Political Calculations
Unexpectedly Intriguing!
July 31, 2015

Do you remember when President Obama said that the U.S. economy, "by every metric, is better" than when he took office?

In addition to that claim having been rated as "mostly false" by the left-leaning Politifact, the Bureau of Economic Analysis rained on the Obama-Stewart talking points parade by, once again, revising their estimate of the nation's GDP growth downward. This time, for every quarter since the second quarter of 2012.

But then, last year, they revised every quarter since the second quarter of 2011 downward too. Let's take an animated look at the U.S.' incredibly shrinking GDP for both those annual revisions.

Animation: 2014 and 2015 GDP Revisions, 2009-Q1 through 2015-Q1

In the chart above, the vertical lines correspond to the first quarter of each year indicated.

Examining the chart, it's rather amazing how bad 2012 looks now, especially in the third and fourth quarters, as the U.S. economy verged on entering into recession where only the Fed's third round of Quantitative Easing (QE) policies kept the nation's then-faltering economy from sinking into outright contraction at the time.

Meanwhile, it's pretty surprising at how large the new downward revisions are for 2013, especially in the first and second quarters of the year, which corresponds with the first six months during which President Obama's Social Security payroll tax hike and other income tax hikes took effect. If not for the Fed's significant increase of its QE program in December 2012, which was made after long anticipation of those tax hikes, we think that these quarters would also have been ones of contraction.

Speaking of which, we'll soon revise our estimate of the GDP multipler for QE where, based on these revised GDP figures, we would anticipate also revising our previous estimate downward to be less than 1.0.

Data Source

U.S. Bureau of Economic Analysis. National Income and Product Accounts Tables. Table 1.1.6. Real Gross Domestic Product, Chained Dollars [Billions of chained (2009) dollars] Seasonally adjusted at annual rates. Last Revised on: July 30, 2015. [Online Database]. Accessed 30 July 2015.


July 30, 2015

When did the earliest advertisement for Campbell's brand new line of condensed soups appear in a local U.S. newspaper?

Previously, we identified what we believe to be is the first newspaper advertisement ever for Campbell's Condensed Soup, but there's some uncertaintly with that ad because the maker of the "new concentrated soup" wasn't identified in the retailer's 12 January 1898 notice that it would be demonstrating the product.

And because of that uncertainty, we wondered when the first ad that ever specifically identified Campbell's condensed soups appeared in an American newspaper. After doing some digging, we have a pretty good candidate for an advertisement that local grocer J.H. Banman in Alton, Illinois placed in the Alton Evening Telegraph on 28 June 1898.

Alton Evening Telegraph (Alton, IL) Advertisement for Campbell's Condensed Soups - 1898-06-28

Speaking of firsts, we think we've identified the earliest local grocer ad featuring a discounted sale price for Campbell's Condensed Soups as the following one, which appeared for Jones Dry Goods in the Sunday, 14 August 1898 edition of the Kansas City Journal (see the third column, in the section for the sixth floor):

Kansas City Journal (Kansas City, MO) Advertisement for Jones Dry Goods with Campbell's Condensed Soups being sold at a discounted price - 1898-08-14

For a discounted sale price of 3 cents per can, a 70% markdown from the typical sale price of 10 cents per can, we're pretty confident this ad reflects the first time that a retailer ever sold cans of Campbell's Condensed Soups to the public at their wholesale cost.

Meanwhile, the first advertisement that featured a picture of a can of one of Campbell's Condensed Soups in an American newspaper would have to wait for the 7 October 1900 edition of the Chicago Daily Tribune, where the ad for Siegel-Cooper & Co.'s "The Big Store" featured a hand drawing of Campbell's Condensed Ox Tail Soup.

Chicago Daily Tribune (Chicago, IL) Advertisement for Siegel-Cooper & Co. 'The Big Store' with hand drawing of Campbell's Condensed Ox Tail Soup - 1900-10-07

It's interesting to note that in the two years from 1898 to 1900, the number of varieties of Campbell's Condensed Soups expanded from 5 to 17 varieties. As a side note, Campbell's had expanded their production from when they started sometime in 1897 of about 10 cases per week, which would work out to be an annual rate of production of 12,480 cans, up to 400 cases per week in 1900, which works out to be an annual production figure of 500,000 cans.

Since Campbell's Condensed Tomato Soup was already established at this point as its leading product, we were surprised that the first artistic rendering of one of its iconic soup cans would feature its Ox Tail variety, but remember that it's Chicago. They've always been somewhat weird about the food they eat there.

Another interesting bit of trivia is that Campbell's was extremely thrifty with their advertising budget, where it focused its limited advertising dollars putting ads on streetcars, which it began doing in 1899. The company wouldn't pursue direct print advertising venues until 1905, and until that time, it left all of its print advertising in the hands of local grocers throughout the country. Most significantly, through the newspaper ads of the Great Atlantic and Pacific Tea Company, a.k.a "A&P", as the following advertisement announcing that the regional grocer would now be handling Campbell's 10 varieties of its condensed soup products in the 3 September 1899 edition of the Atlanta Constitution:

Atlanta Constitution (Atlanta, GA) Advertisement Now Handling Campbell's Condensed Soups - 1899-09-03

Over a century ago, A&P was something like the Trader Joe's of its day. Last week, the remnants of the grocer filed for bankruptcy for the second time in the last five years, which will likely mark the end of the story of that company in the annals of U.S. commerce.

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July 29, 2015

Based on preliminary data, it appears that the second U.S. housing bubble may have entered a new phase. If the trends we observe in the data hold, it would mark the third phase of the bubble, which originally began to inflate in July 2012.

Trailing Twelve Month Average of Median U.S. New Home Sale Prices, July 2012 through June 2015

The initial inflation phase of the second U.S. housing bubble was caused by the sudden influx of investment from a number of hedge funds and real estate investment firms, who made the strategic decision to acquire distressed properties in the U.S. residential real estate market. That first phase lasted a year until the supply of available distressed properties was reduced to the point where they found it difficult to continue their rate of acquisitions as prices rose, where they would no longer have easy returns on their investments.

