Real World Demand Curves

On a recent flight, I listened to the latest Freakonomics podcast in which Stephen Dubner interviewed the University of Chicago economist Steven Levitt about some of his latest research.  The podcast is mainly about how Levitt creatively estimated demand for Uber and then used the demand estimates to calculate the benefits we consumers derive from the new ride sharing service.  

Levitt made some pretty strong statements at the beginning of the podcast that I just couldn't let slide.  He said the following:

“And I looked around, and I realized that nobody ever had really actually estimated a demand curve. Obviously, we know what they are. We know how to put them on a board, but I literally could not find a good example where we could put it in a box in our textbook to say, “This is what a demand curve really looks like in the real world,” because someone went out and found it.”

As someone whose spent the better part of his professional career estimating consumer demand curves, I was a bit surprised to hear Levitt claim "nobody ever had really estimated a demand curve."  He also said, "we completely and totally understand what a demand curve is, but we’ve never seen one."  The implication seems to be that Levitt is the first economist to produce a real world estimate of a demand curve.  That's sheer baloney.  

The most recent Nobel prize winner in economics, Angus Deaton, is perhaps most well known for his work on estimating consumer demand curves.

In fact, agricultural economists were among the first people to estimate real world demand curves (see this historical account I coauthored a few years ago).  Here is a screenshot of a figure out of a paper by Schultz in the Journal of Farm Economics in 1924 who estimated demand for beef.  Yes - in 1924!  I'm pretty sure that figure was hand drawn!

Or, here's Working in a paper in the Quarterly Journal of Economics in 1925 estimating demand for potatoes.

Two years later in 1927, Working's brother was perhaps the first to discuss "endogeneity" in demand (how do we know we're observing a demand curve and not a supply curve?), an insight that had a big influence on future empirical work.

Fast forward to today and there are literally thousands of studies that have estimated consumer demand curves.  The USDA ERS even has a database which, in their words,  "contains a collection of demand elasticities-expenditure, income, own price, and cross price-for a range of commodities and food products for over 100 countries."   

Here is a figure from one of my papers, where the demand curve is cleanly identified because we experimentally varied prices.  

And, of course, I've been doing a survey every month for over three years where we estimate demand curves for various food items.

In summary, I haven't the slightest idea what Levitt is talking about.  

Consumer Research and Big Data

Its been a great week in Boston at the Agricultural and Applied Economics Association (AAEA) annual meeting.  It's always good to see old friends, meet new ones, and learn about a wide array of topics.  

This year, I had the privileged of taking over as president and giving the AAEA presidential address.  I chose to talk about new and emerging data sets that are being used in consumer research.  I presented several short studies using data from the Food Demand Survey (FooDS) to illustrate how we might garner new insights about consumer heterogeneity and demand using new datasets.  A working draft of the paper is here. [Note: I've updated the paper (new draft here) in response to some comments, and some of the elasticity figures have change because I found a small error in my code)   I welcome any comments.   

A few key lessons.  First, there are big differences across consumers in their demands for food at home and away from home, but larger datasets that have a lot of cross-sectional and temporal variability reveals that the "representative consumer" hypothesis is probably false.  Here's a plot showing the distribution of the income elasticitities of demand for food at home and away from how (i.e., how much additional food at home or away from home a household buys as their income increases). For some households, food at home is a "normal" good (they buy more when they make more), but for other households, food is an "inferior" good (they buy less when they make more).  Food away from home is a normal good for more households than is food at home.

One of the main ways economists have studied consumer heterogeneity is by doing surveys.  However, almost all these surveys are conducted at a single point in time.  Thus, they present a "snap shot" of consumer preferences.  Using my survey data, however, I showed (using a so-called choice experiment repeated monthly) that these typical survey approaches might miss a lot of variability over time.   

Finally, one of the problems with many consumer research data sets is that they are not large enough to allow us to learn much about small segments of the population.  If one wants to learn about people with Celiac disease, for example, then a survey of a random sample of 1,000 people will only turn up roughly 20 people with the disease - hardly enough to say anything meaningful.  

In FooDS, we've been asking whether people are vegetarians or vegans for over three years now.  This group only represents about 5% of the population, so one needs a large data set to describe the characteristics of this group.  I used a machine learning method (a classification tree) to predict whether a person self-identified as vegetarian or vegan.  Here's what turned up.  Vegetarians tend to be very liberal, on SNAP (aka "food stamps"), with relatively high incomes, and children under 12 in the house.  

