Putting My Money Where My Mouth Is: A Challenge to Okonjo-Iwealla If I Were The "Economic" Coordinator of the Bantu (and anyone who has two heads can go ahead and challenge me on economic theory) LESSON 1 – the economy in a nutshell: a superlative account America is a number of families. They live in a number of houses. They buy, principally, things required to keep their home livable in. This attended to, they buy exigencies, all classified under what the immortal Adam Smith calls the augmentation of enjoyments. But America is a land of liberty, so at age 18 every family member leaves the home and gets a job using his high school diploma. Within three years, the gestation period, the high school graduate is able to buy a home, and his part time job, supplemented by aid from his parents and support from his extended family, grants him the liberty he requires. Independence is a home and he buys it at 21. And so the cycle continues. This is the fundamental American Econometric Model. Just the paragraph above can be developed to one thousand pages, but we’ll start small and get to the next level of evidential classification. How do we model this in English? We have, as in all models, a static model and a dynamic model. STATIC ECONOMETRICS A basic rule of thumb in modeling is to have, first, what we call a superlative or pristine model. This model takes the world as it should be and then we have a baseline for making predictions that are sound and based upon the obvious. THE SUPERLATIVE MODEL “vocat” The econometric interface is the transaction. We want to list all transactions that connect home buyers with home builders, since this is what we will define as our primary economy, the buying of homes at the age of 21 by every American, having lived three years in a supportive or incubatory environment. To model this state of affairs what we are semantically instructed to do is to account for all items, within scaled emicity, that gives the same information as the paragraph to be modeled. Emicity simply is a number which all items of the same class share. So, for this rudimentary and yet ultimately complex and powerful model, we want to account, using discrete “items,” for the process space for American living. The first thing we do is refactor the paragraph. FIRST REFACTOR Every year X # of Americans turn 18. Every year X # of Americans turn 21. Every year X # of Americans are born. Since it is from the pool of born Americans that Americans who turn 18 come from it is our first metric—birthrate. The power of a rudimentary emic model is that it captures all generalizations needed to fund the complex structure of PhD level modeling. And yet, it IS PhD level modeling, or McCarthy’s “problem” of Generalization—to where do we drill down once the emicity is defined? SUMMARY 1: Economic modeling is done over time. Over time the following statistics define emicity zero or ‘baseline.’ Population growth (average and total) Infant Mortality PG – IM will give us our first security, or assurance for spending any amount of money in the homebuilding industry, assuming for now that nobody dies after birth. We normally say PG-(IM + satan) meaning the death rate of babies plus whoever dies after birth and before 18. We shall call this security HomeBuilder’s Trust 1 and in our software model the primary explanation of any change in the value of this security is PG-IM. There are many factors that influence population growth and there are many factors that influence infant mortality. Incipiently, certainly, we are concerned about feedback: How is population growth influenced by the housing industry itself, and how is infant mortality influenced by the industry too? First we assure ourselves that where the industry affects the predictive statistic positively it is a good thing. PG – IM represents, upon translation, the upper limit of new home construction in a given year. So let us look at new home construction, before we take a glance, if at all, at the feedback loop—how new homes sold affects PG and IM. Items: Architect, Electrician, Plumber, Carpenter, Labor, Digger, Landscaper, Surveyor. The architect designs the house and gives the plan to the surveyor. The surveyor is granted license to develop the plot, and draws its bounds once all is kosher. Then the digger digs a hole for the foundation and labor places the blocks in place and poles that hold up the roof and the entire skeletal structure. Then the carpenter builds the house and the plumber and electrician fit it for quality of life. That’s the homebuilding industry. Then let’s make a first assumption for the sake of developing the paradigm. Let us assume that on average, America grows by 2 percent each year, and that over the amount of time in question America has grown by 20 million people, say over ten years. Then… We have a metric for determining how many Americans in the year in question, say next year, will be moving out of their benchmark location and into the supportive incubator. It is High School graduation rate, and I’m too tired to go into it… so we’ll develop this later, meaning ‘adjunctcies.” But… We continue in short form.. why am I even writing long form in the hospital? Jeez…. In this rudimentary but already complex economy, employment is bounded by the items we demonstrated earlier. And so assuming people go to three year colleges and stay in rented facilities, 2 