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What Food Policies do Consumers Like and Dislike?

I have a new working paper with Vincenzina Caputo in which we elicit consumers’ preferences for 13 different food policies. Here’s our main motivation (references removed for readability).

A variety of food policies have been proposed, and in some cases enacted, in an effort to improve public health, environmental outcomes, or food security. Proposed actions include a spectrum of policies ranging from fiscal incentives/disincentives, bans, labelling programs, and passive policies such as subsides and investments in education. What food policy proposals do consumers prefer? While there have been numerous studies aimed at calculating the welfare effects of individual food policies it is difficult to easily ascertain the relative preferability of numerous policy options, even those that have the same objective (e.g., “fat taxes” and nutritional education both aim to improve public health).


We conducted a nationwide survey of 1,056 U.S. consumers who were asked to indicate the relative desirability of the following food policies.

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Rather than use a traditional approach, where respondents are not required to make trade-offs between policies (e.g., people can approve of all policies or rank all policies as “very important”), we used the “best worst scaling” approach that requires respondents to make trade-offs. The approach requires respondents to answer a series of questions like the one below, where for each question, they have to indicate their most and least preferred policies.

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The results are analyzed using a choice model that allows for preference heterogeneity. The main outcomes are below, reported as “preference shares” - i.e., the percent of people predicted to choose each policy as most preferable. Results indicate the highest levels of support for investments in agricultural research and requirements of food and agricultural literacy standards in public education. Fat, calorie, and soda taxes are the least popular. These preference shares provide a measure of intensity of preference in a population. Funding for agricultural research is 14%/8% = 1.75 times more preferable than symbolic nutritional labeling.

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While the above results are useful in providing intensity of relative preferences, they do not indicate whether people would actually vote in favor of a policy. The table below shows the results of that question; the results largely align with the best-worse scaling approach. Fewer than one-third of respondents are in favor of these three tax policies.

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There are a number of significant demographic correlates with policy preferences. Some are not surprising. For example, Nutrition Assistance (or SNAP) is more desirable to lower income vs. higher income households and Democrats vs. Republicans. As another example, soda taxes are less desirable among lower income households.

Funding for agricultural research was generally supported across all demographic categories except for age: older individuals were more supportive of funding for agricultural research than younger individuals.

A Basket-Based Choice Experiment

That’s the title of a new working paper I’ve co-authored with Vincenzina Caputo.

Much of research seeking to understand consumers’ preferences for food products and attributes relies on “choice experiments”, which are like simulated shopping scenarios. What makes choice experiments different from a true shopping scenario, however, is that respondents are only asked to choose one product out of a set. In reality, people often choose multiple products from different product categories when shopping, and their preference for one product may depend on what they’ve already put in the shopping basket.

Here’s a brief summary from the paper:

consumers make multiple food choices at a time and prefer to choose on average 4.4 out of 21 possible foods items. This is especially the case when looking at fresh meat and vegetables. For example, the selection frequency of salad/lettuce increased as respondents selected more items since salad is often used as a side dish. Further, our results reveal that food items act as complements or substitutes. Finally, while in standard [discrete choice experiments] cross-price elasticities are forced to be positive due to single discrete choices, our results also imply negative cross-price elasticities. Overall, these results suggest that the BBCE [basket based choice experiment] is a promising experimental approach that allows for a richer set of substitution and choice patterns as it brings together the advantages of standard [discrete choice experiments] and the advantages of traditional demand system analysis.

The following figure shows the most common items consumers put in their basket.

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Perhaps more interesting, however, are the combinations of items people place in their baskets. For example, given that someone chooses ground beef, the next most common items in the basket are salad/lettuce, potatoes, and then tomatoes. Given that someone has picked ground beef, there is more than a 50% chance each of these vegetables/vegetables also appears in the basket. These sorts of results illustrate the challenge of suggesting people to just increase fruit or vegetable consumption because their values for these items increase when accompanied with meat. For example, one of our model specifications suggests that the value of lettuce/salad increases by more than $4 if it is also accompanied with ground beef.

There’s much more in the paper, which Vincenzina will present at the Agricultural and Applied Economics Association meetings next month.

Organic Food Consumption and Cancer

A couple of days ago, JAMA Internal Medicine published a paper looking at the relationship between stated levels of organic food consumption and cancer among a sample of 68,946 French consumers.

The paper, and the media coverage of it, is frustrating on many fronts, and it is symptomatic of what is wrong with so many nutritional and epidemiological studies relying on observational, self reported data without a clear strategy for identifying causal effects. As I wrote a couple years ago:

Fortunately economics (at least applied microeconomics) has undergone a bit of credibility revolution. If you attend a research seminar in virtually any economics department these days, you’re almost certain to hear questions like, “what is your identification strategy?” or “how did you deal with endogeneity or selection?” In short, the question is: how do we know the effects you’re reporting are causal effects and not just correlations.

Its high time for a credibility revolution in nutrition and epidemiology.

Yes, Yes, the title of the paper says “association” not “causation.” But, of course, that didn’t prevent the authors - in the abstract - from concluding, “promoting organic food consumption in the general population could be a promising preventive strategy against cancer” or CNN from running a headline that says, “You can cut your cancer risk by eating organic.”

So, first, how might this be only correlation and not causation? People who consume organic foods are likely to differ from people who do not in all sorts of ways that might also affect health outcomes. As the authors clearly show in their own study, people who say they eat a lot of organic food are higher income, are better educated, are less likely to smoke and drink, eat much less meat, and have overall healthier diets than people who say they never eat organic. The authors try to “control” for these factors in a statistical analysis, but there are two problems with this. First, the devil is in the details and the way these confounding factors are measured and interact could have significant effects. More importantly, some of these missing “controls” are things like overall health consciousness, risk aversion, social conformity, and more. This leads to a second more fundamental problem. These unobserved factors are likely to be highly correlated with both organic food consumption and cancer risk, and thus the estimated effect on organic is likely biased. There are many examples of this sort of endogeneity bias, and failure to think carefully about how to handle it can lead to effects that are under- or over-estimated and can even reverse the sign of the effect.

