Another phrase that accompanied these early stories of the recession was “the credit markets are seizing up”. These two phrases may be related. Normally lending occurs  between banks and also from banks to businesses. Some of these loans are short term loans, sometimes paid back in a matter of days, sometimes within a few months. Companies that rely on these short term loans could be forced out of business if they can’t get them. Think of a small retail clothing store. If they don’t have the money on hand to buy their Spring inventory they’ll need to borrow it from a bank. This would normally be a safe bet for the bank if the store had several years worth of sales history from previous Spring sales. The fact that the retail price of the inventory is about double the wholesale doesn’t hurt either. After all, the store wouldn’t need to sell all of their inventory to pay back the loan. So the store buys the clothes on credit and pays the bank off during the sales season as customers buy the clothes. But if the bank is suddenly unwilling to lend the money the store can’t buy as much inventory. Their sales season may end early for lack of inventory and the store may need to lay off staff or even temporarily close after their inventory runs out. Sales at this store would have dramatically dropped from the same period last year. Multiply this across many other businesses in the region and suddenly it’s not just the credit markets that are seizing up, it’s the whole local economy. Or in other words, the economy is going over a cliff.

The question is, is there a way to visualize this? Adding up sales volume of every business in a region (or the nation) is a difficult task. Yet the example above relied on only one business, the banks. If the credit markets did seize up one way to track that would be to track the growth (or shrinkage) of business loans, which is just what the Economist did in a recent graphic. This graph shows business loans for the US, parts of Europe and the UK. The US portion of the curve almost looks like the phrase “going over a cliff”, with the only caveat being the rapid rise just before the rapid plunge. If you ignore that rise, US bank loans dropped by 40%* between mid-2008 and early 2009. However, even if you were to discount the rise before the fall, the fall would still be a 20% fall and furthermore, the fall would end below the no growth line, end in a 10% reduction in loans.

The phrase “falling over a cliff” evokes an image of a long plateau suddenly falling away and so the above graph, with that peak before the fall, fails in conveying that sense. But that peak may be a distraction. The far left side of the graph appears to have a relatively stable 20% growth in business loans before falling away in early 2008. This story reminds us that Bear Stearns collapsed around this time and that undoubtedly put a chill in banks’ willingness to lend money. However within a few months Bear Stearns was forgotten and lending resumed, perhaps even making up for lost ground, hence the spike**.

Banks like to have a certain amount of assets on hand before they lend. If their assets fall below their threshold, they stop lending until those assets rise above above that threshold. With the decline in asset values of the sub-prime mortgages the banks suddenly found themselves with much fewer assets than they thought they had. To remedy this situation the US government came in and provided nearly $1.5 trillion dollars through TARP and reduced Federal interest rates. These funds went to the banks to increase their asset level so they’d start lending again. Where did all that money go? It’s still in the banks. This chart shows the total money available for use in the US monetary system.

As planned, this money did go to increase the banks’ assets so they’d feel more comfortable lending. This graph (also from the Economist) shows us that their asset ratio is crazy high, yet apparently many banks are still not lending.

We still haven’t found (or even simulated) a graphic that would qualify as a smoking gun for the phrase “economy is going over a cliff” yet we’ve come close. The reduction in business loans starting in the latter part of 2009 and continuing through today certainly is unprecedented during the 2 year range of that graph and may be unprecedented for an even longer period of time. Indeed Bernake and Paulson may have had a mental image of a similar graph extending further back in time when they were urging congressional action on the TARP program.  A slightly different perspective, perhaps dollar flows, interbank lending volumes or  total loans (and not loan growth) may yield a more evocative picture. But our hats are off to the Economist for creating these graphs even if they were done 18 months after the start of the recession.

**Astute readers may note that the loan growth chart is a derivative chart (in the calculus sense). “Loan growth” being flat at 20% means that total loans are growing at 20%. Loan growth of 0% means you’ve reached a plateau. It takes a negative loan growth before the total quantity of loans drops or falls over a cliff.

