Relevant and even prescient commentary on news, politics and the economy.

The oil industry is undergoing a major structural change.

Lifted from comments from this post US to be leading producer of oil is Spencer England’s comment about structural change in the oil markets. Obvious to some but bears repeating for a lot of us, as we discuss environmental issues or gasoline prices in the media more than structural economic impacts:

Spencer says:

The development of fracking and the tar sands means that the oil industry is undergoing a major structural change.

Use to be that one of the thing that made the oil industry very unique was that virtually all their costs were sunk or fixed costs and variable costs were relatively insignificant. Under this cost structure if prices fall it still pays to produce oil when prices fell as long as revenues covered the variable costs. So falling prices did not lead to falling output.

But now the marginal supply of oil is from tar sands or fracking where variable costs are very high. Moreover, the marginal costs of bringing in new oil from these sources is now in the $80 to $100 range.

So now, when prices fall, at the margin some producers will withdraw from the market and output will fall.

This is creating a fairly solid floor, the price for oil at about $80 — where oil bottomed last year and again this year.

Very few people are incorporating this structural change in the oil market into their analysis.

Spencer

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Fowl and Fishy Inflation

It has been suggested that the rapid increase in the prices of fish, fowl, meat and eggs for about two years following October, 2009 was the result of QE causing inflation in these items.  From this Calculated Risk graph, we can get the QE date line.  QE was announced on Nov 25, 2008, and expanded in April 2009.  It ended in May, 2010.  QE II was hinted at in Sept, 2010, announced in Nov 2010, and ended in August 2011.

The timing correspondence is less than stellar, since the YoY increase in prices for those food items dropped like a rock from October, ’08 though Oct. ’09.  It then shot up to a 7 1/2 year high in May of 2011.

This can be seen in the red line of Graph 1, which also shows the CPI for all items except food and energy (CPILFESL) in blue.

 Graph 1 YoY Price increases for Selected Food Stuffs and All Items Less Food and Energy

To assume a cause and effect relationship, you have to account for a time lag of a year from the announcement and 6 months from the expansion of QE to the turn around in those price increases from the Oct ’09 bottom.  Remember, through the first 11 months of QE, the YoY change in those prices dropped dramatically.  Between May and November, 2010, while no QE program was in effect, these prices had the steepest part of their rise.  After QE II ended in August, 2011, the YoY price increase remained high for those items until the end of the year, and then fell rapidly.

A longer view reveals that the increase in those food prices oscillates continuously around the All Items Less Food and Energy line.  The trough to trough period is irregular, averaging 3.52 years with a standard deviation of 0.45 year (5 measurements).   The trough to trough time from May, ’06 to Oct., ’09 was a very typical 3.4 years.  It is very hard to look at that graph and see anything unusual about the 2008-2012 region, other than the depth of the trough shortly after the Great Recession.

It appeared to me that the blue line of Graph 1 might be a crude approximation of a long average of the red line.  This turns out not quite to be the case, since the two lines are measuring different baskets of goods.  What we have is the YoY increase for these food items oscillating around its own mean. That sounds like a tautology, but let’s look a little deeper.

Graph 2 shows the same data, along with some long averages of the food stuffs YoY price increase line.   These are the 5 Yr (light blue), 8 Yr (yellow), and 13 Yr (purple) moving averages, and the average for the whole data set, 2.9 (bright green).  I’ve also included an envelope one standard deviation (3.06) above (5.96) and below (-0.17) the mean in dark green.

Graph 2 YoY Price increases for Selected Food Stuffs with Avgs and All Items Less Food and Energy

This (sort of) resembles a control chart.  The +/- Std. Dev. envelope isn’t a hard barrier, but does tend to turn the data path back toward the mean, unless something strange happens.  Frex, the big rise from late ’02 to early ’04 followed the Iraq invasion and resulting disruption in petroleum pricing.  The ’09 trough was the result of the Great Recession.  These are explainable variations.

Note also that the moving average lines tended to run below the CPILFESL line prior to late 2002, and have tended to run above it since.  This is to be expected since these items are basically the top of the food chain and have several layers of fuel dependent contributors in their cost structure.  Recall that until 2002, fuel prices were low, and since then (except for the Great Recession) have increased steadily.

