Now they are suggesting they might use a device which has never been used the for this kind of major systemic reform. We know it would be — the only thing bipartisan about it would be the opposition to it, because a number of Democrats have said, “Don’t do this. This is not the way to go.” — Senator McConnell on FoxNews

Recently the Sunlight Foundation ran an infographic examining the past 20 years of Senate Reconciliation bills. At a glance you can see which bills had bipartsian support and which didn’t, and the list is relatively small (13 bills) so deeper inspection can be had relatively easily. However, what’s not immediately obvious is an indication of what Senator McConnell alleges, namely the “magnitude” of these bills. Most bills seem on their face to be simple budgetary adjustments. The “Balanced Budget Act of 1997″ had wide bipartsian support and a relatively simple title. However the “Jobs and Growth Tax Relief Reconciliation Act of 2001″ had hardly any bipartsian support, perhaps more tax cuts were added in reconciliation than in the original bill? If the graph could be altered to show some sort of significance factor then that would be an infographic!

This significance factor or magnitude could be quantified by providing some sort of comparison of the changes against the original bill. By way of example, there are well established computerized ways to compare documents and a significance factor could be calculated by comparing the size of these changes. Bills with bigger “change files” could show up with fatter lines in the graphic above. Of course this technique would have some problems.  It could give false positives if there were a lot of words to describe a relatively minor change and it could give false negatives if a massive portion of the original bill was removed (describing a removal is a fairly easy task, essentially “delete lines 1-1000″). But even a flawed mechanism in the hands of knowledgable people can be a useful tool as knowledgable people can quickly weed out the false positives, reinstate the false negatives and focus the viewers’ attention on the issues that matter.

Such a tool could be useful here if we were looking at hundreds of bills, but we’re only looking at 13. A responsible journalist would have prepared for an interview with Senator McConnell by digging into these 13 other bills and asked McConnell to choose which of them would take 2nd place behind health care for “significant bills passed through reconciliation”. McConnell could then use that as a spring board to describe how much more of a change health care is from that previous “high water mark” or the question could reveal how hollow McConnell’s talking point was. Instead, we, as consumers of news, get neither.

As informative as this chart is and as powerful as the treemap technique is, the information content in this graphic is about as much as a tabular listing of the various departments and the budget each of those departments is receiving. Yes, the relative sizes of the boxes allow us to quickly see which departments are receiving the most amount of money, but as a news item I’m left a little cold. There’s not enough context to this graphic to make me care beyond the “tinker with the pretty buttons” stage.

One way to increase that context would be to add an input box where I could enter my 2010 tax bill. The infographic then could shift the dollar amounts and turn it into how much of my tax dollars are being spent on various programs. Seeing how much I’m paying for interest on the national debt or National Defense may make me wonder if my money really is being spent wisely.

But while that simple technique provides some context and heightens my interest, it is just as transitory as the original. Shortly after tax season I doubt I’ll care and again I’ll get wrapped up in the headline news cycle of congressmen complaining about various parts of the budget, defecit and debt. But this is our national budget, we should look at it periodically just as we would our household budget. The NYTimes should trot out this graphic every time they run a story on a federal budget item.

Indeed it could prove useful as an index page into budget related stories. I’ve created a mock-up of what I’m envisioning. Click on the image below to be taken to an interactive version. The idea here is that as you mouse over various budget items you’ll be presented with the latest news article about that budget item. Frequently we’ve heard exchanges where one congressman complains about excessive spending in one area while another congressman points out that the amount of excess in that area is a drop in the bucket compared so the spending in another area. Reporting on those news stories while simultaneously referencing this graphic would be an inspired use of the treemap technique.

The blog/news site Good.is is running a series of articles on transportation. Recently they posted an infographic showing where the energy in your car is wasted. I was planning on running a similar article, but they beat me to the punch. Notice the large amount of loss coming from the engine. This, while true, is a bit misleading as the efficiency of an internal combustion engine (ICE) varies with RPM. Also the drive train losses vary depending on the type of drive train. All Wheel Drive is probably much more wasteful than the 5.6% shown below. So GOOD.IS’s infographic is nice, but it doesn’t quite give you enough information to make a decision.

