Blog

How people cognitively group different foods

I ran across this interesting ethnographic study of how people conceptualize different foods. It’s a small sample of only 27 people, so one might take the results with a grain of salt, but nonetheless, I found the following figure interesting. The results are from a “pile sorting” exercise where people were asked to group 42 different foods in terms of their similarity to one another in as many groups as they wanted.

Source: Fox, E.L., Davis, C., Downs, S.M., McLaren, R. and Fanzo, J., 2021. A focused ethnographic study on the role of health and sustainability in food choice decisions. Appetite, p.105319.

Source: Fox, E.L., Davis, C., Downs, S.M., McLaren, R. and Fanzo, J., 2021. A focused ethnographic study on the role of health and sustainability in food choice decisions. Appetite, p.105319.

I thought it was interesting that plant-based burgers and milk were neither classified as “meat” or “dairy” or “plant” but in categories of their own; by contrast the in-vitro meat burger was closer to the meat category.

They also found that:

the most salient considerations cited by our participants were price, health, taste, and time.

These findings are consistent with the broad literature on food values; I should note that we have published several papers in the past where we asked larger samples of consumers to rate and rank different foods along multiple dimensions (e.g., here, here, or here)

Understanding Risks and Vulnerabilities via Food Systems Dashboards

That’s the topic of a recent presentation I gave for the College of Agriculture at Purdue just before the Holiday break.

I talked about several new dashboards we’re developing at the Center for Food Demand Analysis and Sustainability. One such dashboard is described here, and it is based on the paper Ahmad Wahdat and I wrote on describing vulnerabilities to different input supplier segments. Ideally, this would be done at a plant-level, but given limited data available, we look at diversity in suppliers to different food processing sectors at the state level. Feel free to play around with the dashboard yourself.

Here’s a video of me walking through the dashboard.

Year in Review - 2021

As the year draws to a close, a recap of activities here on the blog is due. The five most viewed posts of 2021 were:

The “story of the year” had to be rise in retail food prices witnessed in the latter half of 2021. By at least one count, I was cited in 179 media outlets on the topic of food prices this year (see this piece I wrote for EconoFact for a summary of the issues at play).

Overall, there were over 86,000 page views on this site in 2021, and there were 42 new posts. In addition to the new posts mentioned above, there were a number of older posts that continued to attract high attention in 2021, including:

My co-authors and I wrote 12 academic journal articles that have a 2021 publication date. These include:

  • Dennis, E.J., G.T. Tonsor, and J.L. Lusk. “Choosing Quantities Impacts Individuals’ Choice, Rationality, and Willingness to Pay Estimates.” Agricultural Economics. 52(2021):945-962.

  • Malone, T., K.A. Schaefer, and J.L. Lusk. “Unscrambling COVID-19 Food Supply Chains.” Food Policy. 101(2021):102046.

  • Neuhofer, Z. and J.L. Lusk. “Decomposing the Value of Food Labels on Chicken.” Journal of Agricultural and Applied Economics. 53(2021):229-245.

  • Pappalardo, G., M. D’Amico, J.L. Lusk. “Comparing the Views of the Italian General Public and Scientists on GMOs.” International Journal of Food Science and Technology. 56(2021):3641-3650

  • Lusk, J.L. and R. Chandra. “Farmer and Farm Worker Illnesses and Deaths from COVID-19 and Impacts on Agricultural Output.” PLoS ONE. 16(2021): e0250621.

  • Wahdat, A.Z., M.A. Gunderson, and J.L. Lusk. “Farm Producers’ Household Consumption and Individual Risk Behavior after Natural Disasters.” Agricultural and Resource Economics Review. 50(2021):127-149.

  • Chenarides, L., C. Grebitus, J.L. Lusk and I. Printezis. “Food Consumption Behavior During the COVID-19 Pandemic.” Agribusiness: An International Journal. 37(2021):44-81.

  • Chenarides, L., C. Grebitus, J.L. Lusk and I. Printezis. “Who Practices Urban Agriculture? An Empirical Analysis of Participation Before and During the COVID-19 Pandemic.” Agribusiness: An International Journal. 37(2021):142-159.

  • Tonsor, G.T., J.L. Lusk, and S.L. Tonsor. “Meat Demand Monitor during COVID-19.” Animals. 11(2021):1040.

  • Lusk, J.L., G.T. Tonsor, an L.L. Schulz. “Beef and Pork Marketing Margins and Price Spreads during COVID-19.” Applied Economics Perspectives and Policy. 43(2021):4-23.

  • Lusk, J.L. and G.T. Tonsor. “Supply and Demand Indices and Their Welfare Implications.” Q Open. 1(2021):1-22.

  • Ahn, S. and J.L. Lusk. “Pecuniary and Non-Pecuniary Effects of Sugar-Sweetened Beverage (SSB) Taxes.” American Journal of Agricultural Economics. 103(2021):53-69

Next year, I’m looking forward to continuing to build out the data dashboards and products with the Center for Food Demand Analysis and Sustainability.

Hope you have a Happy New Year and a prosperous 2022!

Visualizing Food Price Changes

Food prices have been in the news a lot over the past couple years. The Bureau of Labor Statics (BLS) is the “go to” source of public information on food prices, but their website can be a bit challenging to navigate, and the data difficult to use, for the non-specialist.

To make these data “come to life,” my team with the Center for Food Demand Analysis and Sustainability (CFDAS) at Purdue. has created a dashboard that auto-updates each month when the BLS releases new price information. Check out the new dashboard here. Click on the food categories you want to see and hover over the dates to see the calculations at different points in time. Click the small arrows on the bottom (that say “1 of 3”) to switch between comparing changes in monthly, annual, and from-a-chosen-base-date prices.