I’m quite sympathetic to the idea that QE has done very little to help ease the economic doldrums following the GR.  But I see no reason at all to believe that it has contributed to the pain and suffering of ordinary citizens at either the grocery store or the gas pump.

Maybe there have been real downsides to QE.  Any thoughts on what they might be and how to quantify them?

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Has America Lost It’s Drive? – Pt. 4

In Part 3 of this series, I wondered a couple of things.

 – With the vehicle/1000 people number in the range of 825 to 845 since 2004, is the market near saturation?
– Is the January sales number of 14.2 SAAR (seasonally adjusted at annual rate) enough to maintain the vehicle/1000 people number?

For the first question, I have to again credit Roger Chittum for pointing me to this 2007 paper, Vehicle Ownership and Income Growth, Worldwide: 1960-2030, by Dargay, Gately and Sommer (32 page pdf, data through 2002.)  There’s a lot to this paper, including projections into the future for vehicle sales and fuel consumption, worldwide.   My immediate interest is in their use of a Gompertz function to estimate vehicle market saturation as a function of per-capita income.

Here is one of their graphs.

 Graph 1  Vehicle/1000 Gompertz Function of Per-Capita Income

Their model indicates flattening above about $30K per year, and leads to a saturation point in the U.S. of about 852 vehicles per 1000 population.  Saturation points for various countries also depend on urbanization and population density.  See the paper for details and background.

This indicates that the U.S market is about 97% saturated, give or take a point.

What does that suggest for vehicle sales going forward?   Karl Smith led off the month pointing to this graph from Calculated Risk, estimating light vehicle SAAR for February at 15.1 million.  With that, on to question 2.
 
I already have the data in hand for vehicles/1000 population (see part 3.)  The data for the Calculated Risk SAAR graph comes from BEA, Table 7.2.5S.  Plotting a scattergram of YoY change in Vehicles/1000 population vs annual average SAAR for the years 1990 to 2009 gives us this picture.  (See notes, below.)

 Graph 2   SAAR and Change in Vehicles/1000 Population

This suggests that the break even point for vehicles per 1000 is right around 14.7 million annual average SAAR.

The official vehicle/1000 numbers are only available up to 2009.  But we have the SAAR data for 2010 and 2011.  Annual average SAAR for 2010 is 11.77; for 2011, it’s 13.05.  You probably don’t want to take the values suggested by Graph 2 too literally, but seeing the vehicle/1000 number slip to around 815 for 2011 should be a reasonable expectation.  This is still slightly above the 95% saturation level.

Average light vehicle SAAR for the first two months of this year is 14.65 – right at the break even point for vehicles/1000 population.  

 Notes on Graph 2

The red dots represent data for 2001 and 2002.  The SAAR values look reasonable.  The changes in vehicles per 1000/population do not.  An increase of 25 in one year, from 800 to 825, followed by a decrease of 10 in the following year with SAAR, nearly identical (17.46 and 17.15) makes no sense.  An average of the two, plotted for both years as yellow dots, by some odd coincidence, lies exactly on the best fit line.

The R^2 value of .43 is less than stellar, but not terrible.

Eliminating the two questionable points raises R^2 to a respectable .65.

Cross posted at Retirement Blues.

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Is America Losing Its Drive? – Pt. 3 Vehicles per 1000 Persons

In private communication, Roger Chittum got me thinking about the vehicle component of gasoline consumption. I’m going focus on the gross vehicle numbers, and not get too deeply into the car/truck/SUV product mix detail.   Data is from the Department of Energy TRANSPORTATION ENERGY DATA BOOK: EDITION 30—2011.   (Warning:  414 page pdf.)

According to Table 3-5 on page 3-9, vehicle ownership, measured as vehicles (both cars and trucks) per 1000 population, peaked in 2007 at 843.57, and dropped by 1.88% to 828.04 in 2009, two years later.  Data presented in the source is from 1900 to 2009.  This graph shows the data from 1950 to 2009.  Recessions are highlighted in red.

During the post WW II era up to 1982, recessions might have slowed the growth of vehicle ownership, but they did not cause a decline.  Even the severe recession of 1958 only caused a flat spot on the curve.


 

This changed with the double dip recessions of 1980 to 1982.

 

The reduction from 1981 to ’82 is miniscule.  But since then, every recession has led to a significant reduction from the previous year.  As an aside, this is one more time series that shows a change in character right around 1980.

