Institutions, motivations, and assumptions in economic analysis
A belated Merry Christmas, everyone! Yes, it’s possible that most of Grist’s readers are not Christian (I’m not, for one). But December 25 is the day celebrated as Christmas by much of the world; it’s a declared holiday in many countries and cultures, whether or not we buy into its religious significance. It is, therefore, Christmas Day in reality, even among peoples of the earth who have never so much as heard of Christianity.
Christmas, in other words, is an Institutional Reality.
There’s nothing scientific about it — in ecological or cosmological terms, it’s just another revolution of the planet. But an economic model that assumes Christmas Day is business as usual across the globe is simply not being realistic.
I don’t know of any mathematical model of the global economy that takes into consideration holidays in different countries and cultures, and incorporates those data into sales projections, output expectations, or other such forecasts. There was some effort by those opposing the creation of a Martin Luther King holiday to demonstrate the economic losses of a new holiday — and there were reasonable arguments that another vacation day resulted in lost GDP.
But the loss of GDP didn’t prevent the creation of a day honoring an icon of the U.S. civil rights movement. Why not? It’s interesting. Assuming most U.S. workers work 5 days per week, that’s 260 days a year. Allow for (a measly) two week’s vacation and we’re down to 250 days of work, excluding holidays. Loss of one of those days means 1/250th of our productivity is lost — a full 0.4% of GDP!
Doesn’t sound like much to you? Hey, many opponents of efforts to mitigate climate change are up in arms over the fact that major investments in mitigation and associated regulations might (note: might, not "will") cost as much as 0.4 to 0.5% of GDP! If we can declare a holiday and lose that much GDP to honor one man, surely we can rationally allocate as much GDP growth loss to an effort to avoid a possibly disastrous change in the global climate and massive population migrations (and probably wars) associated with rising seas due to shrinking ice caps.
Ah, but here we come up against Motivational Realities.
Change is difficult. Declaring a holiday is easy, and adjusting to it is a minor accounting problem: pay overtime for those who work, accept some productivity loss for those who do not, otherwise not much else has to change. Altering how we do business, how we consume, and how we live our lives requires far greater motivation — and that has not yet been stimulated, at least not in most of the U.S. as of late 2008.
Back in the 1970s, the U.S. consciously saved energy. We were motivated by an oil embargo (a real shortage), federal regulations, and a president who made a public display and a virtue of wearing sweaters indoors. The context — shortages and leadership — provided motivation. What we have just learned is that price swings, even unexpected and historically unprecedented price increases, do not motivate as well. (There is some evidence that U.S. demand for large SUVs softened substantially when the price of oil ran up to $140/barrel and gas cost over $4.00/gallon, but there is also evidence that the demand started to climb a bit once again as the price fell — before it became almost impossible to get a car loan.)
This brings us around to Assumptions and Reality.
We are blessed/cursed today with a pair of seemingly unrelated news stories, both belying some of the assumptions underlying economic models.
First, we’ve just had the nation’s largest spill of coal ash, a dubious present from the Tennessee Valley Authority and reminder of the assumptions behind "clean coal." There is argument over how toxic the coal ash is in its relatively solid form, how badly it has polluted drinking water sources, for how many people. But what is not subject to argument is a physical relationship: if we require that the smoke from burning does not contain some products of combustion, then those substances will end up as solids or liquids instead. This is a reality, not an assumption.
When we model the costs and benefits of emissions controls and forget about those residual solids and liquids, we are assuming that they pose no costs. When we decided not to regulate the disposal of those leftovers because doing so would cost $5 billion — according to an Edison Electric Institute report in 2000 — we assumed the benefits were less than the costs. When we translate that $5 billion into an estimated increase in the cost of electricity and ask consumers if they are willing to pay that for the regulation, we are asking the people surveyed to assume that, if the wastes are not regulated, there will never be costs that could drive up the cost of power — or undermine their quality of life.
The reality is that we routinely make such assumptions. Good economic analysis requires that all calculations about future costs of actions:
- also calculate the costs of inaction (doing nothing is always an option, but it has costs);
- explicitly state their assumptions about the probability that certain possibly undesirable events might occur and calculate the costs of them (since excluding them is really a hidden assumption that the events will never occur, a pretty extreme position); and,
- refrain from using consumer preference or other "willingness to pay" survey results when those studies do not clearly explain the risks and associated costs of unexpected events of the seemingly cost-free alternatives presented.
But, you ask, what about future benefits? We’ll come to that, but most efforts to discourage taking action or intervening focuses on costs and forgets about benefits altogether.
Our second example comes from the advance of science, in this case the capacity to predict climate change and its effects. The U.S. Geological Survey has just released a report that predicts a 4.0 foot rise in sea levels by 2100, rather than the previously expected 1.5 foot rise. The report contains an important lesson about the uncertainty of science — a finding that the previously expected sea rise was off by more than 100 percent. We were modeling costs and benefits of action based on sea-level rise only 37.5% of what we now expect it to be!
We severely underestimated the impact. Many more houses and other property will be lost, more ports will have to be totally rebuilt, and the likelihood that ports will have to be relocated and require much more inland infrastructure investment (rail and roads) is much higher.
But a sea-level rise more than 2.5 times what we had anticipated would have off-shore effects as well: vastly greater climate-driven population displacements in the Third World and resulting increases in the likelihood of wars. Should we now include in our cost models the likelihood that land loss in India would result in wars that might escalate into nuclear conflicts, given that both India and Pakistan have such weapons? (Actually, we probably should have done so even for the 1.5 foot sea rise, but now odds are even greater that we need to consider such disasters.)
The real lesson here is that scientific findings are not certainties and cannot be taken as fully reliable predictions. We need to allow for the risk of error — and unavoidable uncertainties — in the science on which our cost and benefit calculations are made. This is, of course, the rationale for the precautionary principle. But that is a principle governing appropriate courses of action, and we are (or at least I was) looking at principles for good economic analysis.
The economists’ equivalent of the precautionary principle might be called the Fat Tail Principle. It is a reference to what is called a "normal curve" of probability distributions (you’ve seen it; it looks like a broad-brimmed hat) and the possibility that the curve does not drop to zero at either end, but has a fat tail (the brim might extend forever). That is a visual representation of the fact that we do not know how extreme the effects of certain scientific or human events might be.
Economists looking at the uncertainty represented by the fat tail — an accurate measure of the reality of scientific exploration — say that we should not use the logic of discounting future costs and benefits when considering options that are low probability but possibly catastrophic. Temporarily, then, until we explore the principles of discounting further, let’s add a fourth item to the requirements for good economic analysis:
4. As an application of the precautionary principle, the future should not be discounted.