The recent Talia Jane story just made me realize we have a possible inconsistently in policy. To get you up to speed, Jane took a low-wage job in the San Francisco Bay Area, hoping to work her way up to her passion of being a social media manager for a major company. But because of rental prices, she paid 85% of per post-tax pay just for rent (!), complained about her employer paying so little, and then was fired.
But as for the inconsistency:
Illegal: paying someone below $X/hour.
Legal: paying someone ($X + $Y)/hour (Y positive) to work in a place where their discretionary income would place them in extreme poverty (e.g. 85% of post-tax on rent).
And yes, that's just an (arguably trivial) corollary of "minimum wage (and tax brackets for that matter) is not automatically cost-of-living-adjusted". But if the goal is to stop people from being taken advantage of with low job offers that hold them in poverty, that seems like a pretty big loophole.
And it's not just that -- let's say someone moves farther out to be able to afford to live there. Then they're traveling an extra N hours just to make each shift which should rightly count against their effective hourly wage.
So, food for thought: what are we really trying to optimize for here? What would the law have to look like to not just avoid these loopholes, but "carve reality at the joints" such that it's fundamentally impossible to scalably circumvent such a law?
If you keep raising the minimum wage for a locality, and people keep commuting greater distances to get that income, what have you accomplished?
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I can't say, but I think there is a way at least to take a stab at the question of whether cost of living (or at least cost of housing) can be plotted as a function of latitude and longitude:
Take a given latitude and longitude as givens (parameters, and this numbered list is pseudocode for a "function").
Draw a circle of radius (say) 20 miles (32 km, or whatever you declare by fiat to be "commuting distance") around that point.
Make a list of all single-unit home sales within that circle during (say) the last 12 months.
Determine (say) the 10th percentile home price from that list.
Return that number as the value of `expectedHousingPrice(latitude, longitude)'
Maybe do this procedure on (say) ten thousand randomly selected locations and interpolate or Delaunay triangulate or whatever to fill in the rest of the map.
Every instance of (say) in parentheses is an arbitrary parameter of the system that can be chosen according to social norms concerning what is reasonable, etc. Just be sure, for the sake of objectivity, to keep them consistent once chosen. It may be that what's a fair figure for commuting distance might be shorter in particularly congested areas, but I'm figuring the more congested areas are also more pedestrian/bike/transit friendly, on average, so maybe it's a wash. It's also the case that there's more to location-dependent cost of living than housing, so maybe there should also be a representative market basket of groceries and other goodies, or perhaps a marketminimalistic basket but not to the point of being health-compromising. This would make the calculation much more complicated than a straight-up cost-of-housing calculation, but I believe there are viable ways to automate much of the necessary data collection.
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