What Consumers Don't Know about GMOs

Yesterday the Journal of the Federation for American Societies for Experimental Biology (FASEB) published a paper I co-authored with Brandon McFadden from the University of Florida.  We surveyed a representative sample of over 1,000 US consumers and probed the depth of their knowledge about GMOs.  

We asked questions about the number of genes affected by different plant breeding techniques, prevalence of use of GMOs for different crops and foods, true/false questions about genetics and GMOs, knowledge of the length of time biotech crops have been grown, regulatory approval times for GMOs, views on public policies directed toward GMOs.  Before and after asking these questions, we asked respondents to rate their self-assessed knowledge of GMOs and to indicate their belief that GMOS are unsafe or safe to eat.  

The overall finding is 1) consumers, as a group, are unknowledgeable about GMOs, genetics, and plant breeding and, perhaps more interestingly, 2) simply asking these objective knowledge questions served to lower subjective, self-assessed knowledge of GMOs (i.e., people realize they didn't know as much as they thought they did) and increase the belief that it is safe to eat GM food.  

The implications are two fold: 1) using consumer opinions about GMOs to guide public policy is problematic given the low levels of knowledge, and 2) using something like the Socratic Method may as effective at changing safety beliefs than simply providing information.    

Restaurant Performance Index

I was recently made aware of the so-called Restaurant Performance Index (RPI) put out by the National Restaurant Association (NRA).  According to their website: 

“The RPI is based on the responses to the National Restaurant Association’s Restaurant Industry Tracking Survey, which is fielded monthly among more than 400 restaurant operators nationwide on a variety of indicators including sales, traffic, labor and capital expenditures. Restaurant operators interested in participating in the tracking survey”

and

“Index values above 100 indicate that key industry indicators are in a period of expansion, while index values below 100 represent a period of contraction for key industry indicators. The Index consists of two components – the Current Situation Index, which measures current trends in four industry indicators (same-store sales, traffic, labor and capital expenditures), and the Expectations Index, which measures restaurant operators’ six-month outlook for four industry indicators (same-store sales, employees, capital expenditures and business conditions).”

I was a bit curious. How well do some of the variables I'm tracking in my Food Demand Survey (FooDS) follow the NRA's RPI?  By looking back at past releases, I was able pull together  monthly data on the overall RPI, the current situation index, and the expectations index, and I merged these with data from FooDS on average reported spending on food away from home and changes in anticipated spending on food away from home each month.  

First, the good news.  Spending on food away from home (as measured by FooDS) seems to track closely with the RPI-current situation index.  

It probably isn't too surprising that the two are positive correlated since two of the measures of the RPI-current situation index are same store sales and traffic volume; however, it is still comforting to see my FooDS data roughly track this measure from the NRI.  One benefit of FooDS is that we release the data in a more timely manner.  Right now, the latest figures available from the National Restaurant Association (NRA) are for March.  However, I already have a measure of April's away from home food expenditures from FooDS (it's $55.43).  A simple linear regression predicts that NRA's current situation index will be 102.3 for April.  We're already fielding May's FooDS right now, so I can make an even more up-to-date forecast in a few days.

Now, the not-so-good news.  In FooDS, we track consumers' stated intentions to increase or decrease spending on food away from home in the following weeks.  One "anomaly" present in the FooDS data is that, every month, people say they plan to spend less on food away from home next month.  Over the three years we've been doing FooDS, the measure ranges from a -2.4% cutback in spending to a -1.05% cutback in spending (average is -1.59%).  I interpret this as people feeling guilty about spending on food away from home, and perhaps even evidence of a self-control problem: people plan to cut back on eating out but rarely actually do.  In any event, despite this "bias", trends in this variable might still be useful as there are some months people plan to cut back more than others.  

Here's a plot of the planned spending change on food away from home from FooDS alongside the expectations portion of the NRA's Restaurant Performance Index:  

While the correlation between the two is positive, it is pretty weak.  The two track each other pretty closely through about mid 2014 and then start moving in opposite directions.  The two measures are getting at slightly different things.  One is measuring how restaurant owners/managers are planning to change capital expenditures, staffing, and what they think sales will be in the future; the other is a measure of how much consumers think they'll change spending.  Maybe all this says is that restaurant operators' expectations are not the same as restaurant consumers' expectations.  

