One (positive) unintended consequence of obesity

An article forthcoming in the journal Health Economics paper by Dunn and Tefft finds a result I would have never considered.  If you're overweight, it takes more alcohol to make you drunk.  If you're less likely to be drunk, then you're less likely to be involved in drunk-driving accidents.  Ergo, growing obesity rates lead to fewer drunk driving accidents.  Now, I hardly think this is a sign to gain weight or booze it up, but it is interesting nonetheless.     

The abstract: 

We develop a model of alcohol consumption that incorporates the negative biological relationship between body mass and inebriation conditional on total alcohol consumption. Our model predicts that the elasticity of inebriation with respect to weight is equal to the own-price elasticity of alcohol, consistent with body mass increasing the effective price of inebriation. Given that alcohol is generally considered price inelastic, this result implies that as individuals gain weight, they consume more alcohol but become less inebriated. We test this prediction and find that driver blood alcohol content (BAC) is negatively associated with driver weight. In fatal accidents with driver BAC above 0.10, the driver was 7.8 percentage points less likely to be obese than drivers in fatal accidents that did not involve alcohol. This relationship is not explained by driver attributes (age and sex), driver behaviors (speed and seatbelt use), vehicle attributes (weight class, model year, and number of occupants), or accident context (county of accident, time of day, and day of week).

HT: Andreas Drichoutis

Mea culpa - fat tax version

About two years ago, I co-authored a paper that was published in the journal Health Economics  with the title "When Do Fat Taxes Increase Consumer Welfare."  

I started work on that paper because I was troubled by the contradictory way in which economists had approached the analysis of fat taxes.  One the one hand, economists estimated the effects of fat taxes by using elasticities of demand that were derived from a rational consumer-utility maximizing model.  In this kind of conceptual model, a tax (or a price increase) cannot make a consumer better off.  However, on the other hand, economists were publishing these papers with the premise that somehow the tax could make people better off.   In the paper, we tried to think about an approach for reconciling these two stances by asking whether a tax could make people better off if we make the reasonable assumption that people also care about (and consider) weight or health effects when choosing the quantity and type of food to buy.   

We had argued that in this situation, it was possible but empirically unlikely a tax could make people better off.  Enter Professor Neill, who wrote a comment on our work, saying "no" - it is concetually impossible for a fat tax to increase consumers' well-being in a "standard" economic model of the consumer.  Here are the first sentences of our forthcoming response to Dr. Neill:

We are flabbergasted at how such a fundamental lesson of mathematical economics escaped our attention, but Professor Neill is right and we were wrong. We apologize. 

Neill pointed out, embarrassingly to us, what should be obvious to any serious student of economics.  A tax is akin to reducing someone's income.  No one is better off with less income.  Even if one wants to weight less, they don't need a tax to do it.  A consumer can achieve a lower weight at current prices and re-allocate their income toward other non-weight-increasing goods and achieve a higher level of satisfaction.  

So, how do economist justify fat taxes?  One approach is to claim that obesity is an externality - that my food choices impose a cost on you (via Medicare/Medicaid) and thus a tax can force me to properly consider those costs.  However, in a paper a couple years ago, Bhattacharya and Sood dismantled the validity of that argument (although it is not well understood and continues to be debated).  

Another response is that the "rational consumer utility maximization" model is incorrect.  This "behavioral economics" approach posits that people fail to properly account for their future well-being and that a tax can force people to make decisions today that their future selves will ultimately find beneficial.  The precise mechanism for this welfare improvement is rarely laid out with any precision.      

In our reply to Neill, we tried to sketch out a behavioral-economics type model to see when a fat tax might be justified on those grounds.  Here is our conclusion:

under this sort of behavioral economics framework, where people naively or myopically optimize utility without considering future weight effects, it is possible to imagine situations where raising prices might increase ultimate experienced welfare. However, this condition occurs only when price is very high and falls in the range where consumption would take place only because people are ignoring the ultimate health impacts; at lower prices, a ‘fat tax’ would only lower welfare.

The fight over whether fat kills

A little after Christmas, Katherine Flegal and colleagues published a big review paper in the Journal of the American Medical Association.  They found that people who are overweight and even a little obese actually live a bit longer than "normal" weight people.  I wrote an article for Townhall.com in response with the tongue in cheek title (Will Fat Taxes Kill You?)

