It is not very often that a new leading indicator hits the market to compete with the Conference Board or the Economic Cycle Research Institute (ECRI), but the Consumer Metrics Institute does just that and does it well.
DoctoRx and I have been watching his data for a while and were impressed by what we saw. Recently we had an opportunity to interview Dr. Rick Davis, a physicist, the founder of CMI, and came away as believers in his approach. A caveat though, we reflect on a lot of data, and we fit CMI into our mental matrices, but we give it a lot of credibility based on what we’ve seen.
Dr. Davis looks at what he considers to be the most important factor in measuring real time growth, consumer spending, which as we all know has accounted for about 70% of economic activity. He measures consumer Internet spending which, he believes, yields the most current indicator of what consumers are doing. In fact he literally measures what happened yesterday, not 5-week old BEA data. Further, he says his data leads GDP by 18 to 20 weeks.
Here is an example of his most recent Growth Index:
(1) The Consumer Metrics Institute’s 91-day ‘Trailing Quarter’ Growth Index -vs.- U.S. Department of Commerce’s Quarterly GDP Growth Rates over past 4 years. The quarterly GDP growth rates are shown as 3-month plateaus in the graph. The Consumer Metrics Institute’s Growth Index is plotted as a monthly average.
As you can see from this graph, their lead time has been fairly consistent.
Dr. Davis has developed a method to capture current Internet buying data from major retailers. His method is proprietary and well thought out. He then weights the data according to a good’s impact on GDP. He weights his data according to the same weighting used in the BEA’s National Income and Product Accounts (NIPA). This weighting gives more importance to goods that have a bigger impact on GDP. Dr. Davis claims his Weighted Composite Index is very accurate with about a 20-week lead time.
Which goods he measures is interesting. He collects data reflecting discretionary spending rather than the daily necessities such as rent, food, and gasoline. He admits that there is a great deal of volatility in his data, but he smoothes it out using 91, 183, and 365-day averages. He not only measures the level of spending but the duration of any “extended deviation” from the norm. This allows him to construct “Contraction Watch” which shows how spending as a leading indicator is expanding or contracting during a cycle. The following table is based on a 91 day average:
Why, you may ask, do we need another leading indicator? Or perhaps a better question is, why did Dr. Davis decide to create a new one?
He said that the current measures of economic growth used by the Bureau of Economic Analysis (BEA), the NBER (National Bureau of Economic Research, a private organization that determines the dates of cycles), and the Conference Board use outmoded data.
If you go back to the old formula used to measure GDP: GDP=C+I+G+(X-M) [GDP = private consumption + gross investment + government spending + (exports − imports)], Davis would say that it is more accurate to look at discretionary consumer spending versus putting more weight on manufacturing (important back in 1937 when the formula was constructed). As CMI explains it:
1) It is important to remember that at the Consumer Metrics Institute we measure only a portion of the “C” in the above equation. In fact, we have intentionally chosen to track a particularly volatile subset of “C” in order to gain signal strength and lead time. … All of the above tells us that we should lead the BEA’s “C” while having an amplified signal that may or may not offset the impact of “I”, “G”, “X” and “M” when the final calculation of GDP is performed.
2) We think that the BEA’s methodologies for imputing “C” are seriously flawed. Their 1937 based focus on factories places their data far downstream from where the real economic action is — probably 4 or 5 months. We understand why a factory focus was chosen in 1937 (given FDR’s constituency and 1937 jobs demographics), but the economy is much more than just factories in 2010. Additionally, the BEA uses a questionnaire approach, which leads to survivor and large firm biases — not to mention lags and revisions when the data does finally come in. And finally their numbers are “annualized” growth that is then seasonally adjusted; while our numbers are strictly year-over-year growth, which require no seasonal adjustments.
