Financial Survival Kit

What do you need to be prepared for the future financial turmoil? How can you survive?

Should I Buy Gold?

It seems like everyone is a gold bug these days, but is it the right thing for you?

What Are The Chances?

With all this "end of the world" hype going on, maybe we should consider the chances. What are the chances of a civilization threatening event?

How Much Insurance Do I Need?

Insurance is an extremely broad topic. Hopefully this generalization on the different types and amounts will help straighten things out a bit.

Iraqi Dinar: Scam or Scoop?

Some say it's an easy way to make a million bucks! But do you understand currency markets enough to take advantage?

The financial world we live in is just as wild, if not more, than the mountains and woods we walk through. We are told that the fundamentals of our economy are strong, but we can feel that something is wrong. My unique financial background and survival passion make Financial Survivalist and excellent place to learn and share.

Thursday, December 5, 2013

Supply-Side Economics

The majority of the discussion about supply-side economics can be summed up by “trickle down.” However, many “supply-siders,” as Karl Brunner called them, would argue that there is much more to supply-side economics than that. The basic doctrine can be described as “a lower tax schedule and , especially, a lower slope of the tax schedule is a necessary and sufficient condition for economic recovery.” Such as tax policy will “increase real economic growth, eliminate inflation, and eventually lower government expenditures.” The reasoning is behind incentives to work, invest and save.
Robert Freeman goes on to specify that that tax cuts should be for those who are wealthy enough that they don’t need to spend the “windfall.” Rather that they will invest their tax savings in factories, office buildings, research and development, etc. To me, it sounds like supply side economics is the exact opposite of “redistribution” of wealth.
Robert Freeman goes on to point out that the theory is that the investment will lead to economic growth, higher employment and higher incomes, and therefore higher tax revenue for the federal government despite the cut in taxes. Anne Krueger elaborates the definition of supply-side economics to consider the measurement of success. If one refers to determinants of aggregate supply, then production or supply is the key to economic prosperity. I should point out that over production leads to a dead weight loss, and so my initial though is that supply is only half of the equation.
Karl Brunner was a tough critic of supply-side economics and the Reagan administration.  He said that supply-side economics “lacks the consistent analytical framework necessary to provide effective solutions for our socio-economic problems.” That it, it won’t fix our problems that we have inherited from past generations.
So what are the problems that we inherited? Karl Brunner attributed the majority of the growth during the 50’s and early 60’s to Keynesian policies. He did not praise Keynesian policies, only eluded to their at least partial success at the time. He said that in the mid 60’s and through the 70’s we drifted into a period of permanent inflation with erratic and high levels of interest rates. He admitted that employment was growing, but the average level of “real growth” (I am assuming GPD) fell below the precedent set by preceding years.
He blamed the waning economic environment partially on shifting policies, but mostly on monetary, fiscal and regulatory policies occurring during the postwar period. As a solution to the woes in the 80’s supply-side economics reared it’s head and provided only a partial solution.
I find it interesting that while Karl Brunner spends his time criticizing the failures of policy makers during these years, Anne Krueger points out that during these years the world experienced that fastest increase in quality of life ever! 50 years ago the world was a very different. I wouldn’t know because I wasn’t alive, but it is easy to deduce a similar conclusion by reading about the time.
The majority of time reading Karl Brunner’s article, I felt as though he was a progressive criticizing conservative parties. Even if he wasn’t a progressive, he at least leaned left. As the article progressed, I realized that Karl Brunner was a staunch conservative, and that his critique of supply-side economics was that it was not extreme enough. It surprised me that he criticized supply side economics, but argued that the key to prosperity was limited government, decreased regulation and decrease in taxes. He made a point that cuts in government spending were more important than tax cuts, and one of the failures of President Reagan is that they didn’t cut government sufficient. He criticized supply-side economics because it didn’t really focus on cutting government spending. Instead it justifies deficits because tax cuts would lead to more revenue and offset it.
Robert Freeman makes an observation that essentially supply-side economics is a modern version of Say’s law. Say’s law has long since been discredited and it is easy to see the similarity between the two. Just because supply is there does not me demand will meet it. Without considering the demand side of the equation, supply-side economics is bound to fail or not succeed fully.
It seems to me that cutting taxes on the 1% wealthiest individuals won’t do anything. The majority of their income is from capital gains. So any affect of a tax cut will have to encompass a significant number of citizens in order to have any effect. As it includes more and more tax payers, the chances that they will spend the “windfall” will also increase. The balance between the two will likely not lead to more investment in factories, machinery, research and development etc., because for the most part it is companies that make these investments, not individuals. So policy would have to be centered on corporations and small businesses, not around individuals. However, these entities often make their decisions based on expected demand for their products. If taxes are cut, then they might expect and increased demand due to increase disposable income of customers. Therefore tax cuts do have the potential to increase investment, but such tax cuts should not be focused on the rich as they cause a relatively small portion of consumption.  
It is always easy to look backward and criticize decisions made in the past. After all hind-sight is 20-20. I seem to grow ever more tired of these extreme unbalanced criticism. Not that someone doesn’t have the right to criticize, but at least when doing so they should not apply their criticism to the extreme niche of the view. What I mean is that there are wackos in every bunch. I’m sure there are “purist” supply-siders, but I could easily argue that the majority of “supply-siders” are not purist. They understand that it is not a complete one size fits all solution.
Anne Krueger points out that the nature of our economic environment dictates that the problems and solutions are not static. As our economy evolves we will continue to encounter new problems that need new solutions. It has been in the past and will be in the future, an extreme error to attempt to apply a single static solution to economic problem. It is the every changing nature of economics that draws me to it. I want to be able to have the proper knowledge and skill set to observe, orient, decide and act to solve the new problems as they come our way.
Because the economy is not static and I believe contains at least some level of chaos, neither is the answer to our economy static. I have more to learn about current application and attempts to apply non-linear analysis to the economic challenges. I look forward to this study as I continue in my educational pursuit.