I’m focusing on the engine and drive train losses because these are the things you can control when you decide on the type of car you want to buy. It would be great to see this same sort of infographic reproduced for several different types of cars. First up would be a hybrid like a Prius. This is sometimes called a Parallel-Hybrid because both the electric motor and the ICE can simultaneously supply power to the drive train. Of course, if you do that you incur some fraction of the engine losses and the drive train losses just to have the ICE hooked up in parallel.

What’s surprising is that the series hybrid is as efficient (if not more so) than a parallel hybrid. In a series hybrid there’s an ICE, but it powers a generator that is connected to electric motors that run the car. Part of the reason why this type achieves its efficiency is that it can run the ICE at the optimal RPM all the time and because it can dispense with much of the drive train that accompanies an ICE when it is connected to the wheels (there’s no gear box for example).

A fuel cell car would just replace the ICE and Generator in the above picture and provide power that way. Up until the BloomBox started getting attention the primary type of Fuel Cell that was being considered was a hydrogen fuel cell. It’s clean, producing only H2O as a waste product and relatively cool, but getting the hydrogen fuel is a big problem. Bloom’s fuel cell in contrast can run off of hydrocarbons which have a ready-made infrastructure, but they do run hot, about 2x hotter than a normal ICE. So it’s not clear if it’s feasible, but it would be way cool to have one.

I like Good.is’s website. They do good stuff. But this article set me back a bit. All it says is “Urmmmm, cars baaaaad”, they don’t give me enough information to decide what’s better or even… ahem good. Hopefully they’ll address this in the remaining 6 parts.

1. They justify their conclusions with graphs and numbers
2. They tackle seemingly contradictory results
3. Sometimes they provide more than a single number to justify their conclusions.

You should read the full article as they address several more issues than we’ll get into here (and they also provide sample photos of the types of images they’re referring to), but we’ll call out some highlights that illustrate these points.

Numerical Justification.

Is it better to smile or not? The standard advice is to smile while looking into the camera. But how effective is that really? It apparently depends on whether you’re male or female. Below is their answer for men. I find their chart a bit confusing as the bars below the line at first blush indicate that you get negative hits… how can someone “take back” a dating request? What they’re doing though is graphing against the average. I’ve redrawn it on the right which may make more sense. And of course the results are that no eye contact for men is almost always better.

Results Conflict With Common Sense

They dispel several common pieces of advice regarding dating photos in their article. One example involves those odd-angle self-portrait shots, what they term “MySpace shots”, from their article:

The universally maligned “MySpace Shot,” [is] taken by holding your camera above your head and being just so darn coy.

The graph below shows how effective these are for women. In short, very very effective. They even do a little control testing of this, just to verify that it wasn’t some other feature of these shots that made them so effective (read the article for details) but the “MySpace shot” still came out on top. Again, they’re graphing these results against the average, but with the previous warning this graph should be a little easier to read.

When Yes/No Won’t Do.

Most of the examples they gave provide information to the effect of:

For best results, men should look away from the camera and not smile.

They provide a sense of how much more effective their suggested strategies are by comparing them with other strategies. But this information may be too homogeneous for general use. “Does it really apply to me?” So for a few types of photos OkCupid dug a little deeper. In one case they analyzed how effective “showing skin” was in the photos. For men this tended to be showing off their ripped abs (assuming they had any). But instead of providing a simple comparison against the average or other types of photos, they broke it down by age. The results are below. In short, if you’re young and have ripped abs it’s better to show them off, but it’s not always that much better.

Conclusion.

Every newspaper has an Entertainment section and why not us? But our entertainment may come with ulterior motives. Here’s ours. After seeing this data, how likely are you to abandon your common sense wisdom regarding effective dating photos in favor of a technique that data says works better? Is the ideology of smiling and looking into the camera any different from any other ideology? Shouldn’t any ideology fall under the weight of contradictory evidence? If you change your strategy will your friends call you a “flip-flopper” or are you instead holding fast to a higher principle, one of science?

