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July 30, 2014

What does it mean when the trends for non-farm payroll jobs and total employment in the U.S. are on two separate and diverging tracks?

We're asking that question today because of something we've observed in the data for the state of Illinois while working on a different project. The chart below shows what we found when we looked at that state's total employment numbers and nonfarm payroll jobs since December 2012.

Illinois: Total Employment and Non-Farm Payroll Employment, Seasonally-Adjusted, December 2012 through June 2014

Here, we observe that the trends for the state's total employment and for the state's non-farm payroll jobs would appear to be on diverging trajectories over time. As we've previously discussed in considering the differences between the Household and Establishment surveys that document employment trends in the U.S., that pattern is largely due to cyclical factors related to turning points in the economy:

One other factor that can contribute to differences between the two surveys' reported data is driven by cyclical factors, which are often present during economic turning points, such as the beginning of recessions or periods of economic expansion. Here, during recessions, the Establishment survey will show job losses as recessionary conditions take hold and workers are laid off, while the Household survey will show gains as those displaced workers move into the kind of marginal employment that is captured by that survey.

That script gets flipped when an economic recovery takes hold, as establishments boost their hiring, pulling workers out of marginal employment, with the results showing up in the data as job gains in the Establishment survey but as a falling level of employment in the Household survey.

The chart above shows those patterns. Here, in 2013, the total employment level declined as the number of people who had been marginally employed declined as the number of people in non-farm payroll "establishment" jobs increased, which suggests that labor market conditions in Illinois improved throughout the year.

But those improving conditions for Illinois' labor market would appear to have reversed since the end of 2013. Here, the number of people employed in non-farm payroll jobs have declined while the total employment figure for the state has increased rather dramatically, which suggests a massive increase in the number of people who are marginally employed in Illinois beginning in January 2014. The chart below takes a closer look at the recent trend in Illinois' employment situation.

Illinois: Total Employment and Non-Farm Payroll Employment, Seasonally-Adjusted, December 2013 through June 2014

Although the data for non-farm payroll jobs and total employment are based on different surveys and cover different portions of the civilian U.S. labor force, we're going to treat them as if they do fit neatly together like jigsaw puzzle pieces in the following analysis to get a sense of how the number of newly created jobs in Illinois would have had to change in order to produce these figures. The chart below shows the change in the number of people counted as being employed for each data series since December 2013.

Illinois: Change in Total and Non-Farm Payroll Employment Since December 2013, December 2013 through June 2014

What's important to consider here is the spread between the disappearing number of jobs counted in Illinois' non-farm establishments and the increasing number of jobs that were counted in surveying Illinois' households. That spread would represent the number of newly generated marginal jobs in the state.

Illinois: Net Change in Total Employed and Non-Farm Payroll Since December 2013, December 2013 through June 2014

The curious thing here is that the magnitude of the net increase in the number of marginal jobs in Illinois through June 2014 is over eight times greater than the actual loss of non-farm jobs at Illinois' establishments during the same period of time. That difference suggests that what we're seeing isn't the migration of workers from establishment to marginal employment after being laid off, but rather a large scale increase in the number of people in Illinois from outside of the state's employed population into marginal jobs that have been created since the beginning of the year.

Where that extra population of job-finding Illinoisans came from will be our next stop in this series.

Data Sources

U.S. Bureau of Labor Statistics. States and selected areas: Employment status of the civilian noninstitutional population, January 1976 to date, seasonally adjusted. [Text Document]. Accessed 25 July 2014.

U.S. Bureau of Labor Statistics. Economy at a Glance: Illinois. [HTML Document]. Accessed 25 July 2014.

References

Abraham, Katharine, G., Haltiwanger, John C., Sandusky, Kristin and Spletzer, James. Exploring Differences in Employment Between Household and establishment Data. Journal of Labor Economics, Vol. 31, No. 2, Pt 2, pp. S129-S172. [PDF Document]. http://www.jstor.org/stable/10.1086/669062. 11 June 2013.

U.S. Bureau of Labor Statistics. Employment Situation Technical Note. [HTML Document]. Last Modified 3 July 2014. Accessed 12 July 2014.

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July 29, 2014

Every month, when the U.S. Bureau of Labor Statistics (BLS) puts out its Employment Situation report, it provides the results of two different surveys that it conducts each month: the Household survey and the Establishment survey. We thought we'd take this opportunity to note the differences between the two.

