David Rosenberg

July advanced retail sales were released by the US Census Bureau on Today. The next charts summarize the 2010 numbers and put them into context by comparing with pre-recession data.

click to enlarge


A realistic analysis of the numbers requires real dollar comparisons to previous years. Real dollar numbers in the next two charts are in January 2005 dollars.

Another way of representing where retail sales are compared to where they might have been had no recession occurred can be seen in the next chart.



retail sales

Last week the Association of American Railroads [AAR] released data that showed that carload volumes were higher than the corresponding week in 2008. Several bulls seized on this data as an indication that things were getting better, whereas realists had a more measured interpretation of the data. You see last week the AAR release was for the week ending July 3. In 2008 the data was for the week ending July 5, i.e. it included the July 4 holiday. Naturally anyone who wasn’t searching for data to fit a narrative would have expected that data from a week that included a July 4 holiday would be lower than from the weeks either side of the release. It follows that it was a no brainer to predict that volumes this week would be significantly down on 2008, and that is what has eventuated. As the AAR said in their press release:

WASHINGTON, D.C. – July 15, 2010 – The Association of American Railroads (AAR) today reported a decline in rail traffic for the week ending July 10, 2010, with U.S. railroads originating 252,963 carloads, down 3.5 percent compared with the same week in 2009 and down 20.8 percent from 2008. The July 4 Independence Day holiday did not affect comparison weeks in 2008 or 2009. In order to offer a complete picture of the progress in rail traffic, AAR reports 2010 weekly rail traffic with comparison weeks in both 2009 and 2008.

The following charts show intermodal and carloads freight volumes since 2007. Growth in carload volumes has stalled now for several weeks and currently shows no signs of reaching pre-crash levels. (Click charts to enlarge)

Intermodal rail freight has been stronger than carloads this year, i.e. closer to pre-crash volumes than carloads.

Based on from what we know about intermodal freight there should be some sort of correlation between container activity at our ports and intermodal rail freight, i.e. containers coming and going from ports have to be transported. The chart below shows moving averages of intermodal rail freight and total container movement at the ports of Los Angeles and Long Beach and New York/New Jersey — a 52 point moving average of the weekly intermodal freight data and a 12 point moving average of the monthly port data.

The carload data is divided by the AAR into 18 specific categories (plus a 19th called “other”). Below are the charts for each of the 18 individual categories. The same color coding used in the first two charts above applies: black — 2007; orange — 2008; green — 2009; blue — 2010. Please click on the images below to enlarge them.

freight · rail freight

Outbound and inbound container movements from the ports of Los Angeles, Long Beach and New York/New Jersey are shown in the next two charts. Of note is that container movement is trending up at both the west coast and east coast. Inbound container volumes have increased quite a lot in the last couple of months in line with an apparent seasonal trend.

freight · trade · US trade

I always find Marc Faber both entertaining and informative. …but would I enjoy listening to him as much if he didn’t have that accent? Not sure. All I know is that he always reminds me of Dr Strangelove. Here is a link to a one hour long speech he recently gave at the Mises Institute titled “Mirror, Mirror on the Wall, When is the Next AIG to Fall?”

Boom · Doom · Gloom · Marc Faber · Mr Bernanke · Mr Greenspan · Mr Krugman

April retail sales were released by the US Census Bureau on Friday. The next four tables summarize the numbers and put them into context by comparing with pre-recession data. A realistic analysis of the numbers requires real dollar comparisons to previous years. Real dollar numbers are in January 2005 dollars. Note that since the CPI for April has not yet been released the April real dollar data has not been corrected, i.e. April inflation of zero percent was used.

There has certainly been some improvement in sales numbers in March and April. We need a couple of months more data but it is possible that retail sales might resume the trajectory they had been on for many years prior to this recession — retail sales barely missed a beat during the 2001 recession. The chart below puts the current retail sales trajectory into context. A significant source of GDP growth has been lost in this recession, and this recovery, such as it is, shows no signs of being strong enough to recapture that lost GDP in the near future.

retail sales

In March blogger Matt Trivisonno wrote an article titled “Payroll Withholding Taxes Surge in March“, which was published at The Big Picture. Somewhat strangely the figure that accompanied the blog actually showed continued decline in tax revenues. Sure there was a rise in the line on the chart but the rise indicated that revenues were less negative, it didn’t indicate positive year-on-year (YOY) growth. Those sort of charts represent a continued source of irritation for me as described here:

• Debunking ‘Some Good News on Jobs: Tax Withholding Improving
• Interpreting Economic Data

So while there has been a YOY increase in withholding taxes in March (see below) the chart accompanying the article showed the opposite.