Alternatively, are you swayed by these results because you believe them to be unbiased? After all, the motivations of OkCupid here are clear, they just want more people to meet each other and they’re reporting on what appears to do that. It’s not like they’re charging more for those “MySpace shots”. Yes, reporting bias does exist and is a real concern, but that cuts both ways, may even cut 3-ways: left-bias, truth and right-bias. Your task is to find the truth and not to blanketly assume your favorite bias is always true. Our task is to help you do that. One way is exemplified by the ripped abs chart above. Rich data, in this case showing where showing the 6-pack helps a lot and where it only helps a little, is believable even if OkCupid had something to gain from pushing 6-pack abs shots. They state themselves that it’s not always tremendously effective. And the results of the “MySpace shot” is another means of verifying unbiased reporting. The results are so stunning that other researchers may very well try to repeat the result, and furthermore the results are so stunning that young women may be able to independently verify its veracity by testing their own dating pics. A marginal result may be hard to independently verify, but a result like this should be fairly easy.

Note, this metric is more analogous to the employment level than a measure of active money supply as the counter is incremented each time a baby sitter is used regardless of whether it’s for one hour or for four hours.

Given the starting parameters of the simulation peak employment occurs with an initial value of 4 or 5 Popsicle sticks per couple. Maximum employment would be 50, where half of the couples are going out on a date and the other half are babysitting. The fluctuations in employment occur for several reasons. One is that many couples are simultaneously interested in going out on a date and in babysitting. If they get asked to babysit before they themselves find a sitter then they are removed from the “dater pool” which results in suboptimal employment (employment would be higher if they instead went on a date hiring someone who had no choice but to sit). Note this effect is minimized when there are vastly more sitters than daters, which occurs when there is only 1 stick per family. (The sparkline is much less volatile.)

Click on the graphic to go to the simulation.

However, identifying problem districts is one thing, reshaping them into more reasonable forms is another. The entrenched politics of gerrymandered districts may require a leader to step forward in order for change to occur. Such leaders may emerge from Congress itself. While the geographical boundaries of all the congressional districts are almost trivially easy to find online, it is much harder to find information regarding when these districts took the shape they currently have. Consequently it is difficult to determine if the district was gerrymandered for the representative currently holding the seat or if it was gerrymandered for a representative who no longer holds that seat. Either way any representative in a gerrymandered district should be ashamed of this regardless of why the district was gerrymandered. Public pressure could be brought to bear on the representatives in those states that will go through a redistricting process as a result of the 2010 census. Weaker politicians may be too squeamish to embrace this change, but powerful ones should be powerful and popular enough to survive even a redistricting.

Thus the question becomes are there any powerful congressmen from apparently gerrymandered districts? The short answer is “yes”, we found four powerful congressmen from geographically fractured districts. We examined the districts of the 55 most powerful congressmen in order to see if any of those districts were also in the top 55 geographically fractured districts. For “fractured” we used the scores at RedistrictingTheNation.com and if 3 of the 4 scores were in the top 55 that was good enough for us. The power scores came from Congress.org, specifically their page here.

George Miller CA-7 rank 14/435
	Shape Rating
Power Rating

The most powerful congressman from a geographically fractured district is George Miller from California’s 7th district. A score card showing his detail is shown at right.  As we’ve mentioned before, it is somewhat disingenuous to cite someone as being an outlier merely because they’re in the top 50. Someone always has to be in the top 50. What is more important is to note how far outside of the normal range these people are. RedistrictingTheNation.com used a sparkline graphic to illustrate just this point for their shape scores. You can see at a glance the shape scores for all the congressional districts follow a bell-shaped curve, with the red-line showing where the score for this particular district lies. We’ve followed that same style for the power rating. To be consistent with the shape scores where highly fractured is on the left, high power is on the left. Representative Miller has a high power score, and his power is well above the average (as is everyone else’s on this list).




