Tag Archives: chart

Today’s Experiment: Crowdsourcing a Blog Post

by BrittanCreswell Posted on December 15, 2016

Mother Jones

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Over the weekend I was diddling around with some charts because that’s apparently what I do now when I’m trying to take my mind off Donald Trump. Here’s one I did that never made it into a post because it didn’t seem to show anything interesting:

So let’s crowdsource this post. What’s interesting or unexpected about this chart? Anything? There sure is a big drop in the number of people getting education degrees.

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Today’s Experiment: Crowdsourcing a Blog Post

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Correction: Obamacare Premiums Are Going Up About 0% For Most People

by JinaGirardi Posted on October 26, 2016

Mother Jones

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Data! You want data! Sure, Obamacare premiums are going up and so are the subsidies. But how much are the subsidies going up? The chart below—which I want everyone to look at because it was a pain in the ass to create—shows this for the 15 states with the highest premium increases:

As you can see, subsidies are increasing more than premiums in every state—and by quite a bit. This comparison data is for a 27-year-old with an income of $25,000, and comes from Tables 6 and 12 here. (Arizona is literally off the chart: premiums increased 116 percent and subsidies increased 428 percent.) Here’s the same chart for the 15 states with the smallest premium increases:

There are plenty of caveats here. Premiums and subsidies will be different for different kinds of households. Upper middle-class families don’t get any subsidies at all. And this doesn’t tell us what the average net increase is, once subsidies are accounted for.

However, it gives us a pretty good idea that for a substantial majority of Obamacare users, the net amount they pay for health insurance in 2017 isn’t going to be much more than it was this year. For many, in fact, it will be the same. For those who shop around, it’s quite likely to be less.

Bottom line: if your income is low enough to qualify for a subsidy, there’s no need to panic over the Obamacare premium news. The higher premiums will help stabilize the market, and the cost will be covered almost entirely by Uncle Sam. Your pocketbook is safe.

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Correction: Obamacare Premiums Are Going Up About 0% For Most People

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Today’s Dumbest Chart, Presented in Chart Form

by JocelynCamp63 Posted on December 14, 2015

Mother Jones

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“Someone on the internet is wrong” isn’t a great mission statement for a blog. I get it. Really. But….National Review posted this in their Twitter feed a few minutes ago:

This is so phenomenally stupid that I figured it had to be a joke of some kind. Or maybe some intern put it up, not understanding how dumb it was. But no. When I backtracked to the PowerLine post that it came from, it turns out that author Steven Hayward wasn’t trying to trick anyone. He was making an explicit argument that this is the right way to view climate change:

When I make charts and graphs, I generally make it a practice to scale the vertical axis of a chart from zero (0) to the upper bound of the range. Compressing a chart’s vertical axis can be grossly misleading…..The typical chart of the global average temperature is usually displayed this way normal chart inserted….But what if you display the same data with the axis starting not just from zero, but from the lower bound of the actual experienced temperature range of the earth?….A little hard to get worked up about this, isn’t it? In fact you can barely spot the warming….If this chart were published on the front page of newspapers the climate change crusaders would be out of business instantly.

Hayward missed a bet by not using Kelvin and scaling the chart from absolute zero at the bottom to the temperature of molten lava at the top. Then the warming would really be invisible.

We all post stupid stuff sometimes. But things are really going downhill at NR if they post charts like this even though the author explains exactly why he’s doing something so dopey. In case they still don’t get it, though, maybe the chart below will clear things up.

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Today’s Dumbest Chart, Presented in Chart Form

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Bee Die-Offs Are Worst Where Pesticide Use Is Heaviest

by BreannaRoemer Posted on May 14, 2015

Mother Jones

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The nation’s honeybee crisis has deepened, with colony die-offs rising sharply over last year’s levels, the latest survey from the US Department of Agriculture-funded Bee Informed Partnership shows. A decade or so ago, a mysterious winter-season phenomenon known as colony-collapse disorder emerged, in which bee populations would abandon their hives en masse. These heavy winter-season losses have tapered off somewhat, but now researchers are finding substantial summer-season losses, too. Here are the latest numbers.

Chart: Bee Informed Partnership/University of Maryland/Loretta Kuo

Note that total losses are more than double what beekeepers report as the “acceptable rate”—that is, the normal level of hive attrition. Losses above the acceptable level put beekeepers in a precarious economic position and suggest that something is awry with bee health. “We traditionally thought of winter losses as a more important indicator of health, because surviving the cold winter months is a crucial test for any bee colony,” Dennis vanEngelsdorp, University of Maryland entomologist and director for the Bee Informed Partnership said in a press release. But now his team is also seeing massive summer die-offs. “Years ago, this was unheard of,” he added.

And here’s a map a map depicting where losses are heaviest:

Chart: Bee Informed Partnership/University of Maryland/Loretta Kuo

The survey report doesn’t delve into why the nation’s bees are under such severe strain, noting only, as USDA entomologist and survey co-coordinator Jeffrey Pettis put it, “the need to find better answers to the host of stresses that lead to both winter and summer colony losses.”

A growing weight of science implicated pesticides—particularly a ubiquitous class of insecticides called neonicitinoids, as well as certain fungicides—as likely factors.

Here are US Geological Survey maps of where two major neonics, imidacloprid and clothianidin, are grown. Note, too, the rapid rise in their use over the past decade.

Chart: USGS

Chart: USGS

A 2013 paper co-authored by the USDA’s Pettis and the University of Maryland’s vanEngelsdorp found that lows levels of two particular fungicides, chlorothalonil and pyraclostrobin, “had a pronounced effect” on bees’ ability to withstand a common pathogen. Here are the USGS’s maps for them.

Chart: USGS

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Bee Die-Offs Are Worst Where Pesticide Use Is Heaviest

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A Simple Chart That Shows We’ve Locked Up Too Many People

by CarenRubensohn Posted on February 16, 2015

Mother Jones

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Correlation is not causation. This has recently become something of an all-purpose comeback from people who want to sound smart without really understanding anything about a particular research result. Still, whether it’s overused or not, it’s a true statement. When two things move up and down together, it’s a hint that one of them might be causing the other, but it’s just a hint. Sometimes correlation implies causation and sometimes it doesn’t.

The inverse statement, however, is different: If there’s no correlation, then there’s no causation. With the rarest of exceptions, this is almost always true. Dara Lind provides an example of this as it relates to crime and mass incarceration.

The chart on the right shows the trend in various states at reducing incarceration. If reducing incarceration produced more crime, you’d expect at least some level of correlation. The dots would line up to look something like the red arrow, with lots of dots in the upper left quadrant.

Obviously we see nothing like that. In fact, we don’t appear to see any significant correlation at all. As Lind says, the scatterplot is just a scatter.

It’s possible that a more sophisticated analysis would tease out a correlation of some kind. You can show almost anything if you really put your mind to it. But if a simple, crude scatterplot doesn’t show even a hint of a correlation, it’s almost a certainty that there’s nothing there. And in this case it demonstrates that we’ve locked up too many people. Mass incarceration hit the limit of its effectiveness in the late-80s and since then has been running dangerously on autopilot. It ruins lives, costs a lot of money, and has gone way beyond the point where it affects the crime rate. It’s well past time to reverse this trend and get to work seriously cutting the prison population.

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A Simple Chart That Shows We’ve Locked Up Too Many People

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