Trailing Twelve Month Average of Median U.S. New Home Sale Prices vs Trailing Twelve Month Average of Median Household Income, December 2000 through June 2015

During that first phase, U.S. homebuilders largely abandoned the production of less expensive homes for the low end of the market, which is what caused U.S. home prices to escalate at rates that were faster than those recorded during the inflation phase of the first U.S. housing bubble in the period from November 2001 through December 2005. The second U.S. housing bubble then entered its second phase, where U.S. new home sale prices began rising at a slower, but still escalated rate.

That second phase now appears to have lasted up until February 2015. A third phase, in which U.S. homebuilders would once again appear to have begun producing a larger number of new homes for the lower end of the market, with the results of that change in business strategy beginning to show up in the national level data after February 2015.

We do not as yet have enough data to quantify the new phase that would appear to be developing for new home sale prices, as the data we do have is still subject to revision. As a general rule, it takes a minimum of six data points to do so, so with monthly data, we should be able to do so after the data for August 2015 is reported.

Alternatively, if we were completely unconcerned by random variation in the data, we could simply project a linear trend by drawing a line through the two most recent data points, but that's clearly a less than reliable approach.

Let's next look at the bigger picture of where the current trends for median new home sale prices with respect to median household income with all the available data we have going back to 1967.

Trailing Twelve Month Average of Median U.S. New Home Sale Prices vs Trailing Twelve Month Average of Median Household Income, December 2000 through June 2015 (Monthly) and 1967 through 2014 (Annual)

As of June 2015, we estimate that the median sale price of a new home in the U.S. is about 27% above the level that would have been consistent with the relationship between new home prices and median household income in the years from 1967 through 1999.


Sentier Research. Household Income Trends: June 2015. [PDF Document]. Accessed 24 July 2015. [Note: We have converted all the older inflation-adjusted values presented in this source to be in terms of their original, nominal values (a.k.a. "current U.S. dollars") for use in our charts, which means that we have a true apples-to-apples basis for pairing this data with the median new home sale price data reported by the U.S. Census Bureau.]

U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [Excel Spreadsheet]. Accessed 24 July 2015.


July 28, 2015

Today, we're going to do some quick, back-of-the-envelope style economics to answer the question of whether the supply or the demand for new homes is what is behind the recent falling trend for their prices. First, let's visually establish that yes, both average and median U.S. new home sale prices have been falling since October 2014:

Median and Average Monthly U.S. New Home Sale Prices, January 2000 through June 2015

Next, lets tap the Federal Reserve's Economic Data to see how the monthly supply of new homes has been changing, as measured by the number of months it would take for all new homes to be sold at their current rate of sales:

Here, we find that the supply of new homes has been rising since February 2015. We now have the information we need to use our tool for telling whether supply or demand factors are behind the change in the prices for new homes in the U.S.

Price and Available Quantity Data
Input Data Values
How has the price of the item changed over a given period of time?
How has the available quantity of the item changed over that same time period?

What's Behind the Change in Price?

What we find is that the prices of new homes have been falling because of an increase in their relative supply. As for the current state of the U.S. home builders, we find that the industry's market capitalization has been increasing, although the most recent data suggests its growth may be starting to decelerate.

Trailing Twelve Month Average New Home Sales Market Capitalization, Constant June 2015 USD, December 1963 through June 2015

After adjusting for inflation, as measured by the Consumer Price Index for all urban consumers in all U.S. cities, it would appear that the U.S. new home market has recovered approximately to the level it was in 1994 and 1995, when it last rose to surpass a market capitalization of $14 billion in terms of June 2015's U.S. dollars.

Data Sources

U.S. Census Bureau. New Residential Sales Historical Data. Houses Sold. [Excel Spreadsheet]. Accessed 24 July 2015.

U.S. Census Bureau. New Residential Sales Historical Data. Median and Average Sale Price of Houses Sold. [Excel Spreadsheet]. Accessed 24 July 2015.

U.S. Department of Labor Bureau of Labor Statistics. Consumer Price Index, All Urban Consumers - (CPI-U), U.S. City Average, All Items, 1982-84=100. [Online Application]. Accessed 24 July 2015.

Federal Reserve Economic Data. Monthly Supply of New Homes. [Online Database]. Accessed 24 July 2015.

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July 27, 2015

How different would the value of the S&P 500 be if not for the amount of stock buybacks that have taken place in the U.S. stock market since the end of 2008?

We're asking that question today because of the recent suggestion that "Wall Street's new drug is the stock buyback":

In the first quarter of 2015, companies in the S&P 500 index returned more money to shareholders than they earned. The last time that happened was in the fourth quarter of 2008, when the entire S&P 500 reported a slight loss for the quarter but still spent $110 billion on dividends and buybacks.

“This is not a normal trend,” said Howard Silverblatt, senior index analyst at S&P Dow Jones Indices. “This is a large amount of money being returned with the majority of it in buybacks.”

In the first quarter, S&P 500 companies spent $237.69 billion on dividends and buybacks, while reporting operating earnings of $228.36 billion, according to data compiled by Silverblatt.


According to recent data from S&P, total buybacks and dividends (assuming those dividends were reinvested) have accounted for 35% of the buildup in market cap for the S&P 500 since it bottomed out in 2009. Without dividends, buybacks alone have accounted for 21% of the market cap’s rise.

To approximate what that change means in terms of the value of the S&P 500 index, we're going to treat the S&P 500 index as if it were a single company, which means that we will not be accounting for the market cap-based weighting of the buybacks that a number of the index' component firms have executed since the end of 2008.

We then calculated the S&P 500's equivalent number of shares by dividing its market capitalization at the end of each quarter since 2008 for which S&P has provided data (Excel Spreadsheet) by the index's value at the end of each quarter (Excel Spreadsheet). We then applied the same math to determine the equivalent number of shares that would have been bought back during each quarter.

