Lather, rinse, repeat

I found this in my Drafts folder. I'm not sure if I copied from somewhere or wrote it myself...

The pattern is

1. Government policy and poor regulation cause (or invents) a crisis.
   
2. The government publicly and violently searches for culprits, aided by the MSM, and names the wrong parties--usually in the private sector.
   
3. The government then rolls out a massive new law and its regulatory children to "fix" the problem as they defined it.
   
4. The new law doesn't solve the real problem, costs a lot, and has massive unintended (but fully predicted) consequences, including setting the stage for the next crisis, which will be bigger and more damaging.
5. Memory of the past crisis fades and everybody reluctantly adjusts to the massive new regulatory overhead.
   
6. A new crisis occurs. The government publicly and violently searches for the culprits, aided by the MSM--looking exclusively in the business community...

and so it goes.

Would you believe? 70% vs 35% tax rate...

For fun, let’s compare the effective rates and a few other values under Carter (1979) and G.W. Bush (2006). Recall that under Carter (1979) the marginal rate went all the way up to 70% paid on income over $215,400 (married filing jointly); under Bush (2006), the highest marginal rate was *only* 35% paid on income over $336,550 (married filing jointly). The CBO puts out a nice paper every year. It starts with data from 1979 and runs up to 2006 (the last year for which I have data, 2005 for the top 0.01%), making this comparison easy.

Would you believe that in 1979, when the highest marginal tax rate was 70%, the effective income tax rate paid by the top 1% was just 21.8% (compare to the marginal rate of 70%). In 2006, after massive tax rate cuts that were accompanied by elimination of a host of deductions, the effective income tax rate for the top 1% had dropped only a few points down to 19% (compared to marginal rate of 35%).

Would you believe that in 1979, when the highest marginal tax rate was 70%, the top 1% paid 15.4% of all federal taxes collected but that in 2006, when the highest marginal rate was a mere 35%, the top 1% paid 27.6% of all federal taxes collected?

Would you believe that the bottom 80% are *all* (statistically, not necessarily as individuals!) paying a smaller share of the both the total federal tax burden and income tax burden in 2006 than they were in 1979? Only the top 20% are paying more?

Would you believe that the bottom 40% went from paying about 4% of income taxes collected in 1979 to paying -3.6% in 2006 (negative share of taxes due to excess refundable tax credits, primarily EITC and child-related credits)?

Believe it!

Share of Total Federal Tax Liabilities
* the lowest quintile (20%) paid 2.1% of all taxes collected in 1979 and 0.8% in 2006
* the second quintile paid 7.2% of all taxes collected in 1979 and 4.1% in 2006
* the middle quintile paid 13.2% of all taxes collected in 1979 and 9.1% in 2006
* the fourth quintile paid 21.0% of all taxes collected in 1979 and 16.5% in 2006
* the highest quintile paid 56.4% of all taxes collected in 1979 and 69.3% in 2006
* top 1 percent paid 15.4% of all taxes collected in 1979 and 27.6% in 2006
* top 0.01% percentile paid 2.7% of all taxes collected in 1979 and 6.5% in 2005

Share of Federal Income Tax Liabilities
* the lowest quintile paid 0.0% of income taxes collected in 1979 and -2.8% in 2006
* the second quintile paid 4.1% of income taxes collected in 1979 and -0.8% in 2006
* the middle quintile paid 10.7% of income taxes collected in 1979 and 4.4% in 2006
* the fourth quintile paid 20.2% of income taxes collected in 1979 and 12.9% in 2006
* the highest quintile paid 64.9% of income taxes collected in 1979 and 86.3% in 2006
* top 1 percent paid 18.3% of income taxes collected in 1979 and 39.1% in 2006
* top 0.01% percentile paid 2.6% of income taxes collected in 1979 and 8.0% in 2005

Share of Pre-Tax Income
* the lowest quintile earned 5.8% in 1979 and 3.9% in 2006
* the second quintile earned 11.1% in 1979 and 8.4% in 2006
* the middle quintile earned 15.8% in 1979 and 13.2% in 2006
* the fourth quintile earned 22.0% in 1979 and 19.5% in 2006
* the highest quintile earned 45.5% in 1979 and 55.7% in 2006
* top 1 percentile earned 9.3% in 1979 and 18.8% in 2006
* top 0.01 percentile earned 1.4% in 1979 and 4.2% in 2005

