Another view on default cascades--Battiston et al. (2011)

This paper (pdf) was recently published in Switzerland, and provides an interesting look at our recent topic--default cascades. Although these papers are mathematically dense, they are worth working through sometimes as they may give some foreshadowing of future economic policy.

Block-slider model of earthquakes

Battiston et al. (2011) have presented a model of the financial system which might look like one of Turcotte's slider-block models of earthquakes, which are comprised of numerous blocks of (possibly varying) masses, connected by springs, having to slide across a surface with a limited (and possibly variable) friction. Motion in one block can change the stress field across the model, possibly triggering slip in one or more other blocks.

The original slider-block model consisted of two blocks connected by a spring, both of which sat on a somewhat rough surface (so there would be friction between it and the blocks). If block A moves some small distance, then it will add to the forces on block B. That force may be enough to overcome the friction which kept block B stable. If both blocks move together, we have a larger earthquake. The simple two-block slider model exhibits chaotic behaviour (Turcotte, 1997). I remember attending a conference a few years before the above volume was published when Turcotte presented a more advanced model that looked something like the one below.

We are looking at a plan view of several interconnected blocks. The frictional forces vary for each block, and each block has its own driver. Once again, the slippage of a single block may trigger slippages in one or more blocs--the more blocks that slip, the larger the earthquake. We might expect such models to satisfy the Gutenberg-Richter law which is an observed distribution of earthquake sizes through time that is consistent with a system at self-organized criticality (SOC). But I'm not sure because I've never seen the results although comments on similar models used to study avalanches were consistent with SOC (there are those avalanches again).

Block-slider model of default cascades

According to Battiston et al. (2011), prior to the financial crisis of 2008, existing models suggested that major financial entities had diversified their debts and obligations sufficiently that the likelihood of systemic failure was negligible. The observed financial crisis suggests that this conclusion was unwarranted, to say the least. The authors attempt to study the effects of diversification on systemic risks using a model conceptually similar to the block-slider model above.*

In the financial model, the blocks represent financial institutions. There are a large number of possible interactions between one institution and its neighbours. Furthermore, there is a richness to the interactions that is missing in the earthquake slider-block model--the debts and credits between institutions may each be long- or short-dated, so that there may be a mismatch in maturities between the credits and obligations of any one institution.

In the above figure, which shows only a portion of the potential interactions among entities A, h1, h2, etc., the arrows point in the direction in which credit has been extended. Credit may be long- or short-term. For instance, entity A has extended long-term credit to entity j1, and short-term credit to entity m1; and in turn has borrowed long-term from entity h1, and borrowed short-term from entity n1.

The authors carry out the following experiment. Assume an initial allocation of assets and liabilities across different participants, and derive (logically rather than empirically) a law of "motion" related to financial robustness of each agent affected by one or more of the initial defaults, as measured by their equity ratio. Models are run and the size of the default cascade is compared to the initial distribution of robustness and risk diversification.

The interrelationships between all the balance sheets of the various financial institutions links the dynamics of the individual equity ratios in ways that are not easily predictable.

The authors identify two "externalities" to the triggers for default cascades: 1) variability of financial robustness of all of the interconnected financial entities; and 2) the average financial robustness of the interconnected entities.

If all parties have similar financial robustness (variability is low), then increasing connectivity makes the system more robust. Stability is even likely through diversification if the individual parties are not very robust. It was only when the initial robustness was highly variable across agents (i.e., some agents are weak and others strong) that increasing interconnectedness tended to stimulate systemic defaults.

The second "externality" is a consequence of incomplete information--and deals with the likelihood that creditors will force a foreclosure on an otherwise solvent entity due to the fear that some of its counterparties might fail. Losses may therefore be amplified along the chain if runs begin on entities which may be technically solvent, but which may then be forced to sell long-dated assets at fire-sale prices to raise cash. Model runs suggest that if the average robustness of agents is high, then increased connectivity is beneficial. For low levels of average robustness, then increased connectivity has no effect. For intermediate values of average financial robustness, increased connectivity tended to stimulate systemic defaults.

The lesson here is diversification is not always a good idea. If you diversify across financial entities with wide risk profiles (i.e., some are weak and some are strong) you actually increase the likelihood of a financial calamity.

