Measuring Project Success

 

 

Motorcycle ride
What counts as success depends on what your goals are. By Andrey Armyagov/Shutterstock

7 August 2019 – As part of my research into diversity in project teams, I’ve spent about a week digging into how it’s possible to quantify success. Most people equate personal success with income or wealth, and business success with profitability or market capitalization, but none of that really does it. Veteran project managers (like yours truly) recognize that it’s almost never about money. If you do everything else right, money just shows up sometimes. What it’s really all about is all those other things that go into making a success of some project.

So, measuring success is all about quantifying all those other things. Those other things are whatever is important to all the folks that your project affects. We call them stakeholders because they have a stake in the project’s outcome.

For example, some years ago it started becoming obvious to me that the boat tied up to the dock out back was doing me no good because I hardly ever took it out. I knew that I’d get to use a motorcycle every day if I had one, but I had that stupid boat instead. So, I conceived of a project to replace the boat with a motorcycle.

I wasn’t alone, however. Whether we had a boat or a motorcycle would make a difference to my wife, as well. She had a stake in whether we had a boat or a motorcycle, so she was also a stakeholder. It turned out that she would also prefer to have a motorcycle than a boat, so we started working on a project to replace the boat with a motorcycle.

So, the first thing to consider when planning a project is who the stakeholders are. The next thing to consider is what each stakeholder wants to get out of the project. In the case of the motorcycle project, what my wife wanted to get out of it was the fun of riding around southwest Florida visiting this, that and the other place. It turned out that the places she wanted to go were mostly easier to get to by motorcycle than by boat. So, her goal wasn’t just to have the motorcycle, it was to visit places she could get to by motorcycle. For her, getting to visit those places would fulfill her goal for the project.

See? There was no money involved. Only an intangible thing of being able to visit someplace.

The “intangible” part is what hangs people up when they want to quantify the value of something. It’s why people get hung up on money-related goals. Money is something everyone knows how to quantify. How do you quantify the value of “getting to go somewhere?”

A lot of people have tried a lot of schemes for “measuring” the “value” of some intangible thing, like getting where you want to go. It turns out, however, that it’s easy if you change your point of view just a little bit. Instead of asking how valuable it is to get there, you can ask something like: “What are the odds that I can get there?” Getting to some place five miles from the sea by boat likely isn’t going to happen, but getting there by motorcycle might be easy.

The way we quantify this is through what’s called a Likert scale. You make a statement, like “I can get there” and pick a number from, say, zero to five with zero being “It ain’t gonna happen” and five being “Easy k’neezie.”

You do that for all the places you’re likely to want to go and calculate an average score. If you really want to complete the job, you normalize your score by weighting the scores for each destination with how often you’re likely to want to go there, then divide by five times the sum of the weights. That leaves you with an index ranging from zero to one.

You go through this process for all of the goals of all your stakeholders and average the indices to get a composite index. This is an example of how one uses fuzzy logic, which takes into account that most of the time you can’t really be sure of anything. The fuzzy part is using the Likert scale to estimate how likely it is that your fuzzy statement (in this case, “I can get there”) will be true.

When using fuzzy logic to quantify project success, the fuzzy statements are of the form: “Stakeholder X’s goal Y is met.” The value assigned to that statement is the degree to which it is true, or, said another way, the degree to which the goal has been met. That allows for the prospect that not all stakeholder goals will be fully met.

For example, how well my wife’s goal of “Getting to Miromar Outlets in Estero, FL from our place in Naples” would be met depended a whole lot on the characteristics of the motorcycle. If the motorcycle is like the 1988 FLST light-touring bike I used to have, the value would be five. We used to ride that thing all day for weeks at a time! If, on the other hand, it’s like that ol’ 1986 XLH chopper, she might make it, but she wouldn’t be happy at the end (literally ’cause the seat was uncomfortable)! The value in that condition would be one or two. Of course, since Miromar is land locked, the value of keeping the boat would be zero.

So, the steps to quantifying project success are:

  1. Determine all goals of all stakeholders;
  2. Assign a relative importance (weight) to each stakeholder goal;
  3. Use a Likert scale to quantify the degree to which each stakeholder goal has been met;
  4. Normalize the scores to work out an index for each stakeholder goal;
  5. Form a critical success index (CSI) for the project as an average of the indices for the stakeholder goals.

Before you complain about that being an awful lot of math to go through just to figure out how well your project succeeded, recognize that you go through it in a haphazard way every time you do anything. Even if it’s just going to the bathroom, you start out with a goal and finish deciding how well you succeeded. Thinking about these steps just gives you half a chance to reach the correct conclusion.

Do the Math

Applied Math teacher
Throughout history, applied mathematics has been the key to human development. By Elnur/Shutterstock

31 July 2019 – Over the millennia that philosophers have been doing their philosophizing, a recurring theme has been the quest to come up with some simple definition of what sets humans apart from so-called “lower” animals. This is not just idle curiosity. From Aristotle on, folks have realized that understanding what makes us human is key to making the most of our humanity. If we don’t know who we are, how can we figure out how to be better?

In recent decades, however, it’s become clear that this is a fool’s errand. Such a definition of humanity doesn’t exist. Instead, what sets humans apart is a suite of characteristics, such as two eyes in the front of a head that’s set up on a stalk over a main torso, with two legs down below and a couple of arms on each side ending with wiggly fingers and opposable thumbs; a brain able to use sophisticated language; and so forth. Not every human has all of them (for example, I had a friend in Arizona who’d managed to lose his right arm and shoulder without losing his humanity) and a lot of non-humans have some of them (for example, chimpanzees use tools a lot). What marks humans as humans is having most of these characteristics, and what marks non-humans as not human is lacking a lot of them.

On the other hand, there is one thing that most humans are capable of that most non-humans aren’t: humans are capable of doing the math.

Yeah, crows can count past two. I hear that pigeons are good at pattern recognition. But, I’m talking about mathematical reasoning more sophisticated than counting past seven. That’s something most humans can do, and most other animals can’t.

Everybody has their mathematical limitations.Experience indicates that one’s mathematical limitations are mostly an issue of motivation. At some point, just about everybody decides that it’s just not worth putting in the effort needed to learn any more math than they already know.

That’s because learning math is hard. It’s the biggest learning challenge most of us ever face. Most of us give up long before reaching the limits of our innate ability to puzzle it out.

Luckily, there are some who are willing to push the limits, and master mathematical puzzles that no human has solved before. That’s lucky because without people like them, human progress would quickly stop.

Even better, those people are often willing – even anxious – to explain what they’ve puzzled out to the rest of us. For example, we have geometry because a bunch of Egyptians puzzled out how to design pyramids, stone temples and other stuff they wanted to build, then proudly explained to their peers exactly how to do it. We have double-entry accounting because folks in the Near East wanted to keep track of what they had, figured out how to do it, and taught others to help. We’ve got calculus because Sir Isaac Newton and a bunch of his buddies figured out how to predict what the visible planets would do next, then taught it to a bunch of physics students.

It’s what we like to call “Applied Mathematics,” and it’s responsible for most of the progress people have made since the days of stone knives and bear skins. Throughout history, we’ve all stood around scratching our heads about things we couldn’t make sense of until some bright guy (or gal) worked out the right mathematics and applied it to the problem. Then, suddenly what had been unintelligible became understandable.

These days, what I think is the bleeding edge of applied mathematics is nonlinear dynamics and chaos. Maybe there’s some fuzzy logic thrown into the mix, too. Most of the math tools needed to understand (as in “make mathematical models using”) these things is pretty well in hand. What we need to do is apply such tools to the problems that today vex us.

A case in point is the Gini-Simpson Diversity Index I described in this blog two weeks ago. That is a small brick in the wall of a structure that I hope will someday help us avoid making so many dumb choices. Last week I ran across another brick in a paper written by a couple of computer science professors at my old alma mater Rensselaer Polytechnic Institute (aka RPI, or as we used to call it when I was there as a graduate student, “the Tute”). This bit of intellectual flotsam describes a mathematical model the authors use to predict how political polarization evolves in the U.S. Congress.

Polarization is one of four (at my last count) toxic group-dynamics phenomena that make collaborative decision making fail. Basically, the best decisions are made by groups that work together to reach a consensus. We get crappy decisions when the group’s dynamics break down.