This source indicates the most recent value for the U.S. is 765, though it’s not clear what “most recent” means.  If this is accurate, then ownership has back-tracked to the 1994 level.   This would correspond to a 7.6% drop from 2009, and an astounding 9.3% drop from the 2007 peak.  I don’t believe it; but that value is indicated with a red dot on the next graph, as a point of reference.

I’ve also included some best fit straight lines to show how the slope has changed over time.  The decreasing slope and more serious response to recessions might result from a market being close to saturation, but that’s just a guess.

One of the reasons I’m skeptical of the red dot point is that new vehicle sales have recovered substantially from the 2009 low, as this graph from Calculated Risk demonstrates.  (The August, 2009 spike is the cash for clunkers event.)

This might not be enough to stop a continuing slide in the vehicles per 1000 population number, but I think it’s enough to keep it from falling off a cliff.

Another perspective on vehicle use comes from Table 3.3 on page 3-5 of the Data Book.  This graph shows the Federal Highway Administration estimate of vehicles in use.

Except for recessions (highlighted in red on the total line) growth of the total vehicle count has been been quite constant over four decades.  But, since the mid 80’s, car sales have been stagnant.  All of the growth since then has come from truck sales.  It will be interesting to see how these trends develop over the next few years.

Part 2
Part 1
Cross posted at Retirement Blues

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Has America Lost its Drive? Part 2

I made a mistake in my original post.   Graph 4 in that post was based on the wrong data set.  As Roger Chittum pointed out in comments, that graph only covers a subset of total gasoline deliveries.

This is the correct graph.  (Source.)  Thanks, Roger!

Graph 1 Gasoline Supplied

The fall off in gasoline delivery is not as extreme as I indicated, but it is still real.  Here is a close-up view of data for the current century, from the same source.

Graph 2 Gasoline Supplied This Century

Seasonal changes are dramatic.  Peaks occur in July or August, valleys in January or February of most years.  May values, highlighted with blue dots, and September values, highlighted with yellow dots, are recurring secondary peaks and valleys, respectively.  July values are highlighted with red dots.  The years 2008 and 2010 are accented with contrasting blue line segments.

In 2008, gasoline consumption dropped dramatically.  May was down slightly, compared to ’07, while July and September were down a lot.  Through 2009 and ’10 there was a slight recovery, with all three highlighted months showing increases.  The 12 month moving average, in pink, stopped falling, but failed to increase very much.

Then, in 2011, gasoline deliveries turned down again. This can be seen clearly in the highlighted months and the moving average. Some of the standard explanations are changing demographics and retail habits. An aging population with more retirees might tend to drive less – though this is not my personal experience. Kids these days cruise on social media rather than pleasure drive through the streets of town as we did in my day. On-line shopping, though only about 5% of total retail, is growing rapidly.

You can’t gainsay any of these trends.  They are probably affecting the big picture.  But it would a stretch to say that they can account for less gasoline use in 2011, but not 2010 or 2009.  Especially so, since this past year was supposed to be a recovery from the previous economic doldrums, and the expectation would be for the improvements of the previous two years to continue.  But it looks like something is happening, economically or culturally, to cause another downturn in travel – though not as dramatically as I suggested in the original post.

The estimate of vehicle miles driven, from the December, 2011 report by the Federal Highway Administration, tells a similar story.

Graph 3 Vehicle Miles Driven – Moving Total

The years 2008 and 2010 are highlighted in yellow.  The pink line traces the November, 2011 low back through the Summer of 2004.  Again we see recovery in 2009 and ’10, and a resumption of the slide in 2011.

The slope change in mid-2005 is intriguing.   This precedes the April 2006 peaks in the Case-Shiller Composite-10 and Composite-20 Indexes by several months, and the October 2007 peak in the S&P 500 by over 2 years.  The new slope remains relatively constant right up to the peak in November, 2007. 

Meanwhile, gasoline prices have increased again in the last month, after sliding about 70 cents from the high in May, 2011.  This gloomy article at Seeking Alpha blames part of the price increase on “stronger demand, courtesy of a growing economy.”  The data simply does not support this opinion. Instead of text book supply-demand behavior, gasoline prices and miles driven exhibit basically similar motion

I still contend that the prices of petroleum products are manipulated on the supply side.  All the data I’m aware of supports this.