percent of the population, less the contextual IM and satan, will apply for home mortgages to finance their acquisition of property, the hallmark of liberty and happiness. The Virginia Bill of rights lists the pursuit of property, not inordinately and foolishly obviously, as a basic human right in America. We agree as economists. So, for the business student who wants to invest in home building, the incipiency asks us to determine, from the population growth and mortality rate statistic, how many new home buyers seeking mortgages will exist in any given year. It is lesson 1 and we will stop there for now. That’s homework. Figure it out using basic English and rudimentary thought. You will be graded, ultimately, on your understanding of what we are trying to determine and why we are using the items chosen to determine it. If you have adjunctcies and can keep up with the logical complexity that more than three items spawn, do so for extra credit. LESSON 2: Trends So, we are at the point where as investors we need to drive home construction by looking at trends in new homes sales. We want to list the factors that affected last year’s new homes sales and make predictions about just how much we should build. This sort of thinking, looking at last year’s, and then with relevant info the year before, is called Bayesian. We are ignoring all the information from years previous for reasons which will become quite obvious in season. The problem is stated in this way: 1990 PG 2% of 200 million 1991 PG 2% of 200+X million 1992 PG 2% of 200+X+Y million To keep complexity secure, we shall pretend to live in 1992, a very glorious year for me, and wish to invest in 1993 in the homebuilding industry. Those are our primary stats. In 1989, the year we began keeping such statistic, the population of America was 200 million. It grew in 1990 by 2% of the population and by the end of 1992 the population had grown by 8 million, which is a compound of a constant 2 %. There is no real reason to have growth constant, it’s just an artifact of the hyperium, the container for the imaginative statistic. Now… We immediately make the superlative assumption, since this is just the superlative model, that because population increased by the amounts as specified, then new home buyers ALSO increased at that level. Clearly, then, our work is defined. We need to look, first, at new home sales for the three years in question and determine which of the myriad of factors EXPLAINS the difference between the population growth model as a source of predictive capability, and the actual empirical figures from the three years in question, then we can predict based upon our precision how many homes we build next year, 1993. 1989-1990 PG 2% of 200 million (4 million) NHS 2.5 million 1990-1991 PG 2% of 204 million (4 mil, 80,000) NHS 2.8 million 1991-1992 PG 2% of 208.08 million (4 mil, 161,600) NHS 2.9 million So we can see, in this imagination, that population growth is constant and yet new home sales increase. This, by eyeballing, is not a traditional model but a complex one, so we should mimic reality a bit more. So we do… 1989-1990 PG 2% of 200 million (4,000,000) NHS 2.5 million 1990-1991 PG 3% of 204 million (6, 120,000) NHS 2.8 million 1991-1992 PG 4% of 210.120 million (8, 404,800) NHS 2.9 million Let us use this model for several reasons I won’t explain save to say that in reality population growth is closer to exponential than constant. Besides, it gives us a better fit for doing what we must do next, and it is the heart of this course, statistical regression analysis. We are to look at new home sales for the three years in question then determine what factors most influence it GIVEN our hypothesis that it ought be influence MOST by population growth. This is a logical assumption and our first task is to find evidence, first, that population growth has some sort of explanatory effect on the number of houses sold then we can go ahead and find explanations for why the proportion of influence is as described in the numbers. We see that each year the population growth increases by 1% of the total population, compounding, but the rate of new home sales is not directly concomitant with this increase. We seek to discover by looking at relevant factors, why, and solely because when we find the factors causing the anomaly we have found our key to predicting NHS for the year in which we seek to invest and maybe even influence the prediction. Let us first do a regression of our predictand, New Home Sales, with our predictor, population growth. Excel to the rescue. The diagram above demonstrates the predictive power of regression analysis. Assuming trends to be real, an increase in population growth should give us a precise idea how many home sales will occur in a given year in the future. But we don’t immediately say that we can predict because of a fit line, which we will draw now. Recall that there should be a perfect correlation between population growth and new home sales, and for now we’ve characterized the discrepancy blaming it on infant mortality and other factors. But first let us draw a fit. This fit line is calculated using the least squares method and gives us a line with the formula y = mx + c, where y is the new home sale, m is the rate of change of new home sales relative to the population growth and c is what the home sales would be when there is