The Household portion of the Employment Situation report is conducted by the U.S. Census Bureau, which surveys some 60,000 American households during the week of the 12th of each month as part of its Current Population Survey (CPS). In addition to determining the employment status of the individuals in each surveyed household, which it classifies as employed, unemployed or not in the civilian labor force, the Census collects data on their demographic profiles, including race, Hispanic origin, age, sex, et cetera.

Meanwhile, data for the Establishment is collected by the U.S. Bureau of Labor Statistics as part of its Current Employment Statistics (CES) survey, which incorporates the payroll records of some 144,000 non-farm establishments and government agencies, covering workers at some 554,000 individual worksites. In addition to determining the number of people employed at the surveyed locations as of the payroll period including the 12th of each month, the BLS collects data on the number of hours worked, earnings and the industries in which individuals are employed at the surveyed organizations.

The BLS notes the following differences between the surveys:

  • The household survey includes agricultural workers, self-employed workers whose businesses are unincorporated, unpaid family workers, and private household workers among the employed. These groups are excluded from the establishment survey.
  • The household survey includes people on unpaid leave among the employed. The establishment survey does not.
  • The household survey is limited to workers 16 years of age and older. The establishment survey is not limited by age.
  • The household survey has no duplication of individuals, because individuals are counted only once, even if they hold more than one job. In the establishment survey, employees working at more than one job and thus appearing on more than one payroll are counted separately for each appearance.

These differences mean that the results from each survey do not necessarily fit together neatly like the pieces of a jigsaw puzzle. Census Bureau statisticians have suggested that in addition to the differences noted above, many of the discrepancies between the surveys may be attributed to the characteristics of marginally-employed workers, such as those who work as independent, self-employed contractors or in "off-the-books" or other types of non-standard occupations, which are often captured by the Household survey but not by the Establishment survey.

One other factor that can contribute to differences between the two surveys' reported data is driven by cyclical factors, which are often present during economic turning points, such as the beginning of recessions or periods of economic expansion. Here, during recessions, the Establishment survey will show job losses as recessionary conditions take hold and workers are laid off, while the Household survey will show gains as those displaced workers move into the kind of marginal employment that is captured by that survey.

That script gets flipped when an economic recovery takes hold, as establishments boost their hiring, pulling workers out of marginal employment, with the results showing up in the data as job gains in the Establishment survey but as a falling level of employment in the Household survey.

But economic turning points like recessions are not the only driving factor that can produce these results, which is an idea that we'll be exploring in upcoming posts.

References

Abraham, Katharine, G., Haltiwanger, John C., Sandusky, Kristin and Spletzer, James. Exploring Differences in Employment Between Household and establishment Data. Journal of Labor Economics, Vol. 31, No. 2, Pt 2, pp. S129-S172. [PDF Document]. http://www.jstor.org/stable/10.1086/669062. 11 June 2013.

U.S. Bureau of Labor Statistics. Employment Situation Technical Note. [HTML Document]. Last Modified 3 July 2014. Accessed 12 July 2014.

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July 28, 2014

We think we may finally have a decent handle on how to compensate for the echo effect in our forecasting of the S&P 500's future.

To briefly recap the story to date, our index value forecasting method incorporates historic stock prices from a year earlier as part of the base reference points from which we project the future value of stock prices. The "echo effect" is something that results from our use of that historic data, particularly when stock prices had experienced a "noise event". A noise event is when stock prices deviate from the level that their fundamental underlying driver, the change in the growth rate of their trailing year dividends per share expected at a discrete point of time in the future, would otherwise suggest they should be set according to our model of how stock prices work.

Those deviations from various noise events are clear when you compare what our model had forecast against the actual trajectory that stock prices took in 2013.

We had previously come up with a filtering technique that initially showed promise, but which turned out to not be able to handle the situation where the stock market had undergone a volatile series of disruptive noise events, which was the case from mid-June through mid-October 2013. So we pulled the plug on it last month.

So we seemed to be up a creek without a paddle for compensating for the echo effect in our forecasting. That much is certainly evident when you compare our forecast with the current trajectory of stock prices, where it would appear that the stock market began experiencing a significant negative noise event on 17 July 2014, but which turns out to really be an artifact of the echo effect in our projections - the echo of the noise events of a year ago.

Alternative Future Trajectories for the S&P 500, 30 June 2014 through 30 September 2014, Snapshot on 25 July 2014

We started thinking about how we recognize the presence of noise in the market in the first place, where we observe it as the deviation from our forecast trajectory of where stock prices would go when investors are focused on a particular point of time in the future. We know what that trajectory looks like and we know what the actual trajectory of stock prices was.

