Fat taxes & modeling

NPR covers a Danish move to tax saturated fat:

So when the tiny Scandinavian country announced it would be imposing a 16 Kroner (about $3 U.S.) tax on every kilogram of saturated fat as a way to discourage poor eating habits and raise revenue, we were left scratching our heads.

How’s that going to work?

Ole Linnet Juul, food director at Denmark’s Confederation of Industries, tells The Washington Post that the tax will increase the price of a burger by around $0.15 and raise the price of a small package of butter by around $0.40.

Our pals over at Planet Money took a stab last year at explaining the economics of our version of the fat tax — the soda tax. They conclude that price increases do drive down demand somewhat.

But couldn’t Danes just easily sneak over to neighboring Sweden for butter and oil and simply avoid paying the tax, throwing all revenue calculations off?

Meanwhile, some health studies indicate a soda tax doesn’t work to curb obesity anyways.

First a few obvious problems: oil is typically not saturated and therefore presumably wouldn’t fall under the tax. And sneaking over the border for butter? Seriously? You’d better bring back a heckuva lot, because there’s the little matter of the Øresund Strait, which now has a handy bridge, and a 36 EUR toll to go with it.

More interesting is the use of models in the linked studies. From the second (“doesn’t work”):

But new research from Northwestern University suggests that soda taxes don’t actually help obese people lose weight, largely because people with weight problems already tend to drink diet soda rather than the sugary kind. So taxing full-calorie sodas may not help many Americans make better dietary choices.

Patel ran computer simulations designed to track how soda prices would affect obesity rates. The findings demonstrated that a sugar tax would cause a negligible drop in obesity, about 1.4%, and that obese people would not lose much weight. “For people going from [body mass indexes] of over 30 to below that…most people are not having massive swings,” Patel said.

For the study, Patel’s team collected data on people with “all ranges of BMI” from the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System, which has tracked health conditions in the U.S. for nearly three decades. They also collected a data set of soda prices and sales to estimate consumer practices, which they used to predict what people would purchase before and after the implementation of a soda tax. Based on the resulting change in total calories consumed per day over a set time period, the team modeled long-term changes in weight using existing nutrition literature.

Kelly Brownell, the director of the Rudd Center for Food Policy and Obesity at Yale University, has doubts about the accuracy of studies such as Patel’s. Simulations of the potential impact of public health actions such as a soda tax are based on a huge number of assumptions — about consumption, spending behavior, weight change — that are, in reality, difficult to make accurately, he explains.

“All of those changes are unknown,” he said. “So it’s not hard to allow those assumptions to create the results you want.”

Patel counters that assumptions are inevitable in research, and that previous studies that have produced results in favor of soda taxes have also made assumptions, typically about consumer preferences. “I’m trying to see if there are any critical assumptions here that really change the results, but so far I haven’t had anything like that,” he said. “It’s a somewhat valid criticism, but the paper is still being fleshed out, and there are a variety of robustness checks.”

But Patel acknowledges that his study could not predict whether a soda tax would help prevent people from consuming sweetened drinks in the first place and becoming fat later on — another point raised by Brownell. “The question of whether a soda tax could prevent people from becoming obese in the future…that’s still kind of an open question because there are some issues on how you model weight change that to my knowledge haven’t been addressed,” he said. “It’s possible that a soda tax could prevent people from becoming obese in the future, but for people already obese it’s not really going to do anything.”

As press coverage of models goes, this is actually pretty good, and Patel is nicely circumspect about the limitations of the work. The last paragraph hints at one thing that strikes me as extremely important though: the study model is essentially open loop, with price->choice->calories->body mass causality. The real world is closed loop, with important feedbacks between health and future choices of diet and exercise, and social interactions involved in choices. I suspect that the net result is that the long term effect of pricing, or any other measure, on health is substantially greater than the open loop analysis indicates, especially if you’re clever about exploiting the various positive loops that create obesity traps.

Brownell’s complaint – that we know nothing, so we can just plug in assumptions to get whatever answer we want – irks me. It betrays an ignorance of models (especially nonlinear dynamic ones), which are typically more constrained than unstated mental models, not less.

There seems to be a flowering of health and obesity models in system dynamics lately, with some interesting posters and papers at the last few conferences. There’s hope for closing those loops yet.

Bananas, vomit and behavioral economics

I just ran across a nice series of videos and transcripts on behavioral decision making, heuristics and biases, psychology and economics, with Nobel Prize winner Daniel Kahneman, Dick Thaler and other masters:

You have to watch the first to work out the meaning of my strange title. I can’t embed, so head over to Edge to view, where other interesting links will pop up.

The envelope please…

The 2011 Ig Nobel in Mathematics is for modeling … it goes to predictors of the end of the world:

Dorothy Martin of the USA (who predicted the world would end in 1954), Pat Robertson of the USA (who predicted the world would end in 1982), Elizabeth Clare Prophet of the USA (who predicted the world would end in 1990), Lee Jang Rim of KOREA (who predicted the world would end in 1992), Credonia Mwerinde of UGANDA (who predicted the world would end in 1999), and Harold Camping of the USA (who predicted the world would end on September 6, 1994 and later predicted that the world will end on October 21, 2011), for teaching the world to be careful when making mathematical assumptions and calculations.