zero population growth or when population growth, in this case, WAS zero. Having added a fit line, we can now predict, at first source, what the NHS figures ought to be given any year outside of the evidence we have. This is business 101. HW: Study regression analysis by viewing online sources that explain it and try to think up, write down in fact, reasons, just reasons, why the projected NHS numbers using population growth as the regressor will only give a so-so prophesy and can not be taken serious by any money-wielding investor. LESSON THREE: Multivariate Regression Analysis—the imperfect world. Alas, due to a software glitch we cannot view precise numbers, but the regression fit line is a measure of how linear the data is. From the above points, it should be above seventy percent, evoking an adjective in the higher realms of probability. For example, at this point we would say “population growth is most likely to explain new home sales.” But now we need to determine the reasons new home sales are not exactly concomitant with population growth. We have mentioned infant mortality as one factor, but is it truly relevant? In the perfect model it would be, as would be murder rates and rates of death due to sickness. But these things all affect the natural world spawning other dependencies in a ferocious cycle that makes such a determination, of what the reasons are for asymmetry, difficult. Science, however, is not about a perfect world. It is about the world we observe, the world we see, feel and hear. And it is often sound science to begin rationally at the first point of complexity, which is where we are situated. We have identified population growth as being a hypothesized explanatory factor in housing starts, but now we want to give our hypothesis a number. At NASA we use –1 0 1. –1 suggesting that the explanation of the regressor has been superceded by other factors. Zero for a match and one to show that it is the prime factor in a regression. In this case it is par for the course, given the total complexity of the items in emic scale to talk through the ideas we have and begin to whittle them down before we settle on the next and probably most important explanation of why housing starts are not symmetric. Recall that mortgage is a French term which literally is a measure of innovation, granting the assumption that the peace of mind which comes with home ownership invokes the latitude for enterprise and thus innovation. It is, in fact, proven that such is the case and so Mr.Obama’s deriding of the “ownership society” was done out of, Sara Lawrence-Lightfoot admits, impatience. A very powerful lady and a credit to the faculty at Harvard, giving me an idea of how my future wife, Sophie Okonedo will look at that age. They are dead ringers! So let us explore mortgage, for at the end of this course we want to be able to characterize all the items which relate to mortgage as indices we can use, periodically, to determine the innovation rate as a function of American enterprise. This is the primary metric in the information age, innovation, and it stems from change and its implications, given a global world. We begin that process. We have made the point that peace produces purpose and purpose drives innovation. The American Econometric Model has at its heart the concept of self-interest. We hold these truths to be self-evident, the word says, and makes sacred the rights to life, liberty and the individual pursuit of happiness. Such pursuit, we then say, has truly begun when one owns a home, because of what, historically and theoretically, the safety of a home implies and has implied. The Alexis DeVeaux model from the Buffalo Business School suggests that the market place is simply the needs of a populace aggregated and classified logically within a facilitating process such as would allow the savvy business man provide solutions to those needs. We might then imagine the primary need for a homeowner, once shelter is taken for granted, to be food and the energy it produces. There are other needs as well, after food. Transportation, clothing and manufactured goods are top of the list, just for basic living, before one’s cares and hobbies and interests are attended to. We call these things, needed as home adjunctcies, quality of life items. Whereas the bible says that of faith, hope and charity, charity is the greatest, what might we imagine the greatest need is of food, transportation, clothing and manufactured goods? Food is correct, for in a triage situation it is obvious that the first necessity is food. And so we make a second hypothesis, coming on the heels of the PG proposition: we say, now, that the food industry ought to have metrics that explain, most effectively after population growth, new home sales. It is a hypothesis and we will spend the entire course seeking justification for this measured conjecture. When we have to items that explain a primary item it is called multivariate regression and when these two items are ordered logically then we have classification and regression. Of course we can use more than two items but you will find the complexity inherent in two items to be plenty for your first brush with PhD level economic analysis. I’ll save us all a major headache and say the magic words. Wheat prices. Bread. Without going too much into nutrition, it is clear that there are staples in the food