The Queensland Government’s troubled Cross River Rail project is a good example of a multi-billion-dollar Megaproject at risk of cost blowouts and falling short of projected benefits, as I posted on last week (Cross River Rail scrutinised by Grattan and AiP) following the publication of a new report by the Grattan Institute. On Monday, I recorded a podcast interview with Marion Terrill, Transport & Cities Program Director at Grattan, who was lead author of The rise of megaprojects: counting the costs. The interview is now available as the latest episode of my Economics Explored podcast via iTunes, Spotify, etc. and you can even listen via the player below in this post (on the QEW website rather than in the email version of post).

Issues of discussion include:

• Why megaproject costs blow out (5:20)
• Optimism bias (12:00)
• What Nassim Nicholas Taleb said about megaprojects in Antifragile (17:15)
• How we can improve infrastructure project selection and management (22:50)

Here’s a sample of some of Marion’s great insights (from 12:45):

…there’s a couple of different stages at which optimism bias can kick in. So when a politician or an official makes the first cost announcement or cost promise, they probably don’t know all the possible complexities, but the margin that they might build in for that is generally nothing like enough. We see that time and time again, that the initial cost announcement is sort of assuming everything’s perfectly simple and goes perfectly smoothly. And the cost estimators are also very hamstrung, I think, in their ability to do good cost estimates, because we don’t collect data on finished projects in any useful format.

And what that means is, for example, in the report, one thing we’ve looked at is business cases that are in the public domain, we looked at the P-50, or the median cost system as compared to the P-90, or the worst case cost estimate if you like. And the typical differences are that a P-90 estimate is about 7% higher than a P-50 estimate. So that is not much more money between what you think is going to happen and the worst-case option. And when we looked in the data for 19 years, what we found is that should be more like 49% higher. That’s massively higher. So, you can see the lack of data supports a process of cost estimation, that makes insufficient provision for what history has told us is quite likely to happen.

And then there’s a final stage to this, which is when this goes to market. A lot of companies have been quite vocal this year and last year about them not making money. When I say to people, well, why is your company bidding for these jobs when you can’t make money? The feeling that comes out is essentially optimism bias. They want the job and they think, oh, we’ll find a way. They’re kind of inclined to see the advantages and to minimise all the things that could go wrong. And that’s perhaps what it takes to get the job. But it’s given rise to a lot of angst in industry.

I hope you enjoy our conversation.

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