Blog

Look at Me, I'm Buying Organic

That’s the title of a new paper I co-authored with Seon-Woong Kim and Wade Brorsen, which was just published in the Journal of Agricultural and Resource Economics.

We know consumers have a number of motivations for buying organic food - from perceptions about health, taste, safety, and environment to perceptions about impacts on smaller farmers. Whether these perceptions are accurate is debatable. In this paper, we were interested in an all together different motivation: the extent to which consumers feel social pressure to buy organic.

In our study, people made simulated choices between apples or milk cartons, where one of the characteristics was the presence or absence of the organic label. People were divided into one of four groups:

1) The control (CTRL): no manipulation.

2) The eye (EYE) treatment. This is going to sound crazy, but following some previous research, we showed an image of a person’s eyes on the screen as people were making their apple and milk choices. Prior research suggest that exposure to an image of eyes creates the aura of being watched, which increases reputational concerns and cooperative behavior.

eyes2.JPG

3) The name (NAME) treatment. Just prior to the apple/milk choices, people in this group were asked to type in their first name, and we ask them to confirm if they lived in the location associated with their IP address. The idea was to remove the perception of anonymity and increase social pressure.

4) The friend (FRND) treatment. Here we used a vignette approach. Jut prior to the apple/milk choices, people were told, “Now, imagine you are in the specific situation described below. Your good friends have family visiting. They’ve asked you to help out by taking their sibling, whom you’ve never met, to the grocery store. While you’re there with your friend’s sibling, you also need to do some shopping for yourself.”

If social pressure is a driver of organic food purchases, willingness-to-pay for the organic label would be expected to be higher in the EYE, NAME, and FRND treatments relative to the control.

Here are some summary statistics showing the percent of choices made by consumers in each treatment group in which a product with the organic label was chosen.

organic_seon.JPG

We see some support for the idea that organic is more likely to be chosen in the social pressure treatments (EYE, NAME, FRND) than the control. The effects are statistically different, but that aren’t huge for every treatment considered. However, the above table doesn’t consider price differences. When we convert the choices into a measure of willingness-to-pay, we find the biggest effect is for the vignette (FRND). For this treatment, we find willingness-to-pay for organic is about 88% higher for apples and 82% higher for milk than the control.

For all groups, we found that education levels moderate the relationship between the social pressure treatment variables and willingness-to-pay for organic. In particular, social pressure is higher for more highly educated consumers. The effect was particularly large in the EYE treatment, where more highly educated consumers valued the organic label between 150% and 200% than less educated consumers when exposed to eyes.

organic_seon2.JPG

These results provide evidence that at least a portion of organic consumption is likely driven by a form of conspicuous consumption. Some might call it a form of conspicuous conservation, but that’s a whole other can of worms.

Trends in National School Lunch Program

Back in 2010, the U.S. Congress passed the Healthy, Hunger-Free Kids Act. The Act, championed by Michelle Obama, provided funding for free and reduced price school lunches and breakfasts, introduced a variety of new nutritional guidelines, and provided incentives for schools to offer healthier options.

The law went into effect at the beginning of the 2012-2013 academic year. There was a fair amount of initial push back. A video from these Kansas students protesting the calorie restrictions has been viewed more than a million times, and other students took to social media with photos of the new lunches using the hashtag #ThanksMichelleObama. This CNN article from 2017 provides some background and also reports on the efforts of a large school food service industry association to roll back some of the guidelines.

The new guidelines may well have generated some positive health benefits for students who ate school lunches. But, how have these policies potentially affected participation in the National School Lunch Program (NSLP)? Students are not required to eat the school-provided NSLP lunches. Students can bring food from home, go off campus, or find other substitutes at school.

I downloaded some data from the US Department of Agriculture (USDA), Food and Nutrition Service (FNS). These data show that in 2017, 4.89 billion lunches were served under the NSLP, with 73.6% being free or reduced price and the remaining 26.4% of students paying full price. How have these statistics changed over time?

As the figure below shows, there was a nearly linear increase in the number of lunches served each year in the NSLP right up until 2010, after which there was a slowdown and then a slow decline.

