How many Fixed Income trading venues are there?  A simple question came up recently in a conversation – how many new Fixed Income t...
Friday, 3 April 2020
Policy frameworks for pandemics - part 1 - frameworks...
Every day we hear the news about the number of cases of people dying from Coronavirus. These numbers are shocking and every death is a tragedy. But not all deaths are equal.
Let's look at that statement by way of a simple thought experiment involving a comparison:
Twenty people die in a single event - a coach crash. A tragedy. Let's replay that:
Coach A crashes, twenty people die. All twenty of the victims are 17 year old school pupils, a 50:50 gender mix of men and women.
Coach B crashes, twenty people die. All twenty of the victims are 87 year old pensioners, a 50:50 gender mix of men and women.
You can stop one crash and only one crash. Which do you prevent? You don't know anyone on either coach and this is all the information you have.
In some anecdotal research, I have found that a non-representative, self-selected, sample of respondents have 100% said they would save coach A. Comments have involved that they are younger, have more life to live, that the older people have had a reasonable innings and other answers.
The point of a fair innings is one to which I will return.
Now, what is the metric we are implicitly using here - it's something along the lines of the "change in the aggregate number of years of life lost". So, in a modern country the life expectancy from time of birth, adjusted for survivorship bias minus the number of years of life lived.
In other words the 87 year olds have reached their life expectancy, the 17 year olds have 70 years to go.
This is moving in the direction of a useful metric - the "Quality Adjusted Life Year" or QALY. The QALY is somewhat controversial, but like any model it's flawed, the true value is found if it's useful, not if it's 100% right.
The usage of the QALY is relevant here as we can estimate the QALYs lost for both coach A and coach B. Let's say the total lost QALYs for A is 70x20 = 1400. For coach B it's 1x20 = 20. Therefore, using this simple metric, the outcome that minimises the QALYs lost is to save coach A.
Note that this has NOT adjusted for quality. Consider the quality aspect of life. This be done simply by those of a certain age by looking back at one's own life and considering the quality of health at age 20, 30, 40, 50 and so on. Ignore quality of life in economic terms (if you were dirt poor at 20 but were a billionaire by age 30 there should be a substantial uplift in quality of life between 20 and 30 but not necessarily of quality of health).
Adjusting the analysis for quality of life may then look like
Total lost QALYs for A
70 years x 20 people x 1 = 1400
Total lost QALYs for B
1 year x 20 people x 0.5 = 10
As a simple measure we index the quality of life for one group to 1 and show a relative quality for other groups. So, in this analysis the quality of life for the group of 87 year olds is half that of the 17 year olds.
Here we have covered a framework which can assist with decision making from the perspective of evaluating the benefits of competing claims to finite resources under a model of full information.
How is this relevant to covid-19?
How does a government decide what changes to make during times of uncertainty? How are the costs evaluated? How are the benefits evaluated? What model exists to evaluate the incidence of costs and the incidence of benefits? Is current policy rational?
Example we will consider:
Why are bicycle shops allowed to stay open in the UK and it appears they are not allowed to open in New South Wales, Australia?
The intention, at time of writing, is to extend this into a series of pieces:
The regular reader may note that this really is rather off-topic. And from the perspective of a blog that has traditionally looked at topics such as Fixed Income Trading: New venues ( How many Fixed Income trading venues are there? ) , Product Management and Fintech and What's the difference between an EMS and an OMS? this may seem a little unusual. However, your author was trained as an Economist and took a keen interest in Health Economics and the interface between rationality, efficiency and morality. As such, with the usual topics somewhat becalmed I decided to repurpose this blog for an intellectual diversion. I can assure the reader that normal service will be resumed sooner or later...