industry, these being items which most people choose not to do without. In Nigeria, for instance, staples include akamu and akara, made from cornmeal, corn then being the true staple. In Thailand, wheat bread (and beer) is a staple. In America, too, bread made from flour and wheat is a staple, and something by which we can gauge the farming and food industry. It is a very convenient item, a loaf of bread, for it is the most purchased item next to cereal and milk, but since bread covers many of the farming staples—wheat, flour, eggs, milk, sugar, salt—then we will just use the end product bread as an indicator of how the food industry is shaping up. What is bread? Bread is something obvious that we have yet to discuss, something we have taken for granted. Bread is employment. We have taken for granted that the housing industry employs a portion of the populace in skilled craftsmanship, but once those have built houses for themselves and schools to propagate their craft, they begin to build houses for the growing population which must pay for these houses and the concomitant living needs employed doing what? Making bread, that’s what. When we speak of the most important industry after housing, we are asking where employment must have access to talent most after the housing pool is dynamically saturated and moving forward. Because food is the staple that comes after housing, then the food industry is where the next set of workers come from, and where the most houses SHOULD be built at the earlier stages of this model. You will find, much later on, that this is an invaluable way of ordering things, for even if the food industry today employs relatively few people, one will find that it can ALWAYS be used as a gauge, in scale, to determine the economic viability of a situation. So we are looking at the food industry—bread. It is delineated as follows. Farming, Fisheries and Husbandry—agronomy, resource optimization, livestock care Farming, Fisheries and Husbandry—administration and labor Farming—commodity harvest and trading, livestock produce post processing Food—unit preparation Food—transportation and delivery Food—distribution Food—retail and service “…if you fallow me…” We need to list the employment necessities in this industry so that we get a sense, on the larger scale, what sort of housing numbers it might account for once those in the housing industry are situated. But judging by the list above, such a task may be cumbersome. Instead of a plain vanilla list, we ought perhaps to bound our model once and for all with another paragraph describing our bizzaro ecomic world, where, and at age 21, all men and women become independent and own a home. Let us assume that the first people in America were the craftsmen, at some point they are all employed teaching the craft or using the craft for their own personal needs and so their industry is saturated enough to absorb only the talented into their guild. At this point there is need to take advantage of another industry, in our model the food industry, in order to account for the living standards of themselves and newcomers to this virgin land. We will now, in a flash, make the architects and craftsmen invisible by the following status report. Every morning homeowners in the virgin and united states enjoy a bowl of corn cereal with sugar and milk, then 2 slices of bread. In the afternoon they eat a bowl of rice and tomato stew with fried chicken. In the evening they enjoy mashed potatoes, steak and corn-syrup flavored malt brew. In the day-time they are either employed as craftsmen or in the food industry. And this is our new model. “…Virginia shall conceive….” Relevant Items: Corn. Sugar Cane. Cows. Poultry. Oxen. Horses. Rice. Tomatoes. Potatoes. Malt. Wheat. Carriage wood. Wooden Wheels. Cured Wooden Poles. Farming implements. Horse feed (for transportation fuel). Carpentry Implements. Milk Vats. Cooking Pots. In bizzaro economic world, all clothes are made from leather, and shoes from the cow’s hide as well, and the cow is also good for milk, cheese, yogurt, meat and bags. Its worth, as in the days of the children of Aaron, is pretty much immeasurable. As well, we assume prescience concerning a plot to sink our national security interests in the future through an oil embargo, and so we use horses and oxen for transportation of goods through carriages. Oxen are also use to plow the land with some farming implements. Fuel is horse feed here and its cost is akin to the cost of oil in the real world. Per barrel as well. With our economy bounded, instead of using just bread, we can say that employment in the food industry and the craft industry, but obviously more the food industry drives new home sales. Much later on we will see how a loaf of bread is almost as good as a dollar in terms of its sensitivity to the precision levels required to determine changes in the economic pipeline that affect the savvy business man’s investment in the housing industry. Now you see that we could have begun with this model and not spoken at the beginning concerning new home sales. Why did we? Because this model is relative to nothing, giving, in that instance, no real predictive or qualitative information. Instead, we can now hypothesize that housing drives productivity and increases the organizational merit and synergistic quality of the food industry so that new home sales have a loop