Willingly suggests there's a choice. When you have livestock indoors, there's no choice. :haha:

Shipping urban teens to the country probably wouldn't go well, I admit. (although I've known a few who benefited greatly). Maybe get them working when they're young? Part of educational requirements, maybe ... spend time on a family farm learning where food comes from.

Ch-ch-ch-ch-Changes...

Hmm.

Instead of a snapshot in time, if I look at my life as a whole, I'm 46, and I spent 21 years living with my parents, and my kids have spent 15 years so far living with me, so that's 36 years in a household with two parents and kids. I spent 1 year as a man living alone, and 5 years in non-family households, and 5 years as a married couple without kids.

I predict that by the time I die at age 80, it will be about 47 years married with children, 1 year living alone (I hope), 5 years non-family household, and 27 years married with no kids.

YMMV.

YMMV for sure, "There are eight million stories in the naked city..."
All of them, would fit nicely into that chart, but explaining the trends would be a challenge.

I can think of a number of reasons for "Married with children" to be cut in half over 40 years. Kids grow up, death/divorce, ageing population, etc. But which reason is primary, has the biggest effect, would be much tougher.

The 120% increase in "Men living alone", is obviously due to an increase in intelligence. :p:

Heh... men living alone has gone up faster than women living alone, because the woman usually gets custody.

What I find most interesting is the threefold increase in "non-family" households. It makes sense that "other family" households go up alongside the rise in divorce and blended stepfamilies. But "non-family" means roommates, which most adults only do if they can't afford to live alone. Then again, it was going up even in the boom years, so maybe the economy doesn't have much to do with it after all.

I think non family is also unmarried couples.

Unmarried with children, too.

I think if there are children, then you become a "family" so the household would be "other family households" if the parents are unmarried.

The graph is somewhat interesting, a bunch of squiggles. But the conclusions drawn by the study are really something else.

In a nutshell, two decades ago, welfare payments were switched from checks to debit cards. No checks meant that there were no checks to cash, so there were fewer people walking around with wads of cash, so robberies went down. This study says that a whopping 10% reduction in crime nationally is directly attributable to the switch from welfare checks to welfare debit cards.

Previous explanations thrown out there for the crime reduction were the elimination of lead from gasoline and paint, and an increase in police and prison spending. But those things didn't correspond anywhere near as closely to the reductions in crime as the timing of this welfare payment switch does.

The only one that jumps out to me as being questionable is Larceny. It also went down, but it had been going down steeply already and its rate of decent seemed to be thwarted by the welfare payment switch.
Attachment 47144

What boom years?

The cultural switch in the ghetto, changing from crack to weed. I talked about this with a guy who was in cop school, and he said it was actually in the book-learning he was doing.

Not as many things wrong with this one as the last one, but:

http://a5.img.talkingpointsmemo.com/...fkeytbwnz0.jpg

1) Again, the misleading Y-axis

2) No mention that the goal had been adjusted to 6 million months ago, after the failed launch.

Both technically correct, but greatly misleading.

They later corrected the first one.

Any chance of an explanation for furriners, please?

The numbers are the numbers of people enrolling in healthcare through the exchanges, (but not moving onto Medicare). The expectation before launch was to have 7 million by now. When the launch was so bad, they revised the expectation down to 6 million.

(As an aside, there were more procrastinators than expected, and the number may reach 7 million after all, but that information came in after this chart was produced.)

The Y-axis issue is putting up a bar graph with an unlabeled vertical axis, and making the difference in size between the bars an arbitrary amount. Someone looking at the graph will get the impression that the axis starts at zero, and (in the example above) the actual enrollees are about a third of the goal, since the one bar is about a third of the size of the other. If you look at the numbers, and do some calculation, you can figure out the actual relationship between the numbers, but you could have done that without the graph. A graph is supposed to communicate an idea visually, and provide the data as reference, not communicate an incorrect impression that can be corrected by interpreting the numbers yourself.