Total Effective Federal Tax Rate (income+payroll+excise+corporate)
* the lowest quintile rate was 8.0% in 1979 and 4.3% in 2006
* the second quintile rate was 14.3% in 1979 and 10.2% in 2006
* the middle quintile rate was 18.6% in 1979 and 14.2% in 2006
* the fourth quintile rate was 21.2% in 1979 and 17.6% in 2006
* the highest quintile rate was 27.5% in 1979 and 25.8% in 2006
* top 1% percentile rate was 37.0% in 1979 and 31.2% in 2006
* top 0.01% percentile rate was 42.9% in 1979 and 31.5% in 2005

Effective Income Tax Rate
* the lowest quintile rate was 0.0% in 1979 and -6.6% (negative 6.6 percent) in 2006
* the second quintile rate was 4.1% in 1979 and -1.0% (negative 1.0 percent) in 2006
* the middle quintile rate was 7.5% in 1979 and 3.0% in 2006
* the fourth quintile rate was 10.1% in 1979 and 6.0% in 2006
* the highest quintile rate was 15.7% in 1979 and 14.1% in 2006
* top 1% rate was 21.8% in 1979 and 19.0% in 2006
* top 0.01% percentile rate was 21.0% in 1979 and 17.0% in 2005

Futility of "Taxing the rich" as the solution to the problems

In the last year for which data is available, 2008, the highest marginal tax rate was 35%. This rate is paid on AGI above $357,700. Certainly someone with AGI over that value is in the well-to-do category, but may not be “millionaires and billionaires” (but this is immaterial to my point). According to the IRS, the number of returns that were in this highest marginal rate was 971,510. So there’s nearly a million households in this country that are, at least by the IRS bracket definition, “rich”. Not too shabby, it seems the USA is indeed the land of opportunity.

According to the IRS, the cumulative amount of AGI subjected to this highest rate was $622,765,389,000, so let’s round up to $622.8B. The taxes generate on this money is therefore $218B (the IRS reported $217,967,886,000). The overall effective rate for these returns (taxes paid / income) was 28.9%.

Let’s assume for a moment (no matter how unrealistic the assumption is) that no one affected would change a lick of their income-generating behavior as a result if we raised the top marginal rate to 100%. How much revenue would that generate? Why, all of $622.8B, if no one modified their behavior in any way that affected their income and tax impact. That is, it would generate an additional $404.8B in revenue relative to the current 35% bracket.

If you added that $404B to the revenue pot, our deficit this year would still be over $1T…

Global Warming? Part II

I have some exposure to some of the models used by climate researchers at NOAA. I can tell you, the models are frequently ad hoc and contain numerous fudge factors and corrections to massage the data, throw out outliers, adjust that term during this time period, this term during that time period, etc. Further, many temperature measurements are based on proxies--e.g. assuming tree rings are wider during higher temperatures, but there's simply no way to determine how much wider per degree C.

I'm not saying that their models are wrong, just that, having implemented models like these before, I understand enough of the math to know that a minor mistake in a fudge factor meant to allow dissimilar measurements to be used as if they were from the same dataset can make a huge difference in the validity of the model. Not to mention simple errors in implementation that can have the results "look right" but still be completely wrong.

For example consider the story told by the data that turned out to be wrong.

In this case, the scientists found out that their ERSST model was producing warmer results, by about 0.2C, than other instruments. It turned out that in 2001, the satellite providing the data was boosted to a different orbit, and the model failed to take that into account. It took 10 years before anyone thought that there might be a problem! Up until then, everyone apparently assumed the earth had warmed by 0.2C suddenly in 2001. Worse, they assumed that the data for 1971-2000 was wrong and massaged it to fit the 2001+ data. "In early 2001, CPC was requested to implement the 1971–2000 normal for operational forecasts. So, we constructed a new SST normal for the 1971–2000 base period and implemented it operationally at CPC in August of 2001" (Journal of Climate).

Just the abstract to that particular paper reveals how fragile the models are, being based on assumptions piled on top of assumptions, and unveiling a tendency to massage data.

"SST predictions are usually issued in terms of anomalies and standardized anomalies relative to a 30-yr normal: climatological mean (CM) and standard deviation (SD). The World Meteorological Organization (WMO) suggests updating the 30-yr normal every 10 yr."

How can a normal be updated--the data is the data, and its normal is its normal? This sentence implies that the data is somehow massaged every ten years or so. There may be legitimate reasons to do so, but anytime you massage data, there have to be questions as to the legitmacy of the alteration.

"Using the extended reconstructed sea surface temperature (ERSST) on a 28 grid for 1854–2000 and the Hadley Centre Sea Ice and SST dataset (HadISST) on a 18 grid for 1870–1999, eleven 30-yr normals are calculated, and the interdecadal changes of seasonal CM, seasonal SD, and seasonal persistence (P) are discussed."