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Applied Chaos

“Chaos: When the present determines the future, but the approximate present does not approximately determine the future.” (Lorenz)
Chaos theory is a phenomenon found in mathematics with applications in many other sciences, where initial conditions of a system, without change of the system or its variables, can have extreme effects on the outcome of the system. This effect is often referred to as the “butterfly effect,” although it’s significance is much greater than what popular culture might cause one to think. When discussing “chaos” in mathematics, generally one is referring to mathematical chaos. Mathematical Chaos is a behavior that looks random (chaotic) but is not. In order for “chaos” to be referred to as mathematical chaos it must have the constraints of sensitivity, determinism and recurrence. There are many important concepts of chaos theory including fractals, strange attractors, sensitivity of initial conditions and bifurcation or minimum chaos.
Chaos exists everywhere. It is found in the weather outside, crowds of people, boiling water, governments and even in the trees. This is also why there are so many applications for the things learned by chaos theory.  It is extremely difficult to use two-dimensional models to study multi dimensional realities. However, this is exactly what our traditional math attempts to do. Just because someone makes his or her two-dimensional model more and more complex, does not necessarily mean it will be more and more accurate. In fact the opposite may be true if they are studying a chaotic system. By accepting that chaos does exist, scientists have created several clever ways to identify, measure, analyze and use it. The study of chaos has led to many new ways to analyze data many of which also have application in linear (two-dimensional) analysis.
One of the most widely used applications for chaos theory is in weather forecasting. As the science of meteorology, and the tools used to analyze the weather (Doppler, satellites, computers, etc.) became more and more advanced, scientists attempted to predict weather with increasing complex models. The problem was that they didn’t work. While working on a weather-forecasting model, Edward Lorenz noticed that a computer model could predict a vastly different weather forecast from what was thought to be the same data. He figured out that rounding some of the initial data to the third decimal point changed the result significantly. He concluded that due to the sensitivity of initial conditions, weather could never be forecasted more than about a week in advance.
Chaos freed the scientists from the obligation of exactness, but they did not give up. Instead of trying to figure out an exact prediction of the weather, they now run iterations of multiple models and then calculate a probability or “most likely scenario.” This kind of forecasting is extremely more effective. However, because of the sensitivity to initial conditions, and the complexity inherent in any weather condition, it is still extremely difficult to predict the weather. A small error of any variable can significantly change the forecast. Sometimes meteorologists don’t see the storm coming until the day before. The study of chaos continues to make advances in weather forecasting, and the science has come leaps and bounds since Lorenz’s discovery in the 1960’s.
Another application of chaos, and one of the most popular aspects of it, is fractals. A fractal is a an object who’s irregularity is consistent over different scales. This is also known as self-similarity. Sometimes something that seems random and chaotic has self-similar properties. If you zoom in the image still looks the same. In that case, though it may not be possible to measure the length of an infinitely complex curve, it is possible to measure it’s “roughness.”
Made famous in the 1980’s by Benoit Mandelbrot, and later clothing designers, fractals have many applications. In 1982, Mandelbrot published a book entitled “Fractal Geometry of Nature.” Fractals are found everywhere in nature including in the mountains, forests, sea shells, plants and bacteria. Mandelbrot could see fractals everywhere, when most people only saw disorder and chaos. His book inspired many applications of fractals including computer graphics from movies and flight simulators, antenna, and even biologists studying the rain forest.
A good example of how fractals can bring order to chaos is in measuring and studying rain forests. Scientists have discovered that the distribution of branches on a single tree is self-similar to the distributions of trees in the forest. By using this self-similarity they can approximate the biomass of the trees in the forest and therefore the amount of carbon dioxide the forest breaths. This allows scientists to more closely measure the effect of the forest, including deforestation, on the global climate.