If you click on the graphic for the district you’ll be sent to the detail page on RedistrictingTheNation’s website where you can judge for yourself whether the district is gerrrymandered or geographically fractured for other reasons. A note, while California is both mountainous and coastal, the twisty part of the boundary doesn’t necessarily line up with these geographical features. Representative Miller is the most powerful congressman from a fractured district, but his district is the least fractured of the four we found. In decreasing order of congressional power the rest are below.

Frank Pallone NJ-6 rank 16/435
	Shape Rating
Power Rating

Representative Frank Pallone from New Jersey’s 6th district is next on the list. New Jersey is a coastal state, but the twisty parts of Representative Pallone’s district face inward. You can see from the shape scores below that his is the most geographically fractured of our four. Not by a little, but by a lot.

Debbie Wasserman Schultz FL-20 Rank 24/435
	Shape Rating
Power Rating

Representative Debbie Wasserman-Schultz from Florida’s 20th district is next. Again Florida is a coastal state but most of the fracturing of Florida’s 20th is inland. The U-shaped hole on the top is suburban Fort Lauderdale, the long peninsula at the bottom picks up some suburbs of Miami. An obvious redistricting would be to compact Representative Wasserman-Schultz’s district to the Fort Lauderdale area.

Jane Harman CA-36 Rank 27/435
	Shape Rating
Power Rating

Representative Jane Harman from California’s 36th district is next on the list. The shape of this district doesn’t look so bad but the smooth part on the left is the coastline! This district seems to pick and choose neighborhoods near Long Beach to the exclusion of neighborhoods near Inglewood and Santa Monica. Additionally Wikipedia mentions that she’s the 2nd wealthiest member of Congress, so if anyone can withstand a redistricting it should be Ms Harman.

A Postscript

Initially the comparison of congressional power with geographically fractured districts revealed 5 powerful congressmen from fractured districts. However earlier this week the most powerful of the 5 passed away following complications from gall bladder surgery.

John Murtha PA-12 13/435 deceased
	Shape Rating
Power Rating

John Murtha came from Pennsylvania’s 12th district. The fractured nature of PA-12 is plainly obvious. Indeed its shape was the inspiration for the examples in the explanatory table at the end of the previous article. Murtha held this district since 1974, despite the fact that each Census since then Pennsylvania has lost 2 congressional seats. Losing 6 seats since the 1980 census had to result in significant redistricting. Now the seat is up for a special election. Clearly someone less powerful will get the seat. If trends continue Pennsylvania will lose another 1 or 2 seats as a result of the 2010 census. Powerful congressmen who can weather a redistricting and want to do right by the people may well be good champions of this cause, however the power of the people up against a weak congressman may also serve as a good champion of this cause. We may never know whether Murtha would have been in favor of reshaping his district, but with a newcomer taking his seat it is clear that the people of PA-12 will have relatively little to lose and fairness to gain in the reshaping of Pennsylvania’s 12th district.

Illinois' 4th District

A district is gerrymandered if its geographic boundaries are re-drawn to accomplish some political goal. Each state has its own method of deciding on the boundaries of district lines and some states may not make their deliberations public. Thus the first task of determining which (if any) districts are gerrymandered is to look at the shape of the district. But examining all 435 congressional districts in the US is a daunting task. And redistricting isn’t restricted to national politics. State and local governments frequently have legislative bodies similar to the US House whereby legislators represent a region in a state, or a neighborhood in a large city. The task of examining these national and local districts is even more daunting. Fortunately computers, math and visualization can be pressed into service to aid in the search.

Maryland's 1st District

A website called RedistrictingTheNation.com is a superb resource for people wanting to know more about individual districts. Many of the images in this article come from their website. They track each district by 4 mathematical scores designed to identify oddly shaped areas. The details of these scores aren’t important (but detail is provided at the end of this article). What is important to note is that while none are perfect, they all are good enough to highlight potential problem areas. All gerrymandered districts have poor scores, but some egregious examples only score moderately poor. And some of the worst offenders are false positives, appearing to be gerrymandered merely because they follow a river, coastline, mountain range or state boundary.

