Where do people eat the most meat?

It seems a fairly simple question: In which U.S. states do people eat the most meat?  Yet, there is surprisingly little good, publicly available data on this question.  Yes, there are fun maps like this one at Slate, but they are far from scientific or data driven.  

I thought I'd try to partially fill this void by turning to data from my Food Demand Survey (FooDS) that has been running now for almost four years.  Because I've surveyed over 1,000 people in the U.S. for about 44 months, that means I have responses from over 44,000 people spread all across the country that I can use to help look for geographic differences.  

In FooDS, each person is told "Imagine you are at the grocery store buying the ingredients to prepare a meal for you or your household.  For each of the following nine questions that follow, please indicate which meal you would be most likely to buy."  Then, they are presented with nine questions that look like the one below.  The only differences across the questions are the prices assigned to each item and the order of the items.      

For sake of simplicity, I counted the number of times each person chose steak, how many times they chose chicken breast, etc.  Thus, the maximum possible "score" a person could have for each item is 9 and the lowest is 0.  To be clear, this isn't a measure of consumption, but rather it is an index of demand.  It is a measure of how much people "like" each of the choice options relative to all the other choice options.  For point of reference, across all the people in my sample, the most frequently chosen option was chicken breast (chosen on average 2.43 times out of 9) followed by ground beef/hamburger (chosen on average 1.33 times out of 9).  The least popular meat items were pork chop and ham, chosen on average 0.80 and 0.68 times, respectively, out of 9.

I won't go into all the hairy details here (email if you want to know more), but I then estimated some statistical models to infer how often, on average, consumers in each state chose each of the meat options.  Then, I calculated how different (in percentage terms) each state was from the mean number of choices, and I created maps.

I'll start with one that has a very obvious regional pattern: chicken wings.

Chicken Wing Demand by State

Demand for chicken wings is highest in the southeast US, where people chose this option 15% to 44% more often than in the average person in the US.  Consumers in western states like Oregon, Idaho, and Arizona chose wings 15% to 27% less often than the average consumer nationwide.

For other products, there is less of a regional pattern.  Below is the map for beef steak.  Demand for steak is highest in California, Nevada, Washington, Oklahoma, Minnesota, Illinois, Florida, and New York.  Steak demand is lowest in Idaho, Utah, Missouri,  and the Appalachian regions, Tennessee, Kentucky, and West Virginia.

Beef Steak Demand by State

While we are on beef, here is the map for hamburger/ground beef.  For ground beef, demand is generally highest in the upper midwest and is lower on the coasts.

Demand for Ground Beef by State

A somewhat similar pattern emerges for deli ham (shown below), although the location of heaviest demand moves a bit south and east relative to that for hamburger.  

Deli Ham Demand by State

Below is the map for pork chops.  This map is interesting in the sense that there are several instances where some of the highest demand states are situated adjacent to some of the lowest demand states (e.g., Oregon next to California; Oklahoma next to Texas; etc.)  However, one thing to note in the case of pork chops is the scaling: there isn't much difference across any of the states.  Consumers in Missouri have the highest pork chop demand, but only chose pork chops 2.7% more than the average consumer.  Consumers in California have the lowest pork chop demand, but only chose pork chops 3.3% less than the average consumer nationwide.  

Pork Chop Demand by State

The last individual meat product is chicken breast.  As shown in the map below, chicken breast demand is generally highest in the west and the northeast.  I'm not at all surprised to learn that chicken breast demand is near the lowest in my home state of Oklahoma (at -4.5%), trailing only North Carolina, Missouri, and Mississippi.  

Chicken Breast Demand by State

Finally, to round things out, here is a map associated with overall meat demand.  This figure was calculated by determining how many times a person chose any of the six aforementioned meat products (recall there were nine total options, three of which were non-meat).  On average people chose a meat option 7.03 times out of 9 total choices.  However, as the map below shows, there is some heterogeneity across states.  Overall meat demand is highest in the Midwest: consumers in Illinois, Indiana, and Iowa chose any meat option 1%+ more often than the average consumer.  Lowest overall meat demand was in places like California, Arizona, Maryland, Utah, New Jersey, and Massachusetts, where consumers chose a meat options at least 1% less often than the average consumer.  

