Yesterday was the first day that the Dow Jones Industrial Average (DJIA) managed to close above 11,000 in a long time. It had been flirting with that level for almost a week, now, and had crossed that level several times intraday, but never held it through the close of trading.

The media, of course, made a significant bit of noise about it - enough that my wife asked me, after reading the headlines in the local newspaper, whether it really was a good thing. Now, my lady is quite bright (she's working on her second Master's degree), but, as a humanities major, her long suit is not the kind of quantitative analysis necessary to interpret what moves in various economic metrics, such as the DJIA, mean to actual people trying to get by.

"Is the Dow over 11,000 a good thing?" she asked.

"Yes, but it doesn't really mean much," I replied. "I predicted it'd spike over 11,000 a month or so ago, then slide back. But, things are pretty much on track."

Analysis I did last fall (see image above) indicates that the DJIA is just about exactly on its long-term track. It should be just peeking above 10,000 right about now. Since we've just experienced a short spike down (You do remember we've experienced a recession over the last year and a half, don't you?). We can expect an overshoot on the recovery, then settling back to the long term trend modified by a chaotic wave with a period of about 20 years.

In the future, we can expect to see a slow rise with a long term trend of zero to a few percent for about the next five years. The trend should steepen thereafter, reaching a maximum about 2020. In the meantime, expect the DJIA to be in a trading range between 9,000 and 11,000.

The important thing to understand is that the huge price swings that many of us capitalized on over the past 18 months are unlikely to repeat, barring exigencies. Since stock traders make money by cleverly exploiting stock volatility, they won't do quite as well as they have over the past 20 years. Expect the real money to be made by investing in dividend-paying stocks. Expect portfolio returns in the 5-15% per annum range to be the norm. A good model for this investing environment would be the rather boring period from about 1965 through 1980, when the DJIA stayed essentially flat, with only short term ups, and downs.

Sorry, folks.

In every economic downturn, Chicken-Little pundits squawk about how we can't have a sustainable recovery until employment figures show improvement. Any investor, and here I use the word "investor" in its broadest sense to include those who put resources to work, not just those who invest in stocks and bonds, who listens to this drivel is destined to fail, and fail disasterously.

Macroeconomics - the study of large-scale economic trends affecting an economy as a whole - is based feedback loops that drive business activity. These loops describe causal relationships between economic factors affecting business. For example, an increase in production levels generally pushes employment up. Each of these causal relationships involve a time delay. So, when production levels increase, especially from a depressed level, employment does not rise until production levels exceed capacity at the current employment level. This takes time, as does the process of hiring new employees.

These delays are what cause business cycles in the first place. If we use, say, buggywhip manufacture as a hypothetical example, we might say that it takes 18 months for the buggywhip business to respond to a sudden change in the overall demand for buggywhips. So, if New York City should pass a law banning motorized vehicles, so all the Yellow Cabs in the city had to be replaced by horse-drawn surries overnight, that would ratchet up demand for buggywhips. Because it takes 18 months for buggywhip manufacturers to respond, actual sales of buggywhips would not stabilize at a level reflecting the new demand until a year and a half later.

Business cycles occur because it is not possible for businesses to precisely meet demand. In the buggywhip example, assume that there are two buggywhip manufacturers in business at the time the New York law passes. They will both attempt to grab more than their fair share of the enormous new market. Part of driving sales is assuring customers that you can actually deliver the goods ordered. So, both manufacturers will expand production faster than necessary to just meet demand. In addition, during that first 18 months, it will be clear that the established manufacturers won't be able to meet demand. Outside entrepreneurs will see this as an opportunity to jump in to the expanding market, by starting rival buggywhip manufacturing operations.

The result is that some 18 months after the new law passes, worldwide buggywhip manufacturing capacity will greatly exceed demand. Inventories of unsold buggywhips will expand. Buggywhip prices will fall. Marginal buggywhip manufacturers will fail. Buggywhip production capacity will drop. By three years into the process, we'd be back to having inadequate production capacity to meet demand, and the whole thing would start over again.

Boom and bust cycles like that are not some aberration, or the result of faulty business strategies, or some market inefficiency that politicians can erase by passing laws, it's how things inevitably work. In fact, most complex systems, such as economies, consist of multiple such cycles that operate on multiple time scales. Basically, they're all chaotic systems, which is why long term charts of practically every economic indicator - from long-term jobs trends to prices for individual stocks - look like profiles of the Andes Mountains. They're all fractals, which is the pattern most often associated with chaotic systems.

Economic expansions, recessions, depressions, and recoveries are actually just business-cycle components. As any Taoist sage could tell you, whenever the economy is expanding, you know that a contraction is on its way. Similarly, a depression always presages a recovery. It's inevitable. The Great Depression of the 1930s was, when looked at from a longer perspective, just a particularly deep bottom of the overall business cycle. The huge expansion we experienced during the 1990s was, conversely, a particularly robust phase of the overall business cycle.