Arizona's 2nd District

Furthermore some may have been gerrymandered for legitimate political reasons. Arizona district 2 is this shape mainly to address possible conflict of interest issues between neighboring Indian tribes. The point is that the algorithms work best when viewed as a hint that something may be amiss. If the algorithms indicate an issue then further study is warranted.

While RedistrictingTheNation has some excellent quantitative analysis on each district, those analyses are primarily numerical and focused only on the district at hand. Another excellent site that tracks broader governmental issues is GovTrack.us. When you pull up a state’s page on GovTrack you can see at a glance all the congressional districts for that state. This provides some context to the districts. For example, one thing that’s a little easier to determine from GovTrack is the party affiliation of neighboring districts. Illinois’ 4th district is democratic. It is bordered above, below and in the middle by IL-05, IL-03 and IL-07 respectively all of which are democratic too. Wikipedia conjectures that IL-04 follows ethnic boundaries even if the surrounding districts are all of the same political party.

Zoom of Illinois from GovTrack.us

The concern over gerrymandering has a growing movement. There’s an online game and a movie in the works. The visual appeal of oddly shaped congressional districts is so powerful it’s hard to imagine this story being told without infographics of the type seen on RedistrictingTheNation.com or GovTrack.us. This bodes well for my New-Years wish list, gerrymandering may be the first main stream news story told with a significant contribution from infographics. However, info graphics alone lack a personal touch. We’ll try to provide that next week.

* Detail On How RedistrictingTheNation Computes Its Scores

Method	Detail	Score
Convex Hull The district is red. The convex hull is the area enclosed by the green outline. (Imagine wrapping string around the shape.) The score is calculated by dividing the area of the district by the area of the convex hull. The score ranges from 0 (very twisty, possibly gerrymandered) to 1 (the district is convex).
Reock The smallest circle that can enclose the district is shown in green. The score is calculated by dividing the area of the district by the area of the circle. The score ranges from 0 (very twisty, possibly gerrymandered) to 1 (if the district is a circle). RedistrictingTheNation.com multiplies all scores by 100 to get a 0 to 100 range.
Polsby-Popper Make a circle where the circumference is the same as the perimeter of the district. Then compare the area of the district with the area of the resulting circle. Scores again range from 0 (gerrymandered) to 1.
Modified Schwartzberg Make a circle where the circle has the same area as the area of the district. Compare the perimeter of the circle with the perimeter of the district. The normal Schwartzberg score has the circle in the denominator, but RedistrictingTheNation modified this to keep it consistent with scores ranging from 0 to 1.

Initially our babysitting co-op was set up because the neighborhood was in a moderate economic down turn. Couples had money for the occasional night out, but not quite enough for a night out if they had to also pay a babysitter. Then someone had the idea of swapping babysitting duties with other families in the neighborhood and to keep track Popsicle sticks would be given for each baby-hour of sitting. Fast forward a few years and we rejoin our babysitting co-op in the middle of a deeper economic downturn. Families now don’t have enough spare cash for a night out even though the babysitting is “free” (paid for through the co-op). Our “keeper of the Popsicle sticks” who we call Paul (in honor of Krugman’s article) is blind to these problems in the real world. All he sees is a sudden dearth of activity of families buying and selling babysitting services. Last time this happened it was because there weren’t enough Popsicle sticks in the economy. Adding some sticks really jump-started the babysitting economy. So he tries that again here, but nothing happens. He’s puzzled.

What we’re trying to model is a lack of demand. In the earlier case where adding sticks stimulated growth there was pent-up demand for babysitting services. People wanted to use the service and were willing to work to do so.  Adding sticks unleashed that demand (play the original simulation). Here though people can’t afford the cash part of the night out and so don’t want to use the service. Adding more sticks won’t help. We need to stimulate demand.

Stimulus could come in a few forms. We could imagine an increase in demand for babysitting services if we imagine a few families are unaffected by the economic down turn and can afford a night out but who also choose to use the babysitting co-op for their babysitting services. These “rich” families might temporarily invigorate the economy, but once the poorer families have a few nights worth of sticks saved up they probably won’t volunteer for additional work as they still can’t afford the main part of the date and so can’t even use the sticks they have.