3) Another problem with using factory data to impute “C” is that the BEA feels compelled to somehow reconcile the downstream data source to upstream demand by tracking inventories as they slosh up and down. Doubly unfortunate is the fact that the BEA’s inventory data is very, very late arriving — and it is by far the largest source of post-2nd revision adjustments to the GDP. So the GDP gets bounced all over the place as inventory building/depleting cycles take place, and our measurements will diverge from the GDP by at least the amount of those swings.
4) In an economy where household leveraging or deleveraging has become commonplace, the relative impact of “C” on the GDP should naturally change. In times of deleveraging, John Maynard Keynes would have us believe that at such times we simply print new money and crank up “G” to offset the drop in “C”. If “G” soars there will have to be some decoupling of the final GDP value from “C.”
5) And finally, “(X-M)” could significantly boost GDP when the value of the dollar is falling, thus causing net exports to grow. Unfortunately, the biggest portions of “M” are either valued in dollars or in currencies defacto pegged to the dollar, and falling values of the dollar don’t actually help that part of the equation as much as one might suspect. And since most other central banks want their export goods favorably priced relative to the dollar, there are compelling international reasons to keep the dollar strong — not the least of which is the dollar’s role as defacto world reserve currency and safe harbor during times of global economic distress, which causes international investors and central banks alike want to preserve the value of their dollar denominated assets.
As Davis says, their data goes as far “upstream” as possible and is measured daily thus making it quite timely.
In looking at CMI’s data, they normalize the data every year. What that means is that every January 1 the data starts on a scale where 100 is the norm and then they measure deviations from that norm. While there is the possibility that this could lead to distortions over time as the data is normalized, he said that they are aware of the issue and they try to smooth the data over in the YoY analysis, but that this is a problem with all such indices. What it doesn’t do is give absolute longitudinal data going back in time as does ECRI. Which is why we believe CMI’s data is useful in a matrix of other data.
So, what is CMI telling about the economy now? Here is their interpretation, and I should mention that their economic framework is Austrian theory economics:
Our own headline for the end of September would be that both our Weighted Composite Index and our Daily Growth Index have turned down again. In fact, our Daily Growth Index has now reached a level exceeding the lowest level recorded during the “Great Recession of 2008″. [Please see the above Contraction Watch chart.]
…[T]he “Great Recession of 2008″ had a total of 793 percentage-days of contraction over the course of 221 days, whereas the current 2010 contraction has already exceeded 690 percentage-days — already over 87% as bad. And the 2010 contraction has already lasted for 261 days, 40 days longer than the entire 2008 event. Additionally, within the past week the 2010 event has reached levels of daily contraction worse than anything recorded in 2008.
But looking ahead, should the 2010 event recover from its bottom exactly like the 2008 event did, it would still experience nearly another 490 percentage-days of contraction before ending — resulting in a grand total of 1180 percentage-days of contraction for the 2010 event, fully 49% more severe than the “Great Recession of 2008.”
Our concern with the above argument is the implicit projection of the blue line “recovering” in a manner similar to the upswing seen in the 2008 contraction. The 2008 “recovery” was aided by stimuli unlike anything currently pending. And the 2008 “recovery” started with a background level of 6% unemployment. Furthermore, we don’t expect any consumer driven U.S. economic miracles at least for the next 30 days — until the results of the 2010 U.S. midterm elections are known. That should add nearly 150 percentage-days of contraction to the above totals before any “recovery” upswing could plausibly start, placing the economic consequences of the 2010 contraction at least 1.67 times the pain suffered in 2008.
Looking at the chart above, the striking difference between 2008 and 2010 is the implied longevity of the current event. Projecting forward, we will probably see another 30 days of political “Fear, Uncertainty and Doubt” (“FUD”) pushing the blue line laterally to the right. And when the blue line eventually starts back up, we face the real possibility that the plateau visible in the left half of the chart’s blue line is the new consumer “norm,” reflecting the realities of a deleveraging U.S. consumer. If that is true, the economy’s “800 pound gorilla” will have gone on a serious diet.