back effect that caused, as its first victory, an entrepreneur, Mary, who WANTED neither to be in the guild of craftsmen or work in the food industry. Find below the US Virgin Territory Treasury inventory for year x. item unit price (gold coins) corn seedling sack 10 Wheat seedling sack 12 cane seedling sack 10 Calf single 14 chicklet single 12 Ox single 18 Horse single 22 rice seedling sack 11 tomato seedling sack 10 Potato seedling sack 10 malt seedling sack 13 wood-flat panel single 6 wood-cured pole single 4 leather-harness single 4 leather-pouch single 4 leather-shoe pair 4 leather-jacket single 4 leather-trouser single 4 leather-belt single 4 iron-pot single 6 iron-plough single 6 iron-shovel single 4 iron-wheel single 7 feed-livestock sack 10 feed-poultry sack 10 iron-nail single 1 iron-screw single 1 iron-hammer single 2 iron-saw single 2 iron-pole single 4 Milk quart 2 cheese pound 2 Yogurt pound 2 Beef pound 4 Cereal pound 2 Sugar pound 2 Bread loaf 1 rice pound 3 tomato dozen 1 Potato dozen 2 corn dozen 1 chicken single 2 iron-waterheater single 10 iron-doorknob single 2 iron-bathtub single 10 iron-hinge single 2 iron-latch single 2 wood-table single 2 wood-chair single 2 At this point, although we had started simple, one can see how complex the matter is, using just two industries. It would be expedient, at this point, to begin afresh having gained the foundational insight required to do more realistic analysis. The point of these first three lessons was to introduce statistical regression analysis and demonstrate the complexity of using more than one regressor variable in the context of a pseudo real word (bizzaro) situation. But the point has been made, that in the natural world there are reasons for everything, and if nothing else is to be learned from our PhD level legoland, remember the words ‘weak convergence.’ This suggests that when things occur, if one scratches beneath the surface one is likely to find a set of factors causing the occurrence that are constantly causing such occurrences and there is a convergence, often weak, of these factors. We will begin anew in the next lesson with a more focused approach. LESSON 4: The Economy of Virginia Soon we shall learn about the median statistic, but for now instead of using the median we shall suppose that EVERY Virginian does exactly the same thing. So when we speak of the Virginian Farmer we mean EVERY Virginian Farmer, since effectively the median farmer represents, to varying extents, EVERY farmer. As well an agronomic farmer represents all who cultivate controlled natural resources including fish farmers. We begin: The population of Virginia grows by one thousand people each year. This is reflected in the admissions at the Craft University of Virginia. This school teaches craftsmanship in architecture, carpentry, electrical engineering, plumbing, public works and foodservice. Each year the school takes in 600 students and each year the school graduates 500 students who are all aged 21. And so the available labor pool in Virginia, each year, is 500. As we can see this is a not so perfect world, and attrition is a reason for this. We shall deal later with those miscreants who don’t make it through the system as well as those who die of malaria before it is time for learning. For now, however, of each batch of graduating students a number are dedicated to the architecture and public works industry—utilitarian as such to speak—and the others are split unevenly in the other industries. They are: Farming, Husbandry and Warehousing in Virginia In April Farm Administrators in Virginia purchase 4 sacks of seedlings of the variety of their choice. They hire 10 people per farming field to till the land using oxen-drawn ploughs and some manual techniques. These people then plant the seeds and water them until the rains are constant. When the harvest is ready 20 people are needed to harvest each farm and the food is kept in local storage warehouses built and maintained by the craftsmen. Other farmers buy calves and chickens and rear them for milk, cheese, yogurt, eggs, leather and to be sold. Once all the food is harvested, then the farmers are done with their part of the bargain. The most important metric for the farmers is the tonnage of harvested food stored in local storage. The Transportation Business Once the storehouses are full, then the carriages built for transportation, again by the craftsmen—this time the transport craftsmen—are deployed to pick them up and bring them to the center of Virginia where they are packaged and placed in retail storage. The amount of available harvested food ought to be enough for the demand of the people in Virginia who all have access to the center and outlying suburban areas where retail storage is also available. Each carriage is driven by a single employee and also requires a conductor to load the carriage and ensure the safety of the food as it journeys from farm to town. Grocery Franchising However it came to be, several Virginians have enough capital to pay the craftsmen to build retail storage and so Virginia has a number of retail choices and these retail houses are the primary customers of the farms. One may in fact, add here, food processing to transform corn, say, to corn flakes. The way things work, in this awada (“awada” is a Yoruba term for dramatic production), there are ten farms that supply food for