A better stimulus would be to provide a product that most families would use. After all couples still want a night out, they just can’t afford dinner and a movie. But they might go for renting a movie and watching it with friends. Or the guys may go for a poker night while the women go for bridge night. To make such dates seem more like real dates the co-op decides to build a small common area in their neighborhood, big enough for several different couples to have a “date” (e.g. 4 rooms suitable for movie viewing/game playing each big enough for 2-3 couples). Access to the room will also be paid for by Popsicle sticks.

The Popsicle stick analogy breaks down a little here. The “stimulus” was the creation of the common building and that probably was paid for with real dollars not Popsicle sticks, but lesson is still valid. The economy is stimulated when dollars and demand are exist. If demand exists, add dollars. If dollars exist, create demand. If neither exist, supply both.

Following the Lessons of WWII

It wasn’t until after World War II that we fully climbed out of the Great Depression. Was WWII a government spending program? Of course it was, who else paid for all the military equipment and soldier salaries? Sure, we borrowed from ourselves (via war bonds) instead of from China, but it was government spending nonetheless. Those dollars made factories which made boats, tanks, planes, tents, uniforms, helmets, guns, bullets, and etc. After the war those same factories were easily retooled to create cars, trucks, radios, TVs, furniture, and etc.

Advocates of the stimulus package like to point out that WWII was a large stimulus program too. But here’s where the analogy to WWII breaks down. The Great Depression started in 1929, the US entered WWII in 1941 and WWII ended in 1945. So for either 4 or 16 years the American people did without. Either because the economy was depressed from 1929 through 1941 or because people sacrificed to support the war effort between 1941 and 1945 (or both). Four or sixteen years of living without created pent-up demand, a lot of it.

At the end of the war the “War Spending Stimulus” left us with companies that had factories which could make products people wanted and it left us with people who had money to buy those products. But as importantly these years of sacrifice left us with a lot of demand. But we don’t have 4 or 16 years to wait for demand to grow. We all want our economy back now. Besides growing demand is hard. Forecasting demand may be easier. We can see where our money is going today and we can forecast where our money may go tomorrow.

One example is energy. People are going to continue buying energy today, we should do what it takes to keep as much of those dollars in the US as possible. People are going to buy green energy in the future. We should do what it takes to be a player in that global market and not leave it all to China.

But let’s also keep in mind the other lesson from the “WWII Stimulus” and that is that we haven’t yet spent too much. The graph at the right shows this. A case could be made that the differences between then and now may mean that we shouldn’t let our debt grow to the same level as in WWII, but we’re still a far cry from that level and so we shouldn’t be unduly concerned with our current debt level.

All stimulus dollars puts money in people’s pockets. What we also need is increased demand and increased capability to meet that demand. We can’t create demand with stimulus dollars, but we can see where the future, what the demand will be then and seek to increase our capability there. We can also see what foreign demand there is and seek to tap into that. Projects that do these things are the best use of stimulus dollars.

Someone very close to me has been experiencing an incredible run of bad luck. Every day for the past few years my friend (let’s call him Paul) gets mugged. The robbers punch him then take his money but they leave his wallet. He’s not hurt enough from these muggings to go to the hospital, but he is sore for the rest of the day. He can never predict when these muggings will occur, sometimes it’s early in the morning, other times early evening or the afternoon, but strangely enough he’s never been mugged twice in the same day. He’s tried altering his route to work, taking a self-defense class, and in general doing everything he can to make himself a less appealing target, but nothing helps. He has found though, that if he has no money in his wallet he gets beaten up badly so he’s taken the tactic of keeping $20 in his wallet just to appease the robbers.

By and large Paul has gotten used to it. He keeps a supply of pain meds at home and his office and he goes out of his way each day to keep his wallet supplied with a $20 bill. Minor inconveniences to be sure, but he figures he’s losing about $6000 each year to these muggers and once a week heads to the police office to lodge a complaint.