There was actually more to the story that I haven't previously touched on.  In particular, a very high profile Harvard Nutrition/Public Health Professor, Walter Willett, came out soon after the Flegal study and said  

This study is really a pile of rubbish and no one should waste their time reading it

And a symposium was pulled together to further criticize the study, which was seen as "dangerous" in some circles because it seemed to undermined the public health community's call to loose weight.  

Here is where it gets really interesting.  In May, Nature, one of the most prestigious scientific journals, actually called out Willett in an editorial and a feature article.  This editorial by Trevor Butterworth has most of the details, and as he summarizes it:

Science is complex, and Willett’s message to his fellow scientists appears to be that the public can’t be trusted with this complexity (except, as noted, when it might be something that he thinks is worthy of research); the question, which the public might ask in turn, is whether Willett can be trusted with complexity given his apparent intolerance for it in other scientists?

The problem seems to be that Willett and others could not separate their normative, ideological position from evidence-based science.  The evidence conflicted with their prior beliefs and commitments, ergo it must have been wrong.

I found this response from this from Mike Gibney (who also seems to have an interesting new book out) insightful:

Leaving the science aside, there is a critically important aspect to this row that needs highlighting. Think back to the BSE crisis. At that point, within the EU we had the risk assessment process and the risk management process both operated by the European Commission. That was then amended to take the risk assessment process away from the Commission and to create a totally science- based independent body, The European Food Safety Authority, to conduct risk assessment. The Harvard group is effectively seeking to be both risk assessors and risk managers. The former is science based and the latter is politically or policy based. If the two are attended to within the same institute, as the Harvard group seem to want, then the risk management process will filter the risk assessment process. Why support a scientific paper, which conflicts with your risk management goals? Indeed, in this week’s Harvard Gazette which covered this controversy, Professor Willett is quoted thus: “If you don’t have the right goal you are very unlikely to end up in the right place”[6]. Clearly, Professor Willett knows what is “right” and those who differ are “wrong”. This is simply bad for science. As I said, dissent is the oxygen of science.

Farm subsidies, commodity types, and obesity

A recent opinion paper in the American Journal of Preventative Medicine takes a look at farm subsidies and draws implications for obesity.  One problem is in how the study (or rather review) is interpreted by media outlets.  For example, one source had the headline:

US Farm Subsidy Policies Contribute To Worsening Obesity Trends, Study Finds.

However, this was not a new "study" and the authors readily acknowledge the economic research showing very little to no link between farm subsidies and obesity. This study by Okrent and Alston in the American Journal of Agricultural Economics, in fact, finds removal of subsidies would increase weight: 

Eliminating all subsidies,including trade barriers, would lead to an increase in annual per capita consumption in the range of 165 to 1,435 calories (equivalent to an increase in body weight of 0.03% to 0.23%) [note, however, my previous comment about their weight calculations]

Okrent and Alston conclude:

These results indicate that U.S. farm policy, for the most part, has not made food commodities significantly cheaper and has not had a significant effect on caloric consumption.

Don't, get me wrong.  I am not a fan of farm subsidies - largely because they are economically inefficient and reduce the size of the economic pie.  But, I think we ought to get the causes and effects right, and it simply isn't true that farm subsidies caused obesity.  Moreover, I am not a fan of re-engineering farm subsidies to meet "public health" goals, as the authors of the AJPM article apparently are.  Here is their recommendation: 

More specifically, sustainable practices should yield biodiverse, quality foods, optimize nonrenewable resources, and sustain the economic viability of farmers. Important policy reforms could direct increasing subsidies to family farms and/or fruit and vegetable growers in the aim of making their prices more competitive

Frankly, I find the recommendation naive, simplistic, and likely to produce unintended consequences whilst simultaneous failing  to produce the kind of benefits the authors desire.  

On a positive note, I found this table in the paper quite interesting.  At first, I thought the table had to be wrong since they have livestock subsidies (and livestock isn't subsidized per se), but apparently they are also adding in payments for crop insurance premiums for different commodities.  I wonder if they did they same for all fruits and vegetables?  It would also be useful to calculate these subsidies as a percentage of the total value (or revenue) for each crop type for a bit of perspective.    