The RPI model is a nonlinear differential equation describing an aspect of the dynamics of decision-making teams. Specifically, it quantifies conditions that determine whether decision teams evolve toward consensus or polarization. We see today what happens when Congress evolves toward polarization. The authors’ research shows that prior to about 1980 Congress evolved toward consensus. Seeing this dynamic at work mathematically gives us a leg up on figuring out why, and maybe doing something about it.

I’m not going to go into the mathematical model the RPI paper presents. The study of nonlinear dynamical systems is far outside the editorial focus of this column. At this point, I’m not going to talk about solutions the paper might suggest for toxic U.S. Government polarization, either. The theory is not well enough developed yet to provide meaningful suggestions.

The purpose of this posting is to point out that application of sophisticated mathematics is necessary for solving society’s most intractable problems. As I said above, not everybody is ready and willing to become expert in using such tools. That’s not necessary. What I hope you’ll walk away with today is an appreciation of applied mathematics’ importance for societal development, and a willingness to support STEM (science, technology, engineering and mathematics) education throughout our school system. Finally, I hope you’ll encourage students who show an interest to learn the techniques and follow STEM careers.

Misconceptions About Maslow’s Hierarchy of Needs

Maslow's Pyramid
Maslow’s pyramid of needs analyzes human needs and arranges them in a hierarchy. By Shutter_M/Shutterstock

24 July 2019 – Abraham Harold Maslow (1908-1970) was a 20th century psychologist famous for describing human motivation as an hierarchy of needs in a 1943 paper entitled “A Theory of Human Motivation” published in Psychological Review. He was a central figure in the founding of Humanistic Psychology, which concentrates on studying mentally healthy humans.

You have to remember that Maslow did his most important work in the middle of the 20th century. At that time there was great popular interest in the works of Sigmund Freud, who worked with the mentally ill, and B.F. Skinner who mainly studied lower animals. Indeed, the entire arts-and-letters school of Surrealism explicitly drew inspiration from Andre Breton’s interpretation of Freud’s work. Despite (or perhaps because of) this interest in Freud and Skinner’s work, there had been little, if any, study of mentally healthy people.

Humanistic Psychologists felt these earlier studies were of limited value to understanding the healthy human mind. Maslow chose to study the workings of healthy human minds from all social strata, but he was especially interested in studying high achievers. For this reason those of us interested in organizational behavior find his humanists of particular interest. We kinda hope our organizations are populated with, and run by, mentally healthy humans, rather than Freud’s neurotics or Skinner’s lab rats!

Maslow’s emphasis on studying high achievers likely gave rise to the first misconception I want to talk about today: the idea that his work gives cover to elitist views. This elitist theory assumes that everyone strives to reach the self-actualization level at the top of the so-called “Pyramid of Needs” used to illustrate Maslow’s hierarchy, but that only an elite fraction of individuals reach it. Lesser individuals are doomed to wallowing in more squalid existences at lower levels.

The second misconception I want to treat today is a similar notion that people start out at the lower levels and climb slowly up to the top as their incomes rise. This theory substitutes a ladder for the pyramid image to visualize Maslow’s hierarchy. People are imagined to climb slowly up this ladder as both their income and social status increase. This, again, gives cover for elitist views as well as laissez-faire economics.

Maslow’s Conception

What Maslow’s Hierarchy really describes is a priority system that determines what people are motivated to do next. It has little to do with their talents, income or social status. To illustrate what I mean, I like to use the following thought experiment. This thought experiment involves Albert Einstein and it’s particularly appropriate because the Grizzled Genius loved thought experiments.

Albert Einstein’s greatest joy was becoming immersed in translating his imaginings about the physical universe into mathematical equations. This is an example of what Maslow called “peak experiences.” Maslow believed these were periods when self-actualized people (those engaged in satisfying their self-actualization need) are happiest and most productive.

Once in a while, however, Einstein would become hungry. Hunger is, however, one of those pesky physiological needs down at the bottom of Maslow’s Hierarchy. There’s nothing aspirational about hunger. It’s what Fredrick Herzberg called a “hygiene factor” or “demotivator.” Such needs are the opposite of aspirational.

If you’ve got an unsatisfied demotivator need, you become unhappy until you can satisfy it. If, for example, you’re hungry, or have a toothache, or need to pee, it becomes hard to concentrate on anything else. Your only thought is (depending on the nature of the unmet physiological need) to go to the bathroom, or the dentist, or, as in Einstein’s case, go find lunch.

The moral of this story is that people don’t sit somewhere for extended periods of time on a shelf labeled with one of Maslow’s categories. Rich people don’t float in a blissful self-actualizing state. Poor people don’t wallow in a miasma of permanently unmet physiological needs. People constantly move up and down the pyramid depending on what the most pressing unmet need of the moment is.

The hierarchy is therefore actually an inverted priority list. Physiological needs are more important than safety needs. When something frightens you – a safety need – the first thing that happens is you feel an urge to pee to take care of a physiological need to prepare your body for running like a scared rabbit. When you see a fast-moving Chevy bearing down on you, you immediately forget pride in that (esteem level) achievement award you just got.

Elitist Fallacy

A combination of confusion about how Maslow’s heirarchy works and his preference for studying high achievers has led many people to imagine his work gives cover for elitist views. If you’re predisposed to imagine that rich people, smart people, or those of high social status are somehow innately “better” than denizens of what 19th century novelist Edward Bulwer-Lytton called “the great unwashed,” then you’re an elitist. An elitist can derive great comfort by misinterpreting Maslow’s work. You can imagine there being a cadre of elite people destined to spend their lives in some ethereal existence where all lower needs are completely satisfied and life’s only pursuit is self actualization.

The poster child for elitism is 16th century theologian John Calvin. In Calvin’s version of Protestant theology everyone was tainted with original sin and doomed to an eternity in Hell. That was a pretty common view at the time of the Protestant Reformation. Calvin added an elitist element by hypothesizing that there was a limited number of individuals (the elect) whom God had chosen for salvation.

It’s called predestination and those folks got tickets into the elite ranks through no merit of their own. There was nothing anybody could do to beg, borrow, or steal their way in. God decided, while making the Universe in the first place, who was in and who was out based on nothing but His whimsey. (Sexist pronoun used specifically to make a point about Calvinism.)

Of course, the requirements of natural selection logically lead to everyone having a desire to be part of an elite. We all want to be different, like the Dada-esque avant garde group King Missile. That’s how DNA measures its success. Only elite DNA gets to have long-term survival.

So, elitism has a lot of natural appeal. This natural appeal accounts for all kinds of rampant racism and xenophobia. Misunderstanding Maslow’s heirarchy provides a pseudoscientific rationale for elitism. To the elitist, the fact that this view is completely mistaken makes no nevermind.

I hope that by now I have disposed of the elitist fallacy.

Economic Ladder Fallacy

Hoping that I’ve disposed of the idea that Maslow’s work gives cover to elitism, I’ll turn to the fallacy of imagining his hierarchy as an economic ladder. This puppy is a natural outgrowth of the Pyramid of Needs image. The top (self actualization) level of the pyramid is imagined as “higher” than the bottom (physiological) level.

This image actually works from the viewpoint that “lower” needs take precedence over “higher” needs in the same way that a building’s supporting foundation takes precedence over the walls and roof. Without a foundation, there’s nothing to support walls or a roof in the same way that without fulfilling physiological needs, there’s no motivation for, say, self actualization.

Think of it this way: dead people, whose physiological needs are all unmet, hardly ever want to run for President.

So, how do you reach something high? You use a ladder!

That’s the thinking that transforms the Pyramid of Needs into some kind of ladder.

If you’re a strict materialist (and way too many Americans are strict materialists) the “high” you care about reaching is wealth. Folks who haven’t understood last month’s posting entitled “The Fluidity of Money” often confuse income with wealth, so there’s some appeal to thinking about Maslow’s Hierarchy of Needs as a metaphor for income levels. That completes the economic-ladder fallacy.

With this fallacy, folks imagine that everyone starts out at the bottom of the ladder and, with time, hard work and luck, climbs their way to the top. There are obvious problems matching income levels with needs levels, but if you’re sufficiently intellectually lazy, you can unfocus your mind’s eye enough to render these problems invisible.

