Government Statistics and Raising A Child – How Not to Read the Data

             We got the call we have been hoping for from our daughter the other day. She said, dad, we have a foster child. Our daughter and son-in-law have passed all the tests and done all the preparation to be foster parents. They have been looking forward to the opportunity with excitement and some concern. Our daughter could have said, dad we have a foster child, now what do we do? But they didn’t. It is a little interesting because they were expecting an older child and instead have an 18 month old. A little different than they were expecting but we are all pleased and so are they. I have been thinking about raising five children (really Margie raised five children and I tried to be helpful and not cause too many problems) and the attendant questions and thoughts I had when first starting out. We are now looking at being empty nesters in just a couple of years.

             So, how did we afford to raise five children? Well, it wasn’t by reading the USDA reports on the cost of raising a child, even back when we started the process. A CNNMoney article titled “Average Cost to Raise a Kid: $241,080” as quoted by Melanie Hicken, August 14, 2013 on money.cnn.com states “From day care to the monthly grocery bill, the cost of raising a child is climbing at a rate that many families can’t keep you with.” The article says that a U.S. Department of Agriculture report released Wednesday (August 14, 2013) says the cost of raising a child from birth to 18 is up 3% from 2011, not including college to as much as $441,100 ($24,505.56/yr). The average cost is $241,080 ($13,393/yr). If we assume each child cost that much (the article doesn’t differentiate between one and several children) then Margie and I would not have been able to afford 5 children during the early years we had all of them at home on my salary.

             The problem with this kind of data and other related and interesting ideas is discussed in a new book I recently picked up by Charles Wheelan titled Naked Statistics Stripping the Dread From the Data. I find his writing informative, entertaining and thought provoking. It sort of reminds me of a book from several years ago titled How to Lie with Statistics by Darrell Huff. The premise of Wheelan’s book is that we need to use statistics correctly and if we do we can gain some important insights into our daily lives and what is happening to us. On the other hand he suggests that there are some statistics that if used incorrectly lead us to very erroneous conclusions or can even kill us. The above article is one of those cases that can lead to some very poor choices. One of Wheelan’s chapters is titled The Importance of Data: “Garbage in, garbage out”, another chapter is titled, Deceptive Description: “He’s got a great personality!” and other true but grossly misleading statements. I think you can see where I am going with these chapters from Wheelan. We need to be particularly careful in just reading the news headlines or even the actual article because the information may very well not give us what we need.

             So, for example, in the above article the discussion didn’t include anything about economies of scale, meaning the 2^nd child doesn’t need all new cloths or need a new bedroom for himself. Both “facts” the government study didn’t seem to take into account. In our case Margie was good at making the money fit the needs. For the record, you don’t need to spend $13,000 / year / child (the average). One article I found more helpful (“Cost to raise a child can be much less than USDA estimate” by Sarah Gilbert, June 14, 2010) suggests costs may be as low as $2,500 / year / child (for 3 children in this article). I think the actual total for each of us may be somewhere in-between. Which also makes sense as all of us do things differently. Again, the above statistics can give us an average but that may not be very helpful. If you are familiar with the Phantom Tollbooth by Norman Juster, in one of his encounters the hero meets the fractional child from the average family. This is kind of like that, the average cost of raising a child is not likely to really give you much helpful or useful information.   

Mark Twain famously remarked that there are three kinds of lies: lies, damned lies, and statistics. I think we could also say something like that about data. There are three types of data: data, damned data, and statistics. That is important to remember whenever you are presented with the “facts”.

Playing in the Sand Pile – Observations About Sand in Your Shoes – Part II

             I really am more organized than I sometimes seem. I had an outline of the topic I wanted to write about tonight. I started about four hours ago thinking I would be done in an hour or so. Well, it isn’t an hour later (as my previous sentence suggests) and I deviated quite a bit from the original outline. However, I feel that I need to lay this groundwork tonight. I cannot over emphasize the importance of being wary of economic and financial models or money schemes or the best investment you could ever make. Regardless of what Ben Bernanke (of the Fed) or Tim Geithner (of the Treasury Department) or leading economists (with lots of letters and abbreviations behind their names) or your neighbor (it is such a hot tip) or your best friend or a member of your religious congregation or your financial planner, tells you - be suspicious (in a nice way if you think you need to).     

