus an additional 8 questions if there are children in the household) like how often, “In the last 12 months, were you ever hungry, but didn't eat, because you couldn't afford enough food?” or how often “I couldn’t afford to eat balanced meals.” A score is then calculated based on the frequency with which people respond affirmatively to the questions. If the score is high enough, the household is deemed food insecure. Seen in this way, food insecurity is probably best interpreted as a measure of a household’s perception of food affordability, although it almost surely positively correlated with hunger. The ERS has more information on how food security differs from hunger, and on the details of their measurement of food security here.

Ahn, Smith, and Norwood point out another issue that is not widely appreciated. They write:

“To avoid overburdening respondents with unnecessary questions in the CPS‐FSS [Census Bureau Current Population Survey - Food Security Supplement] survey, surveyors first conduct a screening process. If a household’s income is greater than 185% of the poverty threshold, and they answer

(1) “no” to “… did you ever run short of money and try to make your food or your money go further,” or

(2) “enough of the kinds of food (I/we) want to eat” from the question “Which of these statements best describes the food eaten in your household …,”

they are assumed to be food secure and are not administered the Food Security questionnaire (ERS 2015b). This screening process varies: In a 2012 design description, the first of the above questions was not used (ERS 2012a), and documentation of the survey suggests sometimes the income threshold is 200% of the poverty threshold. Though it is recognized that some of the individuals screened out of the questions will in fact be food insecure, the screening was still seen as desirable because it reduces respondent burden (ERS 2015a). Thus, the CPS‐FSS food insecurity rates are a function of responses to food insecurity questions conditional on the statistical screening procedures employed.”

Ahn, Smith, and Norwood’s paper is mainly framed around the question of whether opt-in, internet-based surveys can mimic the official government estimates of food insecurity. However, their results make abundantly clear the critical role of the income threshold in setting official food insecurity rates. In short, if we simply counted the scores on the food insecurity questions and ignored income, we would find MUCH higher rates of measured food insecurity. Before applying the income-cutoff, Ahn, Smith, and Norwood find food insecurity rates of 43% (in a 2016 survey) and 31% (in a 2017 survey). After applying the income cut-offs (essentially assuming anyone with an income over 180% of the poverty line can’t be food insecure) and some demographic weighting, the authors find opt-in internet surveys can produce estimates of food insecurity that are similar to that reported by the USDA-ERS.

I’m a little unsure of how to interpret these findings. On the one hand, I’m left with a sense that the official food insecurity statistics are heavily influenced by a somewhat arbitrary income cut-off, and that perhaps the official measure of food insecurity are too imprecise at measuring the construct we are really after. Another, reasonable, albeit alarming, conclusion is that there may a lot more food insecure people than we thought.

Consumer Demand for Redundant Food Labels

That’s the title of a new working paper co-authored with Lacey Wilson. Here is 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 1,122 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.”

The figure below shows a key result. People place a $0 premium on non-GMO salt, gluten-free orange juice, and hormone-free chicken have significantly higher scientific literacy scores than people who place positive or negative premiums on these redundantly labeled products.

Measuring changes in supply versus changes in demand

I just finished up a new working paper with Glynn Tonsor that shows how to determine the extent to which a change in price (or quantity) results from a change in supply and/or demand. For some time, Glynn has been reporting updated retail demand indices for meat products. In this new paper, we show how to calculate an analogous supply index, which might provide a useful way to determine how much productivity is changing over time. The basic idea is that we want a way to separate changes in quantity demanded (or supplied) versus a shift in the demand curve (or the supply curve). We also show how the two indicies can be used to determine changes in consumer and producer economic well-being over time.