This latest contraction, which started about 2005, and will probably not completely play out until 2015, was another particularly nasty dip in the more or less regular cycle. It's as inevitable as the tide.

So, getting back to jobs data, and the usual panicky predictions of a so-called "jobless recovery," the reason employment data have not significantly improved is that it's just too early in the process for it to show up. Those who ask: "How can sales recover when employment is down?" simply don't understand how the business cycle works. Sales aren't driven by jobs, it's the other way around, with a significant time lag between.

Jobs are driven by production requirements. As any industrial engineer could tell you, production is driven by inventories, not by demand. Demand is an intangible that is very difficult to predict or measure. Inventory levels, on the other hand, are easily measured and better reflect a company's ability to sell the products it makes.

In the real business world, the first thing to recover after a recession is demand. It begins to recover when end users have had their belts cinched so tight for so long, that they have no choice but to by new stuff. Demand for food starts to rise, for example, when pantries start to look bare. It makes no difference whether the family bread-winner has a job or not, when there's nothing for dinner, somebody makes a run to the store. Even if you have to beg a cup of sugar from the neighbors, that sends the neighbors off to the store for more sugar, increasing the demand for sugar. Therein lies the disconnect between jobs and demand.

Demand seems to have hit bottom about six months ago. Since then, we've been working off inventory that built up at the start of the downturn, when production still exceeded demand. Next, production has to rise (pulled by further increases in demand) until it exceeds capacity at the present depressed employment levels. Only then will employment figures begin to rise.

Don't look for employment metrics to turn up until at least the end of the first quarter 2010. The reason it hasn't happened yet is that it's just too darn early.

Over the next week or so, I hope to share with you results of studies pointing the directions we can expect technology trends likely will take next year, and in the decade ahead. The good news for Americans, and for many national economies around the world, is that the recovery is exactly on track. Yammering about "jobless recovery" and doubts over the U.S. economy's ability to expand until full employment returns simply demonstrate the commentators' ignorance of how economies work.

Garden variety depressions, which is what we've experienced over the past five years, take many years to play out. Calendar year 2008 saw the acute contraction phase, but things had been unraveling since late 2005. After a contraction, comes a bottoming, followed by an expansion phase.

Economic recoveries - that is the bottoming and expansion phases of a dip in economic activity - start with stock markets, which anticipate the turn around in general economic conditions by some months. The reason stock markets anticipate recoveries is that investment professionals, unlike media commentators, do understand economics, and recognize harbingers of business improvement long before the improvement happens. Just as meteorologists know that when days start getting longer, Spring is just a few months away, investors know that economic harbingers, such as inventory levels stabilizing at high levels, pre-announce changes in economic trends by several months, and stock prices rise as these investors put themselves in a position to capitalize on the new trend.

After stock prices hit bottom and begin to rise, we start seeing signs that the downward pressure on business activity begins to ease off. High inventory levels, for example, begin to drop. Productivity begins to rise as businesses streamline to cut costs. Later, these more efficient businesses begin reporting better than anticipated earnings on still-falling revenue. Still months later, revenues begin to rise as individuals and businesses can no longer put off purchases that have been delayed since the beginning of the downturn. More months later, employment figures, which conventional wisdom seems to think should lead the recovery despite the fact that it never happens, begin to recover as the productivity gains of a few months ago prove insufficient to meet the growing demand for goods and services. Finally, very late in the recovery, large capital investments, such as in real estate, reach their bottoms and start to recover.

At present, the U.S. economy, as well as that of most of the world, is recovering nicely. Trends in measures like corporate earnings are showing the correct patterns in the correct order and with the anticipated timing. Even the jobless numbers are tracking exactly as they're supposed to. Back at the end of 2008, when the depth of the dip became apparent, knowledgeable pundits were able to predict that the unemployment rate would reach just above 10%, which is just what it did, and begin to recover in late 2009, which it also has done.

By the way, don't listen to all that emotional drivel about some fictional "real" unemployment rate being something like 18% instead of the published 10% level. "The unemployment rate" is a real, clearly defined metric that we use to compare one time period with another. The "real unemployment rate" that Chicken-Little types yammer on about is poorly defined and very difficult to measure, so it's useless as an economic metric. It's only use is to give fear merchants something to shoot their mouths off about to their poorly educated audiences.

One extremely useful metric that can provide prescience about general industrial trends is expectations among industrial automation buyers and sellers about their purchases and sales (respectively) in the coming year.

To determine whether the market for industrial automation equipment was beginning to ascend from the depths of this latest downturn, or were destined to remain mired in the muck at the bottom of the pit for awhile longer, our friends at Control Engineering magazine in partnership with analysts at financial services leader Morgan Stanley surveyed participants in the industrial automation market. The reason to look especially at sentiment in this market is that factory automation is arguably the most important trend in industrial technology of the late 20th and early 21st Centuries.