The police tell him they’re doing all they can to catch the robbers, but they’ve had no success. Nevertheless they’ll keep trying. On his most recent trip to the police station, one of the inspectors, we’ll call him George, made an interesting proposition. It would cost the police $80,000 per year to have a policeman following him around all day long. And even that won’t guarantee success at stopping the mugging when the police weren’t around. So the investigators would rather focus their investigation on the larger community and not just on him. But to compensate for his monetary loss the police department would like to pay Paul $100/week while they pursue the true culprit. Sure the extra money won’t quite cover Paul’s losses (he’s frequently robbed on the weekends too) and it won’t compensate him at all for the pain and suffering he experiences by the light beating he endures during each mugging, but it’s better than nothing.

Now lets modify our basic story by a little.

One time during his weekly trip to the police station Paul hit on an idea. He’s going to suggest to the inspector that they compensate him for the money he’s losing. After all it is the responsibility of the police to keep him safe. Over the past 3 years he’s lost about $18000 and he sees no reason why he should bear this cost when there’s nothing he can do to avoid being mugged. He wants as normal a life as possible and losing $6000 of his discretionary cash each year just doesn’t seem right. When he proposes this to the inspector, George at first thinks it’s a good idea but needs to check something on his computer first. When George returns he has bad news for Paul. Turns out that the muggings started before he became a tax payer so he’s not eligible for victims compensation since it’s paid for by tax dollars.

While the above story is fictitious, I know someone who lives with a very similar run of bad luck. He has type-1 diabetes. Unlike type-2 diabetes which is related to factors people can control, namely diet and exercise, type-1 diabetes is an auto immune disease that strikes without warning regardless of your lifestyle or other controllable factors. He figures that he spends about $20/day in diabetic supplies, needles, testing strips, insulin. His insurance company covers some but not all of these expenses. And, of course, he tests himself (finger poke) 4-6 times a day and gives himself 3-5 injections per day. The analogies to poor Paul above should be clear. Fortunately my friend had insurance when he got diabetes, but in the US if he didn’t have insurance he’d personally have to pay for all his medical care and furthermore he’d find it difficult to find insurance afterward.

Poll: Does it make a difference?

I’m not going to run a poll on whether your opinion is changed based on the situation being crime or health care. (If you have an opinion, please leave a comment.) Instead I’ll share my opinion which is both blatant and nuanced.

Blatant:
Health insurance isn’t some form of a deferred savings plan designed to allow you to pay your own medical bills. Many people run their entire lives with low total health costs, others are afflicted with a chronic disease relatively early in life or for many other reasons have large medical bills. Health insurance is there for risk mitigation, everyone pays the average amount so that those with above average expenses can have those paid for by those with below average expenses. Everyone pays, everyone is eligible, no one can be denied coverage.

As a society, we have the ability and compassion to help those who are affected by a disaster to get back on their feet and resume as normal a life as possible. Folks from the US and around the world help those affected by flood, hurricane, tornado or earthquake rebuild their homes and resume their lives. Of the industrialized countries only America denies this sort of aid to those experiencing catastrophic disease or injury.

Nuanced:
Of course, if you make something free or low cost or fixed cost people’s behavior will change from what they would do if that same thing had a variable cost based on their actions. This axiom may even apply to health care. However there is a key difference between this and say “free housing”. Poor health is a cost in and of itself, while the dollars to treat diseases may be free, everyone would rather be disease free than to be disease ridden but have those diseases treated freely. Even with free health care there won’t be a sudden increase in people playing Russian Roulette.

But Russian Roulette is an extreme example. More realistically, some smokers may chose to continue to smoke with free health care while others (those who currently aren’t regularly seeing a doctor) may choose to quit. Some people may indeed choose to abuse the system. The question isn’t whether there will be some abuse, but will there be too much abuse. If even one person tried to get a free ride from free health care would that make you be against it?

In this article we’ll try to put ourselves in the shoes of a would-be investor in the midst of these bubbles and see how we’d react. Consider Dr. Shiller’s original graph. The peak in prices was very large. But let’s suppose you had access to a grapic like Dr. Shillers updated each year from 2000 through 2006. The question is “when would you sell?”