             Previously we touched on the idea of the sand pile effect in nature and modeling. It includes such  concepts as nonlinearity and the critical state, is often known as complexity theory and sometimes called chaos theory. These ideas and concepts are discussed by Mark Buchanan in his book Ubiquity Why Catastrophes Happen, who we looked at briefly last blog and Nassim Nicholas Taleb in Fooled by Randomness who we have discussed several times.

             Let’s illustrate nonlinearity. Suppose we are enjoying a day at the beach with nothing better to do than build a sand tower as high as we can. As the tower increases with each bit of sand we add there comes a point that one more bit of sand causes the entire tower to collapse and slide down. This illustrates a nonlinear effect resulting from a linear force exerted on an object. Our tower suffered a disproportionate collapse from a very small additional input, namely a little bit of additional sand. It the sand pile would have reacted in a linear fashion we would have expected the small bit of sand to have a small impact. There are some idioms that incorporate this idea, the straw that broke the camel’s back or the last straw, or the drop that caused the water to spill. I can remember my father saying something like “that was the last straw” as he explained to me why I was being punished for what I thought was a fairly minor infraction and not worthy of the severity of the particular punishment I was receiving.

Taleb suggests that the nonlinear dynamics has what he calls the bookstore name of Chaos Theory. Taleb further suggests this is a misnomer because the theory has nothing to do with chaos or randomness instead, chaos theory does concern itself mainly with functions in which a small input can lead to a disproportionate response. A little bit of sand generates a massive sand slide. Buchanan suggests a slightly different but similar definition in his comment on what he calls the critical state. He says it represents “…a special kind of organization characterized by a tendency toward sudden and tumultuous changes, an organization that seems to arise naturally under diverse conditions when a system gets pushed away from equilibrium.” Buchanan says this is the first landmark discovery in the emerging science of nonequilibrium physics. Remember he is science writer and has a Ph.D. in theoretical physics.

             Look at the sand pile example again. Suppose you were to apply the nonlinearity principle to your commute home. A trip could take from a few seconds to months. Or suppose you are coming to the corner of the street. What is the likely height of the next person to come around the corner towards you. If we are in the sand pile the person could be from inches to miles high. Yet we have examples that follow this nonlinearity. Why is Bill Gates so rich. Is it because he is an intellectual giant compared to the rest of humanity. Or perhaps he is so much more intelligent than the rest of us. He may very well be of above average intelligence and superior work ethics and have high personal standards. But is he so much better as to deserve to be so wealthy. An element of nonlinearity or luck would better account for it. Economies, markets and social arenas tend to be nonlinear.  There really isn’t a mathematical model that can successfully model this type of activity. The model has to have a random element. Having said that, there are many who try to model parts and bits of things but the full, rich experience which makes up the world around us is difficult and complex. Think of weather models, how successful are we in predicting how much rain will fall on our backyard tomorrow, then one month later. If weather was linear we should be able to predict both time periods with great accuracy. Taleb suggests that one reason we get in trouble with economic and financial models is that some “…intelligent people who felt compelled to use mathematics just to tell themselves that they were being rigorous in their thinking, [and] In the great rush [to develop models] decided to introduce mathematical modeling techniques… without considering the fact that either the class of mathematics they were using was too restrictive for the class of problems they were dealing with, or that perhaps… the precision of the language of mathematics could lead people to believe that they had solutions when there were none.“  The purveyors of economic and financial models may try to convince us that their models do include enough “mathematics” to describe the particular situation but from our examples of tonight it seems very unlikely that the models will stand any test of time or uncertainty.

