This quote from Michael Moss's book Salt, Sugar, Fat accurately sums up one of the main reasons I see economic analysis as preferable to psychological explanations (and it is one of the main reasons I often prefer non-hypothetical economic experiments to hypothetical surveys).
Pg. 150: “There is not a lot to be gained from asking people why they like something because they don’t bloody know.” - Fancis McGlone, former Unilever scientist
Good news. The Oxford Handbook of the Economics of Food Consumption and Policy, which I co-edited, is now out in electronic version, and each chapter can be individually downloaded.
This is a text designed for graduate-students and faculty interested in learning the "state of the art" in the methods and analysis of the food consumer.
Click here to access the electronic version.
Glynn Tonsor at Kansas State University has created a great resource for the readers of Feedstuff magazine. Glynn writes a periodic column where he takes recent research from the academic literature and boils it down to a layman's perspective. I was pleased to see he featured some work by Kate Brooks at the University of Nebraska and myself in his most recent column. Here were the implications Glynn took from our research:
Implications: This study highlights the potential pitfalls of inferring public preferences from private choices. In this particular study private choices suggested stronger preferences than were reflected in public preferences for a ban restricting production practice options. Conversely, in other settings the opposite behavioral differences are observed. One of the clearest examples is the approximate 5% market share held by cage-free eggs (revealing that the majority of egg consumers are not willing to pay cage-free market premiums) and majority of residents expressing support in ballot settings for bans on laying hen cages. There are several reasons researchers may find the same individual to behave differently when making decisions as a food purchasing consumer than when making decisions as a voting resident. Identification of these reasons and the economic implications of these behavioral patterns are an area in need of additional research as there is a growing list of parallel examples that present complex dilemmas for livestock producers.
"Yes" seems to be the unfortunate conclusion that stems from this paper by Mark Cope and David Allison published in the International Journal of Obesity. Scientists may not (or may) distort their own research findings, but Cope and Allison show, pretty convulsively, that there is a general pattern of distorting the findings of others.
They attribute this to a "white hat bias":
which we define to be bias leading to distortion of research-based information in the service of what may be perceived as righteous ends.
Cope and Allison found two studies related to soda consumption that:
had both statistically and non-statistically significant results on body-weight, body mass index (BMI) or overweight/obesity status which allowed future writers to potentially choose which results to cite, and were also widely cited, permitting quantitative analysis of citations.
Then, they looked at how other scientists subsequently cited the findings in their published papers. Did they focus on the negative findings (that soda doesn't affect weight, etc.) or the positive findings (that soda does affect weight, etc.):
The majority, 84.3% for [2] and 66.7% for [3], described results in a misleadingly positive way to varying degrees (i.e., exaggerating the strength of the evidence that [nutritively-sweetened beverage] reduction showed beneficial effects on obesity outcomes). Some were blatantly factually incorrect in their misleading statements, describing the result as showing an effect for a continuous obesity outcome whereas no statistically significant effect for continuous obesity outcomes was observed. In contrast, only four papers (3.5%) were negatively misleading (i.e., underplayed the strength of evidence) for [2] and none were negatively misleading for [3]. Only 12.7% and 33% of the papers accurately described complete overall findings related to obesity outcomes from [2] and [3], respectively.
They went on to document a similar pattern for studies on the effects of breastfeeding. They conclude:
Evidence presented herein suggests that at least one thing has been demonized ([nutritively-sweetened beverage] consumption) and another sanctified (Breastfeeding), leading to bias in the presentation of research literature to other scientists and to the public at large, a bias sufficient to misguide readers. Interestingly, while many papers point out what appear to be biases resulting from industry funding, we have identified here, perhaps for the first time, clear evidence that white-hat biases can also exist in opposition to industry interests.
The answer is: probably not.
This is important question because there are many studies finding that various interventions (from fat taxes to menu labels) have very (though sometimes statistically significant) small effects on caloric intake. Proponents of the policies are often undeterred - and say things like "well, a 20 kcal reduction every day can really add up to big weight loss over time."
As I've already discussed, some of this sort of analysis is based on the faulty logic that 3500kcal = 1lb. But, as was mentioned in that post, our body does not react linearly to caloric changes in the fashion implied by this formula.
Now, there's more on this topic by Trevor Butterworth in a well-written and catchy-titled post Sex And Lies! The Iffy Science Of Measuring Calories. Here is a key excerpt:
Hall was responsible for filling in the crucial measurements that elucidated one of the most widespread myths highlighted by Allison et al.: the idea that small, consistent changes in energy intake or expenditure will, over time, lead to large changes in weight. The assumption appears to have been based on the 1958 calculation by Max Wishnofsky that one pound of body fat gained or lost is equal to 3,500 kilocalories. This seemed to give people a convenient way to estimate weight loss through diet or exercise, while promising extremely convenient results. If you simply knocked off a 100 kilocalories from your energy intake each day—a ten-minute jog, or a mile walk—you'd end up losing over 50 pounds in five years. Little wonder that early proposals for soda and fat taxes promised to save Americans from themselves: pay a little more, consume a little less, watch a lot of weight disappear in a few years.
Hall first heard the claim listening to a dietician make a calculation for an obese patient. His intuition told him that this calculation was incorrect and would lead to exaggerated weight loss predictions. When he asked for a reference, he was pointed to a nutrition and dietetics textbook. "I subsequently found the mistake everywhere I looked." People weren't stopping to think "about the dynamic interaction between energy intake and expenditure, which is complicated," he says. What they failed to take into account was that "the rate of weight loss changes over time and is primarily determined by the imbalance between energy intake and expenditure—a value that also changes over time." To radically simplify his model, this means that cutting calories in your diet leads to a decreasing calorie expenditure, which in turn slows weight loss until weight eventually plateaus after a few years. "Of course," says Hall, "cheating on your diet will cause your weight to plateau much sooner." In the case of soda taxes, Hall and researchers at the US Department of Agriculture showed how static modeling overstated weight loss by 346 percent after five years.