These are just a few examples of the growing number of questions economists can now start to answer as we get our hands on larger, richer datasets.  

Mandatory GMO Labeling Closer to Reality

I've written a lot about mandatory labeling of genetically engineered foods over the past couple years, and given current events, I thought I'd share a few thoughts about ongoing developments.  Given that the Senate has now passed a mandatory labeling law, and discussion has moved to the House, it appears the stars may be aligning such that a nationwide mandatory GMO labeling will become a reality.  

The national law would preempt state efforts to enact their own labeling laws, and it would require mandatory labeling of some genetically engineered foods (there are many exemptions and it is unclear whether the mandatory labels would be required on only foods that contain genetic material or also those - such as oil and sugar - which do not).  Food manufacturers and retailers can comply with the law in a variety of ways including on-package labeling and via QR codes.  Smaller manufacturers can comply by providing a web link or phone number for further information.  

Many groups that have, in the past, advocated for mandatory labeling are against the bill because, they say, it doesn't go far enough (e.g., this group is upset because it doesn't "drive Frankenfoods . . . off the market."). Other anti-mandatory labeling folks also don't like the bill because of philosophical opposition to signalling out a technology that poses no added safety risks.  

I suppose this is how democracy works.  Compromise.  Neither side got everything they wanted, but at least from my perspective, this is a law that provides some form of labeling, which will hopefully shelve this issue and allow us to move on to more important things in a way that is likely to have the least detrimental economic effects.   

I'm sympathetic to the arguments made by folks who continue to oppose mandatory labeling on the premise that our laws shouldn't be stigmatizing biotechnology.  Because a GMO isn't a single "thing" I agree the law is unhelpful insofar as giving consumers useful information about safety or environmental impact.  The law is also a bit hypocritical in terms of exempting some types of GMOs and not others.  One might also rightfully worry about when the government should have the power to compel speech and when it shouldn't.  And, I think we should be worried about laws which potentially hinder innovation in the food sector.  

But, here's the deal.  The Vermont law was soon going into effect anyway. The question wasn't whether a mandatory labeling law was going into effect but rather what kind.   The Vermont law was already starting have some impact in that state and would likely have had nationwide impacts.  Moreover, there didn't seem to be a practical legal or legislative way to prevent the law from going into effect in the foreseeable future.  

The worst economic consequences of mandatory labeling would have come about from those types of labels that were most likely to be perceived by consumers as a "skull and cross bones".   In my mind the current Senate bill avoided this worst case scenario while giving those consumers who really want to know about GMO content a means for making that determination.  That doesn't mean some anti-GMO groups won't use the labels as a way of singling out for protest companies that use foods and ingredients made with the technology, but at least the motives are more transparent in this case.  For some groups it was never about labeling anyway - it was about opposition to the technology.  That, in my opinion, is a much less tenable position, and is one that will hopefully be less successful in the long run.    

When Bigger Isn't Better

One of my Ph.D. students at Oklahoma State (and soon to be faculty member at Mississippi State University) has been working on an interesting paper on the impacts of changing cattle sizes on the desirability of steaks.  The average beef cow now weights more than 300lbs more than it did a few decades ago.  Generally that's a good thing as we can get more meat from fewer animals (which means less resource use, less land, less greenhouse gas emissions, etc. in addition to lower prices for consumers).  

But, there's a downside:

“As a response to varying muscle sizes such as the ribeye, grocery stores and restaurants are often forced to adjust the thickness to which the steaks are cut in order to meet a target weight. Thus, a ribeye steak from a carcass with a large [loin] will likely be cut thinner than a ribeye steak from a carcass with a smaller [loin]. This has led to the introduction of “thin cut” steaks in some grocery stores. Compounding the issue of altering larger steaks are the historically strong beef prices. Some retailers utilize target prices for packages of steaks. Therefore, consumers are not only facing high beef prices, but also an increase in total package price due to the larger dimensions of the steak. This has caused retailers to reduce thickness to meet a target package price. ”

The key question, then, is whether people prefer thicker steaks with smaller surface areas (like those that existed 20 years ago) or thinner steaks with larger surface areas (like those that sell today)?  To address this question, a survey was taken by a representative sample of over 1,000 steak consumers.  We gave consumers choices like the one below, and asked which steak they'd choose.  Consumers answered a number of these questions where the steak thickness, area, and price, systematically varied across choices.  