Playing in the Sand Pile of History – Part I

             A problem in modern economics has been the unpredictability of things. A cacophony of voices have tried from the beginnings of civilization to predict the next big thing. Just imagine the problem an Egyptian stone mason had trying to figure out the next big pyramid job. How was he going to know what and when to order stone so the competition didn’t get the jump on the big contract. Mark Buchanan in his book Ubiquity Why Catastrophes Happen tackles the problem head on and in my mind with that single mindedness of the scientist convinced that there is a mathematical equation somewhere that will hold the answer to life, the universe and everything. Those who model these chaotic systems talk about complexity and the greater problem some call upheavability. Buchanan suggests that chaos is limited in its ability to explain extreme events (I would say Black Swans – you recognize that term) because many models do not generate upheavals.

             Buchanan is good enough to suggest that predicting the long-term future of any chaotic system is practically impossible. I will suggest that it is presently quite unlikely that current models and modeling techniques will successfully model financial and economic systems with any degree of success or accuracy. Further, what little successes we may have is inversely proportional to model time horizon and the complexity of the system.  We will tend to have limited modeling success in the short term with simplistic systems. However, Buchanan does have some interesting suggestions for looking at complex systems which I think may help in understanding both the complexity and the pitfalls inherent in economic and financial modeling. He uses the term critical state to suggest  a special kind of organization characterized by a tendency toward sudden changes, maybe radical changes. Using his physics background he suggests that instead of trying to find mathematical equations to describe these complex systems that an alternative is to use mathematical games, much simpler equations, to understand specific portions of complex systems. We are going to explore one of these modeling technique, the sand pile, in later blogs. Today we need to set some parameters.

             We need to look at some basic principles regarding modeling and models. I want to start with what Emanuel Derman and Paul Wilcott, financial quantitative analysts, term the Modelers’ Hippocratic Oath. Derman and Wilcott are considered part of the elite group of financial modelers and have been in the financial industry before, during and after the great recession of 2008. Many feel that quants as the financial modelers are known are responsible for the severity and length of the recession and its attendant losses. In many respects this is accurate. Derman and Wilcott’s Oath shows some of the problems inherent in trying to model complex financial systems that many people seem to forget. Models are tools – blunt, limited, and easily breakable.

 Modelers’ Hippocratic Oath

·       I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
·       Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
·       I will never sacrifice reality for elegance without explaining why I have done so.
·       Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
·       I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.
 

             I have a copy of this hanging by my desk. Any time I encounter a financial or economic model or a discussion of one I look at the oath and attempt to see if the author / originator has applied all the points to his model and the information I have about it. If there is any part of the oath that I suspect the author did not consider or incorporate in his model I am immediately suspect of the model, its conclusions and most of all its recommendations. All financial and economic models are suspect, period. Always assume there are errors in the model. Errors in logic, in assumptions, in data points included and excluded, in the equations, and in conclusions. Once you have looked at a model in this light it can be reviewed and analyzed to see if there are portions that may have some value. If you get the sense that models are potential death traps to your financial health and to the forecasting of economic conditions, that is accurate. One doesn’t have to be able to break down models to their component parts but one does need to realize that every model has problems, many of them significant problems. What is a person to do, be very skeptical of the output of any financial or economic model and run away from anyone who says, “trust me, you don’t need to know what is in the ‘black box’ “. If the person can’t or won’t explain the black box (for example, a new financial product guaranteed to get you 25% return in today’s environment) you don’t want to be involved, ever. Remember the old adage, if it’s too good to be true it probably is.

             Next time, how to look at a sand pile –  what can we learn and how can we use it.

The Black Swan – Story Possibilities (Part IV)

             We have spent a few posts looking at Black Swans in economics and politics. Look at them as possible story lines or back story items. This can be either in world building or single situations. Can the reader see it if seen from the correct angle? For example, one can see a Black Swan if one is the Thanksgiving turkey (see Part I – Are We Unknowingly the Turkey) but there really isn’t any Black Swan from the farmers standpoint. The turkey is dumbfounded but the farmer is full and satisfied. Is the reader the turkey or the farmer?