Then, starting with the equivalent number of shares we calculated for the S&P 500 at the end of the fourth quarter of 2008, we progressively added the net change in the number of equivalent shares between each subsequent quarter and its preceding quarter to that base figure, while also adding back the equivalent number of shares that were consumed by buybacks for each quarter.

Our last step was to take each quarter's reported market capitalization and to divide it by the number of equivalent shares from our hypothetical "no stock buyback" parallel universe to calculate what the value of the S&P 500 index would be in that alternate reality. Our results are visualized in the following chart.

Quarter-Ending Value of S&P 500 Index, 2008-Q4 through 2015-Q1, With and Without Stock Buybacks

What we see from our highly simplified, back-of-the-envelope math is that through the end of the first quarter of 2015, the most recent for which S&P has reported data at this writing, the value of the S&P 500 would be about 324 points, or nearly 16%, lower if not for the progressive impact of share buybacks over the last seven years.

The actual impact of share buybacks over this period of time though would be less than that amount, because what we would really want to calculate is the impact of "surplus" share buybacks, which would be the difference between the number of share buybacks that have occurred with the number that would otherwise have occurred under "normal" economic and market conditions. And over the last several years, those conditions have been anything but normal, especially given what has been described as an "interesting coincidence" in how many companies came to have the cash needed to execute their share buyback plans in recent years.

Part of the reason for that cash hoard has been QE and near-zero interest rates, which have made it more attractive to take on debt to help fund share repurchases.

While there is no direct relationship between the two, the price tags on QE and buybacks offer an interesting coincidence: S&P 500 companies have spent about $2.41 trillion on buybacks over the course of the current bull market, according to S&P data, compared with a $2.37 trillion rise in the Fed’s balance sheet since the start of QE.

What does that mean for the market going forward? We'll let a JPMorgan analyst's comments from May 2013 (via ZeroHedge) explain:

"The other side effect of elevated dividends and share buybacks is that these distributions to shareholders may reduce the long term potential of the company to grow relative to the alternative of capital spending."

Two years later, that dynamic would be a major reason why the upward growth of the S&P 500 has largely stalled out through the first seven months of 2015, which through Friday, 24 July 2015, is less than 1% higher than it was at the end of 2014. It likely took longer than they expected, but scenario described by JPMorgan's analyst arrived all the same.

Data Sources

Standard and Poor. S&P 500 Buybacks Report. [Excel Spreadsheet]. Accessed 24 July 2015.

Standard and Poor. S&P 500 Earnings and Estimates. [Excel Spreadsheet]. Accessed 24 July 2015.

Update 28 July 2015: Readers should be aware that there are inherent flaws in treating the S&P 500 index as if it were a single company, where the analysis we presented above would only be applicable if it were. We'll have additional discussion on that topic sometime next week, but if you want a primer, see S&P's discussion of the math behind the index!

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July 24, 2015

We always thought that the early 21st century was kind of steampunk cool, what with all the advancements in lasers, artificial intelligence, computer hacking and the automation of stupidity, but perhaps nothing stands out more to us than how a previously unknown cloning technology changed the nature of warfare, opening the door to the world we live in today. (HT: Neat-o-Rama (via Core77)!)

After viewing the video above, it seems strange now to think that Andy Samberg and Bill Hader were ever underappreciated as visionary geniuses. It's a shame that the people of the early 21st century only thought they were some kind of comedic actors and not the originators of what would ultimately become the 21st century's most sophisticated and deadly military doctrine.

Then again, that was exactly what they wanted them to think, until....

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July 23, 2015

Via Barry Ritholtz, we were impressed by the following chart from Bloomberg indicating at "when your city became unaffordable", as measured by percentage of the income earned by a typical 22-to-34 year old American worker within a given major U.S. city would have go to pay the typical rent in that city:

When Your City Became Unaffordable - Source: Bloomberg - http://www.bloomberg.com/news/articles/2015-07-15/the-exact-moment-big-cities-got-too-expensive-for-millennials

So we tweaked one of our favorite charts to go along the data shown in the chart above so that it covers the same period of time.

U.S. Trailing Twelve Month Average of Median New Home Sale Price vs Trailing Twelve Month Average of Median Household Income, Annual Data Spanning 1980 through 2014, with Monthly Data from December 2000 through May 2015

Draw your own insights!

Data Sources

U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [Excel Spreadsheet]. Accessed 8 July 2015.

U.S. Census Bureau. Income, Poverty, and Health Insurance in the United States: 2011. Current Population Survey. Annual Social and Economic Supplement (ASEC). Table H-5. Race and Hispanic Origin of Householder -- Households by Median and Mean Income. [Excel Spreadsheet]. 19 September 2014. Accessed 19 September 2014.

Sentier Research. Household Income Trends: May 2015. [PDF Document]. Accessed 8 July 2015. [Note: We've converted all data to be in terms of current (nominal) U.S. dollars.]


Clark, Patrick. The Exact Moment Big Cities Got Too Expensive for Millennials. Bloomberg. [Online Article]. 15 July 2015. Accessed 22 July 2015.

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July 22, 2015

How much did it cost the producer of the goods you see for sale at a retailer to actually make those goods?

The answer is "it depends," and what the real answer depends upon is how much of a markup has been put on the goods at each step of their journey as they have gone from the producer to the store where you can buy it.

Let's start with the producer. Here, the starting point for determining what will be the retail price of the good is their cost to produce it. They will then add a markup to that basic cost, which not only includes their profit margin, but also includes a safety margin beyond their profit target, which helps cover their costs of keeping their business going if their sales have some degree of volatility to them or if they are cyclical, where sales swing from good to not good and back again with the passage of time.