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.

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.

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.

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

Wikipedia defines an economic bubble as “trade in high volumes at prices that are considerably at variance with intrinsic values”. And it’s clear that Dr. Shiller’s graph shows trade at considerable variance with intrinsic values. (The graph shows only prices and not volumes, but we’ll assume the volumes were high.) Even though the peak for the current boom is crazy-high, you might be tempted to down play it, after all the curve goes back to the 1890s so some inflation in housing prices is to be expected. Unfortunately Dr. Shiller considered that and this curve is adjusted for inflation. The current boom saw housing prices at an all time high by a considerable amount! (Note, I suspect Dr. Shiller used a standard value of 100 as a generic reference value. If you’re confused by what “a house sold in 1890 for $100,000 inflation adjusted to today’s dollars” means try thinking of it in percent. Eg, a house in 1890 sold for 100% of its value, in 1920 for 66% of its value and in 2006 it sold for 199% of its value.)

Where did this money come from?

Part of the money came from world governments in a manner entirely analogous to our Popsicle stick economy. When governmental banks lower interest rates for home ownership (for example) this has the effect adding money to the economy. However, others believe (podcast) that most of the money came from world-wide investors seeking safe returns on their investments when government bond markets were yielding sluggish returns (fast forward the above podcast to around the 10-minute mark and start listening). Couple this with the supposedly safe financial wizardry of collateralized debt obligations and world-wide money poured into the housing market (listen at about the 30 minute mark on the podcast).

At this point, there was a lot more money running through the housing market than normal. But this money was stuck in the housing market, unable to directly flow into say the auto market or the agriculture market because you needed to buy or refinance a house to get access to this money. The Popsicle stick economy provides some insight here if you think carefully about it. With a lot more money in the housing market you’d expect to see housing prices rise AND people willing to pay these higher prices. Dr. Shiller’s graph shows this. His chart is of home values not home prices, people sold and other people bought houses at those prices.

Side Effects

Dr. Shiller’s chart is much more dramatic than our earlier chart showing the increase in the construction labor force during this time. Dr Shiller’s chart also tackles head-on the definition of a economic bubble, it tracks the variance between price and intrinsic value. Yet it still went largely unnoticed by the wider media in 2006 (we’ll address this issue in a future article). One reason may be because it was so unbelievable. However, bubbles have consequences and their effects should be able to be seen in a variety of manners. One such manner was our earlier construction labor force chart, however another chart, almost as dramatic as Dr. Shiller’s chart tracks the housing supply.

In a boom it is easy to imagine that builders build more houses than normal, and possibly more than the market needs. In the latest boom it is also conceivable that more people qualified to borrow money to buy a home than ever before (see the above podcast). These factors should show up in the housing supply. One measure of the housing supply is the “months of housing supply”. It’s a number that reflects both the sales rate of houses and the number of houses on the market. Basically, “how long will it take to sell all the houses on the market at current sales rate?” The data for the graph above comes from the US Census Bureau. The shaded areas match up with those in Robert Shiller’s graph. Notice that housing supply was relatively unchanged during each boom time. Just after each boom ended the supply of houses increased. (Although the 1980s boom showed some swings it largely followed the same pattern of lower inventory during the boom than immediately before or after.) The increase in supply immediately following a boom may be due to several factors:

• the building of houses in progress finished after the boom ended and could not easily find buyers.
• overpriced houses (post-boom prices) took longer to sell.
• people who couldn’t afford their mortgages had their houses repossessed by the bank.

It is interesting to note that visually the long term average months of housing supply appears to be 6 months. Yet during the current boom that number appeared to be 4 months. This seems to corroborate the perspective in the above podcast that people who otherwise wouldn’t have qualified to borrow money to buy a home before the current boom were able to borrow money. Such an effect is not apparent in Dr. Shiller’s graph.

It is also encouraging to see that the current inventory levels are near this “long term average”, hopefully the housing market doesn’t have that much further to fall before bottoming out.