             L.E. Modesitt, Jr.’s series titled The Ecolitan Matter consists of four books; The Ecolitan Operation, The Ecologic Secession, The Ecologic Envoy and The Ecolitan Enigma. These books explore economic, political and ecologic ideas and concepts. I highly recommend all four as a good example of economic and political ideas. The books were written from 1986 through 1997, during a time of several stock market crashes (1987, 1992 and 1997), political upheaval and a renewed concern for the environment. I will admit it is not hard to write books during times of stock market crashes since they tend to happen every four to eight years. Modesitt does a masterful job of including economic and political problems into his stories. I find Modesitt’s use of economic and political principles very well done and very true to form. Notice how he leaves much of the material as back story or world building materials but enough is in the stories to make it easy to follow and enjoy. He doesn’t burden the reader with too much detail.

             Remember, economics and social sciences are full of Black Swans. It is their very nature to be uncertain and unpredictable. Review The Black Swan’s Mask (Part II) to see how we rationalize our incorrect or missed predictions. Can a story be developed from looking backwards through our missed prediction to a situation (reverse the process by creating an outcome and looking backwards through a false prediction to the original situation- which may be incorrect). Remember every world is full of floods that happen only once in a 1,000 years. In The Black Swan – but I saw that Coming (Part III) we looked at the outlier and the uncertain or unknown nature of such.  Again, the most important point is that economics and political science are not true sciences in the way we think of science as being measurable and predictable. Both are full of Black Swans, the unknown and unknowable. Use that to your advantage.

The Black Swan – But I Saw That Coming (Part III)

           We have spent a couple of posts looking at the problem of the Black Swan – the impact of the highly improbable. We have discussed the turkey and the Thanksgiving feast in part 1 of this set. One doesn’t want to be a surprised turkey. We have looked at the problem of induction or inductive knowledge which includes how can we logically go from specific instances to reach general conclusions. According to Nassim Taleb there are traps built into any kind of knowledge gained from observation. He also suggests that those who believe in the unconditional benefits of past experience cannot project into the future. Remember back to our turkey example, was the farmer surprised by the outcome?

           Having made the above observation, I will say there are some situations where we can use past experiences to draw conclusions about future observations. Suppose you are asked to find the average height of the all men in the United States. You could take a sample of say 10,000 men and draw some fairly detailed conclusions. You would be able to take that data and make some conclusions regarding height of men in general. Now if you were to measure one more man from the population in general your estimate of his height would likely be within the parameters you had established. The same would be true for weight. Another way to state this is, one more observation will not significantly impact the predicted results. Now suppose you sample people who are worth more than $10 million. Let’s suppose that your sample does not include Bill Gates. Your sample size may be quite large, relatively speaking, but if it doesn’t include Bill Gates then as soon as Bill Gates is included in the analysis we have a Black Swan situation. A deviation so large that it blows up the study. So, you decide that Bill Gates must be an aberration… (sorry, let me be specific, in the data only), and toss that data point out. Then the next data point you sample includes Steve Job’s estate. The difference between sampling height and let’s say millionaires is one set of data follows a bell curve (predictable or regular) or a variation of such things and one doesn’t. Many who have studied this feel that economics and social sciences cannot be defined by bell curves and its like. Many in econometrics feel that the world can be defined by regular occurring events or activities. Taleb suggests that “a nerd is someone who thinks exceedingly inside the box” or is blind to Black Swans.

           Some other themes arise from our blindness to the Black Swan. Again I turn to Taleb and his book, The Black Swan the Impact of the Highly Improbable. He suggests the following ideas. We tend to focus on preselected segments of the seen and generalize from it to the unseen. We fool ourselves with stories that cater to our thirst for distinct patterns. We behave as if the Black Swan does not exist, human nature is not programmed for Black Swans. What we see is not necessarily all that is there. History hides Black Swans from us and gives us a mistaken idea about the odds of these events. We “tunnel”, that is we focus on a few well-defined sources of uncertainty at the expense of the others that do not easily come to mind.