NSLP.JPG

The effects can be seen even more dramatically by converting the data in the above figure to annual percentage changes in the number of NSLP meals served. From 1990 to 2008, the school lunch program grew every year. Since 2011, the program has gotten smaller every year except in 2016. In the six years prior to the law’s enactment in 2010, there was an average annual increase of 1.4% in the number of NSLP meals served, but in the six years since the law’s enactment (from 2012 to 2017), there was an annual average decrease of -1.2% in the number of NSLP meals served.

NSLP3.JPG

The data also suggest another interesting dynamic at play. Below is the percent of NSLP meals that are free or reduced priced. Throughout most of the 2000’s just under 60% of NSLP meals were free or reduced price. Since then, there has been a fairly steady increase in the share of meals that are subsidized. Given that the increase started prior to the enactment of the 2010 Healthy Hunger-Free Kids Act, it is likely that other factors (such as the Great Recession) were playing a role. However, there has continued to be an increase in the share of NSLP meals that are free or reduced price well after the recession ended.

NSLP2.JPG

These data show there are fewer meals being served that fall under the National School Lunch Program umbrella and that students who pay full price have increasingly chose to eat meals not governed by the program.

The figures presented above do not causality prove that the 2010 Healthy, Hungry-Free Kids Act led to the decline in the meals served under the USDA National School Lunch Program, but they are interesting trends about which I was previously unaware.

Market Impacts of GMO Labeling

Readers might recall the result from the study Jane Kolodinsky and I published in Science Advances earlier this year. We found that the provision of mandatory labels in Vermont appears to have reduced opposition to GMOs in that state. However, as I noted at the time,

Our result does NOT suggest people will suddenly support GMOs once mandatory labels are in place.

Indeed, the data suggest consumers will still want to avoid products with GMO labels, which provides incentives for food retailers and manufacturers to find ways to avoid GMO ingredients.

Colin Carter and Aleks Schaefer just published an interesting new study in the American Journal of Agricultural Economics, which powerfully shows that mandatory GMO labels are already having significant market impacts. They found a creative way to explore this issue by focusing on the market for sugar. They provide the following background:

In the United States, sugar is produced from both sugarcane and sugarbeets. Sugarcane stalks are milled to produce raw sugar. Raw cane sugar is then sent to a refining facility to be transformed into refined sugar. Sugbarbeets, in contrast, have no raw stage; they are processed from beet to refined sugar in one continuous process. The U.S. market share for beet (cane) sugar is approximately 58% (42%). Almost all U.S. sugarbeet production is GE, while cane sugar is GE-free. However, sugar derived from beets is chemically identical to sugar derived from cane.

This summary data they provide on prices of sugar from cane and beet sources suggests “something” change around the same time as the Vermont mandatory GMO labeling law.

 Source: Carter and Schaefer, American Journal of Agricultural Economics

Source: Carter and Schaefer, American Journal of Agricultural Economics

Here are the main findings.

Our analysis supports the explanation that the divergence in U.S. prices for refined cane and beet sugar was the result of Vermont’s mandatory GE labeling. The divergence occurred on or around July 2016— the month the Vermont Act took effect.

Counterfactual price estimates generated by a regression model suggest that GE food labeling initiatives generated a small premium for cane sugar and a price discount for beet sugar of approximately 13% relative to what prices would have been in the absence of such legislation.

These changes in raw ingredient prices will ultimately have impacts on retail food prices. All this is suggests that mandatory labels aren’t a free lunch.

Are Consumers Eating Out Less Frequently?

According to this Grub Street article, the answer is yes.

The average American’s restaurant visits reached a 28-year low this year, falling from an average of 215 a year in 2000 to 186 a year in 2018. Data gathered by the NPD Group shows a particular and precipitous decline since 2008. Today, 82 percent of meals in America are made at home.

And, they speculate on causes in the slow-down on restaurant spending:

Going out to restaurants doesn’t seem like such a good idea when you’re saddled with student debt and contending with wage stagnation. (In fact, many Americans saw their wages decline over the last year.)