We don't have to confine ourselves to financial institutions. If we consider our agents to be sovereign, we expect the same problem. Creating a financial superpower out of a group of Germanys would be perfect--even a group of Greeces might be okay. But creating one out of Germanys and Greeces tends to encourage a financial catastrophe. Who could have predicted that?

The authors suggest that the "fix" for this situation is to concentrate risk rather than diversify it. I wonder--in whose hands will the risk be concentrated? Perhaps if you hold gold, the risk won't find its way into yours.

References

Battiston, S., Delli Gatti, D., Greenwald, B., and Stiglitz, J. E., 2011. Default cascades: When does risk diversification increase stability? ETH Risk Center Working Paper Series.

Turcotte, D. L., 1997. Fractals and chaos in geology and geophysics, 2nd edition. Cambridge University Press.

* one key difference between the default cascade and an earthquake--in an earthquake, the tsunami (if there is one) happens afterwards. The ocean of liquidity in which we find ourselves has preceded the major financial earthquake.

Scale invariant behaviour in avalanches, forest fires, and default cascades: lessons for public policy

We show that certain extended dissipative dynamical systems naturally evolve into a critical state, with no characteristic time or length scales. The temporal "fingerprint" of the self-organized critical state is the presence of flicker noise or 1/f noise; its spatial signature is the emergence of scale-invariant (fractal) structure.  - Bak et al., 1988 (one of the greatest abstracts ever written!)

1987 saw the publication of an extraordinary paper--one which led to a dramatic change in our understanding of the dynamics of certain kinds of dynamic systems. Most importantly  . . . introduced the concept of self-organized criticality, or self-organization to the critical state--which is a condition neither fully stable nor fully unstable, with a characteristic size-distribution of events (or failures). In the kinds of systems that interest geologists, earthquakes and avalanches were quickly recognized as being SOC systems, and SOC was recognized as the most efficient means of transmitting energy through a system.

Avalanches and SOC

An early computational experiment went like this:  imagine a pile of sand, on which single grains of sand are dropped one by one until an avalanche occurs.  An avalanche occurs when the slope at some local point is greater than a defined value.

If your sandpile is two-dimensional (length and height--imagine a cross-section of a real sandpile), you would have to visualize it as a string of numbers, where each value represented the number of grains of sand stacked at that point. In the figure below, we are only looking at half of the pile, from the midpoint to the edge.

In our simple sandpile consisting of four stacks, a grain of sand of thickness dx falls onto the middle stack. If the difference in heights between this stack and its neighbour x1 in the figure above) exceeds some threshold value n, then one grain of sand would drop from the higher stack onto the lower stack. You would then have to check whether the height of the next stack was now more than  n higher than its neighbouring stack. If so, then another grain of sand would drop down one more stack and so on to the end of the pile.

What happens in a two-dimensional sandpile is that eventually the height of the sandpile is such that each stack is exactly n higher than its neighbouring stack. As a new grain of sand is dropped onto the pile, it migrates along all of the stacks and drops off the edge of the pile.

The behaviour of the sandpile is very simple; but what happens when you move to a 3-dimensional model (I'm counting the height of the pile as a dimension--not all authors describing this problem do so!)? You might expect similar behaviour--that the slope of the pile will increase until a single grain of sand causes a rippling cascade through the entire pile. This doesn't happen, for it would imply that the natural behaviour of the system is to evolve towards a point of maximum instability. In the experiment, the behaviour of the sandpile was much more interesting. The pile built up until it reached a form of stability characterized by frequent avalanches of no characteristic size.



Bak et al. (1987) called this condition of minimal stability the "critical state", and pointed out that as it developed independent of modelling assumptions and external parameters, it arose by self organization--the term "self organized criticality" (SOC) was introduced to describe the process. The characteristics of systems displaying SOC are fractal geometry, and flicker noise (also called 1/f noise).

There are many systems in nature--and increasingly in the human environment--which are similar to the avalanche model described above. Real avalanches, and similar mass sliding events (debris flows in the deep sea, for instance) have been recognized as SOC processes; along with earthquakes, volcanic eruptions, and economic events.

Forest fires were quickly recognized to be characterized by SOC--at least in environments without a lot of active management. Curiously, it quickly turned out that the effects of fire management, at least as practiced in the United States, might have had an effect opposite to that which was desired.