Somewhere between 2000 and 2001 the prices in the current boom reached the prices seen at the peak of the last two booms. Investors having access to the 2001 version of Dr Shiller’s graph should have started to get nervous then. However, if they sold their investment properties in 2001 they would have missed out on another 5 years of growth. Suppose you did sell in 2001, by 2003, when the housing market was even higher, would you still have confidence that the bursting of the bubble was near? Or would you be tempted to jump back into the market? Even if you can see the bubble coming, timing the market is hard. Being confident of your convictions is hard too.

And yet this is not the first bubble we’ve been through. Recently we’ve had 3 other bubbles: Oil in the late 1970s and in 2008, Technology/Nasdaq in 2000 and Gold in the late 1970s (and possibly now). I’ve plotted these 3 bubbles on the same scale in the graph below. It is useful to see all 3 on the same graph but some scaling is necessary since the oil high ($127/barrel in 2008) is lower than the gold low ($198/oz  in 1970).  The data is adjusted for inflation (all dollars are in 2009 dollars) and the relative values were set to each item’s respective value in 1996. Why 1996? Because that was a period of relative calm for all 3 items. What can we learn from the graph? (Click on the graph for a larger view.)

History Does Repeat Itself

The gold bubble in 1980, the Tech/Nasdaq bubble in 2000 and the Oil bubble in 2008 each share a similar feature, a rapid rise (over a period of 1 to 2 years) followed by a similarly rapid fall. I don’t have access to Dr. Shiller’s data, so I can’t graph it together with the above graphs but it is possible to compare them side-by-side. The housing booms of the 70s and 80s were twice as long as the gold, oil and Nasdaq booms. Shiller marks the 70s boom as 3 years and the 80s boom as 5 years long. This extra length may be due to houses being harder to sell than gold, oil or technology stocks. Also, the current boom is longer still, Shiller marks it as 9 years long. By the standards of the 70s and 80s booms, 2000/2001 should have marked the end of the current housing boom. Yet it continued for another 4 to 5 more years.

Another interesting feature is that after the gold and tech/Nasdaq booms the prices stabilized somewhat higher than their values before the boom. The oil boom we’re just coming off of looks like it too may follow this pattern, but it’s too soon to tell. The oil boom in the 1980s runs counter to this but it was an odd price spike for several reasons. The US department of energy has an excellent chart with their take on the reasons why. It also appears the 80s housing boom followed this same pattern stabilizing about 5% higher after the boom than before.

Are we now in another gold bubble? If we are, it is unlike the previous gold bubble. The current upward trend in gold prices has been going on for about 10 years; not classic bubble behavior. However, if you look closely at the right hand edge of the graph (click it to see a larger version), it appears that the increase in gold prices has suddenly quickened. I doubt we were in a gold bubble in 2008, but looks like we may just be entering one now.

Summary

One reason why Dr. Shiller’s graph may not have gotten much attention in 2006 was that the bubble hadn’t burst yet. After a bubble bursts, the damage is widely known and it is only natural to then ask what the historical prices looked like. But by then it’s too late. We need to ask these questions before the bubbles burst, hopefully before they get so large the bursting is catastrophic. Think about the what’s happening in the world today. Can you think of a market that may be currently experiencing a bubble? Would you have thought of gold if I hadn’t already included it above? What about health care? (That will be the topic of a future article.) Any other markets?

Thinking of possible markets ahead of time is hard for the general public, though somewhat easier for investment professionals. Getting the data and verifying whether there’s a bubble is even harder. But these sort of things are trivial for computers. Wouldn’t it be nice if someone somewhere set up a system that defines a possible bubble by certain price behaviors and then offers a service where computers monitor all of these indexes attempting to pattern match on just those bubble behaviors. Perhaps one day someone will create such an app and the damage bubbles cause could be mitigated.

Notes

NASDAQ data came from finance.yahoo.com
Gold prices came from the London Bullion Market Association
Crude Oil prices from 1974 to March 2008 came from one Department of Energy Site
Crude Oil prices from Apr 2008 to today came from another Department of Energy Site
Inflation adjustment data came from the Bureau of Labor Statistics