So, here's a little test.  How well do restaurant owner's expectations this month correlate with their own "acutal" (or current performance index) the following month?  The correlation between the lagged NRA expectation index and the current period NRA current situation index is 0.35.  O.k., so there is some accuracy associated with the NRA's expectation index.

Now, let's do the same thing with the FooDS data.  How well do consumers' expected spending changes this month correlate with their own "acutal" food away from spending next month? The correlation between the two is 0.68.  So, despite the fact consumers' expectations are downwardly biased as discussed above, changes in their expectations seem quite predictive of next month's spending on food away from home.    

So, it seems consumers (in aggregate) know their own futures a bit better than restaurant operators know theirs.  

Can I get that with an extra GMO?

That's the title the editors of the Wall Street Journal gave to my piece that was published today.  I touched on the issue of GMO labeling, but also tried to elevate the discussion a bit to delve into the broader issues at play.  

Here are a few snippets:

“Lost in the politics is a deeper debate about the future of our food system. At the core of many anti-GMO arguments lies a romantic traditionalism, a desire for food that is purportedly more in line with nature. Perhaps we should eat only the food that God gave us. Yet manna rarely falls from heaven.

The truth is that what we eat today differs radically from the food eaten even a few hundred years ago. Carrots used to be purple. Random mutations and selective breeding led to their signature color during the 16th century in the Netherlands, where it later was claimed the new varieties honored the King William of Orange. Broccoli, kale, cauliflower and Brussels sprouts all emerged from the same wild plant. Potatoes and tomatoes originated in the Americas and were never eaten in Europe and Asia until after the New World was discovered. Today we eat more and better than ever, precisely because we did not accept only what nature provided.”

and, in conclusion, after discussing the host of new biotech innovations coming to market:

“Food manufactures today may be reluctant to label foods made using biotechnology. But one day soon, when the fad against GMOs fades, they might be clamoring to add the tag: proudly produced with genetic engineering.”

Economics of Food Waste

There seems to be a lot of angst these days about food waste.  Last month, National Geographic focused a whole issue on the topic.  While there has been a fair amount of academic research on the topic, there has been comparatively little on the economics of food waste.  Brenna Ellison from the University of Illinois and I just finished up a new paper to help fill that void.

Here's the core motivation.

“Despite growing concern about food waste, there is no consensus on the causes of the phenomenon or solutions to reduce waste. In fact, many analyses of food waste seem to conceptualize food waste as a mistake or inefficiency, and in some popular writing a sinful behavior, rather than an economic phenomenon that arises from preferences, incentives, and constraints. In reality consumers and producers have time and other resource constraints which implies that it simply will not be worth it to rescue ever last morsel of food in every instance, nor should it be expected that consumers with different opportunity costs of time or risk preferences will arrive at the same decisions on whether to discard food”

So, what do we do?

First, we create a conceptual model based on Becker's model of household production to show that waste is indeed "rational" and responds to various economic incentives like time constraints, wages, and prices.  

We use some of these insights to design a couple empirical studies.  One problem is that it is really tough to measure waste.  And, people aren't likely to be very accurate at telling you, on a survey, how much food they waste.  Thus, we got a bit creative and came up with a couple vignette designs that focused on very specific situations.  

In the first study, respondents were shown the following verbiage.  The variables that were experimentally varied across people are in brackets (each person only saw one version).  

“Imagine this evening you go to the refrigerator to pour a glass of milk. While taking out the carton of milk, which is [one quarter; three quarters] full, you notice that it is one day past the expiration date. You open the carton and the milk smells [fine; slightly sour]. [There is another unopened carton of milk in your refrigerator that has not expired; no statement about replacement]. Assuming the price of a half-gallon carton of milk at stores in your area is [$2.50; $5.00], what would you do?”

More than 1,000 people responded to versions of this question with either "pour the expired milk down the drain" or "go ahead and drink the expired milk."  

Overall, depending on the vignette seen, the percentage of people throwing milk down the drain ranged from 41% to 86%.

Here are how the decision to waste varied with changes in the vignette variables.

The only change that had much impact on food waste was food safety concern.  The percentage of people who said they'd discard the milk fell by 38.5 percentage points, on average, when the milk smelled fine vs. sour.  The paper also reports how these results vary across people with different demographics like age income, etc.