Fire suppression in the United States

“Strange to say, that, obvious as the evils of fire are, and beyond all question to any one acquainted with even the elements vegetable physiology, persons have not been found wanting in India, and some even with a show of scientific argument(!), who have written in favor of fires.  It is needless to remark that such papers are mostly founded on the fact that forests do exist in spite of the fires, and make up the rest by erroneous statements in regard to facts.”   B.H. Baden-Powell

As European settlers spread through what became the United States, they were confronted by an unusual world. Wilderness was something that had to be eliminated so that "civilization" could spread. Forests were to be cut and the land put to the plow. This was more than an economic imperative--it was a moral imperative as well. 


The rapid westward expansion in the 19th century brought railroads, and railroads brought further development and fire. While clearance of the forest was necessary for development, the desire to create a forestry industry based on sustainable harvesting rather than a short-sighted liquidation of old forests was driven by European examples. And thus the American ideas of forestry were transformed by the turn of the 20th century. Forests were resources that had to be tended. And as resources, any fires within them resulted in economic losses.

Fire had been used as a method of maintaining the forest by the native populations--but such a method was far too messy and unpredictable for a modern people--particularly those who looked to the forestry programs of western Europe, where fires were uncommon. The European model worked tolerably well in the eastern forests in North America, where water was plentiful year-round; but this model turned out to be unsuitable for the western forests, the life cycles of which required fire as a controlling element.

Major Powell launched into a long dissertation to show that the claim of the favorable influence of forest cover on water flow or climate was untenable, that the best thing to do for the Rocky Mountains was to burn them down, and he related with great gusto how he himself had started a fire that swept over a thousand square miles. - Bernard Fernow

The forests of the southwestern United States were subjected to a lengthy dry season, quite unlike the forests of the northeast. The northeastern forests were humid enough that decomposition of dead material would replenish the soils; but in the southwest, the climate was too dry in the summer and too cool in the winter for decomposition to be effective. Fire was needed to ensure healthy forests. Apart from replenishing the soils, fire was needed to reduce flammable litter, and the heat or smoke was required to germinate seeds.

In the late 19th century, light burning--setting small surface fires episodically to clear underbrush and keep the forests open--was a common practice in the western United States. So long as the fires remained small they tended to burn out undergrowth while leaving the older growth of the forests unscathed. The settlers who followed this practice recognized its native heritage; just as its opponents called it "Paiute forestry" as an expression of scorn (Pyne, 1982).

Supporters of burning did so for both philosophical and practical reasons--burning being the "Indian way" as well as expanding pasture and reducing fuels for forest fires. The detractors argued that small fires destroyed young trees, depleted soils, made the forest more susceptible to insects and disease, and were economically damaging. But the critical argument put forth by the opponents of burning was that it was inimical to the Progressive Spirit of Conservation. As a modern people, Americans should use the superior, scientific approaches of forest management that were now available to them, and which had not been available to the natives. Worse than being wrong, accepting native forest management methods would be primitive.

Bernhard Fernow, a Prussian-trained forester, thought fires were the ‘bane of American forests’ and dismissed their causes as a case of ‘bad habits and loose morals’. - Pyne (1995).

Through the early 20th century, the idea that fire was bad under all circumstances, and fire control must be based on suppression of all fires came became the dominant conservation ideology. After WWII the idea became stronger still, partially because of the availability of military equipment; but also due to the Cold War mentality. Just like Communism, the spread of fires simply couldn't be tolerated--and it was the duty of America to contain both "red" menaces (Pyne, 1982).


In the latter part of the 20th century, the ideas behind fire suppression once again began to change. The emphasis on "modern" methodologies began to fade, with a preference appearing for restoration of the "old forest" from pre-settler times. Research into the forest had begun to reveal the importance of fire in the natural setting, and that humans had used fire to manage the forest throughout history. Costs of fire suppression had risen dramatically, and the damage done to the forest by the equipment and the methods of fire suppression often exceeded that done by the fires.


Gradually the idea of fire suppression faded, to be replaced by a determination to allow fire to return to its natural role. Major fires in Yellowstone Park in 1988 brought about something of a reversal again in policy, but it was recognized that a century of fire suppression efforts had left the western forests in a dangerous state. Even though fire was to return to that natural cycle, the huge growth of underbrush has created a substantial risk of massive, out-of-control fires. This risk is an indicator of just how unhealthy fire suppression has made American forests.


By comparison, forests in Mexico, where there have been no fire-suppression efforts are far healthier. Fires are more common, but tend to be smaller, due to lack of fuel. 