So what if we used our older forecast as the baseline reference for projecting future stock prices?

Well, that wouldn't make much sense, would it? After all, it's a projection, one that was significantly different from the trajectory that stock prices actually took. We shouldn't be in the business of making forecasts based on the hypothetical path that stock prices could have taken a year ago.

But then we thought about it some more. That forecast incorporated the historic stock prices of a year earlier, which are a very real thing that we could use as our baseline point of reference. Instead of using the stock prices of a year ago as the base reference point of our projections, we would instead be using the stock prices of two years ago.

Better still, we wouldn't even need to complicate our basic math like we did with our initial echo filtering technique - we would just need to adjust all the older data points to use the same, but older data that applied at the older point in time. The same math could work!

So that's what we did. Here's the result of our rebaselining the calculation to incorporate the historic stock data in our projections of today:

Rebaselined Alternative Future Trajectories for the S&P 500, 30 June 2014 through 30 September 2014, Snapshot on 25 July 2014

Suddenly, we find that stock prices would appear to be once again predictable, currently following the trajectories that are consistent with investors continuing to be focused on either 2014-Q3 or 2015-Q2 in setting current day stock prices - just as they were before we ran into the echo effect using our regular one-year ago base reference period!

But now, we appear to have reached a fork for that trajectory, where we'll soon determine which future investors are really focused upon.

Now, there are some obvious downsides with this approach, as in addition to trading the base reference points for our projections, we've also traded one year's noise events for the preceding year's. But since the period of 2012-Q3 was relatively quiet in terms of those events, we should expect stock prices to fall within the expected error range for whichever alternative trajectory that coincide with the actual point of time in the future where investors are currently focused, with any major deviations we observe now being attributable to current day noise events rather than the echoes of noise events past.

Previously on Political Calculations

We've been working on how to crack the echo effect in our S&P 500 forecasting method since November 2013. The posts below, presented in reverse chronological order, describe our experience in that endeavor to date as we've worked out how to compensate for the echo effect in real time.*

Reading through all these, we can see why we stuck with our echo-filtering technique for so long, even though we never really liked it very much because of how much it complicated the basic math behind our S&P 500 forecasting method. In the end, it turned out to be capable of compensating for relatively stable short-term echoes, such as from the Great Dividend Raid Rally and the subsequent Fiscal Cliff Deal Rally spanning 15 November 2012 through 17 April 2013, but not the echoes from the much more volatile series of noise events that defined the QE-Uncertainty and Debt Ceiling Crisis noise events that disrupted the U.S. stock market in the period from 19 June 2013 through 17 October 2013.

* Note: To the best of our knowledge, we're the only stock market analysts who have made our work product fully transparent by developing it in public and sharing our observations and results in near-real time on the Internet. And we've been doing it since we announced our original discovery of what really drives stock prices on 6 December 2007.

Welcome back to the cutting edge!

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July 25, 2014

It was a triumphant story that was first published by the Northport Patch back on 5 August 2011. Then, the tiny community web site of the tiny town located on the northwest corner of Long Island, New York announced that then 13-year old Northport Middle School student Aidan Dwyer had applied a mathematical principle found in trees that could improve the performance of solar panels and had been granted a provisional U.S. patent for an invention stemming from his insight:

Aidan Dwyer has accomplished more in his life than most people three times his age. He sails, he golfs-- and he is a patented innovator of solar panel arrangements.

Dwyer applied the Fibonacci sequence, a mathematical principle widely occurrent in nature, to solar panel arrays in a months-long backyard experiment. He found that small solar panels arranged according to the Fibonacci sequence found in tree branches produced 20 percent more energy than flat panel arrays, and prolonged the collection window by up to two and a half hours.

Most remarkably, the elegant tree design out-performed the flat panel array during winter exposure, when the sun is at its lowest point, by up to 50 percent.

The feel-good story quickly built steam over the next two weeks, when it blew up into the big time as a number of mainstream media outlets, including Popular Science, heralded 13-year old Aidan Dwyer's achievement. The popular environmental news site Earthtechling offered the following lead for its story on the 13-year old's naturist's insight and invention:

One would be excused for suspecting that Aidan Dwyer, said to be 13, is in fact a small, very young-looking, 37-year-old college-educated con-man of the highest order. Such is not the case though for what the young Long Island lad has accomplished in a feat typically associated with much older individuals. As reported on the Patch community website out of Northport, N.Y., Aidan has used the Fibonacci sequence to devise a more efficient way to collect solar energy, earning himself a provisional U.S. patent and interest from "entities" apparently eager to explore commercializing his innovation.And you're wondering what the Fibonacci sequence is. Aidan explains it all on a page on the website of the American Museum of Natural History, which recently named him one of its Young Naturalist Award winners for 2011. The awards go to students from middle school through high school who have investigated questions they have in the areas of biology, Earth science, ecology and astronomy.