I especially get a kick out of efforts to use the idea of Engel curves (from economics) to make this ladder fallacy work. Engel curves map the desireability (measured as the demand side of the economics law of supply and demand) of a given good or product against a given consumer’s income level. If the good in question is, for example, a used Mazda Miata, the desirability may be high when the consumer has a low-to-moderate income, but low if that particular consumer has enough income to pay for a new Ferrari SF90 Stradale. If you want to, it is obvious you can somehow conflate Engel curves with the ladder idea of Maslow’s Heirarchy of Needs.

The problem with this thinking is, first, that the Ladder doesn’t make a lot of sense as a visualization for Maslow’s Heirarchy, since the latter is formost a priority-setting scheme; second, that Maslow’s Hierarchy has little connection to income; and, third, that Engel curves present an incomplete view of what makes a product desirable.

The elitist fallacy and the economic-ladder fallacy are not the only fallacies people, with their infinite capacity to generate cockamamie theories, can concoct in connection to Maslow’s work. They are just two that have come up recently in articles I’ve had occasion to read. I think analyzing them can also help clarify how the Hierarchy of Needs applies to understanding human behavior.

Besides, I’ve had a bit of fun knocking them around, and I hope you have, too.

Computing Diversity

Decision Team
Diversity of membership in decision-making teams leads to better outcomes. By Rawpixel.com/Shutterstock

17 July 2019 – It’s come to my attention that a whole lot of people don’t know how to calculate a diversity score, or even that such a thing exists! How can there be so much discussion of diversity and so little understanding of what the word means? In this post I hope to give you a peek behind the curtain, and maybe shed some light on what diversity actually is and how it is measured.

This topic is of particular interest to me at present because momentum is building to make a study of diversity in business-decision making the subject of my doctoral dissertation in Business Administration. Specifically, I’m looking at how decision-making teams (such as boards of directors) can benefit from membership diversity, and what can go wrong.

Estimating Diversity

The dictionary definition of diversity is: “the condition of having or being composed of differing elements.”

So, before we can quantify the diversity of any group, we’ve got to identify what makes different elements different. This, by the way, is all basic set theory. In different contexts what we mean by “different” may vary. When we’re talking about group decision making in a business context, it gets pretty complicated.

A group may be subdivided, or “stratified” along various dimensions. For example, a team of ten people sitting around a table trying to figure out what to do next about, say, a new product could be subdivided in various ways. One way to stratify such a group is by age. You’d have so many individuals in their 20’s, so many might be in their 30’s, and so forth up to the oldest group being aged 50 or more. Another (perhaps more useful) way to subdivide them is by specialty. There may be so many software engineers, so many hardware engineers, so many marketers, and so forth. These days stratifying teams by gender, ethnicity, educational level or political persuasion could be important. What counts as diversity depends on what the team is trying to decide.

The moral of this story is that a team might score high in diversity along one dimension and very poorly along another. I’m not going to say any more about diversity’s multidimensional nature in this essay, however. We have other fish to fry today.

For now, let’s assume a one-dimensional diversity index. What we pick for a dimension makes little difference to the mathematics we use. Let’s just imagine a medium-sized group of, say, ten individuals and stratify them according to the color of tee-shirts they happen to be wearing at the moment.

What the color of their tee-shirts could possibly mean for the group’s decisions about new-product development I can’t imagine, and probably wouldn’t care anyway. It does, however, give us a way to stratify the sample. Let’s say their shirt colors fall out as in Table 1. So, we’ve got five categories of team members stratified by tee-shirt color.Table 1: Tee-Shirt Colors

NOTE: The next bit is mathematically rigorous enough to give most people nosebleeds. You can skip over it if you want to, as I’m going to follow it with a more useful quick-and-dirty estimation method.

The Gini–Simpson diversity index, which I consider to be the most appropriate for evaluating diversity of decision-making teams, has a range of zero to one, with zero being “everybody’s the same” and one being “everybody’s different.” We start by asking: “What is the probability that two members picked at random have the same color tee shirt?”

If you’ve taken my statistical analysis course, you’ll likely loathe remembering that the probability of picking two things from a stratified data set, and having them both fall into the same category is:

Equation 1

Where λ is the probability we want, N is the number of categories (in this case 5), and P is the probability that, given the first pick falling into a certain category (i) the second pick will be in the same category. The superscript 2 just indicates that we’re taking members two at a time. Basically P is the number of members in category i divided by the total number of members in all categories. Thus, if the first pick has a blue tee-shirt, then P is 3/10 = 0.3.

This probability is high when diversity is low, and low when diversity is high. The Gini-Simpson index makes more intuitive sense by simply subtracting that probability from unity (1.0) to get something that is low when diversity is low, and high when diversity is high.

NOTE: Here’s where we stop with the fancy math.

Guesstimating Diversity

Veteran business managers (at least those not suffering from pathological levels of OCD) realize that the vast majority of business decisions – in fact most decisions in general – are not made after extensive detailed mathematical analysis like what I presented in the previous section. In fact, humans have an amazing capacity for making rapid decisions based on what’s called “fuzzy logic.”

Fuzzy logic recognizes that in many situations, precise details may be difficult or impossible to obtain, and may not make a significant difference to the decision outcome, anyway. For example, deciding whether to step out to cross a street could be based on calculations using precise measurements of an oncoming car’s speed, distance, braking capacity, and the probability that the driver will detect your presence in time to apply the brakes to avoid hitting you.

But, it’s usually not.

If we had to make the decision by the detailed mathematical analysis of physical measurements, we’d hardly ever get across the street. We can’t judge speed or distance accurately enough, and have no idea whether the driver is paying attention. We don’t, in general, make these measurements, then apply detailed calculations using Gallilean Transformations to decide if now is a safe time to cross.

No, we have, with experience over time, developed a “gut feel” for whether it’s safe. We use fuzzy categories of “far” and “near,” and “slow” or “fast.” Even the terms “safe” and “unsafe” have imprecise meanings, gradually shifting from one to the other as conditions change. For example “safe to cross” means something quite different on a dry, sunny day in summertime, than when the pavement has a slippery sheen of ice.

Group decision making has a similar fuzzy component. We know that the decision team we’ve got is the decision team we’re going to use. It makes no difference whether it’s diversity score is 4.9 or 5.2, what we’ve got is what we’re going to use. Maybe we could make a half-percent improvement in the odds of making the optimal decision by spending six months recruiting and training a blind Hispanic woman with an MBA to join the team, but are we going to do it? Nope!

We’ll take our chances with the possibly sub-optimal decision made by the team we already have in place.

Hopefully we’re not trying to work out laws affecting 175 million American women with a team consisting of 500 old white guys, but, historically, that’s the team we’ve had. No wonder we’ve got so many sub-optimal laws!

Anyway, we don’t usually need to do the detailed Gini-Simpson Diversity Index calculation to guesstimate how diverse our decision committee is. Let’s look at some examples whose diversity indexes are easy to calculate. That will help us develop a “gut feel” for diversity that’ll be useful in most situations.

So, let’s assume we look around our conference room and see six identical white guys and six identical white women. It’s pretty easy to work out that the team’s diversity index is 0.5. The only way to stratify that group is by gender, and the two strata are the same size. If our first pick happens to be a woman, then there’s a 50:50 chance that the second pick will be a woman, too. One minus that probability (0.5) equals 0.5.

Now, let’s assume we still have twelve team members, but eleven of them are men and there’s only one token woman. If your first pick is the woman, the probability of picking a woman again is 1/12 = 0.8. (The Gini-Simpson formula lets you pick the same member twice.) If, on the other hand, your first pick is a man, the probability that the second pick will also be a man is 11/12 = 0.92. I plugged all this into an online Gini-Simpson-Index calculator (‘cause I’m lazy) and it returned a value of 26%. That’s a whole lot worse.

Let’s see what happens when we maximize diversity by making everyone different. That means we end up stratifying the members into twelve segments. After picking one member, the odds of the second pick being identical are 1/12 = 0.8 for every segment. The online calculator now gives us a diversity index of 91.7%. That’s a whole lot better!

What Could Possibly Go Wrong?