Fire, water, and government know nothing of mercy. - Latin proverb

Default cascades as avalanches


Economic fluctuations have long been recognized as SOC phenomena. One type of fluctuation that has been recently posited is the "cascading cross default" in which the failure of one entity to repay its debts drives one (or more) of its creditors into bankruptcy, which in turn drives one or more of its creditors into bankruptcy, and so on.


Clearly these default cascades can be of nearly any size. A default may only affect the defaulting institution--or it may take down all institutions in a global collapse. As a conceptual model, the sandpile automaton of Bak et al. (1987) is a pretty good representation--the key difference being that each individual stack in the economic sandpile is actually connected to a large number of other stacks, some of which are (geographically) quite distant. For instance, the failure of Deutsche Bank would likely put stress on Citigroup. Would it cause it to fail? Perhaps. We would model this by assigning a probability of failure for Citigroup in the event of a default by DB. And we would have to do this for all relationships between the different banks.


But we need conditional probabilities--because it may be that DB's failure alone wouldn't topple Citigroup. But suppose it topples ING, and Credit Suisse, and Joe's Bank in Tacoma, and Fred's Bank in Springfield, and Tim's Bank in Akron, . . . and many others, all of whom owe money to Citigroup. Then it might fall. So apart from having tremendous interconnectivity, with each bank connected to many others, there is also tremendous density of those connections, all of which would appear to make the pile very unsteady. 


Instead of dropping grains of sand one at a time on the same spot, multitudes of debt bombs are dropped randomly on the pile of financial institutions, provoking episodic failures. What might we expect of their size distribution?

The experiment as I've described is too difficult to set up on my computer, mainly because I don't know how to establish the probabilities of failure for all of the various default chains that may exist. Furthermore, the political will to prevent financial contagion, although finite, is unmeasurable. Luckily we don't have to run the model, as it is playing out in real life.

Paper now primed to burn


We have lived through a long period of financial management, in which failing financial institutions have been propped up by emergency intervention (applied somewhat selectively). Defaults have not been permitted. The result has been a tremendous build-up of paper ripe for burning. Had the fires of default been allowed to burn freely in the past we may well have healthier financial institutions. Instead we find our banks loaded up with all kinds of flammable paper products; their basements stuffed with barrels of black powder. Trails of black powder run from bank to bank, and it's raining matches.

References

Bak, P., Tang, C., and Wiesenfield, K., 1987. Self-organized criticality: An explanation of 1/f noise. Physical Review Letters, 59: 381-384.


Pyne, S. J., 1982. Fire in America: A cultural history of wildland and rural fire (cycle of fire). 

An economy explodes--unemployment in Ireland 1985 to 2011

Today we look at the historical unemployment rate for Ireland. No word yet on whether they have different definitions for "unemployed" and "out of work." This data I found at this site, which allows you to telescope the record to obtain monthly data from 1984 to the end of 2011 (for Ireland--the range of data differs for other countries).

Through the '80s and early '90s the unemployment rate was pretty high (by our standards). It fell during the tech boom of the late 90s, staying low until early 2008 when the rate very rapidly returned to the levels of the late 80s.

In earlier articles we have used the phase space reconstruction as a tool for interpreting the dynamics of the system. For ease of presentation, we limit our reconstructions to two dimensions even though we recognize that two dimensions is not sufficient for a true reconstruction. Below we see such a plot using the time delay method, with a lag of twelve months.

I'm not sure whether I would characterize the high unemployment as an area of stability, but you could make a pretty good argument for the highlighted area in the low unemployment section of the curve. The unemployment state occupied that tiny region of phase space for nearly six years.

One observation that favours a high-unemployment area of Lyapunov stability is the return to late-1980s levels of unemployment once the economic "miracle" collapsed.

Enlarging the lower part of the graph shows just how compressed the reconstructed phase space is for six years during the real estate bubble.

I'm not sure how much massaging the unemployment numbers have undergone over the past few decades--it's possible the picture is even worse for Ireland that it appears.

When sorrows come, they come not single spies, but in battalions.

That you have nothing to hide doesn't mean you have nothing to fear

The "Harper Government's" new online privacy bill, which requires that telecom companies hand over subscriber information to police without a warrant, had already generated a great deal of excitement--but when Public Safety Minister Vic Toews accused any opposition to the bill as supporting child pornographers, the online chatter went stratospheric.

