Flicking through Google Reader, catching up, something caught my eye in George Monbiot’s latest:
Cost-benefit analysis is systematically rigged in favour of business. Take, for example, the decision-making process for transport infrastructure. The last government developed an appraisal method which almost guaranteed that new roads, railways and runways would be built, regardless of the damage they might do or the paltry benefits they might deliver(8). The method costs people’s time according to how much they earn, and uses this cost to create a value for the development. So, for example, it says the market price of an hour spent travelling in a taxi is £45, but the price of an hour spent travelling by bicycle is just £17, because cyclists tend to be poorer than taxi passengers(9).
I was vaguely aware that the government had complicated infrastructure cost-benefit formulae that included attempts to put value on people’s time, but I wasn’t aware that they had gone so far as to value the time of cyclists versus the time of taxi passengers. So I followed the reference #9. I’ve read some absurd documents in my time, but I wasn’t quite prepared for this.
When deciding whether a transport project — a road “improvement”, a high-speed railway, a bicycle path — is value for money, the Department for Transport consider the value of the time that users of the new infrastructure will save. When deciding how much time to give each phase of the traffic lights, or whether a bicycle lane gets priority over road, transport agencies will consider the value of the time of the competing users. In particular, DafT are interested in the value of the time that employers will save, because your time doesn’t matter, but the time you waste at work does.
How does DafT determine the value of an hour that an employee spends cycling for work, compared to one that the employee spends on a train or behind a steering wheel? All it needs to know is the average hourly wage of employees using each mode.
It uses the 1999-2001 National Travel Survey. Specifically, there is a dataset that counts all journeys made by mode and by income band, so we know whether the rich and the poor are more or less likely to drive, cycle, ride the bus, etc. If a mode is over-represented amongst the rich and under-represented amongst the poor then DafT consider the time of users of that mode to be more valuable.
If you can’t spot what’s wrong with this, and why I am cringing, I don’t know where to start.
Perhaps with the fact that we’re trying to derive the value of time “wasted” on travel-for-work from a dataset of travel modes that the rich and poor use outside of work? A dataset, indeed, that includes students and the unemployed.
Or the fact that we’re specifying the average value of an hour of time to two decimal places, despite the vast range of values being averaged.
Or with the idea of specifying the value of one Great British hour, despite the massive variation in income and modal share between cities and regions?
Or the assumption that time spent travelling is always time wasted (my favourite office is a good long off-peak and under-crowded train ride with a netbook and an android), and that time “saved” by faster journeys is converted into economically productive time? (Rather than, e.g., additional journeys.)
Maybe we should go back to the silly assumption that the work of the highest paid is the most economically important?
Or perhaps point out the critical fact that the demographics of users of a transport mode prior to investment do not necessarily reflect the demographics of users after investment, and that investment which enables modal-shift can “save” time too.
Or question what DafT are doing using inflation-adjusted figures derived from a decade-old version of the National Travel Survey when there is not one but eight more recent datasets? In 1999-2001, the railways, the buses, and cycling were all at their very lowest ebb. Were DafT to use the latest numbers, the value of an hour on a bicycle would be considerably higher.¹
The whole thing is absurd. According to DafT, if I take a taxi to meet my client, there is more value in their giving me a faster taxi ride than in enabling me to get the meeting even quicker by bicycle.
This is cargo cult mathematics and cargo cult economics. These numbers — given on the DafT website down to the exact pennies-per-hour — are pure fantasy. I am actually embarrassed for the department that they are not only using these numbers, but are proudly publicising the pioneering way that they have been derived. Were it not for the fact that transport policy and funding is such an unsexy topic, the press would be in gales of laughter at this nonsense.
And I would be gales in laughter were it not for the fact that, as I understand it, this crap is the foundation of a deeply regressive and damaging political programme. When modelling the impact and benefits of investment in a transport intervention, the Department factors in this hypothetical “value of time” of users.
That is, because a taxi passenger is more likely to be very highly paid than a cyclist, when all other variables are equal the department should invest in schemes that favour taxis ahead of schemes that favour cyclists. Aberdeen Cars is right: traffic light timings remind the economically-inactive cyclist that she does not matter. Stabiliser can find some answers in this policy.
It becomes a positive feedback loop. Invest in trains ahead of buses and the rich will use the trains while the poor ride the bus.
Imagine this happening at the Department for Health.² Should we invest in cutting the waiting lists for lung cancer surgery or prostate cancer surgery? Well, we value the time of the average prostate cancer patient more…
This is more than absurd. This is a fraud. This is a crude imitation of science and statistics being employed to disguise political decisions — to invest in transport for the rich and not for the poor — as pure objective economics.
1. Here is the 2009 NTS. The dataset we want is NTS0705. Do the maths properly if you like, but even a glance at the journeys-by-mode-by-income table and it’s instantly obvious that cycling has flipped from being highly under-represented in the richer categories in 2001 to being very slightly over-represented in 2009.
2. I mean, imagine it happening this blatantly. I would be surprised if there were not many many ways that state healthcare is subtly weighted in favour of the rich, whether designed and deliberate or not.
As usual, this is hastily bashed out heat-of-the-moment blog post, not a careful scholarly article. The thesis I am certain of, but the details are always open to amusing malapropisms and embarrassing subtle errors in calculations. If you are distracted by them, point them out and I will fix it.