Check out the live version here.

Prices, Expenditures, and Quantity: Econ 101

A variety of recent discussions are prompting this post.  My last post discussed recent trends in the market for plant-based alternatives, which has been witnessing lower prices and lower consumer expenditures (or equivalently lower total dollar sales) than this time last year.  I indicated that the trends indicated a downward shift in demand (i.e., a reduction in consumer willingness-to-pay).  Several commenters, however, argued other factors could be at play, but I’m skeptical. At the same time, we’ve been seeing rising overall food prices and rising consumer expenditures on food.  For many observers, the co-movement in these variables seems obvious: of course consumers are going to spend more if prices go up.  However, this seemingly intuitive logic makes very specific assumptions about the elasticity of consumer demand and magnitudes of concurrent supply and demand shifts. Given the level of back-and-forth on this issue, I thought it might be worthwhile to write a little Econ 101 primer on the relationship between price changes, expenditure changes, and quantity changes. Warning - the rest of the post is fairly pedantic.

Let’s start with a setting in which there is no change in the demand curve (i.e., consumers haven’t changed their collective willingness-to-pays) and price is changing due to some non-consumer related reasons.  A well known result (see here for a derivation), taught in many Econ 101 courses, is that the relationship between price and expenditure (or revenue) changes depends on the elasticity of consumer demand (i.e., how sensitive consumers are to price changes around the market equilibrium).  If demand is elastic (i.e., quantity consumed is relatively sensitive to price changes), then a rise in price is associated with a fall in consumer expenditures (or seller revenue) – prices and expenditures move in the same direction.  Conversely, if demand is inelastic (i.e., quantity consumed is relatively insensitive to price changes), a rise in price will be associated with a fall in consumer expenditures (or seller revenue) – prices and expenditures move in the opposite direction.  Based on this simple model, one would take the observations mentioned in the opening paragraphs to conclude that demand for food in general, and for plant-based meat alternatives in particular, is elastic. 

 However, this conclusion might be misleading because we haven’t yet endeavored to explain why prices may be changing.  To address this issue, it is helpful to use a little math, and the basic model I outlined in this paper with Glynn Tonsor. 

 An approximation to changes in any underlying demand function can be expressed as: Q = n*P +d, where Q is the proportionate change in quantity of the good demanded, P is the proportionate change in price, and n is the own-price elasticity of demand.  d is a demand shock representing the proportional change in consumers’ quantity demanded, and it is the magnitude of the horizontal shift in the demand curve expressed relative to the initial equilibrium quantity. 

Now, when are talking about relatively small changes, then proportionate changes in consumer expenditures (E) can be expressed as the sum of the proportionate change in price and the proportionate change in quantity: E = P + Q.

 So, if we observe a change in prices (P) and expenditures (E), as in the motivating examples about plant-based meat alternatives and overall food price inflation, what can we infer about how consumer demand has changed?

If E = P + Q, then a little algebra also suggests that Q = E – P.  Plug our demand curve into this equation and doing little algebra indicates: d = R – P*(1+n). 

 Ok, so what does this mean?  Information from the scanner data company, IRI, indicates sales of meat alternatives (or, equivalently, expenditures on meat alternatives) were down about 8% in November 2021 relative to November 2022.  Thus, R=-0.08.  The same resource suggests meat alternative prices are down about 2% over this same time period.  Thus, P=-0.02.  So, what happened to demand? 

Using our formula above, d = -0.08+0.02*(1+n).  Let’s say demand is very inelastic and n = -0.1 (i.e., a 1% increase in price leads to a 0.1% reduction in quantity demanded by consumers).  In this case, d = -0.062.  That is, the demand curve shifted inward by 6.2%.  This means at the same prices, consumers were willing to buy 6.2% less than they previously were.  It also turns out that this means consumers’ willingness-to-pay is 6.2/0.1 = 62% lower today than it was a year ago. 

By contrast, let’s say demand is very elastic and n = -2 (i.e., a 1% increase in price leads to a 2% reduction in quantity demanded by consumers).  In this case, d = -0.1.  That is, the demand curve shifted inward by 10%.  This means at the same prices, consumers were willing to buy 10% less than they previously were.  It also turns out that this means consumers’ willingness-to-pay is 10/2 = 5% lower today than it was a year ago.

In fact, given the observed price and expenditure changes, there is no plausible demand elasticity that would suggest demand didn’t shift inward. 

In a similar fashion, we can also see what happened to supply.  We can write a change in a supply function as: Q = e*P + v, where e is the elasticity of supply and v is related to the change in the marginal costs of production.  If all we know are price and expenditure changes, using similar logic as above, we can identify the magnitude of the supply shift: v = R - P*(1+e).  As before, let’s set R=-0.08 and P=-0.02. 

If supply is very inelastic and e = 0.1 (i.e., a 1% increase in price leads to a 0.1% increase in quantity firms supply).  In this case, v = -0.058.  That is, the supply curve shifted inward by 5.8%.  This means at the same prices, firms were willing to supply 5.8% less than they previously were (or marginal costs are 5.8/0.1 = 58% higher today than a year ago). 

By contrast, let’s say supply is very elastic and v = 2 (i.e., a 1% increase in price leads to a 2% increase in quantity supplied).  In this case, v = -0.02.  That is, the supply curve shifted inward by 2%.  This means at the same prices, firms were willing to supply 2% less than they previously were (or marginal costs are 2/2 = 1% higher than they were a year ago).