Zero Hedge ran an article on April 7 titles “Now, About This Alleged Increase In Tax Withholdings By The Government…” which began

“We are a little confused by all the recent hype in the media about a surge in individual tax withholdings by the US Treasury. Our confusion is predicated primarily by the fact that this is patently not true.” (their emphasis)

There was no indication in the Zero Hedge article that they were taking a shot at anyone in particular. And there have been several “taxes are rising” type stories recently.

Whether Zero Hedge had Trivisonno in its cross hairs who knows, but Trivisonno decided to rebut the Zero Hedge article, with a rebuttal titled “Zero Edge– Rebutting Faulty Tax Analysis“, which led Zero Hedge to respond with “Some Afternoon Confusion“. Both Zero Hedge and Matt Trivisonno claim to get their numbers from the daily US Treasury statements which are available here. So are taxes rising or falling YOY?

Trivisonno says in his rebuttal that

“The purpose of looking at the withholding data is to try and get an idea of how many paychecks are being cut, not to make an accounting of the federal government’s cash flow.”

So presumably a study of the daily receipts could be used as a proxy to forecast employment changes ahead of the monthly BLS official figures (or other releases such as ADP).

Since the debate seems to be about Q1 receipts in 2010 versus 2009 I’ve taken the monthly Treasury data, specifically data from Table 4 of the monthly Treasury statements (for anyone interested in reproducing this). The first chart shows federal income tax withheld — this is the first line item in Table 4 of the monthly release.

Clearly there is a rise in income tax receipts. The next chart shows total tax withheld, i.e. income taxes plus social security and Medicare taxes. Note that this next chart excludes the 3rd “Individual Income Taxes” line item called “other.” March 2010 receipts exceed March 2009.

When you add the “other” item the story is much the same:

A criticism Trivisonno had of the Zero Hedge analysis was that new tax changes took effect in April 2009:

“And since Zero Hedge did not take into consideration the tax-credit that began in April 2009, their unadjusted numbers understate withholdings for the first three months of this year by a substantial amount.”

On that basis Trivisonno believes that adjustments are necessary to the 2010 numbers. The problem I have with that is that the shortfall in the total Q1 receipts is due to a decline in social security and Medicare withholding, and the tax rates for these items has not changed. Since these taxes are a flat tax shouldn’t they provide a much better indication of how many pay checks are being cut than income taxes which vary depending on the distribution of tax rates among the population? What do readers think?

Social security and Medicare tax receipts are falling. This surely tells us much more about paychecks and employment than some of the other data being reported?

But back to the question: Are Federal Withholding Taxes Rising or Falling YOY? In March total tax withholdings were rising.

federal taxes · Matt Trivisonno · zero hedge

March retail sales and the CPI were both released today. Retail sales data in nominal dollars in the table below:

We see a huge surge in nominal dollar sales Year-on-year. After seasonal adjustments the numbers look like this:

Even with inflation is running at 2.3% the March number is pretty good. Interestingly when you look at the breakdown of the sales most of the increases came from higher gasoline and motor vehicle spending. The next table shows where we are in real dollars relative to before the crash:

This months data confirms that a recovery is occurring but remains very shallow.

retail sales

In recent weeks rail freight data has been looking more positive for intermodal freight, while carloads are stagnating. There seems to be a school of thought that because the amount of coal being shipped is well down in previous years, we should ignore coal and only examine rail carload volumes ex-coal. I haven’t read a well argued justification for this; arguments seem to be framed solely at producing a set of data that conforms to a certain narrative. In any case there are many other categories of rail freight that are not looking good at the moment; so why stop at coal if you want to massage data?