As a general rule of thumb, a producer will double the cost of producing the good in their markup to trade buyer or retailer, where the result of this math represents the wholesale price of the good. Goods that have high production costs though will often have a multiple between 1.5 and 2 as their markup, given the higher values involved.

Retail Prices - Source: USDA - http://www.choosemyplate.gov/budget/pricetag.html

The retailer will then apply another markup to the goods they buy from producers based on the wholesale price, which in addition to their profit and safety margin, covers their other costs of doing business, such as rent, insurance, taxes, utilities, staff, and so on.

The size of that markup will often fall between 2-1/2 to 3 times the wholesale price of the good, and the result of that math is called the retail price.

Now, here's where it gets interesting. In order for a product to be successful, that markup math has to work both forwards and backwards.

By backwards, we're referring to the situation where the good is being sold in a competitive marketplace, where the actual price that the good will sell at is set not by the producers, wholesalers and retailers, but by the market, where the price that customers are willing to pay for it may not leave enough of a profit and safety margin for the producers and retailers to justify continuing their efforts to produce and sell it.

Our latest tool is designed to estimate what the production cost that works with these typical markups where, if that turns out to be less than a producer's actual cost to make a good, would be an indication that they shouldn't make it. Our default numbers are those for Campbell's Condensed Tomato Soup, circa 1897-1898....

And if you're reading this article on a site that republishes our RSS news feed, click here to access a working version of this tool!

Market Retail Price and Markup Data
Input Data Values
Retail (Market) Price of Good
Markup Multiple from Wholesale to Retail
Markup Multiple from Production Cost to Wholesale

Estimated Prices and Costs
Calculated Results Values
Wholesale Price
Production Cost

Although we've set the default results to only display the results to two decimal places, for our default example using the original prices of Campbell's Condensed Tomato Soup, we find that the wholesale price rounds down from 3.3 cents per can, which in turn, means that the cost to produce a single can would have had to be about 1.7 cents per can.

Coincidentally, that figure would also very likely be the company's maximum profit per can back in 1897 and 1898, which is a result that we'll revisit in the near future as we continue our series into the history of the price for Campbell's Condensed Tomato Soup.

But the reason we create tools like this is so that you can use them to do the math that applies for you. Try it with the retail prices and markups of the products that you want to estimate how much it really cost to produce!

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July 21, 2015

How important is farming to the economy of each of the United States?

Our latest data visualization exercise answers that question by calculating the percentage share of farming with respect to the GDP recorded for each individual state and the U.S. as a whole by the U.S. Bureau of Economic Analysis for 2013, the most recent year for which details for the economic output of each state's agriculture industry is available at this writing!

Economic Output of Farming as Percent Share of Regional GDP, 2013

The chart above represents a single snapshot in time, in this case, reflecting the economic output of U.S. farms during the entire 2013 calendar year. As such, it doesn't tell the story of how the farms in each indicated region achieved its particular share in the accounting for that region's economy.

For instance, are those figures presented in the chart above farming's "normal" share of GDP for each of the states? Could the figures for individual states be inflated by 2013 having been a bumper year for farm goods produced within their borders? Or are they understated because 2013 was a bad year for farmers?

Or was 2013 was a normal year for agriculture, but a banner year for other industries within each state? Or a bust year for those other industries? Those are also things that would affect each state's measure of farming's share of GDP, which wouldn't tell us anything about the condition of farming in each state itself!

Nor can the values tell us anything about the nature of the farming industry in each region. For example, the percentage share of farming output in the three states of Vermont (1.1%), Georgia (1.1%) and California (1.2%) are all within a tenth of a percent of the value of each state's total GDP with respect to each other, but farming in each of these states is very different from each other. They have different crops, different weather, different seasons, different geography, different infrastructure, et cetera.

If we don't know any of these things, valid conclusions about an individual state's farm industry cannot be made from the data that has been presented. In the absence of such relevant context, the chart above becomes little more than a defacto ranking system, which is nice if one thinks substantive analysis is a Top Ten List, but which really doesn't provide much substance from which any valid conclusions about a given topic may be drawn at all.

Data Source

U.S. Bureau of Economic Analysis. Gross Domestic Product (GDP) by State (millions of current dollars). [Online Database]. Accessed 18 July 2015.


July 20, 2015

Last week, we indicated that stock prices in the U.S., through Tuesday, 14 July 2015, were "running hot" and were "almost 3% higher than they might otherwise be" because of positive news regarding the resolution of Greece's debt default crisis.

Through the close of trading on Friday, 17 July 2015, U.S. stock prices, as measured by the S&P 500 index, remain almost 3% higher than they might otherwise be.

How do we know that?

Fortunately for us, the last two weeks have provided nearly optimal conditions for being able to assess what's behind the movement of stock prices using our standard model. With Federal Reserve Chair Janet Yellen set to testify before the U.S. Congress on Wednesday, 15 July 2015, she and a majority of other Fed officials had been working overtime over the preceding two weeks to provide investors with forward guidance on what to expect for when the central bank would begin hiking short term interest rates. The expectation they were seeking to set is that the Fed would act by the end of the third quarter of 2015 to begin raising its effective Federal Funds Rate above the 0-0.25% range where it has been set since December 2008.

According to the WSJ's July 2015 survey of private forecasters, they've been successful in setting that expectation for the future.

WSJ: September's Still the One - http://blogs.wsj.com/economics/2015/07/16/wsj-survey-most-economists-expect-fed-will-raise-rates-in-september/

That's significant information where our analysis is concerned because of how we set the simple scale factor we use in our model of how stock prices work. Given its tremendous influence over the future expectations of investors in recent years, we've used the statements provided by influential Fed officials about its planned future actions for when it would begin increasing the Federal Funds Rate around the times of the Federal Reserve's Open Market Committee's meetings to first empirically determine that scale factor (it's about equal to 5 when used with the CBOE's dividend futures data) and to subsequently verify and periodically confirm the calibration of that scale factor as those meetings occur. We therefore know from the FOMC's last several meetings that our model scale factor is currently very well calibrated.