           As you listen to the news and look at economic projections remember that economists and their associates are working in a world full of Black Swans or full of Bill Gates type situations. It is important to realize that studies done on how successful economists have been in their predictions have shown economists have an exceptionally poor track record of being able to predict things. Just because an economist is able to show past data to support his position may have little or no relationship to future events. It all depends on where the Black Swans are.

The passing of my father

Two weeks ago tomorrow our family laid my father to rest. He was buried with full military honors in Logan, Utah. He was 86 years old, a good age and had lived a good life. I have spent the last two weeks thinking about and reviewing his life and my own. It will be some time before the heartache of his loss subsides. I suspect it will never go away but will fade and will, in time, become just a dull pain. I am grateful for him, his teachings, his life and his example. I know many are also feeling his loss acutely. We can take comfort in knowing that God loves us and that families are in fact eternal. Life is good. My father knew this and I know it too.

My regular blogs will continue tomorrow. Thank you.

The Black Swan’s Mask (problems in induction) Part II

           The marvels (for me anyway) of technology and the benefits of a good internet connection make it possible for me to be in Arizona visiting my grandchildren and their parents. We spent the afternoon at Pima Air Museum where I listened to a marvelous docent, Mr. Miller, tell stories and share experiences from his time in the air service and relating stories of others from earlier times. I marvel again at the willingness of our service people to lay their lives on the line for our great country. I am grateful for their service and sacrifice. I was thinking how it must have felt to live during 1937 – 1939 and watch the world unfold. From our standpoint now in history we know that Germany was going to war, Italy and Japan were gearing up for war and the world was set for an awful time to come. I remember reading that the correspondent William L. Shirer who wrote what is considered by many to be one of the definitive works on Germany’s Third Reich, The Rise and Fall of the Third Reich, was living in Germany during much of the 1930s. If I remember correctly he said that even for those living during that time in Germany and who had a grasp of world events such as a print journalist like himself, didn’t fully grasp what was happening. He and his associates didn’t predict war any better than the others in government, economics or any other branch of science or social science. History tends to look back and say, how could you have missed that. All the signs were there?

           I experienced the lead up and the effects of the recession of 2007 – 2009 from a unique standpoint. I am directly involved in investment banking and many feel that this industry is a big part of the problems that caused that recession. I think the sentiment has some validity. However, most of the leaders or men of knowledge in economics and investment banking never saw it coming. Again, there are some who did see something and made a lot of money on the fact that they felt something was out of alignment in the economy. Again, if one looks at much of the literature on this recession we see individuals drawing conclusions and wondering how we could have missed it. I was in the thick of the information so to speak and I didn’t see it coming. Government leaders, economists, most investment bankers and Wall Street types didn’t see it. We now have Frank-Dodd legislation which runs into hundreds of thousands of words and is so convoluted that we as a people will never fully understand what it does nor do the writers of the legislation really understand its workings or impact. This is supposed to guarantee that we won’t have another financial situation like this happen again. Trust me on this one, it will happen again

           Let’s look at how we rationalize our incorrect prediction (or missed predictions). This is how we protect our self-esteem according to Taleb in his book The Black Swan, where he suggests four methods of rationalization. (1) Tell yourself that you were playing a different game. It is not your skills that are to blame. There is some hidden information or element that if you had known you would have been right. (2) You invoke the outlier. (This is what is generally used by the economists and math guys to explain the recession of 2007 – 2009.) Something happened that was outside the system. Given that it was not predictable you are not responsible. Hey, it was a 1,000 year flood, of course I couldn’t predict it. (3) The “almost right” defense. By looking backwards we assign values after the fact to ideas or events thus applying more or less importance to things, activities, happenings or events. After the fact we say this piece of information is important and this is not. (4) The hedgehog and the fox from Isaiah Berlin and Aesop’s Fables. The hedgehog knows only one thing and tends to exclude all other thoughts and ideas. It may be a single consequential but improbable event. This helps make us susceptible to Black Swans. The fox knows many things and is not married to one idea or course of action. The fox has an open mind (not empty but open) and tends not to get married to one idea. The fox knows history will be full of improbable events but just don’t know what the events will be.

           So, be a fox with an open mind.