Toews argument is yet another iteration of the old saw that you have nothing to fear if you have nothing to hide.

I, and many others, beg to disagree.

The reason I disagree is my long experience with authority figures and a certain creeping inevitability to the abuses that power entails. Once you are put in a position where you are to investigate crime, you begin to colour innocuous events with your own suspicions.

For instance, for years I have collected nickels. I would go to the bank, buy a number of rolls, pick out the ones I wanted, and returned the rest. There is nothing wrong with doing this, apart from whatever annoyance it might cause the tellers at the bank. But now I have these strings of petty cash transactions at the bank--20 dollars out, 18 dollars in, 40 dollars out, 35 dollars in, etc., each one separated by a day or two. I feel I have nothing to hide, but what might a CRA agent or some other suspicious member of the law think of this? How might they interpret all of this moving cash in and out of the account? Who knows.

A few years ago my bank offered me a free financial consultation, so I could talk to an "investment professional" about my progress towards my retirement goals. As per usual I told him that I was a significant shareholder and an insider of a reporting issuer--this is information that they are supposed to know in the event that I trade any securities of a company at which I am an insider. As he looked over my portfolio, he commented on how risky many of holdings were--there were a lot of junior miners there. I told him that I work in the industry, which helps me interpret the press releases and the geology of the various projects, some of which I have visited in the past. He threw off a light comment, "Well, I guess it's easy to make money when you trade on insider information."

Excuse me? Are you accusing me of something? This is a very dangerous accusation to hear from your banker, as he is required by Canadian law to report such suspicions through Fintrac. The guy knows nothing about me, but one look at my portfolio and he's sure I'm trading on insider info. This is the mindset of those who seek to use this information. You are a criminal; they just need to find the right information to prove it.

Suppose you have a favourite niece (or grandchild) going to university near your home. So you go to visit her every couple of weeks, bringing her a small bit of spending cash that you take from your bank on your way to visit her. Perhaps you email her before these visits. Now, suppose that this niece has been caught once with a small amount of some recreational drug (shocking, I know, but not unheard of at university). How might the police look at your pattern of small cash withdrawals from the bank followed by visits to this known drug offender?

Perhaps you are a Muslim youth struggling to keep to a moral path in what might appear to be an amoral world. For support you write your cleric with your questions about your struggle (perhaps you use the word jihad in this context). Might the police look at this discussion as a coded cover for something more ominous? Remember, that's their job.

Has US gasoline consumption really fallen by half in the past decade?

Recent blogs have made much of the following chart from the US Energy Information Agency, which shows a rapid decline in gasoline deliveries to retail stations from refiners.

The decline is significant because it reflects gasoline consumption at the retail level. Gas stations don't store large amounts of gasoline on site--they are normally close to running out when they take delivery. So the chart suggests that gasoline usage has fallen by nearly 50% in the past decade.

There is a problem with the data--it seems to be at odds with reality.

There are about 140 million cars on US roads. An average car is driven about 10,000 miles per year. Average mileage is somewhere around 20 miles per gallon. So each car uses 500 gallons per year, or about a gallon and a half each day. That means about 200 million gallons per day across the country. The chart above shows only about 30 million gallons per day being delivered to stations from refiners.

So either some combination of parameters in the above calculation is off by a factor of ten, somebody else is delivering a lot of gasoline to stations, or the data set is incomplete for other reasons. Need more information before we can draw conclusions though.

Updated reconstructed phase space for US unemployment rate

It has been some time since I updated the graph of US unemployment against real interest rates. Data for the chart have been compiled from the Bureau of Labour Statistics website and our friends at the Fed.

Some interesting changes appear, assuming we can believe the data. As this graph suggests two metastable equilibria for unemployment rate given low interest rates--one low, the other high. It may be that we are beginning to leave the higher "attractor" by way of a rising real interest rate. Of course, it is easy to reduce the unemployment rate if you redefine unemployment.

The reason we appear to have a rising real interest rate is because of the rapid rate of decline in CPI as delineated in the BLS data. I don't know about you, but I haven't noticed prices falling. Of course this is Canada and not the US--also I pretty much only buy food, and tea. Creating the appearance of falling prices might help sell more of those negative interest rate bonds. Sell enough of those and your budget is balanced!

Greek unemployment in phase space

Today's chart is a reconstructed phase space plot in two dimensions of the reported Greek unemployment numbers. The methodology is the same as that used to reconstruct the recent plots of US unemployment, with data available here and here.