Economists, Fat Taxes, and the 3500kcal rule

Economists are often sought out to help determine the effects of fat and soda taxes.  We are generally well-equipped to estimate how much less of a particular type of food will be eaten when prices increase as a results of a tax.  However, we are much less well-equipped to go the next step and figure out how changes in the consumption of a food results in a change in weight - the key statistic of interest.  That last step requires some knowledge of nutrition, biology, and metabolism.  

Unfortunately, it turns out that one of the critical "thumb rules" that we economists have used from those literatures, that a change in 3500 kcal will equate to a change in 1 pound of body weight, is likely highly misleading and overstates the effects of the tax (not to mention that it says nothing of when the weight change will happen or how long it will take to happen).  

I've previously blogged about some of the issues with this thumb rule but I'm not sure how widely the problem is understood or recognized among economists.  For example, here are some quotes from some recent, otherwise well-done papers.     

Okrent and Alston in the American Journal of Agricultural Economics in 2012 (free version here) said:

One frequently used relationship in textbooks (e.g., Whitney, Cataldo, and Rolfes 1994) and academic articles that address the potential impacts of fiscal policies on weight (e.g., Chouinard et al. 2007; Smith, Lin and Lee 2010) is that a pound of fat tissue has about 3,500 calories. We used this multiplier to convert changes in annual calorie consumption into changes in body weight.

Dharmasena and Capps in the Health Economics in 2011 said:

Finally, using the conversion ratio of 3500 cal per pound of body weight, we calculate the induced change in the per capita body weight in pounds as a result of aforementioned change in the per capita caloric intake.

Kulcher et al in Applied Economic Perspectives and Policy (formerly the Review of Agricultural Economics) in 2005 said: 

Assuming that no food would be substituted, at 3,500 calories per pound of body weight (American Dietetic Association), the [estimated] reduction translates into less than a fourth of a pound.

To be fair, I didn't appreciate the problem till only recently.  My own paper with Schroeter and Tyner in the Journal of Health Economics in 2008 stated the following (although we used a different calculation to derive weight changes):

On average, in order to gain (lose) one pound, a person needs to consume (burn) 3500 calories in addition to the typical caloric intake (expenditure). Overall, a surplus (deficit) of 500 kcal a day brings about a gain (loss) of body fat at the rate of one pound per week and a surplus (deficit) of 1000 kcal a gain (loss) of two pounds per week (Whitney et al., 2002).

In this context, I was pleased to see this recent article in the International Journal of Obesity, which we economists can use to derive better weight effects.  Here is the abstract

Despite theoretical evidence that the model commonly referred to as the 3500-kcal rule grossly overestimates actual weight loss, widespread application of the 3500-kcal formula continues to appear in textbooks, on respected government- and health-related websites, and scientific research publications. Here we demonstrate the risk of applying the 3500-kcal rule even as a convenient estimate by comparing predicted against actual weight loss in seven weight loss experiments conducted in confinement under total supervision or objectively measured energy intake. We offer three newly developed, downloadable applications housed in Microsoft Excel and Java, which simulates a rigorously validated, dynamic model of weight change. The first two tools available at http://www.pbrc.edu/sswcp, provide a convenient alternative method for providing patients with projected weight loss/gain estimates in response to changes in dietary intake. The second tool, which can be downloaded from the URL http://www.pbrc.edu/mswcp, projects estimated weight loss simultaneously for multiple subjects. This tool was developed to inform weight change experimental design and analysis. While complex dynamic models may not be directly tractable, the newly developed tools offer the opportunity to deliver dynamic model predictions as a convenient and significantly more accurate alternative to the 3500-kcal rule.

Finally, I will end by noting that there are many papers that use economic models to project how a tax/subsidy will change the consumption of certain nutrients, and similar thumb rules are used to translate to changes in heart attacks, diabetes, etc.  Although I don't know for sure, I suspect many of the exact same sorts of problems exist with these thumb rule extrapolations as exists with the 3500kcal=1lb rule, not to mention the larger difficulty of ascribing causation in those models.