I’m currently working on a chapter for the upcoming O’Reilly book “Beautiful Visualization” (a new book in the “Beautiful” series) and one of the things that I do is walk readers step by step through gathering data and sifting through it in order to create a visualization from the Cash for Clunkers data.

As I was looking through the Cash for Clunkers data, I was fascinated by the extent to which it seemed that the clunkers being turned in were disproportionally from companies based in the US. So I dug into the data and found out that it didn’t just seem that way… 85% of the cars “clunked” came from US based manufacturers.

So I decided to create a visualization to identify which countries gained market share due to the Cash for Clunkers program. So… here it is. Click for a larger view. (caveats below).

You can access the raw data here.

Caveats:

  1. Yes, nearly all Toyota and Honda and Hyundai vehicles are built in the US. I used the “where is the parent company headquartered” as my way of determining country size. That made for a more compelling image.
  2. It makes a certain kind of sense that people would dump a lot of old US-made vehicles because US manufacturers were at the forefront of the SUV boom in the early-mid 2000’s (aughts? oughts? naughts? This next decade will be so much easier), so it seems to make sense that people who bought SUV’s would be most eligible for a Cash for Clunkers rebate. If you bought a fuel efficient Toyota Camry in 2002, you’re not going to be eligible to trade your vehicle in, so it seem unlikely that you would do so.

With all that being said, I think it’s obvious that US manufacturers have lost market share on these transactions. I’d need to do a shade more research, but my understanding is that Ford (which didn’t take any bailout cash) didn’t do too badly while Chrysler and GM saw a large number of their vehicles turned in and comparatively very little purchasing.

What does this mean for the future? I don’t know. This was more for fun and for my book chapter than for anything else. And if you want to learn how to do something like this, just buy “Beautiful Visualization” when it comes out.

The next couple weeks are insane for me, but I’ve been sitting on this idea for some time and I figure its time to let it loose into the wild, spelling errors and all.

First, my sources.

Now for the caveats.

Wait times data are for routine checkups and does not count emergency care or diagnostic testing.

Phyllis Shlafly repeated the line that “The average wait is… the second trimester of pregnancy to see an obstetrician-gynecologist.” It looks like she is using the same documents that I’m using and if that is the case, that statements is absolutely false.

First of all, these wait times apply only to routine checkups (as stated above) and the OB/GYN checkups are “well woman” check-ups. Someone correct me if I’m wrong, but I don’t think that a pregnant woman falls into that category.

Second, the average wait time in that category is 70 days, which is really only the second trimester if you count the “Wait a second, I’m pregnant!” realiziation time, which might be OK if she mentioned that to he readers.

Now for the insurance cost data. This was a statistic I struggled with for quite some time. The reason is because the latest comprehensive data available was collected at the end of 2006 and beginning of 2007. This was so soon after the passage of the Massachusetts health care reform that it is very unlikely that it accurately reflects the results of that reform (which is something the study authors freely admit).

However, I’ve search high and low and cannot find any indication that the premiums have decreased at all. To the best of my knowledge, they have increased faster than the country average.

If this is true,  then the average individual health insurance premium in Massachusetts is somewhere around $830 per month.

But I figured I might as well underestimate in order to flush out people who might complain, so I used the non-specific and drastically reduced number of $600+ per month.

Finally, the most important question:

How close to the Massachusetts health reform is the Obama health reform plan?

Because, honestly, if they weren’t anything like each other, there would be no point in comparing them, would there?

The sad fact of the matter is that the Massachusetts model provides the closest real life approximation to the Obama plan that there is available.

They both have a government agency for providing health care exchanges. They both require business over a certain size to provide insurance for their employees or face penalties. They both require individuals to purchase insurance or face tax penalties.

Like it or not, I think we can look to Massachusetts as a miniature crystal ball to provide a glimpse into the future of health care in the US if the Obama health care plan is passed.

In this video, I take a look at the economic predictions that President Obama made in February regarding the stimulus plan and how those predictions are corresponding to reality.

The answer is: Not well.

But first, some references.

OK… now into the math. The chart that everyone is using does not have a corresponding table with hard number (at least no table that I could find), so I had to guess-timate what they were predicting the unemployment rates would be in May. I assumed that, because their graph divergence began immediately after the Q1, 2009 line, that that line represented the beginning of Q1 2009 (as opposed to the middle). So I estimated that the May would be just a shade before Q3, which is about the same place that Geoff put his May data.

Based on that, I estimated the points on the line like so:

Unemployment Rate Unemployed Population
Predicted Unemployment without the Stimulus 8.7% 13,492,000
Predicted Unemployment with the Stimulus 7.9% 12,251,000
Actual Unemployment with the Stimulus 9.4% 14,511,000

Now… here is the problem. In order to make our data symmetrical, we would have to have another row… a row called “Actual Unemployment without the Stimulus”. This, of course, is a row we cannot have because we sadly live in a space-time of collapsed quantum possibilities. We can never know what that row would hold.

This is where I start getting a little less analytical and a little more irritated. The president’s predictions have been shown to be completely off the mark… almost laughably so. And yet he acts as if he alone knows what would have happened if we hadn’t passed the stimulus because he keeps making statements like “we’ve saved 150,000 jobs“.

It is clear that, if he is referring to the chart we were presented with above, such a claim is absurd. What the president is doing is ignoring the fact that his predictions in the past were horribly inaccurate and simply moving ahead with new predictions. The big difference is that his new predictions can’t be judged against any set of objective reality. He is pitting the actual universe in which the stimulus bill passed against the imaginary universe in which it did not pass. Not surprisingly, the imaginary universe is worse that the real universe and the result is that the President is a hero for saving us from that imaginary universe.

I am not a very anti-Obama person. Predicting the future is tricky business and I think his team should get some leeway on this.

However…

Their predictions were not just kinda wrong. They were horrifically, disasterously wrong. If President Obama is going to use statistics and charts to push nearly $800 billion in spending, I think we should be able to expect his numbers to at least kinda match the reality that comes out of his policies.

At the very least, I’d like to know how his team got those numbers. More importantly, I’d like to know how they have changed their method of prediction. President Obama is fond of saying that we tried tax cuts and they didn’t work, so we should try something else. In that same vein, his team tried predicting the effect of the stimulus and that didn’t work. So I would like to know if they are using the same failed methods they used before or if they are doing something different.