That strong calibration then means that during times when U.S. investors are closely focused on the forward guidance being provided by Federal Reserve officials, as they have been during the last two weeks, we have the ability to isolate and measure the effect of other factors upon U.S. stock prices that might otherwise be indistinguishable from the typical level of noise that exists in the U.S. stock market.

And in the last two weeks, nearly all of that extra noise has come from two places: Greece and China. Between the two, the actions that China's government took to avoid having its stock market crash turn into a cascading failure for the nation's economy would appear to have been somewhat effective in halting its decline. The impact on the U.S. stock market has been neutral however as the momentum needed to fully reverse the declines in China's stock markets has yet not developed.

Meanwhile, the capitulation of the Syriza Party-led government in Greece to that nation's international creditors, in which the divided party's leaders suddenly reversed the course of action they had been following to instead accept even more severe terms from their creditors than those they had previously rejected in order to be bailed out and by doing so, to avoid the full collapse of Greece's economy on their watch, contributed more significant positive noise to the U.S. stock market.

We describe that reaction as noise because as yet, there has not been a significant change in the cash dividends expected to be paid out by U.S. firms by the end of 2015-Q3, the point in time to which the Federal Reserve has succeeded in focusing the attention of U.S. markets, to support the current valuation of stock prices. Unless that changes so that the amount of cash dividends is increased to support stock prices at their current elevated level, we can reasonably expect that stock prices will adapt accordingly. At least, in the absence of additional noise or a shift in focus by investors to a different point of time in the future.

And though we previously said that we would be retiring our alternative futures chart based on our standard model for the year, we're bringing it back one last time because we really don't have a better one to illustrate what our model is communicating.

Alternative Futures - S&P 500 - 2015Q3 - Standard Model - Snapshot on 2015-07-17

We wonder how much of the recent extraordinary effort by Fed officials to focus U.S. markets on the third quarter of 2015 in their efforts to manage expectations was driven by the desire to insulate and provide stability to U.S. markets that might otherwise have become quite volatile in response to the financial crises in China and Greece.

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July 17, 2015

Why would a demonstration for preparing the "new Concentrated Soup" at a department store in the city of Washington D.C., as advertised in the Alexandria Gazette in its 12 January 1898 edition, be so vital for the commercial success of the new product?

To condense the story down to its essential elements, Alexandria, Virginia was the home base of the U.S. Army's Quartermaster General, who was responsible for provisioning the entire U.S. Army to support all its operations. Here, President Grover Cleveland's Secretary of War Daniel S. Lamont had commissioned a board back in 1895-1896 to "consider the merits of various condensed foods as emergency rations for the army", whose use would be patterned after the practices established by the German army.

By 31 May 1896, the board delivered its report to the Secretary of War on what it had learned through its analysis of hundreds of samples submitted by U.S. and foreign firms, including concentrated soups. The Times of Philadelphia, Pennsylvania provided the following report of the board's findings with respect to the soup samples it received:

Another article highly approved by the board is condensed pea soup. This is put up in four-ounce packages, in a dry state, and the contents of one of them will make three pints of rich soup. The stuff is composed of pea flour and beef extract. Trial was made of some that was four years old, and it was quite edible though a little stale. One advantage that it possesses is that it distends the stomach. It is not sufficient to put into a man's stomach so much nutriment; the organ needs to be distended. Four ounces of this material, with water added and a little boiling, will satisfy a soldier's appetite for a day.

But the U.S. Army would need to expand beyond just that product to meet its needs to provision troops in the field, where having a wide range of condensed rations would maximize the effectiveness of limited supply lines.

In 1897, the growing likelihood that the United States would intervene in Cuba's rebellion against Spain drove home the urgency of provisioning U.S. troops with additional, non-emergency condensed rations, which would represent a gold mine for the food companies who could develop and market such a product quickly.

The potential for large government contracts then helps explain what is perhaps the most odd passage that appears in Campbell Soup's official company history:

In 1897, a major milestone occurred when Arthur Dorrance, the general manager of the company, reluctantly hired his 24-year-old nephew to join the company. Dr. John T. Dorrance, a chemist who had trained in Europe, was so determined to join Campbell that he agreed to pay for laboratory equipment out of his own pocket and accept a token salary of just $7.50 per week.

AD Cooper advertisement, Asheville Citizen (Asheville, NC), 3 May 1898, p 4.

Success at gaining government contracts wasn't a given, so to give Dr. Dorrance his due, he was far more visionary than his uncle. And with the subsequent sinking of the U.S.S. Maine in Havana's harbor on 15 February 1898, followed by the U.S. declaration of war against Spain on 21 April 1898 where the U.S. military would be called upon to conduct operations around the world, far more lucky.

Campbell's, which had been losing money in the years preceding John Dorrance's arrival, quickly became profitable within one year after it introduced its line of condensed soups, coincidentally as the U.S. Army exploded in size and needed to conduct far-flung operations in the Caribbean, the Phillippines and elsewhere in the Pacific Ocean, where viable condensed food products would be essential to its success in fielding troops so far from the nation's borders.

And that would certainly account for why Alexandria, Virginia would have been such a crucial market for introducing a new line of condensed soups for the company - it would provide the initial revenue the company would need to market its products to regular consumers to earn its full commercial success!

Speaking of which, the image of the advertisement for "Condensed Tomato Soup" is very likely the first for Campbell's Condensed Tomato Soup, although as with the earlier ad for the "new Concentrated Soup", Campbell's is not identified as the product's brand - it would be another two months before Campbell's would begin to be identified with its new line of products in local grocer advertisements. We thought it was appropriate to include with this post because it was published just 12 days after the U.S. Congress declared war against Spain.


Library of Congress. Chronicling America. [Online Database]. Alexandria Gazette (Alexandria, VA). Vol. XCIX. No. 10. 12 January 1898. p 3. Accessed 5 July 2015.

