alt Hacker News> Do me a favor. Tell me why do you think it's a stretch (to assume that this is a job's issue).
I already have in my prior comment:
>> You could make the same argument that it’s a lack of working enough hours. I’m not saying it’s either, simply that hours worked is not proof alone that the problem is the lack of jobs.
In other words, your logic is:
Assume rent should be this amount -> subtract last paycheck to arrive at difference -> assume hourly wages should be this amount -> divide paycheck difference by hourly wage -> assume the result is the number of hours unavailable for work -> assume lack of hours is the cause for inability to live in a home
Note how many assumptions there are. Some questions that may disqualify any chain of this reasoning:
* How much is the median rent in places where a majority of this population lives? Is it potentially higher where they were living?
* Has the rent to income ratio changed at all, especially in their location?
* Were the majority of these individuals making minimum wage before? Could they have been working gigs for less or more?
* Are the lack of “hours” worked really due to lack of work and not another factor (e.g. ability to work, transportation, skill, etc.)?
* How much is this population spending on other costs that have taken precedence over living in a house? Has that changed at all?
With all that said, a stretch is not implausible. In reality, there is no smoking gun, only a myriad of contributing factors, different for each individual.
Okay I think I understand what happened. A couple posts ago you listed to an executive summary for CASPEH. I don't believe you've ever read the complete report, which is around 96 pages.
If you dig into the details, you'll actually find out that all of your assumptions are spoken about in terms of coming out with a reasonable amount of hours worth inside a California based upon the survey data from this research. The detailed report includes the following:
Median monthly household income in the six months before homelessness: $960 (all participants), $950 for non‑leaseholders, $1,400 for leaseholders. State the obvious if the weighted average is 960 and you have two groups, you can run the math to show that the non-lease holders were 98% of the sample.
Why we do want to think about Least Holders in reality is the renters where 98% of the problems exist. This is a clear application of the Pareto Principle, and so we should look at renters as the core of the homeless issue.
Median monthly housing cost: $200 for non‑leaseholders (0 for many), $700 for leaseholders. Of non-leaseholders, 43% were not paying any rent; among those who reported paying anything, the median monthly rent was $450.
In essence, if you look at the details you'll see where you're assuming are a lot of assumptions are actually somewhat addressed by the detailed report. Unfortunately, I'm going to suggest the detailed report is pretty shabby in terms of forcing somebody to dig out a lot of information which they should offer in some sort of a downloadable table for analysis.
Computationally, we can therefore figure out the minimal amount of hours these people must have been working based on the fact that they must have made at least minimum wage in the state of California.
There's not a lot of assumptions in this. It's based upon the detailed survey data and utilizing California minimal wage, which is where the survey was taken. The issue is digging into the details and computationally extracting information and assumptions that is not blinded by our own biases walking into something.
Again, there is excellent work out of University of Washington to suggest that higher housing costs lends itself toward greater rates of homelessness. That's not under debate here. The issue is from the survey data, it's very reasonable to do some basic computation to put some parameters around the data. It's not assumption, it's critical thinking.