This all started with some import-export data from the United States Bureau of Economic Analysis (BEA) and a couple of import-export graphs from the Economic Research Service (ERS) of the United States Department of Agriculture (USDA). It is not really about imports and exports in general, however, or even agricultural imports and exports in particular. It is about how our international transactions are influenced by the United States’ system of taxation. In particular, it is a discussion of how the United States’ tax system disadvantages labor relative to other factors of production and how that disadvantage affects our transactions with the world.
The graph below is “Export value share of production, 2013-22” for U.S. agricultural and food production. While we are just looking at industry production shares and not total volumes, it is clear that U.S. agricultural and food exports are heavily weighted towards relatively nonperishable commodities, low value food products. The majority of U.S. agricultural products are non-manufactured and non value added. Since 2008, the export share of U.S. agricultural production has remained relatively constant at about 20 percent.
The second graph is “Import share of U.S. food consumption, 2011-21.” The accompanying explanatory notes indicate that imports accounted for 15 percent of U.S. food consumption for the period, and that they steadily grew as a consumption share over the period. The unstated takeaway is that imports must be significantly above 15 percent of U.S. food consumption now.
As in the export graph, we are looking at industry shares rather than total volumes or values. It is still clear, however, that U.S. agricultural and food imports are skewed towards perishable and value-added food products – high value stuff. The explanatory notes also suggest that this is due to, “…numerous factors – including relative competitiveness in production…,” but no explanation for the, “…relative competitiveness of production…,” is given. It is assumed that relative competitiveness is a given – a state of nature.
It is not.
One of the reasons the U.S. exports nonperishable, non-manufactured, low value-added agricultural products and imports perishable, manufactured, and high value-added products is the competitive position of labor within the U.S. We often hear about the competitive position of labor between the U.S. and other countries, but that is not what we are discussing here.
The uncompetitive position of labor within the U.S. is largely a creature of the U.S. tax system. The tax system penalizes labor utilization within the U.S. in a number of ways. In general, taxes on labor are high. Income taxes, which are levied on wages and salaries, earned income, are substantially higher than taxes on capital gains, which are levied on incomes derived from physical and financial capital. This artificially raises the cost of labor in production. It also artificially lowers the cost of capital in production.
Income taxes are also generally levied on gross earned income and are collected immediately upon payment. Labor has very few means of minimizing or deferring their share of taxes. Labor pays gross rates. In addition, the U.S. government funds very large components of its social expenditure package (Social Security and Medicare) with direct taxes on labor. All of these increase the production cost of utilizing labor. It also increases the participation threshold of labor in production, making it less likely that labor will participate in the production process.
Conversely, recipients of receipts from physical and financial capital benefit from multiple incentives that can reduce rates that are already favored over labor (and further distort investment decisions). Among these is a very favorable schedule of depreciation, allowing owners of physical capital to claim a significant portion of receipts during the depreciation cycle as expense deductions. Recipients of returns from capital also have substantial leeway in determining when and how to realize those returns. This allows them to time and combine their receipts in tax-advantaged ways. When they do realize those returns, the recipients pay taxes at filing time rather than upon receipt. They pay net rates rather than gross rates. Furthermore, a large proportion of receipts from returns on capital is self-reported, generating substantial opportunities for tax avoidance.
Taken together, U.S. tax policy raises the relative cost of utilizing labor and lowers the relative cost of utilizing capital in the production process. At this point, one might ask, “What the heck does that have to do with the industry distribution of agricultural imports and exports?”
The answer is relatively simple. High-value food products, perishable, manufactured, and specialty foods, are generally more labor intensive than low-value foods. Through its tax policies, the U.S. disadvantages local production of high-value foods and encourages the production of low-value foods. This is mirrored in the types of food the U.S. exports and imports. There are other factors, but U.S. tax policy is a significant factor in this imbalance.
Tax policy doesn’t just affect employment and production in agriculture. Its effects are economy-wide. Luxury cars and watches are not generally products of the United States. Premium handmade shoes are generally imported, as are handmade suits. We export relatively capital-intensive goods and services. We import relatively labor-intensive goods. Both trends are supported by a domestic tax system that penalizes labor (earned incomes) and rewards physical and financial capital (unearned income).
If you recall your international trade course in undergraduate economics, trade is determined by relative input cost differentials within countries. Movement between countries equalizes internal cost differentials for both partners regardless of single-factor cost differentials between them. That means we can alter our import and export mix with the rest of the world by reducing the tax policy distortions between earned and unearned income. We are often told that unfair competition is stealing American jobs, but before anyone can be accused of cheating, we need to stop driving American jobs away with a distortionary tax regime.
In addition to the artificial cost differentials between labor and capital, the practice of funding social benefits through taxes on labor builds the cost of pensions and health care into the cost of goods on the market. This directly penalizes domestic consumers, and it increases the prices of U.S. exports, making labor-intensive exports even less competitive on the world markets. In most industrial nations, pensions are paid through general taxation and do not directly translate into export prices. In many, such benefits are paid for through value-added taxes that are only levied on goods sold domestically. This makes exports from these countries more competitive than exports from the U.S.
The tax distortions also cause fundamental economic problems and political distortions. While producers face artificially high costs of labor due to taxes, labor gets artificially low returns due to those same taxes. As a result, productive labor is often not an attractive trade-off with respect to the informal economy or household production. High labor costs due to taxes are coupled with low returns to labor due to taxes. We end up with:
A labor to capital cost differential that distorts our production and international trade
Stagnating labor incomes
A shortage of labor
All tied to a tax regime that penalizes earned income and rewards unearned income.
Recipients of unearned income recognize there are distortions in the labor market, but they have no interest in giving up their tax advantages. As an alternative, they insist that U.S. labor is paid too much. They fight to reduce the rights of labor to organize. They fight to reduce labor regulations that address fair payroll practices, overtime payments, working hours, and child labor. In order to get labor costs back in line, they fight to further reduce returns to labor while also defending and expanding the tax differentials that are the root of the problem.