Early in the 20th Century, factory automation was generally non-existent. We (or more accurately, our ancestors) simply did not have the tools available to automate production facilities in any meaningful way.

By the middle of the 21st Century, on the other hand, we anticipate that factories will run essentially fully automatically. That is, there will be no production tasks that are not done by automated machinery. Humans will generally hold supervisory positions. There will be CEOs, managers, engineers, maintenance technicians, and such like, but the population of assembly line workers, for example, will drop to more or less nil.

So, unlike the situation a few decades ago, perhaps the best measure of industrial activity available at the start of the second decade of this century is the level of activity in the industrial automation sector. That is what the survey set out to study, and that is why it's the first thing we looking at as we peer into our crystal ball.

"I'm happy to report that the survey does, indeed, offer more than few rays of hope," wrote David Greenfield, Control Engineering's editorial director, when reporting the survey findings in his article entitled 2010 Global Automation Industry Outlook. "Overall, the findings appear to indicate that a bottom in the market has been reached, pricing is holding firm, and that customers remain loyal - all positive signs for global automation players."

Greenfield cited four key findings of the survey:

1. The automation market has already bottomed; modest growth will return in 2010;

2. There is no evidence of a price war in automation equipment;

3. There is limited differentiation between the spending outlooks for process versus discrete industries;

4. While highly cyclical, automation is a good business to invest in over the long term.

It is important to note that the second finding belies the fear that inflation might be a an immediate threat. Despite concerns over accommodative monetary policies around the world, this survey shows no sign of inflation's return in the immediate future. It's axiomatic that for inflation to appear, prices must rise. This survey of a significant sector of the economy shows no hint of rapidly rising prices.

Greenfield pointed out that the near-term trend in demand for automation equipment appears brighter than it did in early in 2009 because of the percentage of respondents expecting demand to increase, more budgets going up or staying level versus retreating, and increasing demand to replace aging equipment. In addition, pricing appears to be stabilizing in the near term. Few respondents expect to see prices fall, but neither are they expecting out-of-the-ordinary upward price moves by suppliers to help offset losses in the past year.

These results are exactly what we would expect at this stage of the present economic recovery. Pundits prophesying a double dip, an L-shaped recovery, or any similar pattern find no support for their views in this important economic indicator.

During the 1970s, I conducted an (unpublished) meta-analysis of data Charles Greeley Abbot collected from various sources in the early 20th Century to look for cross correlations between his solar irradiance measurements, sunspot index measurements, and weather patterns in various cities. The meta-analysis showed a significant positive correlation between solar irradiance and sunspot data, and a partial correlation between them and the temperature data.

Abbot, like nearly all astronomers and astrophysicists of his time, firmly believed in a negative correlation between sunspot index and solar irradiance, rather than the positive one his data showed. He noted the partial correlation between sunspot index and temperatures, but his prejudice about the correlation between index and irradiance led him to reject the effect as spurious.

By the end of the 1980s, the positive correlation between solar irradiance variations and sunspot index variations had been confirmed by satellite measurements, overturning astrophysicists' previous view. This allowed partial explanation of historically observed climatic variations, specifically the so-called "Little Ice Age" in the latter half of the second millennium, by reduction of solar activity observed through anomalies in the sunspot index, specifically the Sporer, Maunder, and Dalton minima. This research strongly indicates that solar variability is also an important input to the climate system that is certainly not under human control.

Now, it is becoming clear that the climate system is highly complex, with multiple positive and negative feedback loops, as well as a large number of independent forcing inputs, only a few of which are under human control (see "Aerosols Cloud Climate Picture," Science News, v. 176, n. 11., pp. 5-6 for a brief synopsis). These are characteristics of a chaotic system

Paleontologists and geologists have pieced together a fairly complete, though not necessarily detailed, picture of Earth's climate over the 4.5 billion years of the planet's existence. This picture shows a chaotic climate capable of varying over a wide temperature range. On short time scales, weather patterns are now acknowledged to be chaotic, with a horizon of predictability on the order of a week.

Taken together, these bits of information lead one to the conclusion that Earth's climate exhibits chaotic behavior on all time scales. It is, basically, a chaotic system.

Now, let's look at efforts to control climate change. We are attempting to use a chaotic system (global politics) to harness a second chaotic system (social, economic, and technical institutions) to control a third chaotic system (Earth's climate), when not all the forcing variables (e.g., solar irradiance, geology) are in our hands, anyway.

This sounds like a fool's errand.

I suggest that we could much more effectively apply our energies to developing means to react to climate change that is inevitable, than to the fool's errand of trying to direct it. Climate change, in any direction, has both positive and negative affects. It would be far better to direct our efforts toward engineering social systems, laws, and technologies to take advantage of the positive effects, and ameliorate the negative effects.