First up is the time series of unemployment rate since 1998. The data is quarterly except for the last eight months, which is monthly.

And here is the two-dimensional phase space of Greek unemployment plotted with a six-month lag. Recall that we create these plots by making a scatterplot of the data against a lagged copy of itself. In this case, the lag is six months.

The points are quarterly, with the exception of the last two, which are monthly. Note how rapidly the system has evolved into the region of phase space characterized by high unemployment. This rapid excursion was preceded by a steady drop in the unemployment rate from 2004 until about mid-2008. The effect is reminiscent of a tightening spring which is suddenly released.

Ordinarily, after such a large excursion, we would expect to see these numbers stabilize. Stability in this region of phase space would suggest we have reached a long-term state of very high unemployment in Greece.

We see the same thing in the US unemployment rate phase space reconstruction below, with data from BLS.

Unemployment was relatively high in December of '03, and fell until early in 2008, before a sudden move to a much higher rate. Was the earlier decline in unemployment a precondition of the later increase? I think it was a reflection of the gaming of the economy due to low interest rate and the ensuing mortgage-market free-for-all. A lot of jobs were created in real estate sales and renovations--jobs which did not turn out to be based on a sustainable model.

The recent reported decline in unemployment rate appears to be forming some sort of excursion from the more recent area of Lyapunov stability, near the top right of the plot. If this were a natural system, we would look to see if it evolves towards the LSA at the bottom left of the plot. Systems which cycle slowly through two (or more) metastable equilibria are not uncommon in nature. Unfortunately, I still have some residual doubt that the decline in unemployment is not real, but a statistical fabrication.

Systems with more than one equilibrium state are very difficult to control, because their reaction to forcing (think policy) is dependent not only on the mathematical "laws" of the system, but also its past history. Arguably, each moment in history is unique--so the response of the system to the same policy will be different at each point in history. A Keynesian fixation on creating credit in response to all problems cannot be a viable solution at all times. Sometimes it will work. Sometimes new and unexpected behaviours will result, causing central bankers to apologize for their failures.

Unemployment

I have previously discussed methods of reconstructing phase space portraits to study the dynamics of complex systems, including economic systems. An earlier application of this approach applied to the state space of real interest rates and unemployment rate suggested that the economic system has more than one equilibrium state, which is at odds with conventional (Keynesian) economic thought.

Naturally, in performing this sort of analysis, we are assuming that the methodology by which our data are collected remains constant. Any changes (or manipulation) in the data and we begin to have problems. Unfortunately, there is reason to doubt the reported rate of unemployment. Many people who have been out of work for a long time are dropped from the workforce and no longer count as unemployed, as discussed by our resident experts, Drs. Abbott and Costello below (dialogue by CIGA Lew, originally here)

COSTELLO: I want to talk about the unemployment rate in America.
ABBOTT: Good Subject. Terrible Times. It’s 8.3%.
COSTELLO: That many people are out of work?
ABBOTT: No, that’s 16%.
COSTELLO: You just said 8.3%.
ABBOTT: 8.3% Unemployed.
COSTELLO: Right 8.3% out of work.
ABBOTT: No, that’s 16%.
COSTELLO: Okay, so it’s 16% unemployed.
ABBOTT: No, that’s 8.3%…
COSTELLO: WAIT A MINUTE. Is it 8.3% or 16%?
ABBOTT: 8.3% are unemployed. 16% are out of work.
COSTELLO: IF you are out of work you are unemployed.
ABBOTT: No, you can’t count the "Out of Work" as the unemployed. You have to look for work to be unemployed.
COSTELLO: BUT THEY ARE OUT OF WORK!!!
ABBOTT: No, you miss my point.
COSTELLO: What point?
ABBOTT: Someone who doesn’t look for work, can’t be counted with those who look for work. It wouldn’t be fair.
COSTELLO: To who?
ABBOTT: The unemployed.
COSTELLO: But they are ALL out of work.
ABBOTT: No, the unemployed are actively looking for work… Those who are out of work stopped looking. They gave up and if you give up, you are no longer in the ranks of the unemployed.
COSTELLO: So if you’re off the unemployment rolls, that would count as less unemployment?
ABBOTT: Unemployment would go down. Absolutely!
COSTELLO: The unemployment just goes down because you don’t look for work?
ABBOTT: Absolutely it goes down. That’s how you get to 8.3%. Otherwise it would be 16%. You don’t want to read about 16% unemployment do ya?
COSTELLO: That would be frightening.
ABBOTT: Absolutely.
COSTELLO: Wait, I got a question for you. That means there are two ways to bring down the unemployment number?
ABBOTT: Two ways is correct.
COSTELLO: Unemployment can go down if someone gets a job?
ABBOTT: Correct.
COSTELLO: And unemployment can also go down if you stop looking for a job?
ABBOTT: Bingo.
COSTELLO: So there are two ways to bring unemployment down, and the easier of the two is to just stop looking for work.
ABBOTT: Now you’re thinking like an economist.
COSTELLO: I don’t even know what the hell I just said!