To illustrate, suppose an unmeasured variable like health consciousness is driving both organic purchases and cancer risk. A highly health conscious person is going to undertake all sorts of activities that might lower cancer risks - seeing the doctor regularly, taking vitamins, being careful about their diet, reading new dietary studies, exercising in certain ways, etc. And, such a person might also eat more organic food, thus the correlation. The point is that even if such a highly health conscious person weren’t eating organic, they’d still have lower cancer risk. It isn’t the organic causing the lower cancer risk. Or stated differently, if we took a highly health UNconscious person and forced them to eat a lot of organic, would we expect their cancer risk to fall? If not, this is correlation and not causation.

Ideally, we’d like to conduct a randomized controlled trial (RCT) (randomly feed one group a lot of organic and another group none and compare outcomes), but these types of studies can be very expensive and time consuming. Fortunately, economists and others have come up with creative ways to try to address the unobserved variable and endogeneity issues that gets us closer to the RCT ideal, but I see no effort on the part of these authors to take these issues seriously in their analysis.

Then, there are all sorts of worrying details in the study itself. Organic food consumption is a self-reported variable measured in a very ad-hoc way. People were asked if they consumed organic most of the time (people were given 2 points), occasionally (people were given one point), or never (no points), and this was summed across 16 different food categories ranging from fruits to meats to vegetable oils. Curiously, when the authors limit their organic food variable to only plant-based sources (presumable because this is where pesticide risks are most acute), the effects for most cancers diminishes. It is also curious that the there wasn’t always a “dose response” relationship between organic consumption scores and cancer risk. Also, when the authors limit their analysis to particular sub-groups (like men), the relationship between organic consumption and cancer disappears. Tamar Haspel, a food and agricultural writer for the Washington Post, delves into some of these issues and more in a Tweet-storm.

Finally, even if the estimated effects are “true”, how big and consequential are they? The authors studied 68,946 people, 1,340 of whom were diagnosed with cancer at some point during the approximately 6 year study. So, the baseline chance of any getting any type of cancer was (1340/68,946)*100 = 1.9%, or roughly 2 people out of 100. Now, let’s look at the case where the effects seem to be the largest and most consistent across the various specifications, non-Hodgkin lymphomas (NHL). There were 47 cases of NHL, meaning there was a (47/68,946)*100 = 0.068% overall chance of getting NHL in this population over this time period. 15 and 14 people, respectively, in the lowest first and second quartiles of organic food scores had NHL, but 16 people in the third highest quartile of organic food consumption had HCL. When we get to the highest quartile of stated organic food scale, the number of people with HCL now dropped to only 2. After making various statistical adjustments, the authors calculate a “hazard ratio” of 0.14 for people in the lowest vs. highest quartiles of organic food consumption, meaning there was a whopping 86% reduction in risk. But, what does that mean relative to the baseline? It means going from a risk of 0.068% to a risk of 0.068*0.14=0.01%, or from about 7 in 10,000 to 1 in 10,000. To put these figures in perspective, the overall likelihood of someone in the population dying from a car accident next year are about 1.25 in 10,000 and are about 97 in 10,000 over the course of a lifetime. The one-year and lifetime risk from dying from a fall on stairs and steps is 0.07 in 10,000 and 5.7 in 10,000.

In sum, I'm not arguing that eating more organic food might not be causally related to reduced cancer risk, especially given the plausible causal mechanisms. Rather, I’m arguing that this particular study doesn’t go very far in helping us answer that fundamental question. And, if we do ultimately arrive at better estimates from studies that take causal identification seriously that reverse these findings, we will have undermined consumer trust by promoting these types of studies (just ask people whether they think eggs, coffee, chocolate, or blueberry increase or reduce the odds of cancer or heart disease).

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.

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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.

New Published Research

I've had several new papers published in the last month or so that I haven't had a chance to discuss here on the blog.  So, before I forget, here's a short list.

  • What to Eat When Having a Millennial over for Dinner with Kelsey Conley was published in Applied Economic Perspectives and Policy.  We found Millennials have higher demand for cereal, beef, pork, poultry, eggs, and fresh fruit and lower demand for “other” food, and for food away from home relative to what would have been expected from the eating patterns of the young and old 35 years prior.  I'd previously blogged about an earlier version of this paper.
  • A simple diagnostic measure of inattention bias in discrete choice models with Trey Malone in the European Review of Agricultural Economics. Measuring the "fit" of discrete choice models has long been a challenge, and in this paper, we suggest a simple, easy-to-understand measure of inattention bias in discrete choice models. The metric, ranging from 0 to 1, can be compared across studies and samples.
  • Mitigating Overbidding Behavior using Hybrid Auction Mechanisms: Results from an Induced Value Experiment with David Ortega Rob Shupp and Rudy Nayga in Agribusiness.  Experimental auctions are a popular and useful tool in understanding demand for food and agricultural products. However, bidding behavior often deviates from theoretical predictions in traditional Vickrey and Becker–DeGroot–Marschak (BDM) auction mechanisms. We propose and explore the bidding behavior and demand revealing properties of a hybrid first price‐Vickrey auction and a hybrid first price‐BDM mechanism. We find the hybrid first price‐Vickrey auction and hybrid first price‐BDM mechanism significantly reduce participants’ likelihood of overbidding, and on average yield bids closer to true valuations.