There are two main ways to screw up group diversity: group-think and group-toxicity. These are actually closely related group-dynamic phenomena. Both lower the effective diversity.

Group-think occurs when members are too accommodating. That is, when members strive too hard to reach consensus. They look around to see what other members want to do, and agree to it without trying to come up with their own alternatives. This produces sub-optimal decisions because the group fails to consider all possible alternatives.

Toxic group dynamics occurs when one or more members dominate the conversation either by being more vocal or more numerous. Members with more reticent personalities fail to speak up, thus denying the group their input. Whenever a member fails to speak up, they lower the group’s effective diversity.

A third phenomenon that messes up decision making for  high-diversity teams is that when individual members are too insistent that their ideas are the best, groups often fail to reach consensus at all. At that point more diversity makes reaching consensus harder. That’s the problem facing both houses of the U.S. Congress at the time of this writing.

These phenomena are present to some extent in every group discussion. It’s up to group leadership to suppress them. In the end, creating an effective decision-making team requires two elements: diversity in team membership, and effective team leadership. Membership diversity provides the raw material for effective team decision making. Effective leadership mediates group dynamics to make it possible to maximize the team’s effective diversity.

The Free Press, and How You Get It

Free Press Image
The right to sit in a cafe, drinking coffee and reading newspapers is wasted unless that press is free! By Impact Photography/Shutterstock

10 July 2019 – ‘Way back in the late 1960s I spent an entire day as a news hawker. That is, I stood on street corners shouting things at passersby intended to induce them to by copies of a newspaper I was selling. The newspaper was something called The L.A. Free Press. It was produced and sold in Los Angeles, and the street corners I stood on had names like “West Hollywood Boulevard and Sunset.”

I’d recently transplanted from Boston, Massachusetts to the Los Angeles, California area and had never heard of The L.A. Free Press before. A small gang I’d been hanging out with that morning heard that I had a driver’s license on me, and knew that we could use it as collateral to get a great whacking stack of those newspapers to sell at a profit.

Seemed like a good idea at the time.

I initially thought the newspaper copies were somehow free for the taking (as so many local papers are today). I was quickly disabused of that idea because I got pretty decent money for buying copies of it at a low price, then selling them on street corners for a higher price. It clearly wasn’t that kind of free!

Then, I imagined that was (like so many thin publications of the time) some hippy-dippy propaganda rag full of free-love manifestos and ads for beatnik-poetry venues. Being a veteran hippy-beatnik-biker, that was okay with me. I didn’t care as long as there was coin to be had. I wasn’t one of Donovan Leitch’s “beatniks out to make it rich,” but I was interested in coming up with lunch money!

The main headline on the first page of the copies we got in exchange for a mortgage on my driver’s license sounded like a local-interest story that I was not embarrased to wave at potential newsprint buyers, so it didn’t seem to be some hippy-dippy propaganda rag, either. The papers actually sold pretty well!

I needed the money (being dead broke at the time), so I swallowed my pride and did the job. I kept the last copy from my stack, however, to read when I got back to wherever I was sleeping that night.

By the time I’d finished reading the thing I’d realized why the publication was called The L.A. Free Press. It was an independent newspaper founded by a small group dedicated to investigative journalism with nobody to answer to but their readers. I became proud to be working with them.

If I’d been smart and ambitious I would have tried to get a job with them writing copy. After all, part of my reason for relocating was to find some kind of writing gig. But, as is typical with homeless eighteen-year-olds living on the streets, I was more frightened and depressed than smart and ambitious. The next day I moved on to doing something that turned out to be another stupid career move.

Sometimes depression is not a sign of mental illness, but a rational response to the way your life is going.

What I learned from that episode of my misspent youth (What’s the point of misspending your youth if you’re not going to learn something from it?) was what intellectuals mean when they talk about “the Free Press.” It’s not just some empty slogan you hear once in a while on CNN. It’s how we, as citizens of a free country, keep track of what’s going on outside of our individual hovels.

The difference between we citizens of a free country and downtrodden medieval serfs slaving to feed their “betters,” is that we have some say in what goes on outside our hovels. We can’t affect things in a way that’s good for us and the people we care about unless we find out what’s actually going on out there. For that we hire independent journalists who have at least half a brain and make it their business to find out for us.

We pay them a living wage and (if we’ve got at least half a brain ourselves) listen to what they tell us is happening. The Free Press is not, as some dishonest demagogues try to tell us, “the enemy of the people,” but a necessary part of a free democratic society.

For this reason, the journalistic profession has been called “The Fourth Estate” since the Enlightenment. Originally, the term was meant to indicate that a Free Press was available – in addition to the three original estates of clergy, aristocracy and commoners – whose writ was to frame the debate upon which society made common decisions. Later political systems still had (usually) three competing authorities explicitly charged with governing, along with a Free Press implicitly charged with framing the debate about what to do next.

In the United States, our Constitution explicitly delineates a government made up of three co-equal branches: Legislature, Court System, and Executive. The Founding Fathers (If that’s not a sexist term, I don’t know what is!) realized they’d forgotten the Free Press in the original document when they couldn’t get anybody to ratify (agree to) the thing without immediately amending it to include a Free Press (as well as the rest of the Bill of Rights).

The Free Press was considered so important that it was included in the first amendment.

Before anybody gets the idea that I’m criticizing the Founding Fathers as incompetent, I want to point out that this error just goes to prove that those guys were human, and humans make mistakes. Specifically, they were exceedingly bright guys to whom the need for a vibrant Free Press was so obvious that they forgot to mention it. The first ten Amendments – the Bill of Rights – should be seen as an “Oh, Shit!” moment.

“How could we have left that out?”

Having a Free Press, and making good use of it, is the first thing you have to have to set up a democracy. In a sense, it’s not the “fourth” estate, but the first. All the rest is afterthought. It’s bells and whistles designed to be the mechanical parts of a democracy. They’re of no value whatsoever without a Free Press.

On the other hand, once you have a functioning Free Press and a society that makes good use of it, the rest of the bells and whistles will inevitably follow. In that sense, the Free Press is not an afterthought or a result of democracy. Instead, it’s the essence of democracy. That’s why the first thing would-be authoritarians seek to eliminate is the Free Press.

Constructing Ideas

Constructivist pix
Constructivist illustration with rooster’s head. By Leonid Zarubin/Shutterstock

3 July 2019 – Long time readers of my columns will know that one of my favorite philosophical questions is: “How do we know what we think we know?” Along the way, my thoughts have gravitated toward constructivism, which is a theory in the epistemology branch of philosophy.

Jean Piaget has been credited with initiating the constructivist theory of learning through his studies of childhood development. His methods were to ask probing questions of his children and others, in an attempt to understand how they viewed the world. He also devised and administered reading tests to schoolchildren and became interested in the types of errors they made, leading him to explore the reasoning process in these young children.

From his studies, he worked out a model of childhood development that mapped several stages of world-view paradigms they seemed to use as they matured. This forced him to postulate that children actively participate in constructing their own ideas – their knowledge base – based on experience and prior knowledge. Hence, the term “constructivism.”

Imagine a house that represents everything the child “knows.” Mentally, they live in that house all the time, view the world in relation to it, and make decisions based on what’s there.

As they experience everything, including the experience of having someone tell them something verbally or through written words, they actively remodel the place. The operant concept here is that they constantly do the remodeling themselves by trying to fit new information into the structure that’s already there.

My own journey toward constructivism was based on introspective phenomenological studies. That is, I paid attention to how I gained new knowledge and compared my experiences with experiences reported by others studying the same material.

A paradigm example is the study of quantum mechanics. This subject is difficult for students familiar with classical physics because the principles and the phenomena on which they are based seem counterintuitive. Especially, the range of time and distance scales on which quantum principles act is not directly accessible to humans. Quantum mechanics works at submicroscopic distances and on nanosecond time scales.

Successful students of quantum mechanics start by studying human-scale phenomena that betray the presence of quantum principles. For example, the old “planetary model” of atoms as miniature solar systems in which electrons revolve in stable orbits around the atomic nucleus like planets around the Sun is a physical impossibility. Students realize this after studying Maxwellian Electrodynamics.

