Input-output models are fairly standard economic models used by government and industry predict the impact of an economic event on a region, such as the addition or loss of a major industry. I'm not an economist so I'm not the one to ask about the details of these things, but I can tell that as with all economic models, a certain set of assumptions are made about the nature of the economy and variables are added.
Socio-economic impact statements as part of the overall environmental impact statement (EIS) put forward by project proponents use I-O models to come up with their job and economic benefit numbers before later processes like the Northern Gateway JRP hearings convene. I-O models were used in the Mackenzie Gas Project and the assorted diamond mine impact assessment projects. Lee explains the first part of the problem with Enbridge's numbers with reference to how I-O models work:
So how do we get from an average of 1,850 workers for three-years to 63,000 person-years of employment (construction only)? To answer this question we have to understand input-output models, which use GDP data to proxy the flow of income through the economy. Modellers “shock” the I-O model to estimate an increase in economic activity. The important pieces are (a) that direct expenditures on the pipeline also lead to employment in upstream industries that provide the goods and services that are inputs to construction and operations (called “indirect employment”); and (b) income to workers, whether direct or indirect, support jobs in the local economy on food, housing, cars, entertainment and so forth (called “induced employment”).
The second part is where the Sherlock Holmesing comes into play and pertains to the question of indirect jobs and how that relates to the 63 000 person years figure. Lee contacted Statistics Canada and writes the following:
So I am scratching my head a bit, in particular as it relates to direct jobs and how all of those reported indirect jobs could include such large numbers of in far-flung industry categories. There is some kind of flaw in how this is being modelled but without deeper information I cannot get at it. It could be that Oil and Gas Construction Industry [code 2300D0] in the I-O Model is broader than pipeline building (in the NAICS, 23712, Oil and Gas Pipeline and Related Structures Construction).To translate, every job, yours, mine, and your neighbour's falls under a job classification code. NAICS is the North American Industry Classification System, a coding system for classifying jobs that is standardised across Canada, the US, and Mexico. It isn't as simple as it looks as a person's job title doesn't necessarily correlate with the most obvious industry they're involved in. For example, a plumber working at an airport might be coded as working in air transportation, not in something more intuitive like home or commercial construction.
So when Lee speculates that coding for "the Oil and Gas Industry in the I-O model is broader than pipeline building" he is suggesting that the I-O model Enbridge used to come up with their person-year numbers may have used a much more generous code than it should have. It may have included estimates of indirect employment that really would have nothing do to with the pipeline! I mean, does Oil and Gas Construction Industry include someone welding oil derricks in rural Alberta who would have been employed regardless of what Enbridge wanted to do?
I think there's also something to be said about the difficulty in obtaining from a government department work it did for a private firm regarding something in the clear public interest undergoing a massive public consultation. It appears to put Statscan in the unenviable position of protecting Enbridge from potential scrutiny of the support for its claims. It will be telling if Enbridge obfuscates the release of their I-O models and what went into them.