One of the most widely used applications of fractals is in antenna. When cell phones were first made popular they were big and bulky. In order make the antenna strong enough, they had to loop wire back and forth hundreds of times, and it still didn’t work very well. After attending a presentation by Mandelbrot, and during and antenna dispute with his landlord, Nathan Cohen decided to try and make an antenna in the shape of a fractal. Not only did it work better, but it was also smaller and it could receive more frequencies simultaneously. He continued to develop this idea and the technology soon found use in CB radios, cell phones and many other radio devices.
A typical smart phone receives Bluetooth, Wi-Fi, data (3G, 4G, etc.), calls, texts, and sometimes radio signals. These signals are all on different wavelengths, but because of fractal antenna a smart phone does not need multiple giant antennas. It only needs one small fractal antenna. In fact, the multi-functionality of modern smart phones would be extremely difficult, if not impossible, without fractal antennas.
Another application of Chaos theory is in organizational behavior. Lynn Adams, then the Governor of Heber City, Utah, observed principles of chaos, including strange attractors, in local governments. His observations during specific political efforts to reorganize local governments led to work in a model of revolution he called “The ARM.” Dr. Adams was in the right place at the right time to observe patters from within the organization during revolution. He was able to isolate certain aspects of the organization that led to success and or failure. This led to a detailed explanation of the cycle of revolution and the elements within.
Future applications of Dr. Adams work might include social analysis. If one was able to measure the level of chaos in different stages of Dr. Adam’s model, then it could help give an idea of where society is in the cycle and where it might go next. This kind of analysis could possibly have applications in political policies including economic, monetary and taxation policies. However, one must consider the ethical consequences of such a model. Not only could it help lead better policies to cope with chaos in society, but it can also be used to thwart an ethical revolution and or enact an unethical or false revolution. A model that allows policy makers to understand the chaos within their country can also help a dictator maintain power.
            This same research could possibly be expanded and adapted to analyzing business cycles of individual companies, markets or entire economies. A business cycle of an individual company is similar to a sector’s and an economy’s business cycle. Therefore in some ways economies may be self-similar. By analyzing the business cycle of an individual company one can learn things about the entire economy. If it is possible measure the level of chaos within the organization, one can estimate what stage of the business cycle the organization is in and where it might go next. This can be added to other analysis to better valuate companies.
            Another widely used application of chaos theory is in financial markets and financial analysis. Security prices seam completely chaotic. For years statistical (linear) analysis has been applied to try and predict where the price will go next and “beat” the market. Linear analysis has also been used for years for investing strategies. A perfect example of this is Modern Portfolio Theory, which uses statistical analysis to minimize risk and maximize gain. The problem is it doesn’t work because security prices are not linear.
            The Prediction Company, or PredCo, mentioned page 146 of “Chaos: A Very Short Introduction” (Smith), was founded with the intent of finding a better way to analyze financial markets. They theorized that if there was chaos in the financial markets, then non-linear analysis could create better strategies. A confidentiality agreement keeps secret exactly what they are doing, but as Smith mentions, “ The continued profitability of the company indicates that whatever it is doing, it is doing well” (Smith).
            This world is not a linear world, and yet society continues to teach linear math. Chaos theory and non-linear analysis will eventually dominate linear math and continue to lead to innovations in mathematics and all scientific disciplines. The more that is learned about chaos the more it is understood, and the more applications are found for it. Science has only begun to scratch the service of chaos, and advances in medicine, finance, physics and biology will continue to come forth. Chaos is an entirely different way to look at the world. It is more effective and more productive to study a multidimensional system in a multidimensional way.


Lorenz, Edward N. (1963). "Deterministic non-periodic flow". Journal of the Atmospheric Sciences 20 (2): 130–141.

Adams, L. Lynn; Adams, Nathanael; Cortez, Jiar (2002) “PATTERNS OF REVOLUTION: COMPREHENDING, CREATING, AND LEADING PERMANENT CHANGE”, Department of Finance and Economics, Utah Valley University, Woodbury School of Business

Smith, Leonard (2007). “Chaos: A Very Short I
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