So, what did we find?  For most consumers, there is a trade off between thickness and size.  Moreover, it seems changes in thickness are more important than changes in size.  As a result, most consumers are less happy with the steaks they see today in the grocery store (holding prices constant).  That is, consumers prefer a thicker, smaller area steak to a thinner, larger area steak.  We use the estimates to do a little thought experiment.  How much additional money would have to be to give to today's consumers to make their steak choices as satisfying as they were 40 years ago (in terms of thickness and area, holding prices constant)?   

“Table 6 reports the estimated welfare changes by moving from a scenario where the choice set include small area and thick steaks (40 years ago scenario) to a scenario where the choice set includes large area and thin steaks (today scenario). Estimated welfare changes were
calculated for the conditional logit model as well as the two classes from the latent class model
which had statistically significant estimates for price per package. The welfare change estimate
from the conditional logit model implies that moving from the scenario representing 40 years ago to today’s scenario decreased welfare by $5.37 per choice, an amount that is statistically
significant at the five percent level. When multiplied by the number of steak purchases in the U.S. each year, estimates from latent classes one and two suggest decreases in total welfare of
$5.8 billion and $2.8 billion respectively, by moving toward a choice set with large area and
thin steaks, though the estimate for class one is not statistically significant at the 5 percent level.”

Now, it should be noted that consumers might be, overall, better off from changing cattle sizes because they now have more ground beef available and because prices are lower than they'd otherwise be.

Josh's paper was accepted for presentation in one of the new lightening sessions at the AAEA meetings this year in Boston.  These are short sessions where authors have only seven minutes to present their work.  Here's Josh presenting this paper in lighting session format.

Millennials' Food Values

I've given a couple presentations recently on food trends, and in each instance I was asked whether the so-called Millennial generation thinks differently about food issues than older generations.  I haven't spent a lot of time delving into this question because a lot of the willingness-to-pay research I've been involved with over the years suggests demographics don't tend to explain a lot of the variation in willingness-to-pay.

But, given the interest in the subject, I thought I'd take a quick look at some of the data from the monthly Food Demand Survey (FooDS) I've been running for over three years now.  In particular, I pulled the data we ask on so-called "food values."  The question shows respondents 12 issues (randomly ordered across surveys) and asks respondents which are most and least important when buying food.   Respondents have to click with their mouse and drag four (and only four) items in the “most important” box and then do the same for the “least important” box. 

A scale of importance is created by calculating the proportion of times (across the entire
sample) a food value appeared in the most important box minus the proportion of times it
appeared in the least important box. Thus, the range of possible values for a food value is from -1 to +1, where a higher number implies more importance (a +1 would mean the particular food value was placed in the most important box by 100% of respondents). This is a zero-sum scale, and it only reveals relative importance (e.g., how importance taste is compared to price) not overall importance.   

Ok, so here's a graphical illustration of the food values by age group (I've pulled the data over time, so each age group has several thousand observations, yielding margins of error of around +/- 0.025 importance points).

Except for the oldest group, there is agreement in ranking at the top: Taste>Safety>Price.  In the middle-range of importance, there is far less agreement.  Both the 18-24 year old group and the 25-34 year old group could be considered Millennials according to most definitions I've seen.  The Millennials place less relative importance on nutrition than the 55 and older crowd.  However, the top four issues (taste, safety, price, and nutrition) are way more important than the other issues regardless of the generation under consideration.

The Millennials place less importance on appearance but more relative importance on naturalness, animal welfare, convenience and environment than do older generations, particularly the 65 and older group, which compared to the other age groups, places the lowest importance on naturalness, animal welfare, and environment.  There is a big divide when it comes to the importance of origin: the 65 and older group places quite a bit more importance on origin than do people who are 24 years and younger.  

The biggest gap is for origin (there is a 0.30 spread on the -1 to +1 scale) between the youngest Millennials and the oldest group.  The next biggest gap is for naturalness (there is a 0.22 spread on the importance scale) between the oldest group and the 25-34 year old Millennials.  The most agreement is for "fairness."

It might also be instructive to compare all this along another demographic category: gender (margin of error here is +/- 0.014).  

Women place more relative importance on safety, animal welfare, and naturalness than men. Men place more importance on convenience and novelty than women.  The biggest gap is for animal welfare (a 0.19 point difference on the -1 to +1 scale) and then convenience (a 0.16 difference).