Summary

Dr. Shiller directly addressed the housing boom question by graphing price and intrinsic values over a 120 year span. This resulted in a very dramatic graph. But bubbles have consequences and those consequences should also be quite visible. The housing supply numbers over the past 40+ years shows similarly dramatic changes as you’d expect. Our earlier graph of construction sector employment shows another sign of a truly large bubble, but it wasn’t as dramatic as the 2 charts shown here. However when analyzing bubbles it would be great to view all the related data in one place. Seeing multiple independent signs of a bubble may help convince people that a bubble is occurring before it’s too late.

Notes

Footnote Dr. Shiller’s graph shows the 80s boom time occurring between 1984 and 1989. This paper (page 26) from Harvard shows the start date as 1982. For the graph above I’ve split the difference and started it in 1983. As a side note, lest you think Harvard is infallible, that same paper used very bizarre almost laughable graphics (figure 1 page 47). Tufte would not approve.

Krugman creates a hypothetical economy in the guise of a babysitting co-op. It’s easy to imagine an urban neighborhood with lots of young couples each with one or more kids, especially in these economic times. Imagine further that many can’t afford both a baby sitter and a night out on the town, so this neighborhood elects to form a babysitting co-op where each couple who baby-sits one baby for one hour earns the right to have one of their children baby-sat for one hour. To keep track of this they choose to use Popsicle sticks as currency. Thus a couple watching a baby for 4 hours would earn 4 sticks and if they watched 2 babies they’d earn 8 sticks.

We finish setting the stage by hypothesizing that there’s “pent up demand”. All the couples in this neighborhood have wanted to go out on dates, but couldn’t afford both the date and a baby sitter. It was only recently that someone suggested the babysitting co-op. Indeed the person who’s idea this was (let’s call him Paul) is putting the finishing touches on his proposal. An economy like this needs an initial dollop of capital. (Unlike the real-world economy which has been running so long that an initial dollop of capital is hard to conceive.) The reason is simple… if no one has any Popsicle sticks then no one can pay for their dates. All that’s left for Paul to do is decide how many sticks to give each family at the start.

Too Few Popsicle Sticks

If he gave each family one stick (or even better one stick per child) then this may not be enough to get their economy rolling. If a typical date is dinner and a movie, that’s probably a 3-4 hour event. Until a family has 3 or 4 sticks then a date that long is out of the question. Sure, some couples, with only one stick, may opt for a quick dinner out and eventually other couples may amass 3-4 sticks for themselves. If their chosen babysitter is another couple who wants a 3-4 hour date then there will quickly be a flurry of activity as this pool of 3-4 sticks circulates its way through their economy. But with only a small pool of 3-4 sticks circulating, it may be several months before any given couple is able to have another 3-4 hour date. It is clear that one stick per family is too few.

Too Many Popsicle Sticks

If he gave each family twenty sticks (or again, 20 per child), then he may have given so many away that inflation starts. Consider, at 20 per family (a clearly excessive number) each couple would believe they have enough sticks to afford five 4-hour dates. In this neighborhood, that’s a lot, so much in fact that very few couples would feel any need to babysit. What would they do with the extra sticks? Yet if that’s how most folks feel then again, no one would be able to go out since everyone would be looking for sitters and no one would be willing to work. Or at least no one would be interested in babysitting at the rate of one stick per child-hour.

Eventually someone will get so desperate to go on a date they’d offer two sticks per child-hour of babysitting and for some that extra incentive may be enough to get them to sit even though the sitting family doesn’t really need the extra sticks. When the price of the “good” goes from one stick per child-hour to two sticks per child-hour that’s inflation.

Conclusion

Many people who believe in the gold standard do so because they do not understand the constraints that limit how much money a government can print. In our “economy” Paul is the person responsible for “printing money” and the examples above show that there are constraints on how many Popsicle sticks Paul can effectively introduce into his economy. Too few and the economy doesn’t live up to its potential. There are both more people willing to earn sticks and more people wanting to spend sticks than what is happening in their economy. On the other hand, start with too many and inflation starts.