Overall Meat Demand by State

AAEA Early Career Professionals Workshop

Are you a relatively new faculty member?  A new government or NGO employee working on the economics of food, agriculture, health, or the environment?  Within 6 years of having received your Ph.D?  Then I have a deal for you!  

On May 31 and June 1, 2017 the Agricultural and Applied Economics Association (AAEA) will be hosting an early career professionals workshop in Vail Colorado.  Come meet fellow early career professionals and hear from some of the luminaries in the profession about how to effectively get grants, publish, teach, and more.

Registration is only $85 and resort room rates are only $129/night.  Stay tuned because we may even  have some opportunities for some travel grants.  

On behalf of the workshop organizers - Norbert Wilson, Cheryl Devuyst, and myself - we'd love to see you there!

For more details and registration, check out the workshop website.

What's Going on With Wheat Futures?

One of the primary ways farmers have to manage price risk is via the futures market.

Before getting to a potential problem that has emerged, I'll first provide a short primer for those unfamiliar with futures markets 

An Oklahoma or Kansas wheat farmer is likely to begin planting sometime in September or October, but when planting they don't yet know what the wheat price will be at harvest in June or July the next year.  So, to protect themselves against adverse price fluctuations, a farmer might turn to the Kansas City Hard Red Winter Wheat Futures Contract.  The CME Group has a futures contract that settles every year around harvest in July.  Right now, the July 2017 contract is priced at about $4.50/bushel.  

For simplicity sake, let's say a farmer faced the same July 2017 futures price back in September of 2016, and they wanted to protect the price associated with (i.e., hedge) 5,000 bushels of wheat (which is exactly the size of one futures contract).  In September 2016, the farmer would sell one July 2017 contract, receiving  5000*4.50=$22,500.  This action has now contractually obligated the farmer to deliver 5,000 bushels of wheat come July to "offset" their selling position [addendum: while other futures contracts work in this way, this isn't true for winter wheat; rather than delivering wheat, the farm has contracted to deliver a "registered electronic warehouse receipt"].  Normally, however, a farmer doesn't want to go through the hassle of actually having to deliver physical wheat to a delivery point, so they instead buy back (in this example) one futures contract to offset their position when June or July rolls around.  If the price of the July 2017 contract falls from September to July, the farmer makes money from the futures market (e.g., if the price falls to $4.00, the farmer has has to spend 5000*4=$20,000 to offset their original position of $22,500, making $2,500), which helps them offset the loss in expected wheat price they receive when they sell their wheat in the cash market.  Exactly the opposite happens if the price of the July 2017 contract increases - the farmer looses money from the futures market, but receives a higher than expected cash price.  This is why it is said that using the futures market "locks in" the price at the time of planting.  

Although most farmers never actually delivery their wheat to settle their futures contract, this threat of delivery is what ties the futures price to reality.  If, for example, a farmer notices that come July 2017, the July 2017 futures contract is trading at a price well above the cash price being paid for wheat "on the ground" in grain elevators, they have a strong incentive to offset their futures position by actual delivery rather than buying a futures contract.  These arbitrage opportunities are what should force the futures market price to eventually equal the cash market price when July 2017 rolls around.   

All of that is a lead in to this video put out by Art Barnaby at Kansas State University.  It seems that farmers, at least in some situations, are not actually able to deliver wheat to offset their futures positions.  Aside from fundamental concerns about what is being measured by futures market in this case, one farmer in the video says:

“A lot of us were relying on that and felt very betrayed by the fact that what we understood to be a contract was not. ”

[Addendum Barnaby sent me a note of clarification.  The underlying issue here is that farmers have been generally taught and told that they can settle wheat contracts by the delivering physical commodity, when in fact the underlying contract says something different. He indicated: "Farmers are not obligated to deliver 5,000 bushels of wheat; they are obligated to deliver a registered electronic warehouse receipt issued by warehousemen against stocks in warehouses.  This is the reason farmers can’t deliver wheat on a short futures.  You will find this in the contract  . . .The market is trading the value of a CME approved warehouse receipt because that is the only thing that can be delivered."]  

Do you plan to spend more or less eating out in the next two weeks?

The title of this post is based on a question I ask of food consumers every month in my Food Demand Survey (FooDS).  If I had to guess your response, I'd go with "spend less."  Why?  Because every month, for almost four years, that has been the average response to the question (the exact question is: "Do you expect to spend more or less on food bought during grocery shopping in the next two weeks as compared to the previous two weeks?" and response categories are: "I plan to spend about . . . 10% less, 5% less, the same, 5% more, or 10% more").   