Notice that the authors of Limits to Growth aren’t here, not because they were snubbed, but because Limits didn’t actually predict the end of the world. Update: perhaps the Onion should be added to the list though.

The Medicine prize goes to a pair of behavior & decision making studies:

Mirjam Tuk (of THE NETHERLANDS and the UK), Debra Trampe (of THE NETHERLANDS) and Luk Warlop (of BELGIUM). and jointly to Matthew Lewis, Peter Snyder and Robert Feldman (of the USA), Robert Pietrzak, David Darby, and Paul Maruff (of AUSTRALIA) for demonstrating that people make better decisions about some kinds of things — but worse decisions about other kinds of things‚ when they have a strong urge to urinate. REFERENCE: “Inhibitory Spillover: Increased Urination Urgency Facilitates Impulse Control in Unrelated Domains,” Mirjam A. Tuk, Debra Trampe and Luk Warlop, Psychological Science, vol. 22, no. 5, May 2011, pp. 627-633.

REFERENCE: “The Effect of Acute Increase in Urge to Void on Cognitive Function in Healthy Adults,” Matthew S. Lewis, Peter J. Snyder, Robert H. Pietrzak, David Darby, Robert A. Feldman, Paul T. Maruff, Neurology and Urodynamics, vol. 30, no. 1, January 2011, pp. 183-7.

ATTENDING THE CEREMONY: Mirjam Tuk, Luk Warlop, Peter Snyder, Robert Feldman, David Darb

Perhaps we need more (or is it less?) restrooms in the financial sector and Washington DC these days.

Are environmental regulations the real constraint on US energy output?

When times are tough, there are always calls to unravel environmental regulations and drill, baby, drill. I’m first in line to say that a lot of environmental regulation needs a paradigm shift, but this strikes me as a foolish hair-of-the-dog-that-bit-ya idea. Our current problems don’t come from regulation, and won’t be solved by deregulation.

On average, there’s no material deprivation in the US. We consume more petroleum per capita than any other large nation. Our problems are largely distributional – inequitable income distribution and, recently, high unemployment, which causes disproportionate harm to a few. Why solve a distributional problem by skewing environmental policy? This smacks of an attempt to grow out of our problems, which is surely doomed to the extent that growth relies on intensifying material throughput.

Consider the system:

The underlying mental model behind calls for deregulation sounds like the following: environmental regulations create compliance costs that drive up the total cost of resource extraction, depressing the production rate and depriving the people of needed $$$ and happiness. Certainly that causal path exists. But it’s not the only thing going on.

Those regulations were created for a reason. They reduce environmental impacts, and therefore reduce the unpaid social costs that occur as side effects of oil production and consumption, and therefore improve welfare. These effects are nontrivial, unless you’re a GOP presidential candidate. One could wish for more efficient regulations, but absent that, wishing for less regulation is tantamount to wishing for more environmental consequences and social costs, and hoping that more $$$ will offset that.

Even the basic open-loop rationale for deregulation makes little sense. Resource policy is already loose, so there’s no quantity constraint on production. With the exception of ANWR and some offshore areas, most interesting areas are already leased. Montana certainly doesn’t exercise any foresight in the management of  its trust lands. Environmental regulations have hardly become more stringent in the last decade or so. Since oil production in 1999 was higher than it is today, with oil prices well below $20/bbl, so compliance costs must be less than that. So, with oil at $100/bbl, we’d expect an explosion of supply, if regulatory costs were the only constraint. In fact, there’s barely an upward blip, so there must be something else at work…

The real problem is that there’s feedback in the system. For example, there’s balancing loop B1: as you extract more stuff, the remaining resource (oil in the ground) dwindles, and the physical costs of extraction – capital, labor, energy – go up. Technology can stave off that trend for some time, but prices and production trends make it clear that B1 is now dominant. This means that there’s a rather stark better-before-worse tradeoff: if we extract oil more quickly now, to hoist ourselves out of the financial crisis, we’ll have less later. But it seems likely that we’ll be even more desperate later – either to have that oil in an even pricier world market, or to keep it in the ground due to climate concerns. Consider what would have happened if we’d had no environmental constraints on oil production for the last three or four decades. Would the US now have more or less oil to rely on? Would we be happy that we pumped up all that black gold at under $20/bbl? Even the Hotelling rule is telling us that we should leave oil in the ground, as long as prices are rising faster than the interest rate (not hard, at current rates).

Another loop is just gaining traction: B2. As the stock of oil in the ground is depleted, marginal production occurs in increasingly desperate and devastating circumstances. Either you pursue smaller, more remote fields, meaning more drilling and infrastructure disturbance in sensitive areas, or you pursue unconventional resources, like tar sands and shale gas, with resource-intensive methods and unknown hazards. A regulatory rollback would accelerate production via the most destructive extraction methods, right at the time that the physics of extraction is already shifting the balance of private benefits ($$$) and social costs unfavorably. Loop B2 also operates inequitably, much like unemployment. Not everyone is harmed by oil and gas development; the impacts fall disproportionately on the neighbors of projects, who may not even benefit due to severance of surface and mineral rights. This weakens the argument for deregulation even further.