The feel-good bubble for solar power enthusiasts burst just days later when it became clear that 13-year old had made mistakes in his science work backing his invention, which if corrected, would almost entirely diminish any advantage that 13-year old Aidan Dwyer's invention might provide for deploying solar panel technology. Earthtechling summarized the rapid debunking that followed the previous triumphant stories:

Welcome to the digital age, Aidan Dwyer, where a hero becomes a bum in a blink of an eye and you need a neck brace to protect against media whiplash. One day, credulous news outlets – including the one you’re reading now – were glomming onto 13-year-old Aidan’s award-winning science project and advertising it to the world as a solar-power breakthrough. Now, a veritable cottage industry of Dwyer-debunkers has sprung up, and his work is being called way off base.

“Was Our Beloved 13-Year-Old Solar Power Genius Just Proven Wrong?” asks Gizmodo. “Why 13-year-old’s solar power ‘breakthrough’ won’t work,” writes Tuan C. Nguyen on Smart Planet. “Blog Debunks 13-Year-Old Scientist’s Solar Power Breakthrough,” says The Atlantic Wire. “This is where bad science starts,” headlines an exhaustive, nearly 4,000-word takedown of Aidan (and, even more pointedly, the media who grabbed his story and ran with it) on the No One’s Listening blog.

Keeping in mind that it took the effort of people with considerably more scientific knowledge and experience to even detect the errors that 13-year old Aidan Dwyer made, it is perhaps not so surprising that a 13-year old made mistakes in his scientific investigation. People who do real science in real life often go down what turn out to be blind alleys on the path to discovery all the time. Failure is both an integral and illuminating part of the scientific process.

But while the story of how 13-year old Aidan Dwyer's invention turned out to be one of those blind alleys, a more remarkable story is that the original story of 13-year old Aidan Dwyer's insight and invention refuses to die. Instead, scientifically-illiterate environmental activists are continually recycling it! Here's a quick sampling:

Recycling a 13-Year Old's "Solar Breakthrough"
Date Link
17 October 2012 Fibonacci Department: 13-year-old spirals tree branching into solar panels
21 March 2013 13-Year Old Replicates Fibonacci Sequence to Harness Solar Power
23 September 2013 13-Year Old Replicates Fibonacci Sequence to Harness Solar Power
21 November 2013 13-Year-Old Replicates Fibonacci Sequence in Trees
29 December 2013 13-year-old investor cracks the secret of trees to revolutionize solar energy
30 December 2013 13 Year Old Invents Fibonacci Solar Panel Designs
12 March 2014 This Boy Walked Into A Forest. What He Found Could Change Mankind's Future
14 April 2014 Biomimicry - Tree Solar Panels
9 June 2014 13-Year Old Replicates Fibonacci Sequence to Harness Solar Power

It's always the same story too. It's as if the community of environmental activists are too scientifically illiterate to even be curious to find out more about it, much less what can be found on the first page of Google search results.

But do you know what the best part of all this recycling is? Aidan Dwyer never gets any older! Even though it's three years later (at this writing in 2014), he's *always* 13-years old!

Somehow, in failing to make a solar breakthrough with the Fibonacci sequence, 13-year old Aidan Dwyer would appear to have accidentally discovered the fountain of youth!

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July 24, 2014

Previously, we found that the worst state for jobs for teens in the U.S. is California.

But we have bad news for the teens in that state. In 2014, the worst state for job-seeking teens, California, is getting worse:

Labor Market for Teenage Californians, January 2005 - June 2014

Here is how that compares with the job market for adults in California (Age 20 and over) since the total employment level in the U.S. peaked in November 2007, just ahead of the U.S. economy's peak of expansion in December 2007, marking the official beginning of the so-called "Great Recession":

Change in Number of Employed in California by Age Group Since U.S. Total Employment Peak Reached in November 2007, Through June 2014

To make it to the bottom in this category takes a unique combination of poor government policies and poor economic growth prospects, the latter which can be demonstrated by their not being sufficient to offset the negative effects of the former. For the state's teen population, California would appear to have developed both in spades.

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