These principles are illustrated through the simulation that lies behind the image to the right. It simulates a babysitting co-op of 100 families where the main variable is the number of sticks each family initially gets. The economy just limps along when each family only gets 1 stick. Sometimes up to 6 couples are able to go out on a date each week, but sometimes it shuts down and after a flurry of activity no one can afford a date of their preferred length. Start with 4-5 sticks and the economy eems to be going quite well. At most 5 or 6 families are unable to go out on a date when they want to.  But start with 6 or more sticks and the economy starts to wane. More specifically there are some families (looks to me to be about 10%) who have enough sticks for a date, but can’t find a sitter. (These are the houses that are green.) More detail is available on the simulation page.

I find it refreshing to consider the economy from this point of view (what economists refer to as “macroeconomics”). The economics problems we’re facing seem understandable. But more than that, this point of view gives new insights on other economic issues.

For example, in our simple economy we need both people who are willing to babysit and people who have enough sticks to pay for a sitter. Hold that thought while you consider whether there’s a market for “ultra durable goods” (in the real world). People buy a new car every 2 to 8 years and over a person’s lifetime she may buy 5 or so cars. Would people pay a little extra if they could own a car that would last 40 years? How would this affect the auto industry? If across the board… if everything were “ultra durable” would our economy suddenly shrink once everyone owned all the ultra durable goods they will ever need? Can you posit an answer in light of the “Popsicle stick economy”?

[An update to the simulation is available here.]

The US government collects plenty of data on various aspects of the economy and it seems only natural to ask if there’s a smoking gun in the historical data. Is there a difference in a housing bubble vs genuine growth in the housing industry? Sure, growth wouldn’t suffer an immediate decline as bubbles do when they burst. But ideally we’d have some data, metrics, charts and graphs that can give us a warning sign ahead of time. So we’re looking for leading indicators of a bubble. What would those be?

One candidate may be found in the employment data. Genuine growth should happen organically. Slow increase in hiring, enough time to train qualified workers. Genuine growth in housing could occur because of an increase in population. Unless there was a burst of immigration, that sort of increase happens gradually. Families have babies, babies grow up, buy houses and repeat. A significant increase in the birth rate 20 to 30 years ago could foretell an increase in housing demand today.

Genuine growth in the housing market could also occur with wide spread prosperity across the country. If all sectors enjoyed increased wealth people may choose to spend some of that wealth buying a larger home or remodeling their current home. Growth in this way may be called “genuine” especially if the increase in wealth came from a diversity of sources and/or specifically from sources other than the housing market itself. However many believe that people borrowed money against the increased value of their homes to buy a larger home or fund a remodel. Housing itself begot more housing, not increased population, productivity or Gross Domestic Product.

This article is part one of an occasional series where we’ll to put the current economic situation in perspective with data graphics. In this article we’ll examine the employment data.

–

Once again we’ll turn to the Bureau of Labor Statistics to help us answer the question whether there was an increase in employment in the construction sector during the years leading up to the rise and fall of the housing market. It stands to reason that there should be an increase, so the real question is, is there anything unusual about the increase in employment in the housing industry?

The BLS collects data on employment from 2 main sources. For one, they query the employers. This provides a lot of data since a single employer employs tens or hundreds of people (or more). Data collected here is remarkably broad and detailed, in many cases breaking down the occupation to a level as detailed as “Plasterers and stucco masons”. But by querying employers, this source of data misses one key statistic, the unemployed. Thus, the 2nd source of data the BLS uses comes from directly querying the population. Through this mechanism they can determine who is working and who is not in addition to an occupational cross section similar to (but not as detailed as) the employer survey. Irritatingly these two surveys are not compatible with each other. So for reasons that will become apparent we are using the population based survey. The details of which are available here.