If you're out of work, and you can help the economy improve so you can find a job--stop looking for work!

Temperature records from the North Atlantic show cyclicity

Let's consider this post to be an update of an earlier missive about possible climate change from Arctic records.

Change in state for Arctic sea ice?

In the article linked above we agreed that the recent change in the extent of Arctic sea ice was consistent with warming over the duration of the records (since 1979), but this warming was not conclusively anthropogenic. Below, with minimal comment I present some records presented in a recent International Council for the Exploration of the Sea (ICES) Report on Ocean Climate (2010). (Available as a pdf here).

The following graphs are all screen caps from figure 1 of the ICES report.

I have only selected a few of the records available. What I would take from this, especially from the longer records, is that ocean temperatures have varied in a cyclical fashion over multi-decadal periods. I would also note that most of the records show warming since 1979 (although many suggest cooling over the past few years).

This is to reinforce the point I raised last time--the time-frame since 1979 is not sufficient to conclude that anthropogenic warming is responsible for the reduction in sea ice extent over the past three decades. I would go further and point out that the temperature records we look at today are not sufficient to exclude anthropogenic warming--for instance, it is not clear whether the amplitude or period of the cycles is increasing, which is an important question. But we need to recognize the importance of natural variability in any discussion of recent climate change.

Can oil and agricultural land really replace gold?

Last time we discussed the ratio of government debt to gold holdings for select countries (primarily the PIIGS, but also the US and Canada). The article appeared on Zerohedge, and among the comments was this one:

Gold is just one asset class among many. TO divide debt by a single asset puts in play the grand assumption that gold is the only thing of value in the country that any creditor would want. I would argue that, while gold has its place, barring the breakdown of the financial system there are many other assets that are fungible with credit and paper money.

Re Canada: Canada has many fungible assets. How about dividing debt by oil reserves?  Or  fecund and cultivatable land? or other minerals and metals. 

The comment was made in response to the graph I had showing that Canada's debt was about 4000 x greater than its gold holdings.

There are points here worth addressing--some of which crossed my mind in an earlier article about gold mining and exploration. Ask geologists why they spend so much time looking for gold (at least 50%, by exploration expenditure) and your answers will probably be something along the lines of that is what pays the bills. But why should there be so much effort to finding gold, as opposed to, say, copper, nickel, aluminum, and zinc--all of which we use in much greater quantities than gold?

The answer is that gold, unlike any of the other metals listed above, is money. As money, it is irreplaceable, for reasons posited by Aristotle. In exploring for other metals, like lead or zinc, there is always a chance, however unlikely, that the industrial demand will fall dramatically due to technological advancement. Such risk affects every decision made from exploration to mine development for the different metals in different ways.

While it is true that gold is just one asset class among many, it is the only one that extinguishes debt. Credit and paper money merely transfer the debt from one debtor to another. Neither speak to the ability of the debtor to make good the debt.

What about other physical goods like oil or land? One problem is that, as I understand it, the oil and most agricultural land are private property and not something the government can properly use to pay down debt. But even if these things were public, rather than private property, they are not as good as gold for repaying debt. For one thing, the livelihood of Canadians depends on the present (and future output) of agricultural lands and oil fields. Selling them to repay debt would be like a carpenter selling his tools to repay his debts.

To tax the output of our mining, oil extraction, and agricultural fields to repay debt is to subject ourselves to a lower standard of living--which is the result in any case of incurring debts with no counterbalancing assets. Gold, as a non-productive asset, could have been sold without lowering our standard of living (were there any).

There is a lot of Crown Land which might be sold--although at what price is unknown. The only way to find out is to put it on the market.