Here is the problem with the above results.  They are almost certainly false.  If people are continually, month after month, saying they plan to spend less on food away from home, the cumulative effect would ultimately be a negative amount of spending.  

Moreover, another question I ask on the survey relates to how much the respondent says they spend (in dollars) on food away from home (exact question wording: "What has been you (or your household's) usual WEEKLY expense for meals or snacks from restaurants, fast food places, cafeterias, carryout or other such places?"  The response categories are: less than $20, $20-$39, . . . $140-$159, $160 or more).  

In the most recent issue of FooDS, we estimate the average level of spending on food away from home in January 2017 was $53.26/week.  The average answer from the previous month (December 2016) was $50.89/week.  So, in terms of stated expenditure, there was a $53.26-$50.89=$2.37 increase (or a (2.37/50.89)*100=4.66% increase). Yet, (and here is the problem), In December 2016, people said they planned to reduce spending on food away from home by, on average, -0.59%, and in January 2017, they said they plan to reduce spending on food away from home by, on average, -1.47%.

Here is what I get if I calculate "actual" changes in reported levels of spending on food away from home against people's stated plans to increase or decrease spending (the blue bars are the same blue bars as in the above graph, they just look different because the vertical axis has been re-scaled).       

So, what is going on here?  One possible answer is that consumers suffer from a type of self-control problem.  We tell ourselves we want to reduce the amount we're spending on food away from home in the future.  But, when the future arrives, we forget our plans and have fun eating out with our friends and keep spending as usual.  If this is correct, eating out is a sort of "guilty pleasure" - something we enjoy but wish we could force our future selves to cut back on.     

The propensity of an individual to say they plan to reduce spending on food away from home relates to a variety of demographic variables (even after controlling for the month-to-month effects that may be driving changing spending patterns).  Income is a major determinant.  Lower income people are much more likely to say they plan to reduce spending on food away from home than higher income respondents.  Indeed, for the highest income households, there is no consistent upward or downward bias in planned spending patterns for food away from home.  Other (smaller) determinants include gender, age, and participation in food assistance programs with women, older, and SNAP participants being more likely to say they plan to reduce spending on food away from home.

A less nefarious explanation for the above phenomenon might be that our survey is conducted in the middle of the month, and if people are paid at the beginning of the month (or at the end of the previous month), then there might be less remaining in the food budget for "splurges" like spending on food away from home by the time the middle of the month arises and they rationally plan to spend less in the following two weeks.  

I doubt this is true for two reasons.  The first is that results from other surveys back up the "self control" explanation.  For example, this article in the Wall Street Journal a couple years ago pointed to a survey of higher income consumers that asked what kept them from saving more money each month.  The most common answer, given by 68% of respondents, was "dining out".  The second reasons is that we observe no such phenomenon in our survey for stated changes in spending on food AT home.  Here is the average response each month for how consumers expect to change spending on food at home.  As can be seen, the value goes up and down and is neither consistently negative or positive.     

If you have other explanations for why people consistently say they plan to spend less eating out next month, I'd love to hear them.

Economics and Obesity Policy

The International Journal of Obesity just released a a short review paper I was invited to write, which discusses the economics of policies aimed at reducing obesity. In the paper, I touch on the economic approach for thinking about government intervention in this space and whether there are market failures that would justify intervention.  I then move on to discuss a variety of specific issues that are often discussed in relation to obesity such as farm policy, soda taxes, healthy food subsidies, food assistance programs (and proposed restrictions on them), and information policies. 

Here is the conclusion:

“This article presented a somewhat pessimistic view on the ability of government policy to substantively influence obesity prevalence. Obesity is a complicated and multifaceted issue. So too are the effects of anti-obesity policies. One response is to argue for an all-out ‘war’ on obesity. It probably is true that government policy mandating what farms grow, restricting the
supply and type of food to consumers, and controlling prices, offerings and advertisements by food manufacturers could reduce obesity prevalence. But, is this the type of coercive
society in which we would like to live? Society faces very real tradeoffs between economic freedom, technological progress, and obesity prevalence. These sorts of tradeoffs are unfortunate, but they reflect very real constraints to effective economic policy making.”

My paper joins several others that critically evaluate anti-obesity policies.