August 25, 2009
You may have seen the Paul Krugman post “How Big is $9 Trillion” in which he attempts to defend the Obama administration’s recent announcement that they expect that their policies will increase the national debt by $9 trillion. His tack is to “explain” that $9 trillion isn’t really all that much when you understand it in context.
it’s being treated as an inconceivable sum, far beyond anything that could possibly be handled. And it isn’t.
What you have to bear in mind is that the economy — and hence the federal tax base — is enormous, too. Right now GDP is around $14 trillion. If economic growth averages 2.5% a year, which has been the norm, and inflation is 2% a year, which is the target (and which the bond market seems to believe), GDP will be around $22 trillion a decade from now. So we’re talking about adding debt that’s equal to around 40% of GDP.
Right now, federal debt is about 50% of GDP. So even if we do run these deficits, federal debt as a share of GDP will be substantially less than it was at the end of World War II.
I defer to Paul Krugman on a lot of things because he is transparently smarter than I am. But it is precisely because of this fact that I know he is conscious of the obvious reasons his analysis is hogwash.
First of all, the national debt in WWII was initiated by an existential threat to the very continuation of our country. Mr. Krugman does not make even attempt to make the case that we have a similar crisis that justifies this kind of debt.
Second, implicit in his observation is the concept that since we did fine after WWII, we’ll do fine now. But the years after WWII saw drastic reductions in the inflation-adjusted debt driven by drastic reductions in spending. Mr. Krugman points to no similar possibility in the post-Obama world.
Third, we have something now that we didn’t have in the 1940’s. Back in the 1945, at the height of the spending that saw our national debt rise so dramatically, entitlement spending and interest on the national debt made up a meager 5% of our total budget.
By the end of President Obama’s term (if he runs two terms) we’ll be looking at a federal budget that is 70% mandatory spending. (I assume for the purposes of consistency that mandatory spending includes interest on the national debt because we don’t really have a choice in not paying it.)
Here’s a quick visual of the difference in the budgets in 1945 and 2016. (Ugly, because I did it fast… I’m on vacation.)
If you look at the 1945 budget with the single question “How are we going to reduce our debt?” you can identify the major problem. It’s the defense budget, which is almost 90% of the budget. Interestingly, reducing the defense budget is exactly what we did in order to reduce the debt, cutting it over 80% in 3 years (it helped that we won the war).
As a contrast, President Obama’s solution to reducing overall spending is… well, I don’t think he really has a plan. His projected budget in 2016 has reduced the defense budget as a percentage of the overall budget from 20% to 14%, but military spending isn’t what is killing us. The president has no plans to reduce mandatory spending whatsoever. In fact, his only change to entitlement spending is to increase it.
My problem with Mr. Krugman’s “How big is $9 trillion?” is that he is aware of all the problems I pointed out. He didn’t explain how much $9 trillion is; he obfuscated it. By comparing the debt load in the heart of a world-shaking war to a debt load that was accumulated in (relative) peacetime, he has misled his readers to the real significance of the data.
(By the way… if you would like to blame the debt load on the Iraq war, you should know that those costs have raised our debt by 5% of the GDP. Comparing this to WWII, which raised our debt by 70% of the GDP, is a pretty weak argument.)
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.
- Wait time data – Merrit Hawkins and Associates 2009 Survey of Physician Appointment Wait Times
- Cost of Insurance Premiums – AHIP Center for Policy and Research Individual Health Insurance 2006-2007: A Comprehensive Survey of Premiums, Availability, and Benefits
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.
June 24, 2009
I’ve been pretty quiet recently because 1) I’m on vacation and 2) I’m trying to wrap my head around the health care issue before I talk about it at length.
But today I saw something on healthreform.gov that bothered me:
Here’s the thing, Mr. President. There is such a thing as visual lying. That is when you show a graph and you show the numbers but the two things are not in any way related to one another.
That is the problem here. If someone looks at this graph, they see that the sky is falling because the bars have increased so dramatically. On the left, your team has represented a 30% increase with a graphic that shows a 966% increase. On the right, your team has represented a 63% increase with a graphic that shows a 308% increase.
And are the two sets bars related in any way? You might think so, given that they show up next to each other and are supposed to measure the same thing. But from a data perspective, they are not even remotely close to being right.
It is possible to use graphs and numbers in such a way that is honest. That’s an important part of transparency. So, I fixed your graph for you.