I am a recovering economist. I spent most of my adult life with production functions, growth models, economic impact studies, and such. Traditional economic practice and the concepts of progress it embodies assume things grow. Sure, there are recessions. There are depressions. There are areas of the world that stagnate and decline. Those, however, are aberrations. They are failures of individuals or small groups of people. They grow out of market failures that can be identified and cured. At least, that’s the theory…
While we studied the concept of scarcity when we learned price theory in introductory microeconomics the production functions in upper-level micro seldom have explicit limits. It is assumed that individual players are too small to affect total supply or demand, so individual players appear to have an unlimited supply of resources at their disposal. It is only a small conceptual jump for most of us – even those of us who are economists – to internalize the assumption that the resources of production are unlimited.
Input-output systems utilized in economic impact modelling explicitly assume unlimited linear production relationships. It is possible in the context of these models to define an economic event that will employ an additional 10,000 workers in areas having populations of less than 10,000. The model will dutifully crank out an impact to scale (for a quick look at some of the implications of this, see my blog post at Regional Strategic, Ltd.)
Beyond economic impact modeling, whenever we perform cost-benefit analysis looking forward, we discount future costs and benefits on the basis of some factor (generally an assumed future interest rate). The effect of this is that current benefits nearly always swamp future costs. Future costs approach zero the farther out we look because the discount rate reduces them at a geometric rate. In a world of limited resources, one might think that future costs should approach infinity as we run out of valuable stuff to consume, but that is not how we model the future. Our modeled perceptions are probably some distance from our grandchildren’s coming realities.
The Coming Depopulation
These ubiquitous assumptions of continuous growth are running headlong into the reality of worldwide population decline. This is not the Malthusian catastrophe or Paul Ehrlich’s population bomb. It is not due to famine or war ignited by overpopulation, poverty, and despair. The simple fact seems to be that as people around the world are getting better off they are having fewer children.
This reinforces theories of economic demography which suggest that in poor societies children are demanded as a form of insurance against illness, injury, and incapacity. Where infant and child mortality are high, this generates a substantial demand for more children as a hedge against both poverty and mortality. On the other side of the coin, in rich societies with low infant and child mortality and significant insurance infrastructures, the increased demand for children is channeled into quality rather than quantity. In this sense, children in rich societies are acquired much like luxury durable goods – why have a Chevrolet when you can have a Mercedes? Fewer children have greater opportunities to participate in elite sports or arts groups, get more prestigious educations, and, later in life, receive larger inheritances. This increases the status of their parents as an exclusive cadre of exceptional children is paraded about town.
This, to varying degrees, increasingly appears to be the situation around the world, at present. For individual families and children, it is a favorable development. Society wide, perhaps, not so much.
Worldwide population decline has not been experienced since the 14th century when the great famine and the bubonic plague savaged the known world in quick succession. That episode, however, generated substantially different demographic effects than we can expect from the current depopulation of choice.
The famine and the plague inevitably victimized weaker segments of the population – likely the elderly and the infirm – at greater rates than they affected the stronger. As population declined, the working-age population increased relative to other population segments. This increased the productive potential of the population that remained. As total population declined, it also bid up the wages of the working population that remained. Finally, because they inordinately victimized the elderly, the twin catastrophes accelerated intergenerational transfers of wealth.
As a result, the famine and the plague increased the productivity of the remaining population and redistributed wealth towards the working portion of the population. It is quite likely that the rapid population and wealth redistributions of the 14th century were substantial factors in the coming of the renaissance in the 15th and 16th centuries. In other words, increasing the proportion of the productive labor supply in the population and increasing the proportion of society’s resources available to that productive labor supply helped usher in two centuries of phenomenal economic innovation and growth.
The coming depopulation of choice will have very different ramifications. The coming depopulation will be the product of millions of decisions to have fewer children. That will mean fewer people entering the labor force. At the same time, increasing life expectancies will result in an increasing number of older people who have left the labor force. Unlike the 14th century situation in which the relative size of the working-age population increased and the relative size of the elderly population decreased, in the coming depopulation there will be an increasing proportion of elderly matched against a decreasing labor force.
Increasing the relative size of the elderly population can also be expected to decrease the resources available to the working-age population for two reasons. First, it will be necessary to commit an increasing share of output to a population that is no longer productive. Second, as people live longer, the intergenerational transfer of wealth slows. When people die at 50, inheritances accrue to people who are 30 and improve the lives of the dependent children of those beneficiaries. On the other hand, when people die at 80, the inheritances accrue to people who are 60 and will be held by those beneficiaries to support their coming unproductive years.
In the coming depopulation, we can expect the remaining population to become less productive because of the resulting age distribution and the reduction in resources that can and will be dedicated to support the productive portion of that population.
The Current Situation
In affluent countries, where life expectancies are high and premature death rates low, the replacement birth rate is approximately 2.1 children per adult female. This replaces two parents and accounts for pre-adult mortality. In less affluent countries, where pre-adult mortality rates are higher, the replacement rate must also be higher. In 2022, fertility rates in every region of the world except Africa, the Middle East, and Central and South Asia were below affluent-society replacement rates (2.1 children) and falling. Even the fertility rates in regions above the replacement level are falling. With the exception of Sub-Saharan Africa, fertility rates are below 3.0 for all of the above-replacement regions.
The population in China is already falling. East Asia as a whole has a fertility rate of only one-half the replacement level. The fertility rate in India is already below the replacement rate, as is true of all four of the world’s most populous countries (India, China, the United States, and Indonesia). While these trends are increasingly becoming the subjects of attention, they have been recognizable in every region of the world since at least the 1960s.
The United States is not experiencing depopulation. Fertility in the United States, at 1.6 births per female, is far below replacement levels, but strong immigration supports continued growth in the United States population. On the basis of current trends, the United States will continue to have an increasing population until about 2080, but current trends are largely dependent upon immigration.
As outlined in the previous section, the coming depopulation will be accompanied by significant aging in the population. Over the past 30 years, the proportion of the population over the age of 65 has increased by about 15 percentage points in East Asia, 10 percentage points in the European Union, and 5 percentage points in the United States. At varying rates, this trend is occurring in every region of the planet.
As the elderly live longer, working-age people move into the ranks of the elderly, and low birth rates fail to replenish the labor force, fewer and fewer productive people will be supporting larger and larger unproductive populations. In addition, as the elderly live longer, intergenerational transfers of wealth will slow and will become almost entirely transfer cycles within a multigenerational elderly population – continually concentrating resources within the ranks of the unproductive population.
The two demographic solutions to this are higher fertility and accelerating elder mortality. Neither of these appear to be on the horizon anywhere in the world.