In 1864, James Clerk Maxwell succeeded in summarizing everything physicists of the time knew about electricity and magnetism in four concise (though definitely not simple) equations. Taken together, they implied the feasibility of radio and not only how light traveled, but even predicted its precise speed. Maxwell’s Equations were enormously successful in guiding the development of electrical technology in the late nineteenth century.

The problem for physicists studying atomic-scale phenomena, however, was that Maxwell’s Equations implied that electrons whizzing around nuclei would rapidly convert all their energy of motion into light, which would radiate away. With no energy of motion left to keep electrons orbiting, the atoms would quickly collapse – then, no more atoms! The Universe as we know it would rapidly cease to exist.

When I say rapidly, I mean on the time scale of trillionths of a second!

Not good for the Universe! Luckily for the Universe, what this really means that there’s something wrong with classical-electrodynamic theory (i.e., Maxwell’s Equations).

The student finds out about dozens of such paradoxes that show that classical physics is just flat out wrong! The student is then ready to entertain some outlandish ideas that form the core of quantum theory. The student proceeds to piece these ideas together into their own mental version of quantum mechanics.

Every physics student I’ve discussed this with has had the same experience learning this quantum-electrodynamical theory (QED). Even more telling, they all report initially learning the ideas by rote without really understanding them, then applying them for considerable time (months or years) before piecing them together into a mental pattern that eventually feels intuitive. At that point, when presented with some phenomenon (such as the sky being blue) they immediately seize on a QED-based explanation as the most obvious. Even doubting QED has become absurd for them!

To a constructivist, this process for learning quantum mechanics makes perfect sense. The student is presented with numerous paradoxes, which causes cognitive dissonance. This state motivates the student to seek alternative concepts and fit them into his or her world view. In a sense, they construct an extension onto the framework of their world view. This will likely require them to make some modifications to the original structure to accommodate the new knowledge.

This method of developing new knowledge dovetails quite nicely with the scientific method that’s been under development since Aristotle and Plato started toying around with it in the fourth century BCE. The new development is that Piaget showed that it is the normal way humans develop new knowledge. Even children can’t fully comprehend a new idea until they fit it into a modified version of their knowledge base.

This model also explains why humans’ normal initial reaction to novel ideas is to forcefully reject them. Accepting new ideas requires them to do a lot of work on their mental scaffolding. It takes a powerful mental event causing severe cognitive dissonance to motivate them to remodel a mental construction they’ve been piecing together for years.

It also explains why younger humans are so much quicker to take up new ideas. Their mental frameworks are still small, and rebuilding them to fit in new concepts is relatively easy. The reward for building out their mental framework is great. They are also more used to tinkering with their mental models than older humans, who have mental frameworks that have served them well for decades without modification.

Of course, once they reach the point of intolerable cognitive dissonance, older humans have more experience to draw on to do the remodeling job. They will be even quicker than youngsters to make whatever adjustments are necessary.

Older humans who have a lifelong habit of challenging themselves with new ideas have the easiest time adapting to change. They are more used to realigning their thinking to incorporate new concepts and have more practice in constructing knowledge frameworks.

The ASEAN Community

ASEAN Summer 2019 Logo
The 34th Asean Summit Bangkok: Advancing Partnership For Sustainability, 23 June 2019

Apologies to all the folks whose words I’ve expropriated for this piece with insufficient attribution – mostly from Wikipedia and ASEAN sources. It’s already taken three days to piece this essay together and I’m trying to get it published while the dateline is still good! Just ONE more editing pass.

26 June 2019 – This is an appropriate time to visit a little-known and -acknowledged regional international community being developed in Southeast Asia: ASEAN. Last Sunday (23 June 2019) marked the 34th meeting of the ASEAN Summit in Bangkok, Thailand

ASEAN was established on 8 August 1967 with the signing of the ASEAN Declaration (Bangkok Declaration) by the five founding member states, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. Five additional member states – Brunei Darussalam (1984), Viet Nam (1995), Lao PDR and Myanmar (1997), and Cambodia (1999) – joined later to complete the ten member states of ASEAN today. An eleventh nation, Timor-Leste (in English: East Timor) has applied for membership.

The creation of ASEAN was originally motivated by a common fear of communism among the original five founding member states. ASEAN achieved greater cohesion in the mid-1970s following a change in the international balance of power after the end of the Vietnam War in 1975. The region’s dynamic economic growth during the 1970s strengthened the organization, enabling ASEAN to adopt a unified response to Vietnam’s invasion of Cambodia in 1979.

ASEAN’s first summit meeting, held in Bali, Indonesia in 1976, resulted in an agreement on several industrial projects and the signing of a Treaty of Amity and Cooperation, and a Declaration of Concord.

The end of the Cold War between the West and the Soviet Union at the end of the 1980s allowed ASEAN countries to exercise greater political independence in the region, and in the 1990s ASEAN emerged as a leading voice on regional trade and security issues.

ASEAN has a total population of 642 million people, which is nearly double that of the United States (327 million), and twenty-five percent larger than that of the European Union (513 million). Its average annual income per person, however, is only $4,308.00, putting it between the Israeli-occupied West Bank and Mauritania in the Western Sahara as far as average wealth per person is concerned. That means its people still have a long way to go! Its GDP growth rate, however, is 5.3% per annum, which is comparable to that of Egypt or Pakistan and ahead of the average for even emerging and developing countries.

Why Do We Care?

Why should Americans care about ASEAN?

First, it has aspirations to be a regional intergovernmental organization similar to the European Union in an region where the United States has economic and political interests. Their charter specifically calls for adherence to basic principles in line with those of the United States and other Western democracies. Notably the ASEAN charter specifically calls for adherence to democratic principles and maintaining the region as a nuclear-free zone.

Second, as a large and (aspirationally) politically and economically cohesive regional intergovernmental organization, ASEAN can provide a large and (again aspirationally) economically powerful ally in Southeast Asia to counterbalance Chinese efforts to extend its hegemony in the region. Especially, their actions reveal a desire to cooperate with the United States and its allies. For example, the charter refers in numerous places to working with United Nations principles and protocols, and establishes English as the ASEAN working language.

Organization

The ASEAN Community is comprised of three “pillars:” the ASEAN Political-Security Community, the ASEAN Economic Community and the ASEAN Socio-Cultural Community. Each pillar has its own Blueprint, and, together with the Initiative for ASEAN Integration (IAI) Strategic Framework and IAI Work Plan Phase II (2009-2015), they form the Roadmap for an ASEAN Community.

The figure below shows ASEAN’s top organization levels. At the top is the ASEAN Summit, comprised of the heads of state or government of the member states. By charter, they meet together twice a year, hosted by the member state holding the ASEAN Chairmanship, which cycles through the member states. At present, that is Thailand (Prime Minister General Prayut Chan-o-cha), so the latest meeting was held on 23 June 2019 in the Thai capital, Bangkok.

ASEAN Org Chart
ASEAN Organizational Structure.

At the next level, ASEAN is divided into three Community Councils that represent the three pillars of ASEAN activity:

  1. The ASEAN Political-Security Community Council

  2. The ASEAN Economic Community Council

  3. The ASEAN Socio-Cultural Community Council

Each of the three Community Councils has their own makeup and sphere of activity. The ASEAN Coordinating Council, for example, comprises the Foreign Ministers of the ASEAN member states and meets at least twice a year, not only to prepare the meetings of the ASEAN Summit, but to undertake other tasks provided for in the Charter, or for such other functions as may be assigned by the ASEAN Summit. For example, the Coordinating Council coordinates implementation of agreements and decisions of the ASEAN Summit.

In order to realize the objectives of each of the three pillars of the ASEAN Community, each ASEAN Community Council ensures the implementation of the relevant decisions of the ASEAN Summit; coordinates the work of the different sectors under its purview; ensures implementation of Summit decisions on issues that cut across the other Community Councils; and submits reports and recommendations to the ASEAN Summit on matters under its purview.

Each member state designates its own national representatives for each ASEAN Community Council. In addition, each ASEAN member state establishes an ASEAN National Secretariat that serves as a national focal point, the repository of information on all ASEAN matters at the national level, coordinates the implementation of ASEAN decisions at the national level, coordinates and supports the national preparations of ASEAN meetings, promotes ASEAN identity and awareness at the national level, and contributes to ASEAN community building.