UPDATE: In the comments section, James quickly identified the problem… the graph starts the y-axis at 1000 instead of 0. I double checked and it looks like he is spot on. Thanks!
With that in mind, the graph is more of a rookie mistake than a conscious attempt to deceive. I’ve edited my post to reflect that (I left my original comments in so everyone can see what a smart-ass I tend to be).
June 7, 2009
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.
- I got all my data from one extremely boring source, the Bureau of Labor Statistics. At the moment, all the data is in the economic summary, but I assume that will change as the weeks go on, so you can also check the historical unemployment tables.
- The chart that President Obama used is in this document on how the stimulus will effect the economy.
- Special thanks to Geoff at Innocent Bystanders. Last month, he started charting how the numbers Obama used to sell the stimulus were matching to the actual numbers. So I can’t claim the idea as my own.
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.
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.
May 21, 2009
Today the Obama administraion launched Data.gov, a new website designed to make governmental data easily accessible to normal people (who love looking at data) and in formats that allow software developers to mine the data.
This is an excellent step towards transparency in government. The ultimate utility will matter on how many databases they allow us access to and how often they are updated, but it looks like the new go-to site for government data.
Just at a glance, we’ve got extensive data for:
- USA Spending Contracts and Purchases (searchable database)
- Benefits Data from the Benefits and Earning (Social Security Benefits)
- Patent Application Bibliographic Data (2009)
- Graphical Database of Tornados (1950-2006)
- Rain, Hail and Snow Observations
- Energy Consumption Survey (RECS) Files (1978-2005)
- Migratory Bird Flyways for the Continental United States
Lots of government gathered scientific data and a couple things that look like they might have some actual “responsible government” implications. I’d love to see more of this.
Very well done.
May 18, 2009
This graph has been going around a good deal in the last week. (Source)
Basically, the light blue line is the unemployement rate the Obama administration predicted would happen if we didn’t pass the stimulus bill back in . The dark blue line is the unemployment rate the Obama administration predicted would happen if we did pass the stimulus bill. (Here’s the raw document.) And the red triangles are the actual unemployment rate as it has panned out. Not only are they worse than the Obama adminstration expected, they’re worse than what they expected even if we didn’t pass the stimulus bill.
I think it is fair to say that the stimulus bill has not been as stimulating as they told us it would be. Now, it could certainly be the case that the unemployment rate would be even higher than this if we hadn’t passed the stimulus bill, but that is about as non-falsifiable a statement as you can get.
(UPDATE: The author of this graph explains why he thinks there has been little effect … we’ve spent almost none of the stimulus money yet. I’m trying to figure out where he’s getting his data because I don’t see any infrastructure projects on there. I’m certain that there is infrastructure spending going on right now because there is a stimulus project not 3 miles from my house causing daily traffic jams.
UPDATE 2: Here’s the best I could find on stimulus money currently being spent.)
I don’t really feel like dogpiling on the adminstration on this particular issue, so I want to hit a broader topic here… the administration’s use of numbers. This graph tells us some simple things that are scary and a complex thing that is scarier.
The simple thing it tells us is that the Obama administration was completely unable to predict the economic conditions four months into the future. They thought we would be at about 8.0% unemployment if the stimulus bill passed and at 8.5% unemployment if we sat on our hands.
As it turns out, we passed the stimulus bill and we’re at 8.9%. The easy lesson is that they didn’t get that one right. But, as Robert Strom Petersen said, “It’s tough making predictions, especially about the future.” And I probably couldn’t have done any better.
But no one is hanging the weight of hundreds of billions of dollars around my neck, which makes it more OK that I can’t project the future economic conditions. It seems fair to demand a slightly higher level of predictive accuracy from an administration that is using their predictions to push trillion dollar policies.
The complex thing that this graph tells us is that the Obama administration is comfortable using graphs that don’t really have a basis in reality in order to bolster support for their decisions. Graphs make us think that something is scientific and studied and therefore more reliable. But reliability is something that has to be earned. The team that put this graph together should be questioned on what they got wrong and what they would do next time to get it right.
Basically, the next time the president uses projected figures to push his policies, I would like to see someone ask the following question:
“Mr President, the last number predictions you threw at us turned out to be pretty far off the mark. What assurances do we have that these new numbers are accurate?”