Potential for Immigration and Replacement
In spite of sub-replacement fertility, the United States still experiences population growth due to strong levels of immigration. This immigration is driven by the need for labor force replacement in the United States coupled with a positive wage differential between the United States and the immigrants’ home countries.
These two factors are in place throughout the Western industrialized world. While Europe is increasingly concerned about immigration, Europe needs immigration to support its labor force. Hostility towards European Union immigration rules was a major instigator driving the Brexit movement, but Great Britain still needs to fill the labor gaps generated by an aging population with a declining labor force.
The wage differential between the United States and the undeveloped areas of the world (particularly Latin America) all but guarantees immigration, legal or illegal, into the United States. The same is true for Western Europe, particularly with respect to the Middle East and Africa. No nation has ever been able to stop economically driven immigration. In fact, dedicating significant resources in the attempt to stop such immigration will almost certainly exacerbate the internal labor force issues that are the major drivers of immigration in the first place. At best, nations can hope to shape the quality and quantity of their immigrant packages through carefully integrating national entrance and foreign policies.
A major issue with the labor force needs of the developed world is the skillset of immigrants from areas of the world with surplus populations. In an increasingly technical world, labor force needs will require immigrants with substantial skills. In contrast, it is estimated that 90 percent of young people in Sub-Saharan Africa and India, the two largest pools of remaining non declining population in the world, lack basic skills. To a lesser extent, this is also true of Latin America, the Middle East, and the rest of South Asia. These people are drawn to the labor shortages and wage differentials of the developed world, but they are not equipped to contribute at the levels required. The immigrant labor the developed world desperately needs is woefully educationally deficient.
What the Situation Requires
At its base, a solution to the problems that will accompany global depopulation and aging must revolve around increasing economic productivity. This is such an urgent need that the September 2024 issue of the International Monetary Fund’s Finance & Development publication is titled, “PRODUCTIVITY and how to revive it.” As smaller and smaller populations of productive people are left to support the needs of larger and larger unproductive aging populations, we will need to either generate increased levels of output from the productive population or succumb to decreasing standards of living for the population as a whole.
There are basically three means of increasing productivity:
Increase the availability of resources.
Increase the technical ability to transform those resources into goods and services that generate income and wealth.
Increase the skills of the population engaged in resource transformation.
The first of these offers, at best, limited potential. Having treated resource availability as infinite for over three centuries since the industrial revolution, the world’s available resources are becoming harder and harder to locate, develop, and acquire. In fact, the world is dedicating increasingly large shares of the second and third factors of increased productivity to the acquisition of the first. It is not a zero-sum game, but diminishing returns are certainly a reality.
The second requires investments in basic research that almost certainly will have to come from public initiatives. Private entities will willingly invest in the specialized research and development needed to bring marketable products into production. They will not, however, invest in the basic research and scientific progress that underpin their specialized research activities. Basic research results in knowledge available to any private entity. An individual corporation cannot engage in basic research because the successful results of that research do not accrue specifically to the corporation. The results might actually give competing corporations an edge in the marketplace.
Without public investment, basic research falters. Without basic research, productivity falters.
Unfortunately, in both North America and Western Europe, the cores of the world’s basic research infrastructure, levels of funding for basic research are falling. The public appetite for funding basic research through the expenditure of tax revenues has diminished. As a result, it will be very difficult to significantly increase the efficiency with which we transform resources into income and wealth as the world depopulates and ages. This does not augur well for the future.
In addition to funding, increasing basic research capabilities requires maintaining and improving the quality of students entering the various fields of basic research. This fits hand in hand with the third means of increasing productivity: increasing the skills of the population engaged in the transformation of resources.
Both the second and the third means of increasing economic productivity require continuous improvement in basic education. This should drive increasing commitments to and investments in education. As the working-age population diminishes, it is absolutely necessary that the skillset of the working-age population expands. This is immediately true in the developed world, where populations are aging rapidly, and working-age populations are shrinking rapidly. It is also necessary in the less developed world, which will inevitably be asked to replenish the shortfalls in developed nations’ productive workforces.
Unfortunately, public investments in education are also in decline in the United States and across Western Europe. The simple truth is that all three of the necessary components for increasing productivity and maintaining incomes in a depopulating and aging world are going in the wrong direction in the only areas of the world with the resources to improve them.
It gets worse. Even if the developed world invests in domestic education and research to improve productivity, it is almost certain that domestic productivity cannot be raised fast enough. The rapid declines in productive populations juxtaposed against the rising economic needs of an expanding elderly population will almost certainly outstrip even extraordinary productivity improvements. Additionally, the ability to raise the productivity of what are already the highest productivity populations in the world will be limited.
The simple fact of the matter is that maintaining income and wealth for the developed world will require augmenting the developed world’s labor force with imported labor, immigrants. In a world where most available immigrants lack the basic skillsets to be productive participants in a technical industrial and service economy, those skills will have to be improved before they immigrate.
This will require the developed world to invest in the educational advancement of less developed nations which can provide immigrants. This will have to be done by developed nations for two reasons. First, less developed nations simply cannot afford to upgrade their educational systems and human capital fast enough to address the immigration requirements of depopulation and aging in the developed world. Second, educating potential immigrants from the less developed world, like basic research discussed above, will not generate value that the host countries can directly capture. In fact, the very premise, here, is that the people educated will migrate from their less developed home countries to the developed world.
If they desire to maintain their income levels in the face of depopulation and aging, developed countries will have to invest heavily in improving both their own education and human capital development systems and education and human capital development in the less developed world. In addition to maintaining their own income levels, this will have an impact on improving income levels for the world as a whole.
Not all newly educated youth in the less developed world will migrate out. That will improve the productive human capacity of their home countries. It can also be expected that a substantial proportion of the immigrants to developed countries will remit earnings to their home countries. This may not sound familiar, but it has parallels to the situation discussed above concerning the twin catastrophes of the 14th century. By increasing the productivity of the workforce in less developed countries and providing additional capital to that workforce (through both external investments in education and emigrant remittances), we might create conditions for at least some of those less developed countries to take off.
Conclusion
The coming world depopulation appears to be set in stone. Over half of the people in the world live in countries where fertility rates fall below replacement rates. All four of the most populous nations in the world (India, China, the United States, and Indonesia) fall within this group. Only three broad regions on Earth have fertility rates above the replacement level: Africa, the Middle East, and South Asia. Fertility rates are falling in every nation on Earth, even in countries where fertility remains above replacement levels.
