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.

Malthusian Inversion

I’ve noticed several articles in the past few weeks talking about slowing or even falling population growth. This article in the Economist discussed the fact that South Korea’s fertility rate is now below replacement level (i.e., fewer babies are being born than there are parents), and their figure shows the strong negative correlation between income (or GDP) of a country and a country’s fertility rate. The richer we get, it seems the fewer babies we want or need. Then, came this opinion article in the New York Times a couple days ago about the “Chinese Population Crisis,” in which Ross Douthat argues, “Unlike most developed countries, China is growing old without first having grown rich.”

We’ve all probably been adequately exposed to the concerns and problems associated with over-population from the writings of Malthus to Ehrlich’s Population Bomb. Less well appreciated are the benefits and costs associated with a falling population. For one take on the potential concerns, see this 2013 piece by Kevin Kelly entitled the “Underpopulation Bomb.” He states the problem as follows:

“This is a world where every year there is a smaller audience than the year before, a smaller market for your goods or services, fewer workers to choose from, and a ballooning elder population that must be cared for. We’ve never seen this in modern times; our progress has always paralleled rising populations, bigger audiences, larger markets and bigger pools of workers. It’s hard to see how a declining yet aging population functions as an engine for increasing the standard of living every year.”

A smaller population would no doubt produce some benefits. Probably the most obvious benefit is that a smaller population would lessen human’s demands on our environment and natural resources. There is already evidence of this de-materialization. Check out Andrew McAfee’s book More from Less, where he argues that technology has led us past peak demand for many natural resources.

Among the adverse consequences, however, of falling population is likely to be downward pressure on farm incomes. The Malthusian concern implied a large population that was incapable of sufficiently feeding itself. For humanity writ large, this outcome would have been a tragedy. Farmers (or at least land owners), however, would have likely benefited from this dire outcome. More people demanding more food would have driven up food prices, land prices, and farm income. Innovation and productivity growth, fortunately, prevented the hunger problems that would have accompanied a rising population.

One crude way to see whether population growth is out-pacing the effect of innovation is to look at the long-term trend in food and agricultural prices. Here is a graph I created based on USDA data on three major commodity crop prices over time. The long-term trend is negative, suggesting innovation has outpaced the effects of population and income growth.

Lower prices seems bad for farmers, but if they are able to sell more output using fewer resources, they also benefit (see this paper by Alston for some discussion on the farmer benefits and costs from innovation). Still, it is probably safe to say that farmers and the agricultural sector have largely come to expect a rising world population to support demand growth and offset some of the downward pressure on prices. Population projections suggest that expectation may not hold out into the future.

Indeed, research by my Purdue colleagues Uris Baldos and Tom Hertel suggests the effects of population on crop prices is likely to be much lower than what we’ve experienced in the past. They consider the effect of three factors on crop prices and production: population, income, and innovation. From 1960 to 2006, their findings indicate that the effect of innovation pushed down crop prices more than rising income and population pushed up prices, leading to an overall fall in prices, consistent with the graph above. What do they predict for the future based on expected trends in innovation, population, and income? Falling prices. They predict that, going out to 2050, the price increasing effects of population will be about half what they were from 1960 to 2006. Thus, rather than the Malthusian population bomb, we seem to be heading to a sort of Malthusian inversion.

It’s also important to note that population growth will be unevenly distributed across the world. Data and projections from the United Nations show very different anticipated population trends in low-, middle-, and high-income countries. Here is a figure I created based on their data. In 1950, population was 45%, 70%, and 82% lower than it is today in high-, middle-, and low-income countries. By 2010, the UN is projecting population will be 3%, 23%, and 220% higher in high-, middle-, and low-income countries.

The implications for U.S. farmers are many fold. For one, demand growth (at least from population growth) is likely to occur outside this country, highlighting the importance of trade. Within the U.S., rising income, and demand for quality, may play an increasingly important role in supporting farm incomes in the years to come. Finally, flat or declining population, along with innovation, have the potential to have positive environmental outcomes, and it will important to think about appropriate farm policy in light of these trends.

Migration, Agriculture, and Local Food

I recently listened to an episode of a Radiolab podcast entitled There and Back Again. The episode is about the history of science related to the migratory patterns of birds and other animals. It seems our forebearers had some far-fetched answers to the question “where do the animals go in the winter?” Some folks apparently thought birds transformed into other species or even flew to the moon to escape winter. The episode also delves into the modern day science of tracking animal migratory patterns using sensors and satellites. It’s amazing how many thousands of miles animals, birds, fish, and insects travel on an annual basis; there are even some birds that fly annually from the north to south pole.

All this got me thinking about our modern-day discussions about food and agriculture. A popular notion today is that we should eat what is local and eat what is in season, and there is a notion that eating in this manner is more natural, perhaps closer to the “good old days” when our ancestors weren’t plagued by the modern conveniences of hyper processed food.

It is interesting to contrast this local, seasonal view of what is presumably natural for humans, with what is actually quite natural for birds and many other animals who expend great effort to avoid eating seasonally. In our modern world, we have figured out how to specialize food production in areas with comparative advantages (e.g., veggies in California, citrus in Florida, cherries in Michigan, corn and soy in Indiana) and then move the food to the people by boat, rail, and truck. That is, we’ve learned to migrate food to people rather than the old “natural” way of migrating people to the food. Indeed, before humans discovered agriculture, we spent our time following our food while it migrated across the landscape. For example, some native American tribes seasonally followed the bison across the Great Plains. Eating locally and seasonally is decidedly not natural.