I expected the original post to be a one-off, but the current picture is interesting, with no obvious explanation.  This might bear looking into in another 6 months, or so.

Cross posted at Retirement Blues.

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Has America Lost its Drive?

Yesterday,  Karl Smith posted on Oil and the Structural Recession.  This seems to be one of Karl’s thinking-out-loud posts, with more questions than answers, some convoluted reasoning, and a conclusion that higher gasoline prices are in our future.  If I read him right, this will be due to a demand pull.

He included this graph from Calculated Risk.

Graph 1.  U.S. Vehicle Miles

The number of miles driven tends to flatten during a recession, then recover quickly when the recession is over.  At least, that’s the way it used to be.  The Miles Driven curve seems to have been losing slope since the late 90’s, and was close to flat-line during the housing bubble last decade, when everyone supposedly felt rich.  There has been no recovery after the recent Great Recession, which officially ended 32 months ago.

 The same CR post cited above also includes this next graph.

Graph 2.  YoY Change in Vehicle Miles

This confirms my eye-ball assessment that the slope in the first graph has been in decline since long before the oil price bubble of recent years.

But here is a contrary development.  Calculated Risk also reports that the truck tonnage index is way up for all of 2011, and especially in December, when it posted an all time high.

Graph 3. Truck Tonnage Index

Truck traffic is way up, but total miles driven, per graph 2, has been mostly in decline for four years.

This suggests that discretionary personal driving has been sharply curtailed.  I’m having a hard time coming up with any alternative explanation.  Can anybody suggest one?

Just in the last couple of months, it seems that discretionary driving has taken a deep plunge that has not yet shown up in the data posted above.  Deliveries to retail gas stations have been slumping for well over a decade, and now have fallen off a cliff.  If gasoline delivery is just-in-time, as I believe it is, then deliveries are an excellent proxy for consumption.

By the Way, improved fuel economy cannot account for more than a small fraction of this change.  The big improvements in fuel economy happened during the 80’s, when fuel deliveries were in an upswing.  Since 1990, fuel economy improvements for the actual fleet on the road have been on the order of 0.5% per year.

 Graph 4.  Gasoline Retail Deliveries

I made my own graphs of the retail delivery data (not posted,) and there is, surprisingly, no particular response to the recessions of 1991 and 2001.  It’s not easy to find any recession on Graph 4.  Deliveries were slumping even before the Great Recession, so whatever effect it might have had on its own was subsumed by the general trend.  The above graph is noisy, due to lack of seasonal adjustment.  The lowest row of dots over most of this graph represents January data.  Summer months cluster at the top of the array, as you would expect.  Those two lonely points in the lower right corner are October and November, 2011, the most recent data shown.

It’s remarkable that gasoline deliveries are now substantially lower than at any time available in this data set. spanning about 30 years.  And I would never have guessed that anything like this was happening, based on my many trips on I-75 between Detroit and Toledo.  That route must not be a representative sample.

In a comment on Karl’s post, I said that I see all petroleum prices as highly manipulated on the supply side, with demand as a follower.  This data makes me think that the same is true of gasoline, in particular.  But it can’t be the entire story for the decline in consumption.  There is no clear connection between deliveries and gasoline prices over the last several years.

I don’t know where gasoline prices are going.  Karl might be right that they are going up.  But I don’t see any way that this can be due to a demand pull.

Mish also has a couple of recent posts relevant to this topic.

H/T to commentor rjs at Karl’s post, who got me thinking about this, and provided a key link.

Cross-posted at Retirement Blues.

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A Deeper Dive into Oil Pricing

In the previous post, I suggested that speculation is driving oil prices higher than they should be.  In this follow-up, I think I can show that oil prices are not behaving in a completely supply-demand determined way.  We’ll look at price activity, volatility, and an estimate of what rational pricing might be.

First, here is Brent Crude spot price activity since 1987, data from the U.S Energy Information Administration.  I’ve taken a weekly average of daily data, and plotted it as of each Friday (it was just a lot easier than trying to work their data table into a daily data plot.) Also included is a 55 week moving average.

