Notes from Regional Strategic, Ltd.

The Moveable Middle, Statistics, Information, & Progress

Posted on November 24, 2024 - 11:25 pm by Mark Imerman

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.

An Inquiry into Farmland Value Streams

Posted on October 18, 2024 - 2:19 pm by Mark Imerman

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.

A Truckload of Hogs

Posted on June 20, 2024 - 3:23 pm by Mark Imerman

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.

Economic Impact Analysis – Some things to think about

Posted on June 18, 2024 - 6:54 pm by Mark Imerman

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.

Data in Context: Notes on Employment and Labor Force Data

Posted on June 8, 2024 - 3:02 pm by Mark Imerman

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 –

https://app.box.com/s/szybhn1fnaeeebsw0xdl8kl57fim9sop

Red Lights, Green Lights, and Development

Posted on June 5, 2024 - 4:58 pm by Mark Imerman

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.

Meeting the Banker

Posted on April 30, 2024 - 6:09 pm by Mark Imerman

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.

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