The BLS has data through the population survey that goes as far back as 1983. Graphing the raw data results in the chart shown below.

This graph is a stacked graph, meaning, for example, that bottom of the Service curve starts at the top of the Construction curve. If this still isn’t clear a portion of the raw data is shown in a table format below.

Thus, on the left side of the graph (1983), the size of the Construction sector runs from 0 to 11,463,000. The size of the Service sector runs from 11,463,000 to 27,502,000 (11,463 + 16,039). Adding in the size of the Sales & Office sector, another 28,000,000 (approx) we get something close to 60,000,000 as a cumulative total which is where the top of the yellow Sales/Office line in the graph above is.

Both the Population survey (CPS) and the Employer survey (CES) collect data at a detailed level, but the CPS combines many more of the lower level details into a single category. This is because there is less data (fewer people surveyed) in the CPS so the accuracy of the details may be off. But the CPS is a bit broader than you might expect. You may be wondering (for example) what category your job falls into. Teachers, for example are in the Professional side of the Management and Professional categorization. The full break down is available here.

Examining the data, we realize that spotting a unique growth trend will be harder than we expected because all these sectors as a whole grew over the 25 year time frame. One way to factor out this growth and dig deeper into the sector specific growth is to determine the growth of the labor force as a whole and then see if any sectors grew faster or slower than that. Notice, in the graph above, that the “Management & Professional” category grew significantly more than say “Sales and Office”. While both grew, Management grew more. So this approach may yield fruit. But there’s an easier way to get a similar restructuring. Since the population survey also includes the unemployed, we have a picture of the total labor force. So we can instead plot each sector as a percentage of the total labor force.

Additionally we’ll zoom in our scale a bit, using the full vertical range to graph a difference of 12%. Why 12? It turns out that the difference between each sector’s high and low fits within a 12% range. The largest is “Management and Professional” which had a low of 26% of the labor force to a high of 34%, a swing of 8%. The only oddity that this introduces is that on some of the graphs the bottom of the range will NOT be zero.

The graph below shows the Construction sector and if you click on it, you’ll be taken to an interactive application where you can click on the tabs at the top of the graph to see a close up of the other sectors. Note, for this graph we’ve also included an estimate of the 2009 data by merely averaging the monthly data available for 2009.

Here the full effect of a bubble in the Construction sector becomes visible. Firstly we notice a flat or somewhat declining influence in construction on the US labor force from 1984 till 2002 when it reached a low of 9.4%. It grew from there till 2006 when it reached a high of 10.5%. At the end of 2008 the annualized fraction of Construction workers in the labor force was 9.6% and the 2009 estimate appears to be 8.7%.

Taken all by itself it’s hard to tell if this is unusual behavior or not. Fortunately we have data from 4 other sectors to compare it to. When you do that the unusual nature of the construction sector becomes clear. First, except for Sales, all the other sectors follow their trend line for the entire 25+ year period studied. They are all either flat, growing or shrinking across the entire range. Secondly, all have bumps, but the bumps are significantly less than the 1% swing we see in the Construction sector¹. Thirdly, Sales is the only other sector that experienced a rise and fall, but it seems to be what you’d expect from a slow systemic change, and not from a bubble. While it rose 1.4% during a 4 year period it took 14 more years before it fall back to its 1983 levels².

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We have found a hint of the impending doom for the housing market in the employment data. Interesting, but not enough. The construction employment bubble should betray additional details. Where did those workers come from? The construction sector added 1.5 million jobs between 2003 and 2006. It seems reasonable to suppose that these workers were not as highly trained as those who were already in the construction industry in 2002. If this is true, we’d expect to see signs of this untrained labor force. Were there more complaints filed against the construction industry during this time? Was the work below par? Did the work take longer to complete than expected? We will have to see.

