Luckily smart people have resources and a mandate to study each of those questions at scale. You might be interested in the work that the US Bureau of Labor Statistics (https://www.bls.gov/) does on the economic side of labor conditions, and that the Census department’s American Community Survey (https://www.census.gov/programs-surveys/acs) does on the demographic and social side. Both of these groups study questions like those you asked in ways designed to be broadly comparable over time.
The Housing and Urban Development department calculates fair market rents at the county level to support their rent assistance programs (e.g. [0]), although that starts to get at part of the complexity: for your metric do you care about the economic cost or the amount families actually have to pay after assistance?
On the NGO side, groups like the NBER (https://www.nber.org/) disseminate more exotic socioeconomic studies, and there are others.
And of course you can find a mountain of data at data.gov, the federal portal for such things.
I think the harder part (and the part the policy community specializes in) is grappling with the nuances of those kinds of numbers. What do such high-level observations actually mean in something as complex as a continent-wide collection of 10^9 people, and how much human messiness polluted their measurement?
Yes, the data exists, but it’s so nuanced and complex that you can interpret it to fit almost any narrative.
Take California, for example: asking rents have increased by 26% over the last two years. But this figure reflects only asking rents, not what people actually pay—especially in areas with rent control.
If you’re looking to upgrade to a bigger place (e.g., moving in with a partner or preparing for a baby), it might feel like the economy is in shambles. On the other hand, if you’re staying put in a rent-controlled apartment, things might seem manageable.
The data is there—it’s the context and perspective that shape the story.
The Housing and Urban Development department calculates fair market rents at the county level to support their rent assistance programs (e.g. [0]), although that starts to get at part of the complexity: for your metric do you care about the economic cost or the amount families actually have to pay after assistance?
On the NGO side, groups like the NBER (https://www.nber.org/) disseminate more exotic socioeconomic studies, and there are others.
And of course you can find a mountain of data at data.gov, the federal portal for such things.
I think the harder part (and the part the policy community specializes in) is grappling with the nuances of those kinds of numbers. What do such high-level observations actually mean in something as complex as a continent-wide collection of 10^9 people, and how much human messiness polluted their measurement?
[0] https://www.huduser.gov/portal/datasets/fmr.html