As you can see, the price really took off after 2000.  Coincidentally, the Gramm–Leach–Bliley (Financial Services Modernization) Act of 1999, which undid portions of the Glass-Steagall of 1933, was signed into law on Nov 12 of that year, and the Commodities Futures Modernization Act of 2000 was signed into law on Dec 21 of that year.  These new laws allowed mega-consolidation in the finance industry and prohibited the regulation of certain speculative activities.

Here is the same data, separated into two graphs around the year 2000.  Also shown are the 55 week moving average, and an envelope one standard deviation above and below the average.  St Dev is based on the same 55 data points as the moving average.  The sections of the price line that extend above the {Avg + St Dev} line are highlighted.

The entire data set contains 1166 points.  Of these, 428, or 36.7%, lie more than 1 Standard deviation above the moving average.  For the segment through 1999, 156 of  574 points, or 27.18%, lie above the St Dev envelope.  For the segment 2000 on, 272 of 592 points, or 45.95%,  lie above the St Dev envelope.

For data normally distributed around the mean, about 1/3 of the data points should lie outside the 1 Std Dev envelope, half of them (1/6 of the data set) above and half below.   I’m no statistician, but this is not a well behaved data set.  Clearly, there is a powerful high-side bias.  What could be the cause?  Here are some possibilities.

1) Supply-demand forces in a growing world economy are so skewed to the demand side that this is that natural result.
2) External forces, such as panic due to war and instability in the Middle-East, have irrationally raised prices.
3) Speculative forces with a strong long-side bias have skewed the market away from a supply-demand determined price level.
4) Withheld supply due to OPEC activities, contango (hoarding on leased tankers), and the disruption of Iraqi supply for the last decade have unnaturally skewed the supply component.

My view is that possibilities 2 – 4 are all operating to some degree.

I have no way of evaluating 2 and 4. However, 4 seems reasonable, in view of the classic description of inflation: too many dollars chasing too few goods.  This effect could also spill into into futures speculation, where the amount of oil traded is finite, but the amount of speculative money available appears not to be.

To be clear, I’m not talking about CPI inflation, I’m talking about commodity-specific inflation. I believe that financial tail-chasing has not been limited to oil speculation. There is enormous wealth in the world, and to a large extent, it is not being devoted to legitimate investment. It is being devoted to computer generated program trading that capturess tiny fractional percentage gains millions of times per day, to skim money away from those who use exchanges for valid purposes.

And maybe we can get a handle on speculation.  My hypothesis is that deregulation in the 1999-2000 time frame has enabled and encouraged speculative rent-seeking activities in the oil futures market, which has inflated the price of crude.  One way to go at it is to have a look at volatility.  We already have standard deviation in our hip pocket.  Let’s see what we can do with it.

Here is standard deviation, based on 55 consecutive data points, divided by the average of those data points.  Just for kicks, included are a 55 point moving average of the St Dev in red  (for what it’s worth –  not much, I’d say) and a best fit (least squares) trend line. 

Well – the trend line slopes up a bit, but that’s not really a lot to go on.  On the other hand, the entire 90’s lie below the trend line.  In fact, except for the 1990 price spike, most of the of the St Dev values prior to about 1999 lie below the trend line.  Let have a closer look.

Here, the data are divided into two segments,  up through 1999, and 2000 and beyond.   For the early segment, the St Dev/ Price line is in dark blue and the 55 week average is in red.  For the latter segment, the lines are light blue and yellow, respectively.  Now, the trend lines tell an interesting story.  For the early period, the trend line is essentially flat, with a slight downward slope.  For the latter period, the slope is clearly upward.  Despite the localized gyrations, we can see that prior to deregulation, volatility had no trend.  After deregulation the trend is up.

Up to 1999, St Dev / Price averaged 12.65% (exclusive of the 1990 spike, taken as August, 1990 through January, 1991 the value is 12.01% )  From 2000 until now, the St Dev / Price averaged 15.06%.

So, what we see is that since since deregulation, prices have gone up, volatility has gone up, and upside bias in the data set has gone up.  Let’s resurrect possibility 1) and see if demand pressure can be the cause.  To get a handle on this, I took a closer look at my speculative idea from the previous post, and extrapolated prices forward from 1990, based on hypothetical constant growth rates.  Originally, I took a SWAG at the 1990 average price, and came up with $30 per barrel.  With a growth rate of 4% over 21 years, that would result in a current price of $68.36.  The 4% growth rate came from a generous estimate of World GDP growth over the period, assuming a direct, linear link between GDP growth and demand for petroleum.