This says that data is being assembled from widely disparate data sources, with different measurement techniques, and that some of the data was made with instrumentation that simply cannot be validated (data from 1854?).

"Both PDO and NAO show a multidecadal oscillation that is consistent between ERSST and HadISST except that HadISST is biased toward warm in summer and cold in winter relative to ERSST."

Now we see that different data sets, ostensibly of the same population, disagree. And the fact that one data set exhibits bias to the extreme (too warm in summer and too cold in winter) raises questions about the proper use of this data. One scientist may be able to make a valid claim that the more stable data is in error and "correct" it to be more in line with the more volatile data; another scientist may do the opposite. And their personal bias will play a role as to which way they go.

Global Warming?

I for one don't deny that the globe may be warming (or at least that the climate may be changing), nor even that man may have exacerbated this trend. And I am willing to do my part to help minimize my impact: I telecommute when I can, I minimize my driving (e.g. walk to lunch when I'm at the office), we have a garden, we compost, I drive a car with decent gas mileage (albeit 11 years old--trading the footprint to produce a new car vs the slightly better mileage I could get with my next car, which will be a Jetta TDI), and we're planning for our retirement house to be as off-grid capable a possible.

On the other hand, the earth has gone through severe climate shifts even within the last few thousand years--none of which were precipitated by man-made pollutants. 11,500 years ago, much of the Northern Hemisphere was covered with mile-thick ice sheets. Yet that ice all melted. Why? Was the ice an aberration (no) or was the warming an aberration (no)? What is the earth's "correct" temperature? If the earth was cooling and glaciers were expanding and the seas retreating (as ice build-up captured more and more water) would these same scientists and politicians be recommending that we burn more stuff to put more CO2 into the atmosphere? Why is water vapor--the #1 grenhouse gas by a 7-1 margin--never mentioned? What caused the medieval warm period when olives last grew in England and Vikings last lived on the shores of Greenland and silver mines in Sweden were not covered by glaciers? What caused the subsequent Little Ice Age that saw the Thames freezing solid every winter for 200 years, and farms and villiages in northern latitudes were destroyed by expanding glaciers?

At Copenhagen, the AGW crowd lamented sea-level rise: "Just two years ago, the UN Intergovernmental Panel on Climate Change predicted a worst-case scenario rise of 59 centimetres. But the accelerated melting of ice sheets in Antarctica and Greenland caused by faster warming means the worst case is now put at 1.2 metres. " When? By 2200... Current satellite data going back to 1993 has sea-levels rising about 3mm per year. The average, based on a set of tidal measurements over the last 220 years is 2mm/yr.

Fifty-nine centimeters in 190 years is just 3.1mm per year; a rise of 2 feet over 200 years seems like something we could plan for and adjust to and not at all like a geologically recent event. About 8500 years ago the largest lake in the world, Lake Agassiz, which once covered almost half-a-million square kilometers (about 180,000 square miles) of central Canada simply drained, virtually overnight in the geologic timescale, into the Arctic ocean. "The last major shift in drainage occurred about 8,400 calendar years before present (about 7,700 14C years before present). The melting of remaining Hudson Bay ice caused lake Agassiz to drain nearly completely. This final drainage of Lake Agassiz contributed an estimated 1 to 3 meters to total post-glacial global sea level rise. Much of the final drainage may have occurred in a very short time, in two or one events, perhaps taking as short as a year. [emphasis mine]"

In India, we find that "Useful data on sea level fluctuations have been collected during the present expedition. Three wavecut benches were encountered at depths of 11.22 metres, 4.6 metres and 1.34 metres. The proto-historic city was built on the lowest bench, the early historic and the medieval townships on the higher benches. The island of Bet Dwarka, 30 km north of Dwarka, which is also famous as the pleasure resort of Sri Krishna, was connected with the mainland between Otha and Aramda. The reclamation referred to in ancient texts was made in this zone when the sea level was lower 3,500 years ago." The sea has risen 120 meters over the last 20,000 years, albeit with virtually all of that rise taking before about 5000 years ago. All without the help of man.

I'm not against "doing something" and certainly agree that pollution is bad. I'm just not willing to agree that the proposed so-called cap-and-trade legislation is going to do anything about pollution, and if it does have a minor impact on pollution and/or warming (by the bill's supporter's own admission), will it do so without crippling our alreadywounded economy.