Political-Security Community

ASEAN member states pledge to rely exclusively on peaceful processes in the settlement of intra-regional differences and with regard to their security. They are fundamentally linked to one another and bound by geographic location, as well as by a common vision and objectives.

The ASEAN Political-Security Community (APSC) aims to ensure that countries in the region live at peace with one another and with the world in a just, democratic and harmonious environment. The APSC Blueprint envisages ASEAN to be a rules-based community of shared values and norms; a cohesive, peaceful, stable and resilient region with shared responsibility for comprehensive security; and a dynamic and outward-looking region in an increasingly integrated and interdependent world. The APSC’s normative activities include: political development; shaping and sharing of norms; conflict prevention; conflict resolution; post-conflict peace building; and implementing mechanisms.

Economic Community

The ASEAN Economic Community (AEC) has a Consolidated Strategic Action Plan (CSAP) that includes strategic measures in the AEC Blueprint 2025 that takes into account the relevant sector work plans, and is reviewed periodically to account for developments in each sector.

The inaugural issue of the ASEAN Economic Integration Brief (AEIB) was released on 30 June 2017. The AEIB provides regular updates on ASEAN economic integration progress and outcomes, and is a demonstration of ASEAN’s commitment to strengthen communication and outreach to raise stakeholder awareness of the AEC.

The ASEAN Good Regulatory Practice (GRP) Core Principles was adopted at the 50th AEM Meeting in 29 August 2018 and subsequently endorsed by the AEC Council. It provides a practical, non-binding set of principles to assist ASEAN member states to improve their regulatory practice and foster ASEAN-wide regulatory cooperation.

Socio-Cultural Community

At the heart of the ASEAN Socio-Cultural Community (ASCC) is the commitment to lift the quality of life of ASEAN peoples through cooperative activities that are people-oriented, people-centered, environmentally friendly, and geared toward the promotion of sustainable development through member states’ cooperation on a wide range of areas including: culture and information, education, youth and sports, health, social welfare and development, women and gender, rights of the women and children, labor, civil service, rural development and poverty eradication, environment, transboundary haze-pollution, disaster management and humanitarian assistance.

Free-Trade Zone

The AEC aims to “implement economic integration initiatives” to create a single market across ASEAN member states. Its blueprint, adopted during the 13th ASEAN Summit (2007) in Singapore, serves as a master plan guiding the establishment of the community. Its characteristics include a single market and production base, a highly competitive economic region, a region of fair economic development, and a region fully integrated into the global economy.

The areas of co-operation include human resources development; recognition of professional qualifications; closer consultation on macroeconomic and financial policies; trade financing measures; enhanced infrastructure and communications connectivity; development of electronic transactions through e-ASEAN; integrating industries across the region to promote regional sourcing; and enhancing private sector involvement.

The AEC is the embodiment of the ASEAN’s vision of “a stable, prosperous and highly competitive ASEAN economic region in which there is a free flow of goods, services, investment and a freer flow of capital, equitable economic development and reduced poverty and socio-economic disparities.”

The average economic growth of member states from 1989 to 2009 was between 3.8% and 7%. This was greater than the average growth of APEC, which was 2.8%. The ASEAN Free Trade Area (AFTA), established on 28 January 1992, includes a Common Effective Preferential Tariff (CEPT) to promote the free flow of goods between member states.

ASEAN member states have made significant progress in the lowering of intra-regional tariffs through the CEPT. More than 99 percent of the products in Brunei Darussalam, Indonesia, Malaysia, the Philippines, Singapore and Thailand, have been brought down to the 0-5 percent tariff range. ASEAN’s newer members, namely Cambodia, Laos, Myanmar and Viet Nam, are not far behind.

ASEAN member states have also resolved to work on the elimination of non-tariff barriers, which includes, among others, the process of verification and cross-notification; updating the working definition of Non-Tariff Measures (NTMs)/Non-Tariff Barriers (NTBs); the setting-up of a database on all NTMs maintained by member states; and the eventual elimination of unnecessary and unjustifiable non-tariff measures.

I led this essay off with the comment that ASEAN does not seem to get the attention it deserves, at least in U.S. national media. Certainly, U.S. President Donald Trump seems to feel it’s not worth a tweet. The closest I was able to find with a quick Internet search was a report that he insulted Philippines President Rodrigo Duterte before meeting him on the sidelines of the Winter 2017 ASEAN Summit meeting!

That said, I must report that I became interested in ASEAN through a segment in Fareed Zacharia’s GPS show on CNN. So, not everybody is completely ignoring what I’ve come to realize is potentially an important regional intergovernmental organization.

I encourage you to learn more about ASEAN by visiting the various links peppering this column. Maybe together we can generate more interest in what could be a powerful U.S. ally in the Eastern Pacific.

Stick to Your Knitting

Man knitting
Man in suit sticking to his knitting. Photo by fokusgood / Shutterstock

6 June 2019 – Once upon a time in an MBA school far, far away, I took a Marketing 101 class. The instructor, whose name I can no longer be sure of, had a number of sayings that proved insightful, bordering on the oracular. (That means they were generally really good advice.) One that he elevated to the level of a mantra was: “Stick to the knitting.”

Really successful companies of all sizes hew to this advice. There have been periods of history where fast-growing companies run by CEOs with spectacularly big egos have equally spectacularly honored this mantra in the breach. With more hubris than brains, they’ve managed to over-invest themselves out of business.

Today’s tech industry – especially the FAANG companies (Facebook, Amazon, Apple, Netflix and Google) – is particularly prone to this mistake. Here I hope to concentrate on what the mantra means, and what goes wrong when you ignore it.

Okay, “stick to your knitting” is based on the obvious assumption that every company has some core expertise. Amazon, for example, has expertise in building and operating an online catalog store. Facebook has expertise in running an online forum. Netflix operates a bang-up streaming service. Ford builds trucks. Lockheed Martin makes state-of-the-art military airplanes.

General Electric, which has core expertise in manufacturing industrial equipment, got into real trouble when it got the bright idea of starting a finance company to extend loans to its customers for purchases of its equipment.

Conglomeration

There is a business model, called the conglomerate that is based on explicitly ignoring the “knitting” mantra. It was especially popular in the 1960s. Corporate managers imagined that conglomerates could bring into play synergies that would make conglomerates more effective than single-business companies.

For a while there, this model seemed to be working. However, when business conditions began to change (specifically interest rates began to rise from an abnormally low level to more normal rates) their supposed advantages began melting like a birthday cake left outside in a rainstorm. These huge conglomerates began hemorrhaging money until vultures swooped in to pick them apart. Conglomerates are now a thing of the past.

There are companies, such as Berkshire Hathaway, whose core expertise is in evaluating and investing in other companies. Some of them are very successful, but that’s because they stick to their core expertise.

Berkshire Hathaway was originally a textile company that investor Warren Buffett took over when the textile industry was busy going overseas. As time went on, textiles became less important and, by 1985 this core part of the company was shut down. It had become a holding company for Buffett’s investments in other companies. It turns out that Buffett’s core competence is in handicapping companies for investment potential. That’s his knitting!

The difference between a holding company and a conglomerate is (and this is specifically my interpretation) a matter of integration. In a conglomerate, the different businesses are more-or-less integrated into the parent corporation. In a holding company, they are not.

Berkshire Hathaway is known for it’s insurance business, but if you want to buy, say, auto insurance from Berkshire Hathaway, you have to go to it’s Government Employees Insurance Company (GEICO) subsidiary. GEICO is a separate company that happens to be wholly owned by Berkshire Hathaway. That is, it has its own corporate headquarters and all the staff, fixtures and other resources needed to operate as an independent insurance company. It just happens to be owned, lock, stock and intellectual property by another corporate entity: Berkshire Hathaway.

GEICO’s core expertise is insurance. Berkshire Hathaway’s core expertise is finding good companies to invest in. Some are partially owned (e.g., 5.4% of Apple) some are wholly owned (e.g., Acme Brick).

Despite Berkshire Hathaway’s holding positions in both Apple and Acme Brick, if you ask Warren Buffet if Berkshire Hathaway is a computer company or a brick company, he’d undoubtedly say “no.” Berkshire Hathaway is a diversified holding company.