all of Virginia, and there are one hundred retail outlets that purchase from these ten farms and process the commodity. Food Service The main industry outside of the housing industry and the farming industry in Virginia is food service. The food service industry provides onsite facilities for the craft university for meals and catering of events. It also provides restaurants for Virginians and sundry and diverse eateries. It employs line cooks, short order cooks, chefs class I and II, kitchen workers (cleaners etc), waiters and event staff. Some of the more adventurous and context driven franchises venture further away from the domain of food, but most of the employment is done in the context of franchises. These franchises place orders with the retail outlets which place orders with the farm houses at which point the bulk of transportation necessity is deciphered. The Housing Market revisited Each year a number of graduates of Craft University pick a trade, with a relative minority choosing to teach crafthood. Many will pick a trade in the farm industry either as farmers and administrators, or building barns and designing gadgetry, or in the foodservice industry as the employees mentioned or building retail outlets and the sundry things needed for foodservice, or in the transportation industry building carriages for transporting goods and services or building the roads from the farms to the city and from the center to the suburban areas. Most, however, will be engaged in building houses for the new home owners, and maintaining houses for old homeowners. A degree from Craft University serves as a credit voucher so that over the course of ten years a graduate will pay for his new home in installments. As you can see, there are a lot of employment opportunities that come with a degree from Virginia Craft University, and so one imagines that it is a well sought after school for the mortgage it brings. The diagram below shows the “transactional” model. Use this, always, as a starting point, Cousin “Fallow” Malia, for understanding your econometric model. The different words represent “Sectors” of the economy and what I’ve demonstrated before is a ‘weak’ model, just so that you can always have the various sectors “in mind” and their minimal interaction space. The Size of Virginia’s Economy Because the model above accounts for all staples, except for healthcare, which we make an invisible entrant at the classificational level of “farming,” once there is employment saturation in these industries as demonstrated, then an economy we will suggest to be ‘sound.’ We are now concerned, as students of business and finance, with how much it costs to run a business in any of the aforementioned industries and what the profit margin is likely to be in this complex mish mash of interactions. We start that journey: At this juncture it is opportune to note that econometric models are often meant for predicting things. But also they can be used to trigger warnings. Looking at the New Home Sales figures from the years 2003-2009 one is ASTOUNDED to find that from a peak of 128,000 in March of 2005 new home sales had dropped by an incredible 80,000 by March of 2008 when the economic meltdown had become a global phenomenon, and from year to year economists did not have warning triggers based on the suspicious drops beginning after 2005. Neither have they, in attempting to solve the problem, focused on the first year after the peak to determine what factors most influenced the beginning of a negative trend! Let us now begin to make precise our model. In particular we are going to determine the costs of running a farming business and the costs of running a transportation business and then perform analysis to see how changes in the farming industry affect the transportation industry and vice versa. This should give us a new generic model for accurately modeling the virgin economy and determining the influence of motion in any industry relative to the other. Entrepreneur Type: Farmer Startup (Capital) Expenditure: Land –100 Acres @ 100 Gold Token/Unit Storage Barn –2 @ 50 Gold Tokens/Unit Irrigation Trough –10 @ 10 Gold Tokens/Unit Oxen –10 @ 10 Gold Tokens/Unit Harnesses –10 @ 2 Gold Tokens/Unit Ploughs –10 @ 3 Gold Tokens/Unit Total Startup Capital: 10350 Gold Tokens Seasonal Expenditure: Employment Task: Fertilization, Tiling, Planting Personnel: Ten (10) Oxen Supervisors for 30 days @ 1 GT/Supervisor/Day Items: One hundred (100) bags of corn seedling @ One hundred (100) bags of horse manure fertilizer @ 1 GT/Unit Employment Task: Watering and supervision Personnel: Three (3) supervisors for 90 days @ 1 Gold Token/Supervisor/Day Employment Task: Harvest Personnel: Fifty (50) harvesters for 30 days @ 1 Gold Token/Harvester/Day Items: Fifty (50) leather harvesting bags @ 2 Gold Tokens/Unit Employment Task: Primary Distribution Personnel: Ten (10) carriage loaders for 30 days @ 1 Gold Token/Loader/Day Ten (10) carriage drivers for 30 days @ 1 Gold Token/Driver/Day Items: Ten (10) rented carriages for 30 days @ 5 Gold Tokens/Carriage/Day Maintenance ~ 80 Gold Tokens/Season Total Seasonal Costs: 3100 Gold Tokens Entrepreneur Type: Transportation Service Provider Startup (Capital) Expenditure: Carriage –10 @ 100 Gold Token/Unit Dispatch Center –1 @ 100 Gold Tokens/Unit Dispatch Equipment (misc.) –100 Gold Tokens Total Startup Capital: 1200 Gold