Library of Congress. The World of 1898: The Spanish-American War. [Online Article]. 22 June 2011. Accessed 5 July 2015.

Liggett, Lori S. Mothers, Militants, Martyrs, & M'm! M'm! Good! Taming the New Woman: Campbell Soup Advertising in Good Housekeeping, 1905-1920. Dissertation Submitted to the Graduate College of Bowling Green State University. [Online Document]. December 2006. pp 74-76. Accessed 5 July 2015.

New York State Education Department. The Spanish American War: Remember the Maine. Citizen Soldier: New York's National Guard in the American Century. [Online Article]. Accessed 5 July 2015.

Newspapers.com. Aluminum for the Army. The Times (Philadelphia, Pennsylvania). 31 May 1896. [Online Database]. p. 23. Accessed 5 July 2015.

Newspapers.com. Advertisement: A.D. Cooper - The War Business. Asheville Citizen (Asheville, North Carolina). 3 May 1898. [Online Database]. p. 4. Accessed 5 July 2015.

Sidorick, Daniel. Condensed Capitalism: Campbell Soup and the Pursuit of Cheap Production in the Twentieth Century. Cornell University ILR School Book Samples. 2009. [DigitalCommons]. p. 16.

Smith, Andrew F. The Oxford Companion to American Food and Drink. Oxford University Press. 2006 [via Google Books]. p 88. Accessed 5 July 2015.

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July 16, 2015

Since 4 August 2011, the U.S. stock market, as measured by the value of the S&P 500 index versus its trailing year dividends per share, has been in a stable, upward trend. On 8 July 2015 though, that trend came within 12 points coming to a sudden end after enduring for nearly four years.

S&P 500 Index Value vs Trailing Year Dividends per Share, 2011-

Some quick notes. First, this isn't technical analysis, and although it sort of looks like it, it shouldn't be confused with that nearly useless form of tasseography. It's really statistics, and more specifically, what we've presented in the chart above is really a visualization of a statistical hypothesis test.

Second, we're making a pretty important assumption that the trajectory of stock prices with respect to their underlying dividends per share follows a power law relationship during relative periods of order in the stock market, where order can be said to exist when the variation in stock prices with respect to its central trend trajectory can be described by a normal (or Gaussian) distribution.

Or more accurately, when the hypothesis that such a normal distribution might apply under a particular set of circumstances or a limited period of time cannot be rejected.

In reality, stock prices do not closely follow a true normal distribution, as they're not really random. And of course, order in the stock market can break down suddenly, as the leftmost portion of the data shown on our chart partially illustrates for the period between 30 June 2011 and 4 August 2011, as stock prices broke their previous trend and went into a Lévy flight as they dropped a couple hundred points as dividends continued to rise before stabilizing onto their current long-term trend.

At their current level, it wouldn't take much for the current period of order in the U.S. stock market to break down. All stock prices would need to do is to either dip below the lower dashed red curve shown in our chart above, or alternatively, simply continue to drift sideways to slightly higher as they have since the beginning of 2015.

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July 15, 2015
Federal Reserve - Source: http://pubs.usgs.gov/gip/stones/fed-reserve.jpg

Just for the record, we think the Federal Reserve is really trying to set one particular expectation for investors.

How do we know? Let's just say that we haven't missed how the Fed's top officials have been flooding the zone during the last two weeks to direct investors to focus their attention upon the near term....

And also for the record, what they have succeeded in doing is focusing investors on 2015-Q3. And while the news from Greece has added positive noise to U.S. stock prices since 10 July 2015, boosting the value of the S&P 500 almost exactly 3% higher than they might otherwise be, that figure is still within the typical range we would expect if investors were tightly focusing their attention on the expectations they have that are associated with 2015-Q3.

Which is to say that the stock market is running somewhat on the hot side of things. Plan accordingly.

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July 14, 2015

In June 2015, the economy of Earth continued to cool off as recessionary forces concentrated in China resulted in another reduction in the rate at which carbon dioxide is added to the planet's atmosphere for the month. That rate has fallen steadily since it last peaked in October 2013 and is now at the lowest level that has been recorded since May 2012.

Trailing Twelve Month Average of Year-Over-Year Change in Parts per Million of Atmospheric Carbon Dioxide, Jan 1960-Jun 2015

That most recent peak coincides with China's political leadership's third plenum, or leadership meeting, in which China's political leaders committed to a plan to put the nation's economy on a "sustainable growth path" after years of rapid economic growth.

Analyst's Notes

After realizing the close correlation between the timing of the Chinese Communist government's third plenum and the onset of a declining rate at which carbon dioxide as measured at the geographically remote Mauna Loa Observatory in Hawaii was being added to Earth's atmosphere and the correlation we had previously noted between that rate and economic recessions in the U.S., we went back and reworked our previous presentation of the chart presented in this post to consider China's potential contribution to the variation in that rate.

The challenge for us in doing that is the secretive nature of China's totalitarian communist government, which hasn't acknowledged any recession in the nation's economy since the disaster of the Great Leap Forward in the early 1960s.

To get around that limitation, we applied the data we regularly track for the exchange rate-adjusted, trailing twelve month average of the year-over-year growth rate of the value of goods that China imports from the United States, where we indicated any month since January 1986 in which that growth rate was recorded as being negative with blue vertical bars. The result was an almost uncanny correlation between a reduction in China's imports and a negative change in the trailing twelve month average of the rate at which carbon dioxide is added to Earth's atmosphere.

Our next problem was that our source trade data only extends back to January 1985. To fill in earlier periods of time, we focused on periods during which China was experiencing political turmoil, which we believe translated into economic turmoil. These periods are also indicated with blue vertical bars.

Our main takeaway from this portion of our analysis is that the reduction of economic growth in China is remarkably well correlated with negative changes in the rate at which carbon dioxide is added to Earth's atmosphere.