Here is a graph based on the data instead of a SWAG.  It shows constant price increase rates of 3, 4 and 5% per year, based on the actual 1990 average price of $23.66.  The first thing to note is that my $30 SWAG was more than $6 too high.  The next thing to notice is that 1990 was the worst possible year to select, given the point I’m trying to make.  Due to the local spike, the 1990 average of $23.66 is almost $5 higher than the 1987 to 1993 average of $18.84.  So, if anything, my estimate of $68.36 is artificially high.

Still, I went with the 1990 average for this chart.  Extrapolations are based on growth rates of 3 (yellow), 4 (red) and 5(green)% from the 1990 average of $23.66.  This gives Mid 2011 price estimates as follows.

At 3%   $43.34
At 4%   $53.02
At 5%   $64.09

I’m not suggesting that this is a fool-proof method.  However, it is gratifying that it is more-or-less consistent with the oil industry estimation of a supply-demand determined price.  Further, these price growth estimates are quite generous, since estimates of petroleum demand growth are in the range of 1.6 to 2.3 %.

My conclusions:
1) The price of oil is far above rational, market-based pricing.
2) While other distortions and manipulations are likely to play a part in an inflated price, it’s not clear how they could contribute to increased volatility.
3) Unregulated, excessive speculation, with a long side bias is indisputably taking place.  I believe this is a major contributor to excessive price inflation, and the sole contributor to excess volatility.

Do you have a better idea?  Let’s hear it.

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Speculation About Oil

Last Spring, some Democrats and liberals (Ed Schultz and Bernie Sanders spring readily to mind) who have somehow resisted the enlightenment of unfettered free markets suggested that high oil prices are due to speculation.

Noah Smith took this subject on, asking the question: “Do speculators cause oil and/or gas prices to rise above their “natural” or fundamental level?” Noah’s take is that speculation is innocent, and he cites some corroborating experimental evidence. I’m a big fan, but this time I think Noah missed the point. First, I’ll state right up front that futures markets play a vital role in allowing the producers and first-line purchasers of various commodities to be able to stabilize their cash flows and construct realistic business plans. So – yes, futures markets are a good thing.

On the other hand, when quizzed by Senator Cantwell on why big, trans-national oil companies should continue to receive multiple billions of dollars in tax breaks, Exxon CEO Rex Tillerson admitted that a good estimate of a supply-demand determined price (considering the price of the next marginal barrel) for crude is in the range of $60 to $70 per barrel.

For reference, here is a chart and data table for Brent crude, going back to 1987.

At the depth of the global recession, on Boxing Day 2008, when the world was coming to an end, the price dipped below $34. Be that as it may, with recent prices back over $100, we’re looking at premiums over a rational value estimate of from 60 to 85%. Let’s just call it 75% for convenience.

Now, back to the point that Noah misses, and that Senator Cantwell suggested. What is the effect of unregulated speculation on the price of oil? The Senator estimates 30% activity by concerned stake-holders, and 70% by profit-seeking (in my view rent-seeking) speculators who are playing the market for a profit. The graph on Pg 5 of this study (18 Pg. pdf) suggests a ratio closer to 45% commercial and 55% non-commercial interest. Also it looks like open interest, which had been relatively flat for years, increased by a factor of 6 or 7. This financial tail chasing, aided and abetted by deregulation, is a direct manifestation of the asset misallocation that, in my view, is the real cause of The Great Stagnation.

A look at the oil price chart shows 10 to 15 years of more-or-less flat line in the range of $20, followed by a classic bubble and post-bubble bounce. As an aside, this is typical Elliott wave behavior. I can easily trace a five wave rise to the peak, and what looks like the recent end of a counter-current B-wave since the Dec. ’08 bottom. If this is anywhere near correct, the price of crude a decade from now will be eye-poppingly low, and fundamentals be damned.

As an example of a classic bubble peak, consider the Dow Jones Industrial Average during and after the 1929 crash.

But let’s look at fundamentals, anyway. Global GDP growth since 1980 has been in the range of 2 to 5%. Let’s generously call it 4%. The price of crude in 1990 varied from about $15 to $41. Let’s generously call it $30, on average.