Tokens Seasonal Expenditure: Employment Task: Dispatch Management Personnel: One (1) Dispatch Manager for 30 days @ 2 GT/Manager/Day Items: Carriage Maintenance – 100 Gold Tokens Driver Equipment –40 Gold Tokens Total Seasonal Expenditure: 200 Gold Tokens Which business would you be in? We make the following observations: Farmer Enson Reagan went into farming to satisfy a burgeoning need—with the population increasing at a thousand people per year, Reagan’s capital investment, with the help of friends, family and a bank loan, is to be repaid within ten years. This begins to set the template for setting the prices of the commodities he wishes to sell. The list we had earlier on was arbitrarily set, as though by the Communist Politburo. In reality, however, prices are determined by the need to recoup investment as well as competition in a free market absent any ethical violations. We imagine that in virgin country there are enough farmers to take advantage of the need for corn and so the price of corn will reflect, at baseline, the cost of producing corn and some margin of profit intended to keep the investment in good stead and provide quality of life for Farmer Reagan. Like the craftsmen, he wishes to augment his enjoyments, his particular flavor of augmentation being malt liquor and fine wine. So, how many ears of corn will Enson and the median farmer produce during any given season? 100 acres is quite some land, so let us guesstimate that the farmer produces 50 ears of corn per acre. Then in 100 acres the farmer produces 5,000 ears of corn a season. This is how we shall start this portion of the analysis. As for the question of what business is more profitable to be in, it is clear that it requires a larger capital investment to go into farming, and clearly the returns can be tremendous. An investment in 100 acres will yield 5000 gold tokens after the first season from which seasonal costs are subtracted and by the 10th season the investment can have been paid off. This arrangement allows the farmer to schedule repayment of his loan with a flexible schedule and provides for a lifestyle second only to the architects and homebuilders of Craft University. The profit margin for farmers is thus 5000-3100, which is 1900 gold tokens per season. But because the transportation service business requires very little startup capital, as the number of farmers grows it can be a very profitable business with little expenditure required to scale up. At some point will be a magic number of carriages when it is no longer wise to accept new customers, and that is the number that determines the profitability of one business relative to the next. We will be precise about our numbers in comparing the Virginia transportation service industry with the farming industry, for what we hope to begin to see, in the context of regression analysis, is the effect of changes—modest and pronounced—in one industry against the other. For example, and this is our first test, assuming the price of corn to be 1 gold token as listed, what would be the effect of the increase the cost of transportation labor from one gold token per day to two? We might say here that this change in labor was effected because of an increase in the cost of a loaf of bread, the primary staple. The way it works is something like this. The cost of processing wheat to bread using, as well, eggs, milk and salt has increased for any number of reasons. Although the price of wheat, or corn in the event of corn-bread, has not changed, the retail cost of bread is affected by this ad hoc change. Part of the benefits package in working for the transportation service industry happens to be providing free meals to the drivers who are rented by the farmers to drive the carriages. This cost has now increased and so to maintain the original standard the cost of hiring a driver every season increases from one gold token—the minimum wage—to two. It doubles. How does this change matters for the farmers? Employment Task: Primary Distribution Personnel: Ten (10) carriage loaders for 30 days @ 1 Gold Token/Loader/Day Ten (10) carriage drivers for 30 days @ 2 Gold Tokens/Driver/Day Items: Ten (10) rented carriages for 30 days @ 5 Gold Tokens/Carriage/Day The added cost is 600 gold tokens, which is 20% of seasonal costs. That’s a significant increase so let us think of the various ways in which this ad hoc and probably permanent increase affects, minimally, Farmer Enson Reagan, and consequently the price of his staple commodity, corn. But instead of going ahead with our bizzaro virgin world where people are paid in gold tokens, it would be smarter to actually capture the generalizations that this scenario have brought to the fore, with the intent of taking the actual US farming yield numbers and plug into or software package, which by then ought have no glitches, different changes that we hypothesize CAUSED the increase in the price of the commodities in the yield table. And this is what exactly we shall do. Lesson 5: Gross Domestic Product—Non-Durable Goods, Durable Goods and Services Before we continue our economic observations, but now of the real America, here are some terms we should be familiar with, for these are the real indicators of economic health. 