It would also be interesting to learn what parts of construction these workers were engaged in. Were they building new homes? Remodeling existing homes? Building more commercial offices? While the answers to this question may not point to bubble vs growth, it will point to additional areas to probe. If an unusual amount of growth occurred in the residential housing market then probing residential statistics may yield fruit. Are there ways to determine if there was a genuine pent up demand for houses? Or was the only sign of demand the increase in home prices?

It remains a goal of this website to examine important news stories with data visualization tools in the hopes that one day we’ll be able to see the signs of situations like this before they turn into full fledged disasters.

1 A swing of 1% in Construction is 1% of the entire labor force. Since Construction represents about 10% of the labor force, this 1% “total” swing represents a 10% swing for construction workers.

2 There may be a larger story here, but it is also longer term. If Construction swung by 10% during the 4 year period around the bubble, Production decreased by 28% over the 25 year period while Management grew by 27% over the same interval. This long term shift in employment, while interesting, is not the topic of this article.

But this is only half the story. The stimulus program has already spent about $140 Billion and Recovery.org has an excellent website that shows how the money has been spent. A sample of one of their graphics is below and clicking on it will take you to their website.

The obvious question arises, how do these two images relate to each other? In other words, is the stimulus spending targeting the states with the highest unemployment?

The unemployment graph from FlowingData.com correctly uses the unemployment rate for each county. Consider North Dakota which appears to have a few counties with zero unemployment. But North Dakota has a relatively small population, so you might be wondering if the benign coloring of North Dakota is due to its low population. The answer is “no”, because the rate is being graphed. Sure parts of North Dakota are sparsely populated, but most of the folks who live there have jobs. Contrast this with Alaska which is also a state with a small population. Many of its counties are colored dark red and I’d imagine the population of these counties is no larger than the population of the average county in North Dakota. If FlowingData graphed the total unemployed instead of the unemployment rate then the resulting image would essentially be a population density graph, providing us with little information about unemployment.

But we’re interested in tracking if the stimulus spending is getting to the right places and to answer this we need to compare to the total unemployment. By way of example, 100% unemployment in Rhode Island probably means fewer total people unemployed than 10% unemployment in California. Even in this extreme case it would make sense for more stimulus to be applied to California than Rhode Island.

Recovery.org (and the government’s own Recovery.gov) site shows the stimulus spending to date and the Bureau of Labor Statistics has comprehensive data on unemployment. If you take the $137 Billion in stimulus spending so far and divide by the 14.8 million people who are unemployed you find that on average $9,200 of stimulus has been spent per person unemployed. Our question then becomes how do the states compare to this average? Which states are near the average? Which are significantly above this average and which are significantly below this average? The graph below answers this question.

Yellow is neutral here, these states are close to the average. Green states received more than $9,200 per unemployed person while red states received less. The news is in the deep red and deep green states. Wisconsin received less than 1/3rd of the national average. It is way behind the curve here and its congressmen and senators should get see about attracting more stimulus dollars. Illinois and Indiana are about 1/2 of the national average and, while not quite as dire as Wisconsin, are still way behind the curve and again their governmental representatives should be much more proactive.

Where should the money for Wisconsin, Illinois and Indiana (have) come from? Taking a look at the greener states, the heaviest hitters are Nebraska and New Mexico. While the stimulus per unemployed is large in Alaska, North Dakota and Wyoming, those states have such low populations that the total dollars “wasted” there is relatively inconsequential. Contrast this with Nebraska. It received almost 5x the national average but by population it is a much larger state than North Dakota. Consequently, if it received only half what it did, it would still have received more than twice the national average (per person) but that would have freed up more than $1B to go to Wisconsin. This would have put Wisconsin in the realm of one of the dark tan states like New York. Still below average but better.