We conducted a separate study (with another 1,000 people) where we changed the context from milk to a meal left-over.  Each person was randomly assigned to a group (or vignette), where they saw the following (experimentally manipulated variables are in brackets).

“Imagine you just finished eating dinner [at home; out at a restaurant]. The meal cost about [$8; $25] per person. You’re full, but there is still food left on the table – enough for [a whole; half a] lunch tomorrow. Assuming you [don’t; already] have meals planned for lunch and dinner tomorrow, what would you do?”

People had two response options: “Throw away the remaining dinner” or “Save the leftovers to eat tomorrow”.

Across all the vignettes, the percent throwing away the remaining dinner ranged from 7.1% to 19.5%.  

Here are how the results varied with changes in the experimental variables.

Meal cost had the biggest effect.  Eating a meal that cost $25/person instead of one that cost only $8/person reduced the percentage of people discarding the meal by an average of 5.8 percentage points.  People were also less likely to throw away home cooked meals than restaurant meals.  

There's a lot more in the paper if you're interested.

Where do we like to shop?

I thoroughly enjoyed reading this paper by Rebecca Taylor and Sofia Villas-Boas, which was just published in the American Journal of Agricultural Economics.  The research makes use of a new data set - the National Household Food Acquisition and Purchase Survey (FoodAPS) - initiated by the USDA to study where people of different income levels prefer to shop for food.  This question is relevant to the debate on so-called food deserts.  Are poorer households eating less healthily because of the lack of "good" food outlets in their area, or are there no "good" food outlets in an area because people there don't want that kind of food?  To sort this out, you need to know where people of different incomes prefer to shop, and that's precisely what Taylor and Villas-Boas estimate.

Their data suggest that, if anything, lower income households tend to have more stores near them, and at least one store closer to them, than higher income households.  For example, in a 1 mile radius, low income households have, on average, 1 superstore near them, whereas higher income households have, on average, only 0.58.  Using the USDA's definition of a food desert, the authors calculate that only 5%, 8%, and 3% of low, medium, and high income households live in a so-called food desert.  Whereas low income households live, on average, closer to the nearest farmers market than high income households (10.7 miles vs 11.93 miles), high income households are more likely to actually visit a farmers market.  

The authors go on to estimate a consumer demand model.  Where do consumers prefer to shop given the distances they have to travel?  When economists say "prefer" - they don't mean how one feels about a location or the images it conjures up, but rather what is actually chosen.  The authors find that people prefer going to locations that are closer to home.  That is, people don't like to travel too far to shop.  This estimate, then, lets them calculate how far one is willing to travel to shop at one type of store vs. another.  The authors consider 9 types of stores (including restaurants and fast food outlets), and find farmers markets are the least preferable shopping outlet in that people are willing to travel the least distance to get to a farmers market.  

Using the authors estimates, I calculated how much people would be willing to pay ($/week) to shop at each of the 8 other types of food outlets instead of the (least preferable) farmers market.

Both low and high income households would be willing to pay around $25/week to shop at a superstore instead of a farmer's market.  The data also suggests that higher income households prefer farmers markets more than do lower income households.  Across all the outlet types, low income household are willing to pay $18.67 to shops somewhere other than a farmers market, but for higher income households, the figure is only $13.95.  The figure also shows that higher income households are more willing to pay to eat at restaurants than are low income households.  This suggests that farmers markets and restaurants are normal goods - the more income you get the more you want to shop in these kinds of outlets. 

The authors write in the conclusions:

“the households in this sample have low WTP for Farmers Markets to be closer to home, and high WTP to pay for Fast Food to be closer to home. This implies that simply building Farmers Markets will not induce households to shop there. ”

The authors interpret this finding to mean, "low-income households may need to be compensated to shop at Farmers Markets."  But, why?  Why would we use tax payer dollars to encourage shopping in food outlets people least prefer?  Perhaps some would say that farmers market sell healthier food.  Maybe, but the highly desirable superstores sell healthy food too.  And, if the problem is healthy eating, where is the market failure, and why would farmers markets be the most efficient solution to solve that failure?  

In any event, I look forward to seeing the authors' follow up work on the subject, which they discuss at the end of this paper.