If we compound $30 at 4% for 21 years we get (are you ready for this) $68.36. And this is based on generous numbers.

Not a rock-solid price algorithm, for sure, but it ought to be in the ball park. Maybe it’s just a coincidence that this number corroborates Rex Tillerson’s off-hand estimate.

Maybe it’s another coincidence that oil prices took off after Phil Graham pushed through legislation (signed by Billy-Bob Clinton at tail end of his battered second term) that exempted some speculative trading from certain regulations dating back to the Commodities Exchange Act of 1936. One of these exemptions was removing this requirement: “Either way, both the buyer and the seller of a futures contract are obligated to fulfill the contract requirements at the end of the contract term” from oil and other energy products. In case this is not crystal clear, it means that back in the bad old days of regulation, a contract had to be closed by executing the opposite transaction from the original prior to expiration, to avoid either supplying or receiving the physical amount of the contract. But after deregulation this requirement was not in force for oil.

Maybe it’s another coincidence that Morgan Stanley became the largest oil company in America. Plus, another point that Noah explicitly missed is that big, speculative finance entities did, in fact engage in physical hoarding. Here is a 20 month old news flash.

Oil traders are taking advantage of a market condition known as contango, in which the price for future delivery is greater than the price for spot (immediate) delivery. If the difference between the two prices is more than the cost of chartering an oil tanker, traders stand to profit. The difference between the price of crude oil for June delivery and the price of crude oil for July delivery is more than $2.00 a barrel; that’s enough to defray the cost of chartering a very large crude carrier (VLCC), which holds about 2 million barrels of oil and, as of April 23, cost $43,876 per day, according to the Baltic Exchange.

How much of an incentive to keep prices artificially high do you suppose is provided by a cost of $43,876 per day? That’s $1.31 million per month.

And that’s why I think Ed Schultz, Bernie Sanders, and Maria Cantwell might actually be on to something.
________________________________
An earlier (and to be honest, inferior) version of article was posted on Retirement Blues back in May.

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Spencer on Petroleum Exports and US trade balances

Some of you may remember that last cycle I sometimes posted charts showing real exports and imports versus trend to show how trade was doing. It is now over two years since imports and exports bottomed so I thought It was time to update those charts for this cycle. But as I started looking at the data I made a very interesting discovery.

Real petroleum exports — both crude and refined product — are exploding. After being flat for years, petroleum exports have been growing at some 33% to 50% rates since 2006. They have leaped from about $2 B (2005 $) to over $5 B (2005 $). This is a significant development that few people have recognized.

To but this in perspective, petroleum exports had been a relative small factor in trade and the economy for years. For example from 1994 to 2005 petroleum exports had fallen from a sum equal to about 10% of petroleum imports to under 5%. But they now amount to almost a third of petroleum imports.


Consequently, when you look at US dependence on foreign oil it makes a big difference in the analysis if you just look at the data on gross imports or if you adjust the data to look at net petroleum imports — imports less exports. Gross imports show that since the last cyclical peak the combination of falling demand and even expanded domestic production over the last couple of years had produced a significant drop in real petroleum imports. I’ve long used oil imports as a measure of the US marginal demand for oil. This approach shows that real petroleum imports have fallen to their level of about ten years sago — a rather import ant development. But if you look at the net data, real petroleum imports are back to levels of 20 years ago. This shows that the US is making much more significant progress in reducing its dependence on foreign oil than is generally recognized.

This also has a significant impact on the US balance of trade. In recent months the real trade balance has appeared to be bottoming and this has been starting to get economists and analysts optimistic that trade could make a significant positive contribution to growth in coming quarters.

But the improvement has almost all stemmed from petroleum. The real trade balance excluding petroleum is still deteriorating, although maybe not as severely as it had been earlier in the cycle.

Moreover, if you look at real non-petroleum exports they appear to be rolling over. When I tried to do a trend analysis neither a linear or an exponential equation generated a good fit. I had to resort to a polynomial equation to get a good fit. But real non-petroleum exports shows significantly worse prospects for trade adding to growth in coming quarters that the consensus expects.

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The Effect of Oil Prices on Oil Drilling in the U.S.

by Mike Kimel

The Effect of Oil Prices on Oil Drilling in the U.S.

