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“The State’s in a fiscal hole: will they raid their insurance trusts?” can be found here. Please bookmark my new site.

The U.S. Census Bureau released the Advance Monthly Retail Trade and Food Services Survey late last week and on the face of it it seems like good news. By that I mean good news that IS good, whereas “less bad” has often been taken as being good in recent times. Seasonally adjusted retail sales were up 1.3% from the previous month and 1.9% year-on-year. Of course this is a survey of retail sales, with a reported error margin of ±0.5%, not a tally of actual data. So how reliable is it?

At the risk of sounding like I wear a tin-foil hat, should we believe government surveys? I figured that the best way to satisfy myself that this survey gives a credible picture of main street was to compare the survey data with states sales tax receipts. To do this I downloaded all the data via Mathematica and used Mathematica to analyze the data and make the plots shown below.

Quarterly sales tax data reported by the states is collected by the US Census bureau and can be found here. Historical survey data is available here.

The first step in making the comparison was to convert the monthly survey data into quarterly data. The next chart plots total quarterly sales taxes and quarterly retails sales survey data. Note that I’ve included items such as motor fuel sales taxes, and taxes on alcohol and tobacco, in the total sales tax number.

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At first glance the contraction in the trade deficit for October looks positive. I’m generally wary of seasonally adjusted data — not because seasonal effects don’t exist but because I prefer to see the raw data and the algorithm used to do the smoothing. Non seasonally adjusted data for the trade in goods is available here.


A seasonal plot of the trade deficit, derived from 22 years of data, is shown in the next chart.

seasonal deficit

From that chart we see that October is typically a larger deficit month. That should mean that a reduction in the month-on-month deficit in October 09 is good news. However October (along with March) is typically the biggest month for exports, and October is the biggest month for imports. While exports of goods grew 9.7% in October, compared to an average October growth of 7.4% from 1987-2008, imports were up 3.9% rather than the 8% seen on average from 87-08. Does that signal weak consumer demand? That interpretation seems consistent with what we know about retail sales and rail freight movements.

Outbound and inbound container movements from the ports of Los Angeles, Long Beach and New York/New Jersey are shown next. Note the clear seasonal pattern in the inbound container data.

inbound containers

The outbound container data, while oscillating a bit, seems fairly flat.

outbound containers

Is this data positive? At best we need to see how the data tracks between now and the end of Q1 2010 but to me it looks more indicative of an L shaped recovery than an indication of a V shaped rebound that many are wishing for.

(Mathematica was used for all retrieving, processing, and presenting of data in this article)

Total carloads and intermodal rail freight data are plotted in the two charts below as 4 week moving averages. The improvement we’re seeing needs to be viewed in the context of a trending increases in freight that occurs throughout the year through to October. The data certainly lacks the characteristics of a V shaped rebound. At this stage L shaped looks like a better letter to describe the economic path.

intermodal freight

carload freight

While the Australian economy is doing better than most at the moment, it still has its problems — while unemployment is low, a large number of people were forced into part time work during the short recession, and has its concerns — economic growth is too heavily dependent on China.

Interesting coverage of a tour of Australia by a former member of the China Central Bank Committee can be found here:


On a related note an interesting article “China has now become the biggest risk to the world economy” from the UK:


Some valid points/concerns in both articles. I’ve never understood why we have a World Trade Organization that acts as a global policeman/adjudicator, why tariffs and subsidies are generally banned, why dumping of goods is banned, yet currency manipulation by countries is allowed. It doesn’t make sense … to me anyway.

These are buoyant times for (some) commodity investors, and (some) commodity producers, what does it mean in terms of a global recovery?

A common feature among people drawing conclusions about the economy from commodity markets tends to be a sole focus on price, and even then only prices in exchange traded commodities. If buyers out number sellers prices rise, so rising prices mean a return of global demand which in turn means global recovery — right? Well not quite, despite the enthusiasm displayed in many articles pushing that line. The flaw in this line of thought is that the buyers who are out numbering sellers in commodity markets need not be end users of the commodity. There can be speculative demand from speculative buyers just as there can be economic demand from industrial buyers. From the point of view of the markets, or the commodity producer, it doesn’t matter who the buyer is: more buyers than sellers means rising prices. The problem arises when you use market price — at face value — to draw conclusions about the economy. Since most of the people who connect price to economic recovery without digging deeper tend to be economists, I’m assuming economic theories don’t allow for speculators.

Let me focus on metals and minerals (e.g. iron ore, coal). People don’t eat these (unlike food commodities), they are used as inputs for industrial production. As global demand picks up we would expect to see increasing consumption of metals and minerals. When buyers out number sellers prices rise. This is clearly what is occurring at the moment, you only need to look at price charts–which I haven’t reproduced here because we’re all familiar with them. But what if the majority of buyers are not end users of the commodity and do not take delivery, i.e. speculative buyers? If the buyer doesn’t take delivery then you would expect stockpiles to rise. In other words if speculators are out numbering industrialists you would expect to see quantities of the commodity that are available, i.e. stockpiled, to increase. In the first chart we have the base metal stockpiles on the London Metals Exchange.

LME stockpiles
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