And further, I think that the cap-and-trade bill expects some magic technology to spring into being simply because the bill mandates it--how else to get 65% reduction in emissions from coal-fired plants (except to shut them down). Why not mandate 500MPG cars (and $1M/year unemployement benefits to stimulate the economy)? So while I'm willing to "do something", I'm not willing to put my faith in a demonstrably ineffective Government, nor am I willing to live in a cave, naked and eating dirt (but my carbon footprint would be pretty low if I did), nor am I willing to cripple our economy.

Crazy Eddie

"The pattern was always the same. First they wished for the impossible. Then they worked toward it, still knowing it to be impossible. Finally, they acted as if the impossible could be achieved and let that unreality influence every act."

"The Mote in God's Eye"--Larry Niven and Jerry Pournell

This sounds like some liberal politicians to me...

45000 without insurance die?

I often see the proclamation that 45 thousand people die every year because they don’t have health insurance. This number comes from a Harvard study, so it is pronounced with great reverence and seldom questioned or considered. The study is widely available on the internet, and I suggest you read it. While reading it consider the point-of-view of one of the authors: according to the NIH website "Dr. Stephanie Woolhandler helped found Physicians for a National Health Program, a not-for-profit organization for physicians, medical students, and other health care professionals who advocate a national health insurance program."

Of course, the number quoted is the highest number in the study, and comes about only when using criteria suggested by the Urban Institute. The lower end of the estimated range is 27424, or a just about 50% less, albeit this covers only ages 25-64. Ignoring the Urban Institutes guidelines, the number provided is 35327 deaths annually for the non-elderly (ages 18-64), compared to the larger number 44789.

But the study also had several severe limitations.

First, the study relied on self-reported insurance status (unverified data). Further, the study only included insurance status at a single point in time, without determining whether the participants were actually insured at the time of their death. If a person had not been insured at the time of the initial interview, but got insurance later in the 6-year study period and may have actually had insurance at the time of death, they were counted as if a lack of insurance had contributed to their death. The authors have no information as to the duration of insurance coverage or lack thereof--only that the person was uninsured during the initial interview. To be fair, it may also be true that some people who reported having private insurance later dropped or lost their insurance, but the study does not consider either case.

Next, the study made no effort to determine that the cause of death was related to health insurance status. Deaths due to auto accidents, homicides, etc. were counted the same as deaths due to untreated diabetes. This is made more problematic because the study oversampled blacks and blacks are six times more likely to be victims of homicide than whites.

The study also excluded people on Medicare, which is reasonable as the study focused on the non-elderly. However, the study also excluded "nonelderly Medicare recipients and persons covered by Medicaid and the Department of Veterans Affairs/Civilian Health and Medical Program of the Uniformed Services military insurance, as a substantial proportion of those individuals had poor health status as a prerequisite for coverage." These people were excluded because they were probably already sick and the fact that they had health care coverage, albeit government supplied, would likely have skewed the results. If they had died during the study, the hypothesis that lack of coverage is associated with increased likelihood of death may have been less strongly supported; also, it may well have indicated that having government-provided coverage is even more strongly associated with increased likelihood of death.

Although the study seemed to indicate that “uninsurance is associated with mortality,” it also points out that “uninsurance was associated with younger age, minority race/ethnicity, unemployment, smoking, exercise, self-rated health, and lower levels of education and income. Regular alcohol use and physician-rated health were also associated with higher rates of uninsurance.” Or, as the authors put it “unmeasured characteristics (i.e., that individuals who place less value on health eschew both health insurance and healthy behaviors) might offer an alternative explanation for our findings.”

Finally, it is worth noting that of all those who were included in the study, a total of 3.1% died. Of those who died, 83.8% had private insurance (but still died). The overall rate of death was 3.0% for the privately insured, and 3.3% for the uninsured. So, 97% of the privately insured were still alive after the study period ended, and so were 96.7% of uninsured.

In other words, there may be a 0.3% increased chance of death associated with instances of unverified periods of uninsurance (of unknown duration) and where cause of death may or may not be related to health-care related factors.

By way of comparison, in 2008 there were 43,313 deaths in auto accidents in the U.S. That number is typical for the annual loss of life on our roads. Each of those deaths was 100% preventable by simply banning automobiles. We could save many of them by simply lowering the speed limit to 25MPH on all roads. Are we willing to spend $1T to save those people over the next 10 years?

For further comparison, the CDC says that approximately 90,000 people die each year as a result of acquiring an infection while in a hospital. Almost all of those deaths could be avoided if doctors, nurses, and other hospital staff would simply wash their hands and use hand sanitizers regularly and properly.

P.S. According to the numbers in the Harvard report, being male had almost the exact same “risk” as being uninsured.