Right after Sandy passed through the East Coast, I wrote:
One area that will be interesting to observe following this Hurricane is the insurance market, as insurers revisit the premia they charge for wind, water, and fire insurance. If they decide that the threat of hurricanes on the highly populated East Coast is higher in a changing or different climate, than the cost to reinsurers to, in turn, insure themselves through catastrophe bonds or other insurance-linked-securities will drive primary market prices higher. Higher premia for these things will send a signal that it is better (all other things equal) to live or develop elsewhere.
For anyone who, like me, is interested in how our global economy will adapt to climate change, there is a must read article in the new york times that addresses many of the questions I raised in a previous post.
First, risk premia have been rising.
Because of the quickening pace of disaster, those who want insurance or are required to buy it now face much higher costs in risky areas. Premiums for homeowners’ insurance (which covers wind damage) doubled in Florida between 2002 and 2007, tripling in some areas after the 2004-5 hurricane seasons, if insurance was available at all.
Many insurers have raised their premiums because of increased risk estimates, higher cost of reinsurance (insurers transfer part of their risk to international reinsurers), the requirement by regulators and rating agencies that insurers hold more capital in order to reduce the likelihood of insolvency, and the need to provide shareholders with an attractive return.
I speculated that this might be the case, but it appears that rising prices are already occurring. This is the most objective indication that we have that climate change is already underway.
Insurance market prices are effectively representations of distributions. The price of car insurance is a representation of the likelihood that you’ll get into an accident. The distribution for 18-25 year-olds is different than for 30-40 year-olds, and the prices are different. Since our climate is a distribution, insurance price changes for climate-dependent phenomena should be an indicator that the distribution has changed (or, in other words, that the climate has changed).
Assuming that there is a competitive market for climate-dependent insurance in Florida, the evidence cited above is unbiased evidence that climate change has occurred and is occurring. Setting a price too high will mean lost business for the company, and judging by how many people have dropped their wind coverage, this consequence has been suffered by the insurance companies.
Why is this important? In my opinion, it’s that the market is telling us that climate change is happening. Although the scientific evidence is staggering, it has never been quite enough to convince many. Market evidence is rarely cited, and I think it deserves more attention.
A second point that the authors make is relevant to adaptation: subsidized public flood insurance (i.e., flood insurance that not only is administered publicly, but is also run at below break-even prices), and homeowners dropping other forms of climate-dependent insurance (flood, wind, etc.) is skewing incentives and is promoting increased development and the perpetuation of development in areas that are relatively more exposed to climate risks. This is a problem. The market signals of climate change should be felt and increased prices borne by those who are ultimately exposed to the risks. Why? People need the incentive to move, and as the world changes, our geography and our economy need to change along with it (and, actually, ahead of the changes if possible).
Nate Silver’s forecast would have done better had he gotten a few wrong.
I really like 538 and they have done a great job. I have one minor quibble with how they represent their forecasts and their results. 538’s forecasts are not binary predictions, they are probability estimates. Meaning, that of all the races he projects to be at 70% probability, the winner should be the leading candidate 70% of the time – not 100% of the time. But 538 and many others are saying that he got 50/50 states right, and that confuses the picture a lot. What does it mean to call Florida “correctly” when the model gave Obama a 50.1 chance of winning?
The example I used when explaining it to some friends was that if a weatherman, for 10 straight days, said there was a 55% chance of rain, and it rained every day – you could say that the weatherman correctly predicted the weather every day, but the correct interpretation is that his estimates were too low.
The code below simply takes 538’s estimates right before the election, with the probability figure being that which 538 assigned to the eventual winner. The code simply assumes that 538’s probability estimates were exactly correct, and shows the distribution of states that would be in error in a run of 10,000 simulations. As you can see, if 538’s estimates were exactly correct, it’s significantly more likely that he would have gotten 1, 2, or 3 states wrong than none at all.
This is not to say that his estimates weren’t spot on – there is a good chance they were – I only want to point out that there is a little bit more going on than saying that 538 got 50/50 states “right”.
One of the great joys of this election for nerds like me is following the debate about how to track the election. There are a few competing models available to look through, such as the Princeton Election Consortium, Votamatic, FiveThirtyEight – and even UnSkewed Polls.
Each of these modelers has taken a different approach to a difficult problem – namely how to estimate the outcome of an election using available data. There are tradeoffs around each approach and there’s a fairly lively debate about which approach is most enlightening. I am excited to see how they fare tomorrow.
On the other hand, you have a bunch of contributors making proclamations about what will happen, seemingly untethered to the evidence available to them. They only show the results of their internal model (should there be one), not the model itself. Obviously many of them are motivated by something other than the “search for truth”, but even if they were, their contribution is generally useless. Here’s why.
First, a model can be useful even if its predictions are wrong. Especially when it is predicting things that are compositional in nature (i.e. not single events in and of themselves, but composed of many events). Second, a model can make a correct prediction even when the model itself is wrong.
Most importantly, when two projections disagree, it’s impossible to work out why they disagree unless we know how the models that produced them work in the first place.
This last point is the most important. Without a model, you are treating projection like opinion.
So: come with a model.
In my last post I mentioned that climate change is defined as a change in a distribution. I thought it might help to see what that looks like. The data that you see here is observed data, not a projection or a model.
What it shows is the distribution of summer temperatures in North Americarelative to what is normal for that time of year. The horizontal units are in terms of standard deviations, instead of degrees. So a very hot day (i.e., 10 degrees greater than normal) would be plotted as +2 standard deviations (obviously I’m making up the numbers here for effect).
The gist is that climate has already changed (the probability of the shift you see being a product of random variation is vanishingly small). If you believe that humans are the cause of this shift, then you should believe it will continue to change.
(borrowed from the great James Hansen)