Now, I’ve argued that whether something is “natural” has no moral bearing per se, but this episode helps draw out some of the apparent contradictions in our beliefs about naturalness and historical reality. By the way, I’m looking forward to reading Alan Levinovitz’s forthcoming book entitled Natural: How Faith in Nature’s Goodness Leads to Harmful Fads, Unjust Laws, and Flawed Science.

Looking for a Faculty Position? Agricultural Economics vs Economics

‘Tis the season for economics job hunters.  The annual ASSA meetings kick off tomorrow, brining together, for a few days, what is probably the largest collection of economists in the world (scary, I know!).  The meeting also marks the start of the job market for academic economists.  For a variety reasons, agricultural economics departments have increasingly moved hiring to coordinate with the ASSA meetings, and agricultural economics departments appear to be more apt these days to hire new faculty from general economics programs.

There is ample advice for prospective job market candidates online (this paper by John Cawley is among the best), and I won’t attempt to add to it here.  Rather, the purpose of this post is to provide a bit of perspective for job market candidates from traditional economics departments who may be considering jobs in agricultural economics. 

There are two main factors that create different incentives for faculty in agricultural economics departments compared to faculty (often just down the hall) in general economics programs. 

The first is funding.  The existence of separate agricultural economics departments stems from federal and state monies specifically allocated for agricultural research and education.  Unlike general economics departments, where revenues to the department primarily flow from general university revenues, agricultural economics departments have federal dollars for research (these are often referred to as “Hatch” or “experiment station” funds”) and extension or outreach activities (these are often referred to as “Smith-Lever” or “extension service” funds), which are then matched with funds from the state government where the university resides.  This is what people are referring to when they talk about “Land Grant” universities – the dedicated, funded, missions not just for teaching, but for applied research and outreach and extension. 

The second differentiating factor is the promotion process.  Typically, faculty seeking tenure and promotion in an agricultural economics department must ultimately be evaluated by a committee made up of faculty and administrators in a college of agriculture or natural resources.  This can be contrasted with assistant professors in general economics programs who go on to be evaluated by other faculty in colleges of business or colleges of arts and science.  This distinction implies that faculty in agricultural economics departments, when going up for promotion, are more likely to be evaluated by “bench” scientists running labs.

These two combined factors go on to create different outcomes and incentives for faculty in agricultural economics departments than in general economics departments.  Here are a few that come to mind:

• In general, faculty in agricultural economics departments teach less than in general economics department.  The reason is straightforward: agricultural economics faculty are literally paid to spend their time doing other things (research or outreach). 

• The expectations to bring in grants is higher in agricultural economics departments than in general economics departments.  This differential incentive stems from the aforementioned evaluation by “bench” scientists at the college level but also from greater availability of funding for agricultural research, the differential incentives to focus on applied problems, and the closer connection to companies, farm groups, and NGOs in the agricultural sector that fund research. 

• In the “quality”-quantity tradeoff, agricultural economists will normally face greater incentives to generate “quantity” than will a faculty member in a general economics department.  The reasons are multi-faceted including the need to show productivity and returns on grant dollars, the greater interest from immediate stakeholders (think farmers, policy makers, agribusinesses) in applied research results, and the evaluation by “bench” scientists who tend to have many more publications.  By the way, this incentive shows up in salary differentials (see this paper by Gibson and Burton-McKenzie showing salaries in agricultural economics departments are positively related to the quantity of journal articles, but numbers of papers (after adjusting for “quality”) have no independent effect in general economics departments; see also my co-authored paper with Tia and Mike Hilmer comparing salary structures between econ and ag econ programs).

• In the previous bullet “quality” is in scare quotes because in general economics programs “quality” often means only one thing: publication in one of the so-called top five journals.  I won’t get into the tyranny of the top five, except to say that quality has a broader definition in many agricultural economics departments.  Part of the reason is that an agricultural economist often has a broader audience for their work, including non-economists, policy makers, farmers, agribusinesses, etc.  Sometimes this results in greater impact.  For example, general interest science journals, and interdisciplinary science journals, often have much higher impact factors than do the “top five” economics journals, and this is considered differently in agricultural economics departments that, on the margin, are more focused on applied results with real-world impact (at least in our narrower domain).

• In agricultural economists, it is relatively more common to have journal articles with many co-authors, and to include graduate students in publications, than is the case in among general economists.  Again, this partially stems from the closer connection to “bench” scientists, who include everyone in the lab who worked on the project on the publication.  Another incentive at play is that agricultural economists are more likely to work on multidisciplinary problems with ecologists, animal scientists, agronomists, etc. who bring different expertise to problems, resulting in papers with more co-authors. 

• In agricultural economics departments, there is often greater incentive to focus on more local issues relevant to the region and state where the university resides.  Federal monies must be matched with state dollars, and the political support for providing the state dollars indirectly relates to the willingness of faculty to work on issues deemed relevant to local stakeholders.

• The extension mission in agricultural economics departments can often be among the most mystifying difference to students coming out of general economics departments, which typically have no such explicit mission.  One way to think about it is that faculty with extension appointments have class as well, it’s just that their students are farmers, local government officials, agribusiness managers, etc. and agricultural economists reach these “students” in non-traditional ways by going out to their meetings or writing newsletters or doing podcasts or media interviews. Another potential way to think about extension or outreach work is the model of economist as consultant, where the goal isn’t to publish an academic paper, but to take the academic knowledge and convert it into decision aids, tools, white papers, etc. that improve managerial and policy decision making.

I hope these few thoughts help add some clarity for folks from general economics programs who may be considering employment in an agricultural economic department.  Agricultural economics departments are a great place to work, where there are tangible rewards to working on real-world issues affecting a sector of our economy that touches every living person through our dinner plates.   Good luck with the job hunt!