Even with a broken monitor and a sprained finger, I can't let that type of foolishness go

You might think I am joking, but the "Happiest States" list annoyed me so much that I had to go and rail a line of GABA before I could deal with it.

I don't know if the original authors are as dumb as the media reports of it, but something tells me that they were not as critical of their own research as they should be. As for the media...

One problem with lists like these is that they are ranked cardinally, by order. Which is how sports teams are ranked. But, unlike making the playoffs, being ranked statistically is not so clean cut. When I get my hands on the actual data, I will plot that too, but as it is, I am just plotting the rankings, which might be very deceptive. There is a good chance that the separation on this list is by a small amount of degrees. This also comes up with lifespan measurements: a country may be ranked 30 places behind another country because people live three months shorter.

Anyway! I plotted the numbers against suicide rate. Suicide rate, is, of course, a bad way to measure people's general happiness. Suicide numbers are thankfully low, so even in a place where 99.9 of the people are very happy, a small subset might be suicidal. However, since suicide also probably correlates pretty well with suicide attempts, and major and minor depression, any measure of happiness that didn't somehow relate to it might be flawed.

So, I worked up this diagram:
On this diagram, the further to the left a state is, the more happy it is. The further up, the higher the suicide rate is. With that in mind, something should be noted: first, there is not much general trend at all. Second, what trend there is is towards the "unhappy" states having the lowest suicide rates. In fact, New York, the unhappiest state, has the lowest suicide rate. A big outlier like that should probably be a pretty big hint that there is something wrong with the data.

Of course, there are not many people reading this blog, so I am sure that BIG FANCY EAST COAST LIBERAL STATES=UNHAPPY BUT GOOD RELIGIOUS TRADITIONAL SOUTHERNERS=HAPPY will embed itself in at least some people's popular wisdom.

Oh, and the suicide statistics came from here:
http://www.scrippsnews.com/node/43026

I certainly have been slacking off, haven't I?

I have to admit that I have been slacking off.
One thing is, after doing so much research, I had lots of scatterplots, but I wanted to make them SPECIAL. No reason to just throw stuff up here.
Also, I sprained my finger
And, my monitor is broken.
But I will be back. Oh yes, I will be back, mostly because of this:

http://www.livescience.com/culture/091217-happy-state-measures.html

Which annoys me no end. In 2009, people ACTUALLY believe this? My god.
Of course, things like this will get repeated endlessly. And. The original data isn't even available. And. About three people read my blog of scatterplots. But. I must continue to FIGHT.

U-3 and U-6, about what you would expect

As you might know, the "unemployment" numbers that are usually published are only one of the ways that unemployment is measured. It is technically called "U3", and covers people who are conventionally considered "unemployed". However, there are other numbers, ranging from U1 (Which is people who have not worked a single hour for pay in the last x months) to U6 (people who are underemployed).
I wondered how the different rates would match up, and thanks to the labor department I was able to find out:
As could be expected, the U-3 and the U-6 rates are very close to each other. Which makes sense, since U-6 by definition includes U-3. Since this diagram doesn't immediately tell us a lot, I took the U3/U6 ratio and plotted it against U3.
This diagram has some minimal good news: as a vague, general trend, as U3 goes up, the relative increase in U6 goes down. If the trend was flat, or even upwards, the underemployment ratio in Michigan could be over one-quarter.
But the trend isn't super important: as it is, it seems that within the limits we usually have, U3 and U6 go up pretty much up in tandem, regardless of what the number is.

Slight detour, Part II: the same thing, but different

So after yesterday's look at the urban/rural divide and college education, I did the obvious and did the same scatterplot for high school.
That is kind of a messy scatterplot! And not just because I made it at 2 AM! Within the expected confines, there seems to be a lot of variation in these numbers, more so than there is with college graduation rates. There is also some regional clumping, as could also be expected.
But, perhaps due to the lateness of the hour...I am not coming up with any magic bullets for this data.

A slight detour: rural and urban education

I became so besotted with the ERS and their gigantic stream of data, that I had to go slightly off the topic of farms, to find out about educational statistics, as they pertain to urban and rural America.
Did you know? "urban" and "rural" are hard statistics to operationalize. Which the ERS admits, they have an entire complex county-coding system. I can say, having lived in Montana and Vermont, both states that are considered "rural", that the word can mean very different things in different places.
But with those caveats aside, lets look at our scatterplot:As expected, urban areas have a higher percentage of people with bachelor's degrees (except in Massachusettes, where the rural population is almost non-existent, probably being the population of one resort community or something).
Also, Wyoming has perfect parity. The differences vary, from Virginia, with 5 urban degree holders for every 2 rural, up to states where the difference is almost unnoticable.
As could be expected, there seems to be some regional differences. New England and a chunk of Mountain/Plains states (Montana, Wyoming, Idaho, South Dakota) seem to have the smallest gaps, while the biggest gaps seem to be in Appalachia.

Part II- hobby farms versus hobby farmers

I was thinking of a different way to phrase the question of how and why people farm...and a way that would work with the data presented by the USDA ERS. As I was drifting off to sleep, it occurred to me that it was pretty easy to operationalize "hobby farms" and "hobby farmers" with the data presented. One of the statistics is farms underneath 10,000 a year in SALES (that is gross, not net. 10K a year gross isn't a lot of money) and another is farmers who, as the saying goes "have kept their day job". The numbers of farms that produce less than a living amount, and the number of people who have to seek their living elsewhere, should more or less add up.
But you have been reading for a while, so lets see what really happens...


The basic view is, as is usually the case, more or less correct, but the trend doesn't jump out at me. Also, many of the states add up to much more than 100, meaning that there are lots of people whose primary income comes from a farm making less than 10K a year gross. I guess that would make these people more subsistence farmers than hobby farmers. At least in some cases; although it is hard to tell from the data presented.
Arizona is especially curious: I at first assumed that it was probably due to some loophole in zoning or tax laws in that state, but I later realized it might have to do with Native American subsistence farmers. Or, the data could have been a typo! Who knows!
It is also interesting to note that in very few states are most farmers primarily farmers, and in most states, most of the farms don't make much money.
"Further research is needed"

Welcome back! For Laurel- Farm size versus ownership

I was gone trotting around Portland for two weeks, which meant that I had to leave you all scatter plot-less.
I hope this wasn't too sad.
So, today, I present the first of a series of scatterplots, requested by Laurel, centered around farming and the like. Specifically, she asked me about the link between non-corporate farming and farm output. I think. More or less.
So, thank you to http://www.ers.usda.gov/StateFacts/ , I have been able to start digging into this question. There will be more digging!
The first thing I wanted to look at was farm size versus private ownership. I would think that in the states where agriculture is a big business, farms would be larger and less privately owned.
I was, it seems, wrong. Farm size seems to be a lot more related to population density than anything else. Also, farm ownership seems to be pretty uniformly in the range of 80-90% private, across the board. Of course, some of those might be very small hobby farms. A median farm size would be an interesting thing to know.
Anyway, since this research wasn't very conclusive, I will play with more of the numbers in the coming days.