It’s business is owning other businesses.

To paraphrase James Coburn’s line from Stanley Donen’s 1963 film Charade: “[Mrs. Buffett] didn’t raise no stupid children!”

Why Giant Corporations?

All this giant corporation stuff stems from a dynamic I also learned about in MBA school: a company grows or it dies. I ran across this dynamic during a financial modeling class where we used computers to predict results of corporate decisions in lifelike conditions. Basically, what happens is that unless the company strives to its utmost to maintain growth, it starts to shrink and then all is lost. Feedback effects take over and it withers and dies.

Observations since then have convinced me this is some kind of natural law. It shows up in all kinds of natural systems. I used to think I understood why, but I’m not so sure anymore. It may have something to do with chaos, and we live in a chaotic universe. I resolve to study this in more detail – later.

But, anyway … .

Companies that embrace this mantra (You grow or you die.) grow until they reach some kind of external limit, then they stop growing and – in some fashion or other – die.

Sometimes (and paradigm examples abound) external limits don’t kick in before some company becomes very big, indeed. Standard Oil Company may be the poster child for this effect. Basically, the company grew to monopoly status and, in 1911 the U.S. Federal Government stepped in and, using the 1890 Sherman Anti-Trust Act, forced its breakup into 33 smaller oil companies, many of which still exist today as some of the world’s major oil companies (e.g., Mobil, Amoco, and Chevron). At the time of its breakup, Standard Oil had a market capitalization of just under $11B and was the third most valuable company in the U.S. Compare that to the U.S. GDP of roughly $34B at the time.

The problem with companies that big is that they generate tons of free cash. What to do with it?

There are three possibilities:

  1. You can reinvest it in your company;

  2. You can return it to your shareholders; or

  3. You can give it away.

Reinvesting free cash in your company is usually the first choice. I say it is the first choice because it is used at the earliest period of the company’s history – the period when growth is necessarily the only goal.

If done properly reinvestment can make your company grow bigger faster. You can reinvest by out-marketing your competition (by, say, making better advertisements) and gobbling up market share. You can also reinvest to make your company’s operations more effective or efficient. To grow, you also need to invest in adding production facilities.

At a later stage, your company is already growing fast and you’ve got state-of-the-art facilities, and you dominate your market. It’s time to do what your investors gave you their money for in the first place: return profits to them in the form of dividends. I kinda like that. It’s what the game’s all about, anyway.

Finally, most leaders of large companies recognize that having a lot of free cash laying around is an opportunity to do some good without (obviously) expecting a payback. I qualify this with the word “obviously” because on some level altruism does provide a return in some form.

Generally, companies engage in altruism (currently more often called “philanthropy”) to enhance their perception by the public. That’s useful when lawsuits rear their ugly heads or somebody in the organization screws up badly enough to invite public censure. Companies can enhance their reputations by supporting industry activities that do not directly enhance their profits.

So-called “growth companies,” however, get stuck in that early growth phase, and never transition to paying dividends. In the early days of the personal-computer revolution, tech companies prided themselves on being “growth stocks.” That is, investors gained vast wealth on paper as the companies’ stock prices went up, but couldn’t realized those gains (capital gains) unless they sold the stock. Or, as my father once did, by using the stock for collateral to borrow money.

In the end, wise investors eventually want their money back in the form of cash from dividends. For example, in the early 2000s, Microsoft and other technology companies were forced by their shareholders to start paying dividends for the first time.

What can go wrong

So, after all’s said and done, why’s my marketing professor’s mantra wise corporate governance?

To make money, especially the scads of money that corporations need to become really successful, you’ve gotta do something right. In fact, you gotta do something better than the other guys. When you know how to do something better than the other guys, that’s called expertise!

Companies, like people, have limitations. To imagine you don’t have limitations is hubris. To put hubris in perspective, recall that the ancients famously made it Lucifer’s cardinal sin. In fact, it was his only sin!

Folks who tell you that you can do anything are flat out conning your socks off.

If you’re lucky you can do one thing better than others. If you’re really lucky, you can do a few things better than others. If you try to do stuff outside your expertise, however, you’re gonna fail. A person can pick themselves up, dust themselves off, and try again – but don’t try to do the same thing again ‘cause you’ve already proved it’s outside your expertise. People can start over, but companies usually can’t.

One of my favorite sayings is:

Everything looks easy to someone who doesn’t know what they’re doing.

The rank amateur at some activity typically doesn’t know the complexities and pitfalls that an expert in the field has learned about through training and experience. That’s what we know as expertise. When anyone – or any company – wanders outside their field of expertise, they quickly fall foul of those complexities and pitfalls.

I don’t know how many times I’ve overheard some jamoke at an art opening say, “Oh, I could do that!”

Yeah? Then do it!

The artist has actually done it.

The same goes for some computer engineer who imagines that knowing how to program computers makes him (or her) smart, and because (s)he is so smart, (s)he could run, say, a magazine publishing house. How hard can it be?

Mark Zuckerberg is in the process of finding out.

Fed Reports on U.S. Economic Well-Being

Federal Reserve Building
The Federal Reserve released the results of its annual Survey of Household Economics and Decisionmaking for calendar year 2018 last week. Image by Thomas Barrat / Shutterstock

29 May 2019 – Last week (specifically 23 May 2019) the Federal Reserve Board released the results of its annual Survey of Household Economics and Decisionmaking for CY2018. I’ve done two things for readers of this blog. First, I downloaded a PDF copy of the report to make available free of charge on my website at cgmasi.com alongside last year’s report for comparison. Second, I’m publishing an edited extract of the report’s executive summary below.

The report describes the results of the sixth annual Survey of Household Economics and Decisionmaking (SHED). In October and November 2018, the latest SHED polled a self-selected sample of over 11,000 individuals via an online survey.

Along with the survey-results report, the Board published the complete anonymized data in CSV, SAS, STATA formats; as well as a supplement containing the complete SHED questionnaire and responses to all questions in the order asked. The survey continues to use subjective measures and self-assessments to supplement and enhance objective measures.

Overall Results

Survey respondents reported that most measures of economic well-being and financial resilience in 2018 are similar to or slightly better than in 2017. Many families have experienced substantial gains since the survey began in 2013, in line with the nation’s ongoing economic expansion during that period.

Even so, another year of economic expansion and the low national unemployment rates did little to narrow the persistent economic disparities by race, education, and geography. Many adults are financially vulnerable and would have difficulty handling an emergency expense as small as $400.

In addition to asking adults whether they are working, the survey asks if they want to work more and what impediments they see to them working.

Overall Economic Well-Being

A large majority of individuals report that, financially, they are doing okay or living comfortably, and overall economic well-being has improved substantially since the survey began in 2013

  • When asked about their finances, 75% of adults say they are either doing okay or living comfortably. This result in 2018 is similar to 2017 and is 12%age points higher than 2013.

  • Adults with a bachelor’s degree or higher are significantly more likely to be doing at least okay financially (87%) than those with a high school degree or less (64%).

  • Nearly 8 in 10 whites are at least doing okay financially in 2018 versus two-thirds of blacks and Hispanics. The gaps in economic well-being by race and ethnicity have persisted even as overall wellbeing has improved since 2013.

  • Fifty-six percent of adults say they are better off than their parents were at the same age and one fifth say they are worse off.

  • Nearly two-thirds of respondents rate their local economic conditions as “good” or “excellent,” with the rest rating conditions as “poor” or “only fair.” More than half of adults living in rural areas describe their local economy as good or excellent, compared to two-thirds of those living in urban areas.

Income

Changes in family income from month to month remain a source of financial strain for some individuals.

  • Three in 10 adults have family income that varies from month to month. One in 10 adults have struggled to pay their bills because of monthly changes in income. Those with less access to credit are much more likely to report financial hardship due to income volatility.

  • One in 10 adults, and over one-quarter of young adults under age 30, receive some form of financial support from someone living outside their home. This financial support is mainly between parents and adult children and is often to help with general expenses.

Employment

Most adults are working as much as they want to, an indicator of full employment; however, some remain unemployed or underemployed. Economic well-being is lower for those wanting to work more, those with unpredictable work schedules, and those who rely on gig activities as a main source of income.

