I’d like to see us move into a new economic analysis model for economic development. The old days of comparing unemployment rates, average income level, etc. should be turfed in favour of new comparative measurements that matter.
In my opinion, for example, we should swap out unemployment/unemployment rate data with in-migration data. We should be far more deliberate about measuring a community’s ability to attract people (including immigrants) as that is the new labour force metric that matters most.
A second issue is how we measure income. High income areas brag about high average incomes and lower average income areas brag about a lower cost of living. What really matters these days, in the context of point one about in-migration, is the amount of money left over after you pay out all the stuff you would have to in both locations (the high and the low income areas).
Disposable income is gross income minus income tax on that income and discretionary income is income after subtracting taxes and normal expenses (such as rent or mortgage, food, car payments, and insurance) to maintain a certain standard of living. It is the amount of an individual’s income available for spending after the essentials (such as food, clothing, and shelter) have been taken care of. These two income measurements are far more relevant (particularly the second).
When we look at that data, we see that New Brunswick cities don’t fair all that well when compared to larger urban areas. The average household in Moncton has 31% discretionary income than the average household in Toronto. So, if you are looking at money left over to spend on stuff we have discretion over (big screen TVs, travel, etc.), the average household is stil far better off in Toronto.
However, the good news is that when people move from a place like Toronto to a place like Moncton they are not ‘average’ (usually). Most people fall on a continuum and mostly people that are relocating from Toronto (etc.) to Moncton are not at the lower end of the income scale. So, if you are moving to Moncton for a job in computer programming, for example, you have another calculation to make. Namely, is the income variation offset by the lower cost of living (i.e. do I have more money in my pocket for discretionary stuff). And that looks better for Moncton.
Using ERI data, we can see that while the average programmer in Moncton earns $8,000 less than the average in Toronto, he/she has a far lower cost of living. What this tells us is that if you want the same lifestyle in Toronto as Moncton, you are better off at the Moncton salary level (in Moncton) than the Toronto salary level (in Toronto).
I’ll look at more of these metrics later on but they include things like the “disposible time” index (less commuting), crime rates, etc. Essentially, I think we need to get our comparative analysis of communities right down to where people live (doctors per 10,000 population, proximity to schools, spousal employment opportunities index, etc.).
Yes, you will say, that these are not metrics targeted at the business community, they are targeted at individuals. But as I have said many times before the new battleground is being waged on HR. What community can attract people. This is far more important these days than the old line comparative analysis (average rental rates, etc.).
The Atlantic Cancer Research Institute tells me they have no problem recruiting researchers from around the world to Moncton. That’s the new battleground, folks.
Maybe someone will even pay me to develop this set of indices for their community (hint, hint) and be early out of the gate.