We also more clearly marked other periods of economic contraction in our chart that we had previously identified, such as the dissolution of the Soviet Union and its empire in the early 1990s (vertical green bars) and 1997's Asian Financial Crisis (vertical orange-yellow bars), as it would appear that both events reduced Earth's total economic activity enough to affect the Mauna Loa Observatory's carbon dioxide measurements as they occurred in real time. U.S. recessions and isolated quarters in which GDP growth was recorded as negative continue to be indicated with red shaded vertical bars.

We still have some areas in our carbon dioxide recession correlation chart for which we need to identify the corresponding recession, most notably the period from September 1962 to August 1965. Given that the period of time coincides with the beginning of the Cuban Missile Crisis and its escalation of the Cold War between the U.S. and the U.S.S.R. We suspect it may the result of economic sanctions that the U.S. government may have orchestrated against the Soviet Union in response to its placement of nuclear missiles in Cuba, which could have produced recessionary conditions within the Soviet Union and its satellite nation empire, but which we are as yet unable to confirm given that totalitarian socialist/communist government's secrecy and outright dishonesty about the state of its economy.

Previously on Political Calculations

Political Calculations. A Global Economic Indicator? [Online Article]. 12 February 2015.

Political Calculations. The World in Recession. [Online Article]. 11 June 2015.

Data Sources

National Oceanographic and Atmospheric Administration. Earth System Research Laboratory. Mauna Loa Observatory CO2 Data. [File Transfer Protocol Text File]. Accessed 12 July 2015.

National Bureau of Economic Research. U.S. Business Cycle Expansions and Contractions. [Excel Spreadsheet]. Accessed 12 July 2015.

U.S. Census Bureau. Trade in Goods with China. [Online Database]. Accessed 12 July 2015.

Board of Governors of the Federal Reserve System. China / U.S. Foreign Exchange Rate. G.5 Foreign Exchange Rates. [Online Database]. Accessed 12 July 2015.


Song, Shige. Does famine influence sex ratio at birth? Evidence from the 1959–1961 Great Leap Forward Famine in China. Proc Biol Sci. 2012 Jul 22; 279(1739): 2883–2890. Published online 2012 Mar 28. doi: 10.1098/rspb.2012.0320.

Lorenz, Andreas. The Chinese Cultural Revolution: Remembering Mao's Victims. Spiegel. [Online Article]. 15 May 2007. Accessed 12 July 2015.

Trueman, C.N. The Cultural Revolution. The History Learning Site. [Online Article. Accessed 12 July 2015.

Zheng, Haiping. The Gang of Four Trial. [Online Article]. 2010. Accessed 12 July 2015.

U.S. Department of State Office of the Historian. Milestones: 1989-1992: The Collapse of the Soviet Union. [Online Article]. 31 October 2013. Accessed 12 July 2015.

Kinzer, Stephen. Bitter Goodbye: Russians Leave Germany. New York Times. [Online Article]. 4 March 1994. Accessed 12 July 2015.

Frontline. The Crash: Timeline of the Panic. (Asian Financial Crisis). [Online Article]. Accessed 12 July 2015.

Ong, Ryan. The Third Plenum of the 18th Chinese Communist Party Congress: A Primer. China Business Review. [Online Article]. 16 September 2013. Accessed 12 July 2015.

Bloomberg Brief. China's Transition: The Third Plenum - One Year On. [PDF Document]. October 2014. Accessed 13 July 2015.

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July 13, 2015

How volatile are stock prices?

To answer that question, we tapped Yahoo! Finance's entire history of the S&P 500's open, high, low and closing stock price values for each trading day going back to 3 January 1950. We then calculated the percentage change of each trading day's closing value with respect to the previous day's closing value. Our results are presented below, along with the basic statistics we calculated:

S&P 500 Daily Volatility (Percent Change Between Closing Value and Previous Trading Day's Closing Value), 3 January 1950 - 6 July 2015

For the 16,481 trading days worth of closing values we considered, the mean percentage change from one day's closing value to the next is slightly positive at 0.03%. Through 6 July 2015, the S&P 500 has closed above its previous day's closing value some 8,724 times (52.9%) and closed below its previous day's closing value 7,633 times (46.3%), while closing at the same value 124 times (0.8%).

And that 1,091 difference in the number of up days versus down days explains why the value of the S&P 500 index has increased from $16.66 to $2,068.76 in the sixty-five and a half years since 3 January 1950.

Data Source

Yahoo! Finance. S&P 500 (^GSPC) Historical Prices, 3 January 1950 through 6 July 2015. [Online Database]. Accessed 6 July 2015.

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July 10, 2015

Are being commemorated this weekend at IWM Duxford.


July 9, 2015

Last week, Ricochet's John Gabriel linked to one of our old charts in his article Athens on the Potomac:

Since most graphs look like this, I created my own user-friendly debt chart focused on three big numbers: Deficit, revenue and debt. (My first version was published a couple of years ago. This one is updated with the most recent figures).

America's Finances - Source: Ricochet (John Gabriel) - https://7373-presscdn-0-43-pagely.netdna-ssl.com/wp-content/uploads/2015/06/debtchart2014.png

It’s an imperfect analogy, but imagine the green is your salary, the yellow is the amount you’re spending over your salary, and the red is your MasterCard statement.

Call us surprised, but when we post a chart visualizing the U.S.' national debt, it most often looks like this one, or if we're showing its trend over time, it will look like one of these.

We don't know what it says about the producers of other charts that Gabriel looks at on a regular basis, but the chart of ours he linked to was designed to fulfill a very different purpose - it is a visual tool that may be used to predict how high the top income tax rate in the U.S. would be set by U.S. politicians given the size of the national debt, the national population and the nation's economy.

The way we did that was to mathematically describe how U.S. politicians have done that in the past, where we also identified the margins where some degree of political equilibrium would appear to have been established. Here's the chart as we last featured it in October 2011:

U.S. Maximum Personal Income Tax Rate vs National Debt Burden per Capita, 1913-2011

Using that tool back in 2011, we predicted, based on President Obama's proposed spending, that U.S. politicians would attempt to increase the top marginal income tax rate in the U.S. to the 49-50% level.