Meanwhile, restaurants are becoming increasingly more expensive compared to eating in.

I’m a little skeptical of these data? Why? Well, here’s data from the Bureau of Economic Analysis (BEA) data on personal consumption expenditures (these are the data that feed into calculation of GDP) for food at home and away from home.

spending.JPG

In inflation-adjusted terms, all consumer spending is up about 36% since 2001. Spending on food at home only rose about 24% over this time periods, but spending away from home increased 54%. (I’ve also shown spending on clothing for reference). Spending on food away from home fell during the Great Recession, but it has significantly rebounded since.

Now, it’s not impossible for both of these statistics to be simultaneously true - one can eat out less frequently but spend more money on each trip, and total expenditures could still rise.

But, the BEA also reports quantity indices, which provide an estimate of the volume of food sold away from home. Here are those data. These data suggest little evidence for a slowdown in the amount of food consumed away from home.

quantity_index.JPG

The headline on the Grub Street article asks “Should Restaurants be Worried?” The BEA data suggest the answer is “no.”

Dealing with Lazy Survey Takers

A tweet by @thefarmbabe earlier this week has renewed interest in my survey result from back in January 2015, where we found more than 80% of survey respondents said they wanted mandatory labels on foods containing DNA. For interested readers, see this discussion on the result, a follow-up survey where the question was asked in a different way with essentially the same result, or this peer-reviewed journal article with Brandon McFadden where we found basically the same result in yet another survey sample. No matter how we asked this question, it seems 80% of survey respondents say they want to label foods because they have DNA.

All this is probably good motivation for this recent study that Trey Malone and I just published in the journal Economic Inquiry. While there are many possible reasons for the DNA-label results (as I discussed here), one possibility is that survey takers aren’t paying very close attention to the questions being asked.

One method that’s been around a while to control for this problem is to use a “trap question” in a survey. The idea is to “trap” inattentive respondents by making it appear one question is being asked, when in fact - if you read closely - a different question is asked. Here are two of the trap questions we studied.

trapquestions.JPG

About 22% missed the first trap question (they did not click “high” to the last question in figure 2A) and about 25% missed the second question (the respondent clicked an emotion rather than “none of the above” in question 2B). So far, this isn’t all that new.

Trey’s idea was to prompt people who missed the trap question. Participants who incorrectly responded were given the following prompt, “You appear to have misunderstood the previous question. Please be sure to read all directions clearly before you respond.” The respondent then had the chance to revise their answers to the trap question they missed before proceeding to the rest of the survey. Among the “trapped” respondents, about 44% went back and correctly answered the first question, whereas about 67% went back and correctly answered the second question. Thus, this “nudge” led to an increase in attentiveness among a non-trivial number of respondents.

After the trap questions and potential prompts, respondents subsequently answered several discrete choice questions about which beer brands they’d prefer at different prices. Here are the key findings:

We find that individuals who miss trap questions and do not correctly revise their responses have significantly different choice patterns as compared to individuals who correctly answer the trap question. Adjusting for these inattentive responses has a substantive impact on policy impacts. Results, based on attentive participant responses, indicate that a minimum beer price would have to be substantial to substantially reduce beer demand.

In our policy simulations, we find a counter-intuitive result - a minimum beer price (as implemented in some parts of the UK) might actually increase alcohol consumption as it leads to a substitution from lower to higher alcohol content beers.

In another paper in the European Review of Agricultural Economics that was published back in July, Trey and I proposed a different, yet easy-to-interpret measure of (and way to fix) inattention bias in discrete choice statistical models.

Taken together, these papers show that inattention is a significant problem in surveys, and that adjusting results for inattention can substantively alter one’s results.

We haven’t yet done a study of whether people who say they want DNA labels are more or less likely to miss trap question or exhibit other forms of inattention bias, but that seems a natural question to ask. Still, inattention can’t be the full explanation for absurd label preferences. We’ve never found inattention bias as high as the level of support for mandatory labels on foods indicating the presence/absence of DNA.