Depopulation due to declining fertility rates inevitably means populations will age significantly. Rising proportions of elderly people juxtaposed against declining productive populations will result in declining incomes unless rates of economic productivity increase substantially.
The central component to increasing economic productivity is education. Maintaining income levels in the coming depopulation will require an immediate commitment to increase investments in education. This will be necessary but not sufficient in the developed world. It is also imperative that the developed world make an immediate commitment to increase educational investments in less developed countries. This is necessary because the developed world will be dependent upon technically proficient immigrants to augment its labor force as its populations shrink and age.
Regional Strategic, Ltd. engages in issues of regional economics: economic impact studies for existing enterprises and new initiatives, business planning and pro forma financial projections for new and growing businesses, and market and policy analyses (they are pretty much the same animal, really). We like project work. Every challenge is somewhat unique.
Almost all of our clients are looking to persuade someone: investors, taxpayers, development boards, potential clients, etc. We help them find, create, and interpret the information and data needed to support their interests. Upon occasion, we are asked to evaluate analytical studies done by others that our clients would like to challenge. It is still a persuasion game. It is just sometimes adversarial.
A term that often comes up when I talk to clients is the “Movable Middle.” The idea is that if a client can pull some of the middle of a population in their direction, they can generate sustainable growth or drive sustainable changes in policy.
In marketing situations where the client is only a small fraction of an industry made up of similar small players, this is healthy competition. It is reflective of Adam Smith’s 18th century observation of the “Invisible Hand” – where the individual actions of many small participants will continually move the marketplace towards a better solution for all – a mutually beneficial movement of the middle as a whole. In this case, small movements at the middle or average will be offset by multiple other small movements. The overall middle may actually move, but trends in the middle will reflect trends in the population as a whole. The population remains stable.
The situation can be very different where there are major players with market power. They can peel off so many participants in the middle that other market participants cannot accommodate within the existing population distribution. In these cases, moving participants away from the moveable middle affects the existence of the middle, itself. This can make the population less stable.
This is a particular issue in politics, where individual parties are actively attempting to split the middle in order to attain dominance. In political contexts the idea is that a group will never win over the core members of the other persuasion, but they should be able to whittle away at the population in the middle – to draw these denizens of the middle towards the clients’ points of view. This becomes increasingly critical as the number of competing entities diminishes.
You don’t have to remember much of your college statistics course to visualize this. Most of you remember the graphic below – the infamous bell curve. It is a standard normal population distribution where the mean (average) and the median are the same. They sit in the center (at the peak) and two-thirds of the population sits in close proximity to the mean. This is the starting point for an introduction to statistics course, and this type of population is the basis for most of the statistical analysis any of us has done.
In a marketing context, it may represent public perceptions of an industry-standard product where most people are generally satisfied with the product, some people really love it, and a few people really hate it. In a political context, it might represent a population where the majority of members generally wants the same things but sees multiple ways of getting to those things. It is easy to survey a standard normal population. With a normal population, a random survey of the population is sufficient as long as enough inquiries are made to assure reasonable representation of the central core and the outliers.
The center of the bell is the “Moveable Middle.” The term generally does not mean moving the entire middle. It means moving some members of the population away from the middle. A client might want to move someone slightly right of center to slightly left of center or move someone slightly left of center to farther left of center. This flattens the bell curve, increasing the variance and, perhaps, moving the mean away from the median and the highest point on the bell. The graphic below shows what this might look like.
Our population is no longer uniformly distributed around the mean. We have encouraged distinctions among the members that may depend on several specific factors (age, income, sex, gender, religion, etc.) We can no longer look at the population as homogenous. Also, since we actively advocated for and encouraged this movement, we can no longer look at population members as independent. The answer or perspective of any member we survey may be directly dependent upon the attitudes and perspective of other members we survey. We can no longer assume that each member sampled generates an independent data point for analysis.
At this point, we can no longer just pull a random sample from the population and survey the sample because members of our population are no longer randomly distributed or independent. We need to develop a sample frame. A sample frame is a set of rules regarding what we think we know about the distribution of the population. Do we expect that suburban middle-aged males, in general, have a common worldview? If so, we might treat them as a pool to sample. Do we expect that retired folks living on Social Security have the same interests and needs? Ditto. The more observable actionable distinctions we see within the population, the more detailed or complex the sample frame becomes.
Once we identify our frames of reference, we decide what a given group’s weight is within the population. We sample each group in accordance with the perceived weights. Then we combine our sampling responses to simulate the interests and opinions of the entire population.
Setting up and weighting the sample frame is not scientific. It is based on insights derived from science and observable facts on the ground, but it is not scientific. It requires some special insights and knowledge regarding the non-normal non-independent populations to be surveyed. This is why multiple seemingly redundant surveys of what appear to be the same populations regarding what appear to be the same questions and preferences often generate different results.
So-called “Gold Standard” pollsters or survey firms are “Gold Standard” because they have a track record of correctly setting up and weighting sample frames for evaluating non-normal non-independent populations. None of them are perfect, but they are regarded as consistently better than the rest. This is based on their ability to translate insights and facts on the ground into sampling frames that consistently mirror results revealed after the fact.
In any event, the difficulty in constructing sampling frames increases as we continue to move people away from the middle of the population. The graphic below might show a continuing population movement away from the middle. It may actually be a case where the population has split into two populations. In that case, if each population has a normal distribution and its members are independent, surveying either population becomes simpler, again. If both populations are of interest, however, from a political or marketing perspective, we still have the growing problem of developing accurate and informative sampling frames to evaluate the interests of the two populations together.
These issues are real regardless of whether we are marketing goods and services or political ideas. The wider implications are different in the two cases, however.
If we are trying to move the middle when marketing goods, we want to differentiate the market into two populations (ours and everyone else’s). For decades, Mercedes Benz attempted to split the automobile market. They tried to create a distinction between driving a car and driving a Mercedes Benz. Splitting the population would have made their information gathering easier. Two distinct populations could be polled separately, because they would be clearly identied. Mercedes drivers could be polled to find out what would enhance their experience. Car drivers could be queried regarding what it would take to move up. Separate polls would drive separate marketing campaigns. Successfully splitting the market would, in many ways, simplify the marketing game.