Rather than pretending we can turn the clock back to 1970, we should be thinking carefully about our exit strategy for scarce and climate-constrained resources. There must be lots of things we can do to solve the distributional problems of the current crisis without socializing the costs and privatizing the gains of fossil fuel exploitation more than we already do.

Greater petroleum independence for the US?

The NYT enthuses about the prospects for new oil production in the Americas:

New Fields May Propel Americas to Top of Oil Companies’ Lists

…

Still, the new oil exploits in the Americas suggest that technology may be trumping geology, especially in the region’s two largest economies, the United States and Brazil. The rock formations in Texas and North Dakota were thought to be largely fruitless propositions before contentious exploration methods involving horizontal drilling and hydraulic fracturing — the blasting of water, chemicals and sand through rock to free oil inside, known as fracking — gained momentum.

While the contamination of water supplies by fracking is a matter of fierce environmental debate, the technology is already reversing long-declining oil production in the United States, with overall output from locations where oil is contained in shale and other rocks projected to exceed two million barrels a day by 2020, according to some estimates. The United States already produces about half of its own oil needs, so the increase could help it further peel away dependence on foreign oil.

…

Setting aside the big developments in Brazil and Canada, what does technology trumping geology, “reversing long-declining oil production in the United States” look like? Here’s the latest from EIA:

Somehow it’s not such a compelling story in pictures.

Fight or flight in resource modeling

A nice reflection on modeling in emotionally charged situations, from Drew Jones, Don Seville & Donella Meadows, Resource Sustainability in Commodity Systems: The Sawmill Industry in the Northern Forest:

Through the workshops and discussions about the forest economy, we also learned that even raising questions of growth and limits can trigger strong defensive routines …, both at the individual level and the organizational level, that make it difficult even to remain engaged in thinking about ecological limits and, therefore, taking any action. Managing these complex process challenges effectively was essential to using systems modeling to help people move towards well-reasoned action or inaction.

… We were presenting our base run to a group of mill executives and landowners from five different companies. During the walk-through of the base-run behavior of mill capacity (which begins to contract severely several decades in the future) we found that a few participants quickly dismissed that possibility, saying, ‘‘Sawmill capacity in this region will never shrink like that,’’ and aggressively pressing us on what factors we had included so that (we presume) they could uncover something missing or incorrect and dismiss the findings. Their body language and tone of voice led us to believe the participants were angry and emotionally charged.

… we came to identify a recurring set of defensive routines, that is, both emotionally laden reflexive responses to seeing the graphs of overshoot in which participants did not connect their critique to an underlying structural theory, or simply disengaged from thinking about the questions at hand. … When we encountered these reactions, we found ourselves torn between avoiding the conflict (the ‘‘flight’’ reaction; modifying our story to fit within their pre-existing assumptions, de-emphasizing the behavior of the model and switching to interview mode, talking about the systems methodology rather than implications of this particular model) or by pushing harder on our own viewpoint (the ‘‘fight’’ reaction; explaining why our assumptions are right, defending the logic behind our model). Neither of these responses was effective.

Back to the presentation to the industry group. During a break, after we had just survived the morning’s tensions and had struggled to avoid ‘‘fight or flight,’’ Dana [Meadows] walked up to us, smiling, and said, ‘‘Isn’t this going great?’’ ‘‘What?!?,’’ we thought.

‘‘The main purpose of our modeling,’’ she said ‘‘is to bring people to this moment—the moment of discomfort, of cognitive dissonance, where they can begin to see how current ways of thinking and their deeply held beliefs are not working anymore, how they are creating a future that they don’t want. The key as a modeler who triggers denial or apathy is to bring the group to this moment, and then just breathe. Hold us there for as long as possible. Don’t fight back. Don’t qualify your conclusions about what structures create what behaviors. State them clearly, and then just hold on.’’

Debt crisis in the European Minifigure Union

A clever visualization from a 9-year-old:


Click through to the original .pdf for the numbered legend.

This is isn’t quite a causal loop diagram; arrows indicate “where each entity would shift the burden of bailout costs,” but the network of relationships implies a lot of interesting dynamics.

Via 4D Pie Charts.

Climate Catastrophe

This is an interesting, simple model of global ice age dynamics, from:

“A Catastrophe Model of the Paleoclimate”, Douglas R MacAyeal, Journal of Glaciology, Vol 24 No 90, 1979

It illustrates a pitchfork bifurcation as a slice through a cusp catastrophe. It’s conceptually related to earlier models by Budyko and Weertmans that demonstrated hysteresis in temperature and ice sheet dynamics.

The model is used qualitatively in the paper. I’ve assigned units of measure and parameter values that reveal the behavior of the catastrophe, but there’s no guarantee that they are physically realistic.

The .vpm package includes several .cin (changes) files that reproduce interesting tests on the model. The model runs in PLE, but you may want to use the Model Reader to access the .cin files in SyntheSim.

Catastrophe.vpm