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April 2012 Ceridan-UCLA Index: Improves??

Written by Steven Hansen

The Ceridian-UCLA Pulse of Commerce Index™ (PCI) state their index has improved 0.1%, but this hapless index has actually fallen 2.7% unadjusted year-over-year (see caveats below).

The graph below shows the index’s unadjusted year-over-year decline – and its 3 month moving average. This is the second largest decline since the Great Recession, and according to logic, is indicating the economy is sliding into a recession. However, this is not likely true.

Remember, I am using the UNADJUSTED data – not the adjusted data being reported by other pundits who are showing graphs such as this which too is indicating the economy should be slipping into a recession:

The transport network in the USA is almost exclusively fueled by diesel – and diesel consumption makes a good proxy for forward economic activity (as transport occurs, on average, one to two months before consumption). Caveat is that this index is based on road use of diesel.

The PCI is modeled using Ceridian’s diesel distribution network to forecast economic growth – primarily Industrial Production and GDP. Econintersect extracts the unadjusted (not modeled) diesel index for its economic model.

The sad part of this is that this index should work, and Ceridan provides almost real time feedback – unlike government sources which report 2 months late. As of February 2012, US diesel production and import were up 14.6% year-over-year.

Caveats on the Use of this Index

The authors of the Ceridian-UCLA Pulse of Commerce Index™ (PCI) have thrown in the towel in providing written interpretion their own index. What they wanted the index to do was mimic GDP and industrial production – this turned into a colossal failure as the index decoupled.

The likely reason is that the Ceridan (a wholesaler of diesel across the USA) market share has changed distorting the index. Whatever the reason, transport of goods across the USA (whether imported or domestic) is powered by diesel – and diesel remains a good indicator of economic growth (or lack thereof).

As stated, Ceridan’s market share must have fallen. A statement released:

As mentioned in last month’s e-Newsletter, the results of the Ceridian-UCLA Pulse of Commerce Index (PCI) will no longer include the accompanying monthly data interpretation report. In its place, the report will include a headline regarding the overall direction of the PCI, as well as key statistics, including charts and tables showing the correlation with Industrial Production and a regional summary, all of which should be familiar from previous monthly PCI reports. In addition to the new report format, the release date will be earlier in the month, now on the third business day of each month.

This is a post Great Recession index which has little real time history on foretelling economic activity. This model works in hindsight. A positive point for this index is that there is usually little backward revision.

Diesel consumption per ton mile is improving at rate which Econintersect has no means to quantifying in the U.S. But on a global basis, this improvement is likely well over 1% and could be as high as 5% per year such as:

  • There have been significant inroads for fuel conservation by placing trailers on higher efficiency railroads;
  • There has been some conversion of diesel trucks to using LPG;
  • Not only has current environmental standards forced conversion to more efficient diesel technology – the rising price of diesel alone has forced truckers to upgrade to the higher efficiency trucks / engines / trailers/ use management;

  • Tractor design continues towards more aerodynamic design.

Although it is true that diesel moves the goods necessary for the economy, using diesel data without an efficiency adjustment likely will provide incorrect conclusions. Therefore, it is trend lines, not specific values, which are important. It is very likely this index is UNDERSTATING the economy by an amount equal to the indeterminate efficiency improvement rate.

Monthly diesel use can vary with the weather or other natural causes making is index noisy. For this reason, Econintersect uses the three-month moving average for modeling economic activity.

The PCI diesel consumption is based on roadway diesel sales – not railroads, sea or air transport. The Achilles heel of this index might be its inability to adjust for alternative non-truck transport. Ceridan-UCLA dispute there is any evidence that rail is making inroads into road transport – but did not use a tonnage comparison.

Shifting to Rail? The Regional Data Doesn’t Say So. The railroads are reporting a significant increase in intra-modal activity with containers shipped first by rail for long-hauls and then transferred to truck for the shorter routes. If this shift to rail were a substantial part of the story in 2011, it ought to be evident in the regional data, perhaps in the Mountain region which was crossed by longhaul truckers and now by trains, or in the Pacific where rail might be displacing trucks for cargo destined for the East Coast. The chart below illustrates growth of the U.S. PCI since December 2010 until January 2012. The U.S. PCI three-month moving average peaked in May 2011 and has been on the decline ever since. The regions are sorted according to where they end up at the right, from the West South Central and to West North Central. The regions that seem to have contributed to the shape of the U.S. overall have wide lines and the others have thin lines. The Mountain region has a shape that amplifies the ups and down of the U.S. overall which might the effect of railroads. But the Pacific region was actually improving in the second part of 2011, not what might have been predicted by the shift-to-rail hypothesis. There are also evident shifts around March in the behavior of several other regions too: WSC, MAT and WNC. Thus, there is no clear evidence of the shift to rail in the regional data. It could be critical, but there is no smoking gun in the regional data.

Econintersect determines the month-over-month change by subtracting the current month’s year-over-year change from the previous month’s year-over-year change. This is the best of the bad options available to determine month-over-month trends – as the preferred methodology would be to use multi-year data (but the New Normal effects and the Great Recession distort historical data).

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