This has very few society-wide downsides when marketing goods and services, because we can each acquire the goods and services we want, regardless of the purchases of others. In the political marketplace, however, the population as a whole decides to buy one package of policies. Determining which package of society will “Buy” would ideally be made by an educated and informed electorate, similar to the assumption of perfect information in the theory of competitive markets. Unfortunately, the attempt to “Move the middle” in politics effectively reduces the amount of useful information in two ways.
The first of these is passive. As we move from a normal distribution in the first graphic above to the double-peaked population of the last graphic, it becomes harder to effectively poll or survey the population with regard to their community interests or to weight the importance of these interests across groups within the population. As discussed above, this is a result of the split in the population no matter how the split came about. The result, however, is that different polls regarding the same issue or group of issues will give significantly different results depending upon how the pollster designed the sample frame. Individual poll results will increasingly depend on the subjective insights of the individual pollster. The information provided to the electorate and to policy makers will be inconsistent. Decisions made on the basis of that information will become increasingly inconsistent. Governance on the basis of these decisions will become increasingly inconsistent.
The second of these is active. If we assume the population is differentiating because of an active information campaign to draw members of the population away from the middle, we almost have to assume this information is not representative of the population as a whole. We also might assume that some of this information is developed in the form of opinion surveys where sample frames were defined in such a manner as to generate favorable results. As we discussed above, in a differentiated or split population, the accuracy of polls has a lot to do with the pollster’s definition of the sample frame. All-star pollsters are those who are adept at correctly reading the population and developing appropriate sample frames. Policy influencers, however, can also be adept at defining sample frames that generate directed results, even while utilizing acceptable statistical techniques and avoiding the use of leading questions.
Regardless of whether the active or the inactive effects predominate, the erosion of useful information caused by moving population members away from the “Moveable Middle” will decrease the value of information provided to the electorate and policy makers. As a result, decisions made by the electorate and policy makers will be increasingly inconsistent. Governance based on these decisions will become increasingly erratic and ineffective.
This will go beyond politics. It will inevitably affect economic planning, investment, and growth.
Increasingly erratic and ineffective governance generates environments where it is increasingly difficult to plan. An inability to plan generally results in a reluctance to take risks. Increasing risk aversion tends to retard productive investment. A dearth of productive investment reduces production. Reduced production restricts income.
Remember that the expected return on any investment is the product of potential returns and the probability of attaining potential returns. A major determinant in that probability is government stability. Long-term productive investment tends to be made where there is an expectation of government stability over the lifetime of the investment.
In uncertain environments, investable funds tend to fine their way into financialization (placing bets on future policy direction by buying and leveraging shares or derivatives of existing productive assets), or the pursuit of returns directly through the exploitation of favorable government policies (rent seeking). While both may generate income for the participants involved, neither generates productive value or wealth for the economy as a whole.
None of these observations changes the fact that it is in the client’s best interest to try peeling the population in the middle away and towards the client’s ends. The society-wide implications, however, particularly in a political context, are important issues to consider.
P.S.
All of the images in this post were taken from an online article by Andrey Akinshin, “Normality is a Myth,” dated October 9, 2019 and downloaded from https://aakinshin.net/posts/normality-is-a-myth/ on November 20, 2024.
We are doing some work on farm and farmer value streams here at Regional Strategic, Ltd. The pilot work is using Iowa, but the intent is to take what is found and expand the work across the Upper Midwest.
One of the first major questions regards farmland valuation and appreciation. The graph below shows a simple relationship that leads to a number of complex questions. The graph shows cumulative inflation-adjusted value streams for ag land appreciation (from Iowa State University’s Farmland Value Survey), direct government payments (from the Bureau of Economic Analysis), and farm income net of government payments (derived from the Bureau of Economic Analysis) per acre of farmland (from the Census of Agriculture).
The period runs from 1993 to 2022. The scenario assumes that an acre of land is purchased in 1992 and the purchaser initiates production in 1993. The three lines show accumulations of income and land appreciation over a 30-year period. The endpoint is set as the last year in which complete stable information was available from the Bureau of Economic Analysis.
The first thing that jumps out is that accumulated land value appreciation outruns operating income and direct government payments. Accumulated land appreciation separates from the other two streams in 2002. In addition, Operating income breaks out above direct government payments in 2007.
Over the thirty years, the three inflation-adjusted value streams generated an average of $458 per year. Averages for each of the components were
Of this average value stream, only 38 percent came from income, and nearly a third of this income was in the form of direct government payments. Operating income accounted for only a little over 26 percent of the value stream generated by an average acre of Iowa agricultural land.
Average farm earnings net of government payments (operating income) was only sufficient to pay a 4.72 percent return on the 1992 purchase price of $2,559. Operating income plus direct government payments were only sufficient to pay 6.86 percent return to purchase price. This is all barely enough to cover interest or carrying cost on the investment.
Given these low production returns, what makes land price appreciation average 11.5 percent per year?
What caused land appreciation rates to break away from operating income and direct government payments in 2002?
What caused operating income to break away from direct government payments in 2007?
A portion of these relationships might simply be the result of the period being observed, but the size and consistency of the breaks suggest there is something more. There appears to be a confidence in the value of Iowa farmland that overrides observed farmland productivity. Why is that?
Is it due to indirect subsidies?
Is it due to the conviction that subsidies and relief will always maintain farm income?
Is it because of a belief that the removal or reduction of farm subsidies, both direct and indirect, will inordinately affect other production areas and concentrate production and value in Iowa?
We honestly don’t know the answers to these questions. That is the point of the inquiry. More will come as we noodle this out.
It was summertime in the mid 1960s, and I was standing on the loading chute with Dad. He had a small trucking business and brokered livestock for delivery to Hormel and Rath. We were watching pigs being loaded onto a truck for the trip to Waterloo. As we watched the pigs scramble up the chute and into the truck, I asked Dad how much Rath paid for a truckload of hogs.
He wouldn’t tell me. He told me I had no business asking what a load of pigs paid until I understood what a load of pigs cost. The response stung. I remember it kind of pissed me off.
The day passed, and another. Dad was a busy guy. He had seven kids, a truck line, the stockyards, a small farm, and several hired men. I often woke up at 4:30 or 5:00 to the sound of the morning markets on the radio. Dad always woke up that way. There was a chalkboard grid in Dad’s office in the stockyards. It showed price quotes for hogs from every packer within shipping distance of Hampton, Iowa. Back then there were a bunch of them. It was updated every morning before Dad started buying hogs.