Oil markets have changed dramatically in the past couple of decades or so. Except for a few years following the second Oil Embargo – prices got as high as $60 (in 2005 $) a barrel in 1981 – real prices have tended to be below $25 a barrel through about 1999. Conversely, 2003 prices have been higher than that – in some years quite a bit higher. Now, there are all sorts of explanations for this big change we’ve observed over the past few years, ranging from Peak Oil to the war in Iraq to the rise of the BRICs to market manipulation, but that’s the point of this post…

Instead, I want to look at the relationship between the price of oil and the number of oil rigs, and how that relationship has changed over the last couple of decades or so. Oil rigs, of course, are the machines that dig oil wells; once a well is completed and has begun production, the rigs are removed and either go into storage or move on to drilling another well. Data on the number of oil rigs in operation in the United States used in this post comes from Baker Hughes. Regular readers know I normally do not use data from private sources, but Baker Hughes data are as close to “official” as possible, as the figures you’ll find the Dep’t of Energy’s website on rigs originate with Baker Hughes. Rig count data comes out weekly and begins in mid-1987. I’ve taken annual averages beginning in 1988. Price data are annual averages from Table 5.18 of the 2010 Annual Energy Review put out by the Department of Energy. That data runs through 2009.

Now, a few details. Some time toward the end of the last millennium and the first few years of this one, there was a revolution in the drilling of oil (and natural gas). Two new technologies, hydraulic fracturing and horizontal and/or directional drilling, changed everything. Hydraulic fracturing is the fine art of pumping sand and water mixed with small amounts of some fairly toxic chemicals at high pressure to break apart some types of rock formations (usually shale) in which oil (or gas) is trapped. And the other thing available now are rigs that don’t just drill straight down, but instead can drill sideways once they reach the desired depth.

There is no fine line we can point to and say: this is the point when these two technologies became widespread. Instead, based in part on the numbers, I’m just going to say that until about 1998, those technologies were rarely used in the US, but after 2002 they were in widespread use. So… let me put up two graphs. The first one shows the relationship between the rig count and the price of oil from 1988 to 1998, and the second shows the same relationship between 2002 and 2009.

Figure 1

Figure 2.

(A few comments to the statistically oriented… yes, I know that a single equation regression is nothing more than a correlation, but this was for illustrative purposes. And before you mention autocorrelation, take a look at the graph again and think of exactly what would change if I did correct for it.)

So what does all this mean? A few comments:

1. The relationship between prices and rig count exists because as world prices rise, U.S. producers have an incentive to drill more.
2. In the first period, for every dollar increase in the price of a barrel of oil, on average 25 rigs were added in the U.S. In the second period, for every dollar increase in the price of a barrel of oil, on average only 3.5 rigs were added.
3. Part of the difference noted in 2. is just due to the fact that rigs are so much more efficient today than they were a decade and a half ago.
4. Another part of the difference noted in 2. is that there are only so many resources available to install new rigs in the U.S.
5. Yet another explanation for the difference in 2. may be price volatility; given price fluctuate so much these days, prices today aren’t as indicative of prices in six months or a year as they used to be.
6. Drilling for oil is a capital-intensive and risky operation. The relationship observed in comment 2. might be even greater were it not for the low interest rates prevalent in the second period.
7. The fit is much better (i.e., the relationship between price and rigs is much tighter, as there are fewer points far off the line) in the second period.
8. Do 2. and 7. indicate that perhaps oil drillers are becoming “more professional”?
9. Should this serve as a bit of an automatic stabilizer on price volatility? In other words, do the volatile oil prices reduce volatility in oil output, which in turn might reduce price volatility?
10. One other thought, only semi-related, and I’m not sure how it fits. In the oil market, you can get a lot of price volatility with even a small change in output. If world output falls by, say 1%, there are a lot of users without that many good substitutes (at present) willing to bid up the price on the marginal unit.

And one last thought…. does any of this say anything, one way or the other, about the notion of Peak Oil?

A few notes. First, full disclosure – I am not authorized to speak on its behalf, nor do I necessarily see the big picture, but I believe the company I work for would benefit from increased regulation of hydraullic fraccing. Second, the idea of looking for a relationship between prices and the means of production of a similar commodity came from Craig Truesdell. I’ve found Craig’s insight seems to provide useful intuition in a lot of markets.

Cross-posted at the Presimetrics blog.

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