Catching things up to today, that didn't happen, although a large number of U.S. politicians definitely wanted to do just that. The resolution of the so-called "fiscal cliff" crisis in January 2013 combined with the adoption of President Obama's proposed "sequester" spending levels limited the increase in the nation's top marginal income tax rate to 43.4%, the effective top marginal tax rate that applies when the topmost personal marginal income tax rate of 39.6% is combined with the Affordable Care Act's net investment income tax of 3.8% for the highest income earning individuals in the U.S.

But because of that lesser tax hike, we now have a new data point that we can use to better define where the margins for setting the nation's topmost effective marginal income tax rate are. Our updated chart showing that updated political equilibrium is below:

U.S. Maximum Personal Income Tax Rate vs National Debt Burden per Capita, 1913-2014

It's basically the same as the previous chart, but with the margins drawn to be 1.5 standard deviations away from the curve describing the main historical trend, which coincidentally appears to better describe the debt-GDP-population dynamics that apply to the high end of the nation's political tax equilibrium. With that updated margin, our model predicts a top effective marginal income tax rate of 44%. Being so close to the actual top income tax rate, it is unlikely that the top tax rate will be increased further in the near future, provided that the growth of the national debt is adequately constrained with respect to the nation's economic and population growth.

But if anybody can screw that up by going on an unnecessary spending binge and racking up even more national debt without having adequate economic or population growth to support it, today's crop of establishment U.S. politicians are the ones who can render that short-term forecast obsolete!

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July 8, 2015

After April 2015's data anomaly, the year-over-year growth rate of the value of all the goods exported from the U.S. to China once fell back to levels that suggest that China's economy is experiencing recessionary conditions for the just-reported month of May 2015.

At the same time, the value of goods being imported by the U.S. from China rose back to positive territory, indicating that the U.S. economy experience positive but slow growth conditions in May 2015.

Year Over Year Growth Rate of Exchange Rate Adjusted U.S.-China Trade in Goods and Services, January 1986 - May 2015

Through May 2015, the economic situation in China is clearly not improving, as a nation experiencing robust economic growth would need to import a growing level of goods to sustain its growth. Instead, the decline in the year-over-year growth rate in the value of goods imported by China from the U.S. suggests that a considerable amount of slack has developed within China's economy, which the U.S. Census Bureau's international trade data indicates has not yet really begun to close.

That continuing weakness has persisted despite the Chinese government's interventions. The following chart from the World Bank's June 2015 China Economic Update illustrates the actions that China's government has taken as

World Bank - China Economic Update - June 2015 - Updated 3 July 2015 - Figure 1.4 - p. 5 - http://www.worldbank.org/content/dam/Worldbank/document/EAP/China/ceu_06_15_en.pdf

These are not actions that are taken by the government of a nation whose economy is really growing at a rate of 7% per year, which suggests that once again, China's own reported economic data is highly suspect. Or as Christopher Balding memorably described the mismatch between China's promised GDP figures and the reported profitability of Chinese businesses on Twitter:

1. GDP growth of 7% w/profit growth of 0.6%=really bad managers or 2. GDP growth not really 7%. Choose 1 or 2 #China

We should acknowledge that the Chinese government's new efforts to prop up share prices on the nation's stock exchanges are also not those of a nation whose economy is really growing at a rate of 7% per year. Bloomberg provides a chart giving additional context to the relevant time series here:

Bloomberg: China's Market and Policy Timeline - November 2014 through 5 July 2015 - http://www.bloomberg.com/news/articles/2015-07-07/charting-the-rise-and-fall-of-china-s-equity-market#media-2

In addition, the Financial Times reports that China's government has taken the unusual step of informally banning the phrases "equity disaster" and "rescue the market" from Chinese publications, even as it has acted to halt trading in the stocks of 940 over 1,300 firms. In addition, Bloomberg declares in a headline that "China Tells Investors: Go Ahead, Bet the House" in a report on how China's government is allowing Chinese investors' houses to be put up as collateral for margin loans if they borrow money to buy Chinese stocks, which is also an effort to try to get more Chinese investors to plow more money into the nation's stock markets. As is restricting major stockholders from selling any of their shares.

Here's where China's reported 0.6% profit growth for its businesses is a problem. Back in 2013, China's top companies were encouraged by their government to pay at least 30% of their annual profits to shareholders to lure more investors into the nation's stock markets.

From our perspective, that policy clearly worked. However as Chinese investors are now learning, having directly linked dividends to be a set percentage of earnings provides very little insulation from having stock prices suddenly crash when investors shift their attention to a more distant future and realize that the earnings growth needed to support the cash dividend payments they may previously have been expecting won't be there. That's simply part of the math of how stock prices work and is the cause for the "panic sentiment" that has come to grip the nation's stock markets over the past three weeks.

At this point, what needs to happen for Chinese stock prices to stabilize and reverse their negative trend is for the acceleration of the growth rate of expected future dividends per share to shift from negative to positive (or rather, the rate at which expectations for future dividends are worsening needs to slow down). To enhance stock price stability and reduce volatility in the longer term, Chinese firms would then need to decouple the direct linkage between their dividends per share and their earnings per share, setting their dividend policies to be on a more stable and sustainable growth trajectory as their prices recover.

At least that would be a far more useful path for China's political and business leaders to follow going forward. Take it from us - if we can train the Fed's minions to avoid downturns and stabilize U.S. stock prices by managing expectations, we can certainly do the same for their Chinese counterparts.


Board of Governors of the Federal Reserve System. China / U.S. Foreign Exchange Rate. G.5 Foreign Exchange Rates. Accessed 7 July 2015.

U.S. Census Bureau. Trade in Goods with China. Accessed 7 July 2015.

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