I figured Dad had forgotten my question, but I had not forgotten his answer. It kind of stuck in my craw.
Several days later, I was in the stockyards, and Dad asked me to help him with the chalkboard. It didn’t save him any time. He had to tell me what to do, but it became kind of an intermittent thing. Slowly, I started learning about the differences in pricing: live weight, dressed, grade and yield. He talked a bit about the decisions he made regarding how he paid for a given lot of hogs, whether he turned them over immediately or held them to, “Finish them up,” and the basis on which they were eventually sold.
I got sent out to feed trucks filling bunkers with instructions to bring their tickets back to the office. I started seeing the lists. The lists seemed endless. Dad didn’t do double-entry accounting. He had forgone high school when his family needed him on the farm in the late 1930s. There were no net present value equations or formal return on investment calculations, but the journal lists were immense: dates, time, receipts, expenditures, notes on details to remember.
In the absence of computers and a formal accounting system all of these transactions needed to be compiled and categorized periodically. I became scribe. Dad read off dates, categories, and numbers. I recorded them all on a pad of neatly ruled columns.
This went on over the course of two or three years. It was only in hindsight that I realized I was doing more than chores.
You know something? Dad never did tell me what a load of pigs paid. When it was all said and done, I no longer cared. I had gotten something much more valuable. I understood what a load of pigs cost.
Look around. Anyone can acquire the necessary data to do an economic impact analysis. It doesn’t matter at what level. We can buy RIMS-II coefficients from the U.S. Bureau of Economic Analysis. We can purchase subscriptions to utilize models from IMPLAN. We can lay out retail or services estimates on the basis of regional surplus-leakage analysis or gravity-model analysis. We can rely on rule-of-thumb multipliers from most anywhere. Some of these are valid. Some are not. We can, however, see all of them in action if we look at enough “Economic Impact” reports.
Even sticking to the Input-Output based analyses (RIMS-II or IMPLAN), simply being able to buy access to the data does not assure a good analysis. Getting a good analysis is very dependent on the modeler understanding what the models can and cannot do.
The Input-Output (I-O) Model
While the details of a working I-O model can be quite complex, conceptually, an I-O model is quite simple. An I-O model is basically a matrix of economic sectors. Sectors along one axis represent industrial inputs or suppliers to the industries on the other axis, which represent industrial users or demanders. Suppliers and demanders are connected by an interlocking set of mathematical relationships specifying how much of each input is required to make a unit of any output. When it is decided how much final output an industry will produce or how much labor an industry will employ, the model specifies how much of all necessary inputs are required and how those inputs are sourced from other industries.
It starts out looking like the large system of mileage charts (similar to those that you find in the back of a road atlas). Unlike the numbers in a mileage chart, however, each of the cells in an I-O model contains part of a system of production functions that is linked mathematically to all of the other cells in the model. The values of final goods produced or labor employed can be changed for any of the industries and these coefficients allow the modelled economy (the matrix) to be rebalanced, showing how the initial change affects all of the industries that supply inputs to or demand outputs from the industry altered.
This is the basis of the type of I-O-based impact analysis commonly used to estimate the effect of a given economic change. It is important to understand, however, some basic and very important constraints upon the model.
The Model is Static
While we can change the value of output or employment for an individual industry, the model itself is static and unchanging. The production coefficients in every cell remain unchanged. The changes we made to an individual industry simply changes the scale of overall output and employment. They do not change any of the production relationships in the model.
The Model is Linear
Fixed coefficients (production relationships) make the model linear. If we change the final output of industry “A” by $200, we will get precisely twice the impact we will get if we change the output by $100. If we change the output by $100 Trillion, we will get one trillion times the impact we got with a $100 change. Because all of the production coefficients are fixed, any individual output or employment shock can be scaled up or down.
The Model is Limitless
This means the model is limitless. No matter how big a change we submit, the fixed production coefficients will provide us with an economy scaled precisely to that change. We could take Franklin County, Iowa, for example. Franklin County has a population of just under 10,000 and a civilian labor force of about 5,500. An I-O model, however, would allow us to increase employment in an area industry by 20,000 and scale the economy to match that change. It doesn’t matter to the model that the initial change is somewhat ridiculous.
Similarly, Franklin County is heavily farmed. Nearly all the arable land within the county is cropped in corn and soybeans. There is no real excess capacity with respect to arable land, so production is pretty much fixed. The model, however, would allow us to shock the agricultural system in Franklin County by increasing the value of both corn output and soybean output by 20 times. It would dutifully scale the local economy to accommodate that change even if the change is impossible in the real world.
The static model will allow us to model ourselves into absurdity. It is important to understand the environment and economy in which we are modeling. It is important to define the area modeled such that its economy is somewhat commensurate with the change being investigated. The model knows no constraints of scale, so the modeler must be able to recognize, acknowledge, and accommodate them.
Prices in the Model Cannot Change
Constant production relationships require constant prices. If prices change, the value of production and input costs change. Because the model and all of its calculations are dollar-denominated, changing prices would violate the assumptions behind the structure of production relationships.
Prices cannot change. No matter how severely we shock the environment, the model assumes that the economy can provide limitless resources at a constant price. The model also assumes that the citizens in the modeled environment are ready and capable of purchasing limitless amounts of output at constant prices.
This is never the case in reality, but for small enough shocks it can be reasonably close. Going back to freshman economics, the I-O model is a Micro economic model. In Micro it is assumed that every participant in the economy is too small to affect the economy as a whole. As a result, prices are assumed to be fixed.
Conversely, in the context of the Macro economy, prices change as more or less resources are demanded. These changes cause people to adjust their purchases and producers to adjust their inputs in order to maximize their purchasing power.
The I-O model, however, cannot accommodate price changes and the resulting adjustments. This means that the model overestimates impacts for any event, shock, or change modeled. It only overestimates by a little if the shock or change is small relative to the economy (say, adding a pool hall in Des Moines, Iowa). The overestimation grows, however, as the size of the shock or change grows larger relative to the area economy (removing the entire farm and construction machinery manufacturing sector from Pella, Iowa). The larger the shock is relative to the economy the larger the I-O model overestimate of the economic impact will be.