Here’s the motivation:

“In 2015, per-capita beef consumption in the U.S. reached a record low of 54 lbs/person, falling almost 20% over the prior decade from 2005 to 2015 alone (USDA, Economic Research Service, 2020). Why? Some environmental, public health, and animal advocacy organizations heralded the decline as an indicator of their efforts to convince consumers to reduce their demand for beef; others argued, instead, the change was a result of supply-side factors such as drought and higher feed prices (e.g., Strom, 2017). Per-capita beef consumption subsequently rebounded, and in 2018 was almost 6% higher than in 2015. Dramatic fluctuations in corn, soybean, and wheat prices in the late 2000s through the mid-2010s led to similar heated debates about whether and to what extent price rises were due to demand (e.g., biofuel policy and rising incomes in China) or supply (e.g., drought in various regions of the world) factors (e.g., Abbot, Hurt, Tyner 2019; Carter, Rouser, and Smith, 2016; Hochman, Rajagopal, and Zilberman, 2010; Roberts and Schlenker, 2013). These cases highlight the challenge of interpreting market dynamics and the need for metrics that can decompose price or quantity changes to reveal underlying drivers and consequences.”

We calculate the supply and demand indicies for a number of agricultural markets and time periods. First, consider changes in supply and demand in the fed cattle market since the 1950s, as shown in the figure below. The demand index trended positively from 1950 through the mid 1970s. The demand index peaked at a value of 204 in 1976, and it hasn’t been as high since. Demand fell through the 1980s and early 1990s before rebounding. Since 2010, the demand index has been at values just below the 1970’s peak. The supply index trend was positive from 1950 up till about 2000, but has been stagnant except for the past couple decades. Nonetheless, the 2018 supply index value is the highest of the entire time period since 1950. The figure shows a significant drop in the supply index that began in 2013 and bottomed out in 2015, which is likely a result of drought in the great plains and from high feed prices. The fact that the supply index dropped during this period while the demand index remained relatively flat helps provide insight into the debate discussed in the quote above.

One can also calculate changes in producer and consumer surplus over time. The following figure calculates the year-to-year changes. On average, from 1980 to 2018, producer surplus increased $2.7 billion each year and consumer surplus increased $0.58 billion each year. Despite these averages, there is a high degree of year-to-year variability. The largest annual change in producer surplus was $34 billion from 2015 to 2016; the largest decline in producer surplus was -$28 billion from 2013 to 2014.

Here’s how changes in the supply index compare for the three main meat categories. Chicken supply shifts have far outpaced that for hogs or cattle. The 2018 chicken supply index value is 380, meaning chicken supply is (380-100) = 280% higher than in 1980. By contrast, hog and beef supply are only 66% and 28% higher, respectively, than in 1980. These differences are likely explained by differential productivity patterns in these sectors. The rise in hog productivity since 2000 corresponds with a time period over which the industry became increasingly vertically integrated, increasingly mirroring the broiler chicken sector. The much longer biological production lags in beef cattle (which range from two to three years from the time a breeding decision is made until harvest) and less integrated nature of the beef cattle industry help explain the smaller increases in the supply index in this sector as compared to pork and chicken. We also show, in the paper, that these supply indicies correlate in intuitive ways with changes in factors like feed prices, drought, and aggregate U.S. agricultural total factor productivity.

One of the useful aspects of the supply and demand indicies is that they can be applied for highly disaggregated geographic units. To illustrate, we calculated U.S. county-level supply indicies. Here are the changes in U.S. supply indicies in the past couple years relative to 2000. Perhaps surprisingly, many areas of Ohio, Indiana, and southern Illinois have experienced negative corn supply shocks in 2016-2018 relative to 2000. The expanded geographical area of U.S. corn production (e.g more acres in the Dakotas) over this period helped mitigate national corn market effects of the adverse Eastern Cornbelt supply shocks. Note that corn yields and total production have increased significantly in many of the red counties over time, and this illustrates the importance of calculating a supply index rather than just looking at yield or production. The supply index gives us a feel for how much more (or less) is produced in 2018 relative to what we would have expected if the level of technology, weather, etc. were the same as in the year 2000.

There is a lot more in the paper.