1. Gross Domestic Product 2. Personal Consumption Expenditures 3. Durable Goods 4. Non-Durable Goods 5. Non-Residential Fixed Investment 6. Non-Residential Structures 7. Equipment and Software 8. Residential Fixed Investment 9. Exports of goods and services 10. Imports of goods and services 11. Federal government consumption expenditures and gross investment 12. National Defense (the militia ;-) 13. State and Local Government consumption expenditure and gross investment 14. Private inventories 15. Current-Dollar personal income 16. Current Taxes 17. Disposable personal income 18. Final sales of domestic product (GDP – DELTA(Private inventories)) We should spend a little while placing these citizens of our new economic lexicon in the proper perspective, always never forgetting the little hamlet of Virginia because of which we might be so bold. We begin with GDP. The Gross Domestic Product is the market value of all goods and services, paid for, produced by a nation. By goods we are speaking of things such as commodities, manufactured items and durable goods like cars. Services, especially in the information age, are driven by the demand or need for human expertise as a result of quality of life in the modern world. In fact, we are making an incipient hypothesis that quality of life most directly impacts the demand for services. If this is the case, and since we are making the weak claim it is, then we should at once be more precise about how the GDP of America breaks down with goods relative to services; analyzed in the context of American quality of life—however we can define this—and global quality of life. We ought to see correlations that prove or disprove our point. First, though, let us look at the matter of produced goods. The US Government’s Bureau of Economic Analysis lists two types of goods—durable goods and non-durable goods. Why this distinction? We can determine the appropriateness of their economic classification by placing them in the context of time. Durable goods are likely to be purchased less often in a cyclical sense and so give a measure of information quite different than non-durable goods, which are more likely to be purchased, within the analysis timeframe, in a cyclical fashion of use. It is for this reason, of the explanatory econometric information they provide, that they are classified differently. Each one of us has at one time or the other purchased a lawn mower. The gadget can last one several years, even decades with good maintenance, and so a sudden cyclical “multi” purchasing of lawnmowers in any given year would be surprising. On the other hand, shoes and handbags might be purchased a few times each year by the same consumer, providing a very different context for analysis. A spike in the increase of durable goods might indicate, for example, an increase in wages allowing many more households to afford these items which, by definition, cost more to produce and are thus, on average, more expensive to purchase than their counterparts. This difference in cost of production and frequency of purchase is our first matter of precision. To be sure, what we want to do is explore the sorts of information evoked by the identification of the sales figures for durable and non-durable goods produced both within and outside the US relative to the US consumer. Let us say, for sake of explanatory principle, that the nation of Taiwan manufactures a thousand Grasshopper lawnmowers each year, the nation of China manufactures ten thousand Xu-Chie lawnmowers each year and the United States manufactures five thousand Ohio-Player lawnmowers each year. These numbers are a fiction, of course, although they are designed to remind us of the reality of China and Taiwan’s dominance in the field of manufacturing, relative to the United States of late. Let us say that in the year under analysis US residents purchased five hundred, seven thousand and three thousand lawnmowers from Taiwan, China and the US respectively. These expenditures fall under the category of Personal Consumption Expenditure. The number we want, however, is the number of lawnmowers bought from the US itself by US residents and these sales fall under the all-important category of Final Sales of US Domestic Product. It is an added layer of complexity, this feedback loop, where US residents expend money on US-made items so that there is a percentage of the Final Sales figures that is made up by US-residents. This percentage is important because it gives us a sense of how accessible the US market is for various items, to US residents. Why is this important? It is important because if the US market is more accessible to non-US residents, then there is clearly a problem, which might account for a deficit in the GDP in some manner. Item 1–% of GDP made up by US PCE But in a republic, as goes the household so goes the House. We are thus also concerned with government consumption expenditure of goods and services and how much of that is for US-made goods. Item 2–% of GDP made up by US GCE and fixed investment The FSDP figures might look like this. Fig1 So now we have a basic “initial lexicon” consisting primarily of “durable goods”, “non-durable goods,” “services,” and “final sales of domestic product.” With these we return, in spirit, to our little hamlet of Virginia and see how we can apply the analysis of a small space, as in Virginia, to a large nation and what lessons therewith occur.
Posted on: Tue, 17 Sep 2013 17:50:15 +0000
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