In all cases, if the expected result of an event is stated to be a change in related prices, the modeler needs to be very circumspect in evaluating impact model results. Building an ethanol facility in Iowa, for example, is nearly always promoted as a means to raise the price of the surrounding area’s corn production. Because the surrounding area’s corn production is relatively fixed, this violates two of the basic constraints of the model. First, constant prices cannot be assumed. Second, the area cannot be assumed capable of increasing the necessary inputs to support the event within a fixed area subject to fixed production relationships.
The modeler must be able to place the event within an economy that can reasonably handle the resulting impact in a fixed-price environment. The modeler must be cognizant of where the proposed event violates the underlying constraints of the model. In all cases, these issues must be dealt with transparently in the presentation of model results.
In-area Substitutions
The model cannot distinguish between new economic activity (economic impacts) and changes in existing economic activity (substitution). The modeler must be sufficiently aware of the local economy and the event being analyzed to make these distinctions.
For example, Joe buys a factory from Bill. The factory doesn’t alter its operations or output. It just changes hands. There is no economic impact. We just substituted Joe for Bill.
On the other hand, Bob opens a new grocery store in a town of 8,000 that is already served by two existing grocery stores. An economic impact model evaluates the initial investment involved in opening the store as well as the annual impact of store operations. Within a short time, however, the existing stores begin to struggle from decreased sales, and one of them closes. In the short run, there appeared to be economic impact. In the long run, it revealed itself as a substitution.
It can get murky. If the local opera house draws sixty percent (60%) of its audience from the local area and forty percent (40%) from outside the area, is 60% of its modeled impact merely substitution, because local residents would have spent their money at some other local venue? Or is it reasonable to assume that if the local opera house did not exist, local patrons would travel to nonlocal opera houses? After all, it is reasonable to assume that nonlocal opera aficionados travel into the local area to enjoy the existing opera house.
Similarly, suppose Bill was going to close the factory in the first example. Then is Joe’ purchase and continuance equivalent to opening a factory after the previous one closed? Quite often, “Jobs saved,” is presented as a justifiable impact, even if the property transfer appears to be substitution. Similarly, if our local opera patrons would have traveled out of town, “Entertainment expenditures saved,” might be presented as a justifiable impact.
There are no hard-and-fast rules in these situations. It is the function of the modeler to define the event with respect to the origin of the effects modeled and to present modeled results in a way that make the implications of those origins clear.
A Basic Conundrum of the I-O Model and Economic Impact Analysis
The I-O model lives in somewhat uncomfortable territory. It is a Microeconomic model, so the players are assumed to have no effect on size and price relationships. The goal of economic impact analysis, however, is nearly always to show that an event will have significant effects on the overall area economy. The larger these effects, the more likely it is that we are violating the fixed-price assumption of the model and overestimating the resulting impact.
One way to mitigate this size problem is to define a larger area. Doing so reduces the relative size of the modeled event with respect to the overall area economy. Increasing the size of the modeled area also increases the size of the resulting economic impact, because more expenditures happen within the area before the shock begins to dissipate as transactions go beyond the area.
Increasing the size of the modeled area, however, also exacerbates the issue of in-area substitution. As the area expands, the chance that the modeled event simply replaces existing activity grows.
This basic conundrum is why it is imperative that the economic impact modeler thoroughly understand the modeled event, the modeled area, the modeled economy, and their interrelationships.
Good Luck!
Regional Strategic, Ltd. is always available to assist your community, business, or development group with economic impact modeling and other development needs. As time goes on, we will be posting additional thoughts and information regarding the types of services we are engaged in.
We all talk about employment and quote statistics on a regular basis, but few of us even imagine the complexity of the employment data out there or the substantial differences between sources.
Technically, “Employment” is the commitment of personal effort to the process of producing goods and services. In common usage, a person is said to be “Employed” if the person works for another person, a person is said to be “Self-employed” if the person works for him or herself, and a person is said to be “Unemployed” if the person has no job. This terminology works fine in most casual conversations but breaks down when dealing with data collection and maintenance efforts and utilizing data in analysis.
To maintain more precision and consistency, these terms are more tightly controlled in the official statistical process. Understanding the terms and the sources of data is important to understanding and correctly using the information the data provides. The link below is to a paper that provides definitions of terms and an understanding of data collection and content for major statistics on “Employment” in its most common contexts:
Employment and unemployment rates for the residential labor force
Employment by Industry
Employment by occupation
Employment by place of residence
Employment by place of work
There are several sets of employment statistics available for any specified area. These might originate from multiple statistical agencies and refer to different measures of “Employment.” It is important to understand what the statistics are and what they tell us.
About 20 years ago, I wrote the original version of the linked narrative to assist students and researchers engaged in regional analysis work. Its first home was the Department of Economics website at Iowa State University. When I left the department in 2007, the document stagnated due to a lack of updates. It was eventually taken down. I recently stumbled across the original text and decided it would still serve a purpose if updated. Here is the update –
People want to be first. Good examples can be seen any time I drive through town. From every stoplight, people hit the gas, jockey for position, catch the gap behind me and swoop down in front. They hit the gas, their brake lights flash, they bob and weave, and they are still right beside me at the next stoplight. It’s a lot of work for a questionable rate of payout.
I try to drive a little differently. My objective is to flow with traffic while staying off my brakes as much as possible. That requires being much more judicious with the gas pedal – starting off slower, not riding bumpers, looking a little farther ahead, and coasting more. When I see red lights ahead, I try to slow down and coast up until they turn green. It is kind of a game to see how much speed I can hold when others race up and stop.
I generally get through town as fast as the cars around me. I use less gas. I put less wear and tear on my brakes. I have fewer accidents.
If you think of expenses on gas and maintenance as internal resources, green lights as opportunities, red lights as bottlenecks, and accidents as disasters, driving through town is kind of like community and economic development. Generally, racing towards opportunities (green lights) does not get you more green lights or less red lights (bottlenecks). The only certainty is that the constant yo-yo between acceleration and the brakes burns up internal resources.
The race between opportunities and bottlenecks also takes away flexibility. To understand why, think of the journey between opportunities (green lights) and bottlenecks (red lights) as two lines crossing. One is progress towards completion. One is flexibility. When you pass through a green light your progress towards the next light is zero. Your flexibility is one hundred percent. As you move, your progress goes up and your flexibility goes down. The faster you move, the faster your flexibility disappears.