If coal freight movements are down, doesn’t it follow that coal consumption and power generation are probably down? Isn’t a decline in electricity usage indicative of a struggling economy? Unfortunately there is a 3-4 month lag in obtaining information about energy usage, but as on November 2009 the trend was consistent with many other metrics.

And coal remains a significant contributor to our energy consumption:

So why should we ignore coal?

coal · electricity · energy · rail freight

Week after week, economic data gets released and the mainstream media pundits and blogosphere go to work giving us interpretations of the data. The majority of the time the analysis seems to be relative rather than absolute. By that I mean that data is normally discussed in terms of how it compares to the previous month or previous year. That is fair enough because we want to know if things are getting better or worse, what sort of trajectory the economy is on, and whether we are heading in the right direction. In most cases, due to the seasonal fluctuations in the economy, comparisons to the previous year are the most valid unless the data has been seasonally adjusted. When year-on-year [YOY] or month-on-month [MOM] data is displayed in charts it frequently seems to get misinterpreted by pundits, the most common mistakes being to interpret less bad data as good, and to interpret a slowing down in a decline as a “V” shaped recovery. For some reason economists seem to be the worst offenders. I’m not sure whether that is due to them wanting to be positive or to them genuinely misinterpreting the data (mathematical illiteracy ?).

In the figure below I have some underlying L shaped data and below it the MOM and YOY changes in the data.

The shaded region marked “A” is where those pundits inclined to bullishness would typically be pointing to the data as indicating a recovery. Yet when an economy has been in decline, the first signs of MOM or YOY upturns simply indicate a lessening of worseness as we clearly see in the underlying data. It is mathematically impossible for underlying data to “bottom out” without this V shape in the MOM and YOY data. Even when the V shape in the MOM and YOY data is complete (region marked “B”) this is still not indicating a recovery, it merely indicates a bottoming of the underlying data.

An example of this type of data, complete with erroneous conclusions, can be found here:

In the next example there is some underlying data which recovers after a downturn. Region “A” is the same as in the previous chart: less worse underlying data producing V shaped MOM and YOY data.

In the region where the underlying data is actually recovering (not highlighted but evident in the chart) we see positive numbers in the MOM and YOY data. As the underlying data approaches a full recovery, region “C”, the MOM and YOY data begins to decline. The decline in the MOM and YOY data is not bad news, it just means the rate of change in the data is decreasing as the underlying recovers to the point prior to the decline. Mathematically it must do this.

An example of this type of data can be found here (Note that I’ve been unable to find the original article that is referenced in this link). The article contains this chart which plots 3 sets of difference data:

The change in the leading indicator and the change in the coincident indicator in the chart indicate that the underlying leading and coincident indicators suffered a drop but have now nearly recovered to the values prior to the drop. The declines, while remaining positive, are not bad news, they are a mathematical artifact of the type of chart being used. The third line in the plot is some kind of diffusion index which seems to be substantially more volatile than the other two lines. There is no “underlying” for diffusion indices. They are difference data to begin with and rescaled so that 50 is analogous to zero on MOM and YOY charts. This index has headed below 50. Despite the lack of an underlying set of data, diffusion indices should be interpreted in the same way as MOM and/or YOY difference charts. A fall below zero in coming months for the two difference indicator lines would indicate a downturn. How you react to a fall below 50 for the diffusion index depends on what  is known about the index. Given the data displayed in the chart, this “lurch” below 50 could simply be another example of the wild volatility of this index. Certainly more information is needed before drawing any conclusions.

At some point, maybe not for a couple of years the way this “recovery” is shaping, lots of economic data will start to exhibit this “region C”. What’s the bet that this is accompanied by some hand wringing and concerns from pundits. When those concerns eventuate they will be just as unfounded as the exuberance we see about the V shaped signals in current and recent data.

What I hope readers take out of this is that V shapes in difference charts, i.e. charts showing MOM or YOY data, that occur following a period of decline, don’t necessarily equate to a recovery in the underlying data. If the V is in the negative half of the chart then it simply corresponds to a period of decreasing worseness. It is best to consider both MOM or YOY difference charts with their underlying data before drawing conclusions.

China · data · Deutsche Bank · diffusion indexes · month on month data · Westpac Bank · year on year data