You can explore these details yourself by clicking on the graph. This will take you to an interactive version of the page where you can mouse over the state initials to get more details. These additional details are:

• Unemployment Rate – bar graph, height ranges from 0 to 16%
• Total Unemployed – area graph. Area of the square ranges from 0 to 2.2 million
• Total Stimulus Spending – area graph. Area of the square ranges from 0 to $14.8 Billion
• Stimulus Spending per Unemployed – bar graph. Height ranges from $0 to $48,000. (The line at $9,200 shows the national average)

Furthermore the two area graphs are sized so the squares are the same size if the state is close to the national average of $9,200 per unemployed. For example, consider Texas. It received an average of $8,000 per unemployed in stimulus spending. Close to but just a little below the national average. Consequently the square for the total unemployed is just a little larger than the square for the total stimulus dollars received. Compare this to New Mexico. It has a vastly smaller total unemployed than Texas (the red square is smaller) and the size of the green square is significantly larger than the unemployed square which corresponds to its much larger $41,800 stimulus spending per unemployed.

Finally a brief note about the data. Normally I present all the data, favorable and unfavorable, but here I did what I expect was a minor editorial correction. The national totals were calculated by adding up all the state totals, both from the BLS website for unemployment and for stimulus dollars from Recovery.org, with one exception. I omitted the District of Columbia. But I only did this when calculating the $9,200 average. If you mouse over the District of Columbia the detailed stats are legitimate and hence you can see why I discounted them for the overall average. (If I left DC in would have been about $9,800 per person.)

In fact I do not know why the DC average is so large. I suspect it’s an artifact of accounting because many offices that receive stimulus dollars have a branch in DC and for that reason alone a portion of every stimulus program may be erroneously credited to DC. But again I do not know this for sure. It is certainly an oddity worthy of deeper investigation.

Additionally the Recovery.org site is updated continuously. The data used above is a week old. yet today on their website they show that the total stimulus spending to date is less than the data I’m using above. (But DC is still out of whack.)

A note about the graphs.

The colored map of the US was made using the map creator tool at indexmundi.com

The mouse over application was created using Processing available at Processing.org

Ms. Ostrom’s work challenged the view that when people share a finite resource, they will end up destroying it — what is known as the tragedy of the commons. That view argues that resources that are important for the common good need to be highly regulated or privatized.

It is easy to see why this might be so. The Wikipedia article uses the example of a common grazing area for sheep that are owned by several different herders. Left to their own devices (the argument goes) the herders collectively will add more sheep than the land can support because a herder gets 100% of the profit from an additional sheep while only feeling part of the pain from overgrazing caused by that extra sheep. (The pain of overgrazing is shared with the other herders.) It’s this imbalance, 100% of the gain while only part of the pain that leads economists to the conclusion that use of the commons will devolve into tragedy unless steps are taken.

While this is an idealized situation, there are many other similar examples. Commercial ocean fishing is one where the commons are the fish stock and the herders here would be the individual fishermen or boat owners. Water use, especially ground water where the underground aquifer may run underneath many different pieces of property is another. Pollution is a third.

The classic solutions, privatization or regulation, are on opposite ends of the political spectrum. Regulation is the classical liberal response while private ownership is the classical conservative response. Either could be applied successfully to the sheep herder example above. The commons could be divided up and sold to each shepherd or each shepherd could be restricted to a quota of sheep, but in many situations one of these 2 extremes is just not feasible. It’s not feasible to privatize the oceans for example.

The WSJ article continues by describing the inspiration for Ms Ostrom’s research. She found that water management in Southern California in the 1960s was on its way toward becoming a tragedy of the commons, but was both able to avoid this and able to avoid becoming highly regulated or privatized. Since “commons” type problems exist in many different forms she wondered if other such systems for dealing with them spontaneously arose without government oversight or privatization. Turns out it does.

Ms Ostrom’s work is encouraging for two main reasons. One is that she’s shown that there are more ways than just the 2 classic approaches toward solving this problem. The other is that she’s shown this from studying real-world examples. Both features are encouraging because it gives us hope that the world’s problems can have more solutions than we might imagine and that those solutions can be understood by (or at least implemented by) non-experts.

If we can get our governmental representatives out of their ideological bunkers and start talking to each other perhaps they too can discover these additional ways to solve our problems.