This trade off is always important. It is doubly important when the next milestone is uncertain. The faster you accelerate from the last stoplight, the fewer options you have if the next light becomes a bottleneck (turns red) or if there is a disaster (accident) in your path. There are fewer options to turn off, go around, and avoid delays. The time you expected to save accelerating is irretrievably lost. You have expended more internal resources (gas and maintenance).
Just as driving through town, in economic and community development it pays to stay off the brakes. Accelerate a little more slowly. Make space and options. Focus a little farther ahead.
If you take the time it takes, it takes less time.
When I was a kid in Hampton, Iowa, I belonged to a 4-H club, the Lake Stockmen of Franklin County. My brothers and I had cattle. To get us started, Dad sold us each a heifer. To pay for our heifers, Dad got our second calves. Beyond that, we had to finance anything we added to our herds.
I bought my first feeder calf, a “baby beef,” when I was in fifth grade. Not being an established rancher, I had to borrow the funds. Dad took me to the bank and pointed me toward a desk and a gentleman in a suit. As I grew older, I realized arrangements had been made and the deal was done, but the 11-year-old at the bank didn’t know that. I introduced myself. I was a little scared.
The banker asked me what I wanted to do, how much money I needed, how I was going to feed that calf, and how I was going to pay him back. I didn’t realize it at the time, but I had been well-coached, and I was led down the path by a banker who had helped plan the event. It was “Yes, sir,” “No, sir,” some figures scrawled on a legal pad, a pep talk, a signature, a handshake, and a thank-you. Dad came over, cosigned, said a word to the banker, and we were off. I remember the banker wishing me good luck and Dad shaking my hand with a proud, “Congratulations!”
I owned a feeder calf – my own baby beef. For the next year, the banker asked me how my venture was going every time he saw me. He was my partner. He had an active interest.
That next July, I showed my steer at the fair, it was sold, and I went to my banker to settle up. I had a savings account. The payment could have been made with an accounting transaction at the bank, but I recall withdrawing cash and taking it to the desk. I suspect Dad told me to do it that way. He would have wanted me to understand the magnitude of the event. I remember he didn’t go with me.
The whole experience was profound. It helped shape who I grew up to be. It helped build the confidence and focus necessary to walk into a strange room and make my case, to stand up for what I believed should and could be done, and to make a difference.
It is a hard experience to replicate today. My dad was an independent businessman – a farmer, a trucker, and a livestock buyer. I saw business every day. I could walk across the road to Dad’s office in the stockyards. I could wander through the truck garage. Most of my friends could, and regularly did, walk into the environments where their parents worked.
These days, our kids go to day care while we work. Not many bankers can walk an 11-year- old through the commercial loan process. Children come out of high schools and into college with only a sketchy understanding of what their parents really do and how the working world really works. They spend extra time in school, build staggering college debts, and too often leave school bereft of the working skills businesses require. They never get to learn the game before they start making their own moves.
I have spent a good deal of my adult life thinking about this, working with kids’ organizations, and thinking about this some more. I have been a director of child-care organizations and a school board. I have been a scout master and a trustee for a Central Iowa family assistance organization. I have worked with university student recruitment, placement, and internship programs. All roles where replicating this experience could have paid dividends.
Yet, if you asked my own kids, I am sure they could only give you a rudimentary explanation of what I do for a living.
Somehow, we need to expand immersive contacts between kids and employers. This may be through earlier internship programs, expanded collaborations between educational institutions and industry, or simply more business-centered family-friendly childcare. This will help kids understand how the real world works and the expectations the real world has of them. This will help employers shape the future workforce and connect educational programs with the changing needs of the economy.
These partnerships can improve the environment on both sides of the equation. As a society, we need to figure out how to make them work. We can’t lead every 11-year-old to a commercial banker, but we can do a lot more to bridge the gap between a formal education and an ever-changing world.
When I was in high school, I lived in Guthrie Center, Iowa. It was small and rural. Guthrie County had about 12,000 people. Guthrie Center had less than 2,000. I worked in the local menswear store, Hughes Menswear. It was a good job. It had me downtown where there were lots of opportunities to interact with friends. Darwin and Sheryl Hughes taught me a lot about life, business, and community.
I grew up thinking that was an ordinary little situation. It wasn’t. A decade or so later, as an outreach economist at Iowa State University, I worked with Professor Kenneth Stone, who initiated the university’s retail trade analysis program. I learned that virtually no rural town of less than 10,000 people managed to maintain a full-line menswear store. Darwin and Sheryl probably didn’t realize it, but Hughes Menswear was a point of discussion among the retail trade analysis set. They seemed to be doing the impossible.
The store has been gone for over a decade now. Darwin and Sheryl maintained it for over 40 years, but in the end the store was closed. They couldn’t sell it. While I am certain potential buyers were impressed with the miracle they had maintained for 40 years, those buyers were also aware that it was really their own personal miracle. No one had the confidence they could replicate that success in a declining rural town that, by all rights, shouldn’t have been able to support the store in the first place.
That is why business communities – whether they are rural main streets or urban boutique neighborhoods – need to grow. Had Guthrie Center been growing, had its business community been expanding its reach, Darwin and Sheryl might have found a buyer for that store. If they had had confidence they could sell the store, they could have continued investing in it, improving it, and expected the continued investment to come back to them. That continued investment would have also been an asset to other business owners in downtown Guthrie Center. It would have made the entire area more attractive and valuable. That would have helped the business community continue to grow and expand its reach. Growth begets a virtuous cycle.
But that was not in the cards. Guthrie Center didn’t grow. The business community did not expand its reach. Darwin and Sheryl were probably under no illusions that the store could not be sold. At some point, I am certain they went into maintenance mode – doing what was absolutely necessary but taking everything possible out of the business. The business was their primary retirement asset. If it couldn’t be sold, it had to be stripped. They weren’t alone. One by one, nearly all the businesses that populated downtown when I was in school have been stripped and closed. Just as growth begets a virtuous cycle, decline begets a dismal one.
As an outreach economist, I often found myself making presentations to groups in rural Iowa towns. It was not unusual for someone to ask why growth was necessary. After all, they had lived there all their lives, and it was just the way they liked it. I invariably recounted the story of Hughes Menswear.
Nothing is so certain as change. Get on board with the sort of change you want. Cooperate, investigate, and make it happen.