  • One in 10 adults are not working and want to work, though many are not actively looking for work. Four percent of adults in the SHED are not working, want to work, and applied for a job in the prior 12 months. This is similar to the official unemployment rate of 3.8% in the fourth quarter of 2018.

  • Two in 10 adults are working but say they want to work more. Blacks, Hispanics, and those with less education are less likely to be satisfied with how much they are working.

  • Half of all employees received a raise or promotion in the prior year.

  • Unpredictable work schedules are associated with financial stress for some. One-quarter of employees have a varying work schedule, including 17% whose schedule varies based on their employer’s needs. One-third of workers who do not control their schedule are not doing okay financially, versus one-fifth of workers who set their schedule or have stable hours.

  • Three in 10 adults engaged in at least one gig activity in the prior month, with a median time spent on gig work of five hours. Perhaps surprisingly, little of this activity relies on technology: 3% of all adults say that they use a website or an app to arrange gig work.

  • Signs of financial fragility – such as difficulty handling an emergency expense – are slightly more common for those engaged in gig work, but markedly higher for those who do so as a main source of income.

Dealing with Unexpected Expenses

While self-reported ability to handle unexpected expenses has improved substantially since the survey began in 2013, a sizeable share of adults nonetheless say that they would have some difficulty with a modest unexpected expense.

  • If faced with an unexpected expense of $400, 61% of adults say they would cover it with cash, savings, or a credit card paid off at the next statement – a modest improvement from the prior year. Similar to the prior year, 27% would borrow or sell something to pay for the expense, and 12% would not be able to cover the expense at all.

  • Seventeen percent of adults are not able to pay all of their current month’s bills in full. Another 12% of adults would be unable to pay their current month’s bills if they also had an unexpected $400 expense that they had to pay.

  • One-fifth of adults had major, unexpected medical bills to pay in the prior year. One-fourth of adults skipped necessary medical care in 2018 because they were unable to afford the cost.

Banking and Credit

Most adults have a bank account and are able to obtain credit from mainstream sources. However, sub- stantial gaps in banking and credit services exist among minorities and those with low incomes.

  • Six percent of adults do not have a bank account. Fourteen percent of blacks and 11% of Hispanics are unbanked versus 4% of whites. Thirty-five percent of blacks and 23% of Hispanics have an account but also use alternative financial services, such as money orders and check cashing services, compared to 11% of whites.

  • More than one-fourth of blacks are not confident that a new credit card application would be approved if they applied—over twice the rate among whites.

  • Those who never carry a credit card balance are much more likely to say that they would pay an unexpected $400 expense with cash or its equivalent (88%) than those who carry a balance most or all of the time (40%) or who do not have a credit card (27%).

  • Thirteen percent of adults with a bank account had at least one problem accessing funds in their account in the prior year. Problems with a bank website or mobile app (7%) and delays in when funds were available to use (6%) are the most common problems. Those with volatile income and low savings are more likely to experience such problems.

Housing and Neighborhoods

Satisfaction with one’s housing and neighborhood is generally high, although notably less so in low-income communities. While 8 in 10 adults living in middle- and upper-income neighborhoods are satisfied with the overall quality of their community, 6 in 10 living in low- and moderate-income neighborhoods are satisfied.

  • People’s satisfaction with their housing does not vary much between more expensive and less expensive cities or between urban and rural areas.

  • Over half of renters needed a repair at some point in the prior year, and 15% of renters had moderate or substantial difficulty getting their landlord to complete the repair. Black and Hispanic renters are more likely than whites to have difficulties getting repairs done.

  • Three percent of non-homeowners were evicted, or moved because of the threat of eviction, in the prior two years. Evictions are slightly more common in urban areas than in rural areas.

Higher Education

Economic well-being rises with education, and most of those holding a post-secondary degree think that attending college paid off.

  • Two-thirds of graduates with a bachelor’s degree or more feel that their educational investment paid off financially, but 3 in 10 of those who started but did not complete a degree share this view.

  • Among young adults who attended college, more than twice as many Hispanics went to a for-profit institution as did whites. For young black attendees, this rate was five times the rate of whites.

  • Given what they know now, half of those who attended a private for-profit institution say that they would attend a different school if they had a chance to go back and make their college choices again. By comparison, about one-quarter of those who attended public or private not-for-profit institutions would want to attend a different school.

Student Loans and Other Education Debt

Over half of young adults who attended college took on some debt to pay for their education. Most borrowers are current on their payments or have successfully paid off their loans.

  • Among those making payments on their student loans, the typical monthly payment is between $200 and $299 per month.

  • Over one-fifth of borrowers who attended private for-profit institutions are behind on student loan payments, versus 8% who attended public institutions and 5% who attended private not-for-profit institutions.

Retirement

Many adults are struggling to save for retirement. Even among those who have some savings, people commonly lack financial knowledge and are uncomfortable making investment decisions.

  • Thirty-six percent of non-retired adults think that their retirement saving is on track, but one-quarter have no retirement savings or pension whatsoever. Among non-retired adults over the age of sixty, 45% believe that their retirement saving is on track.

  • Six in 10 non-retirees who hold self-directed retirement savings accounts, such as a 401(k) or IRA, have little or no comfort in managing their investments.

  • On average, people answer fewer than three out of five financial literacy questions correctly, with lower scores among those who are less comfortable managing their retirement savings.

The forgoing is an edited extract from the Report’s Executive Summary. A PDF version of the entire report is available on my website at cgmasi.com [ http://cgmasi.com ] along with a PDF version of the 2017 report, which was published in May of 2018 and based on a similar survey conducted in late 2017. Reports dating back to the first survey done in late 2013 are available from the Federal Reserve Board’s website linked to above.

Why Target Average Inflation?

Federal Reserve Seal
The FOMC attempts to control economic expansion by managing interest rates. Shutterstock.com

8 May 2019 – There’s been a bit of noise in financial-media circles this week (as of this writing, but it’ll be last week when you get to read it) about Federal Reserve Chairman Jerome Powell’s talking up shifting the Fed’s focus to targeting something called “average inflation” and using words like “transient” and “symmetric” to describe this thinking. James Macintosh provided a nice layman-centric description of the pros and cons of this concept in his “Streetwise” column in Friday’s (5/3) The Wall Street Journal. (Sorry, folks, but this article is only available to WSJ subscribers, so the link above leads to a teaser that asks you to either sign in as a current subscriber or to become a new subscriber. And, you thought information was supposed to be distributed for free? Think again!)

I’m not going to rehash what Macintosh wrote, but attempt to show why this change makes sense. In fact, it’s not really a change at all, but an acknowledgement of what’s really been going on all the time.

We start with pointing out that what the Federal Reserve System is mandated to do is to control the U.S. economy. The operant word here is “control.” That means that to understand what the Fed does (and what it should do) requires a basic understanding of control theory.

Basic Control Theory

We’ll start with a thermostat.

A lot of people (I hesitate to say “most” because I’ve encountered so many counter examples – otherwise intelligent people who somehow don’t seem to get the point) understand how a thermostat works.

A thermostat is the poster child for basic automated control systems. It’s the “stone knives and bearskins” version of automated controls, and is the easiest for the layman to understand, so that’s where we’ll start. It’s also a good analog for what has passed for economic controls since the Fed was created in 1913.

Okay, the first thing to understand is the concept of a “set point.” That’s a “desired value” of some measurement that represents the thing you want to control. In the case of the thermostat, the measurement is room temperature (as read out from a thermometer) and the thing you’re trying to control is how comfortable the room air feels to you. In the case of the Fed, the thing you want to control is overall economic performance and the measurement folks decided was most useful is the inflation rate.

Currently, the set point for inflation is 2% per annum.

In the case of the thermostat in our condo, my wife and I have settled on 75º F. That’s a choice we’ve made based on the climate where we live (Southwestern Florida), our ages, and what we, through experience, have found to be most comfortable for us right now. When we lived in New England, we chose a different set point. Similarly, when we lived in Northern Arizona it was different as well.

The bottom line is: the set point is a matter of choice based on a whole raft of factors that we think are important to us and it varies from time to time.

The same goes for the Fed’s inflation set point. It’s a choice Fed governors make based on a whole raft of considerations that they think are important to the country right now. One of the reasons they meet every month is to review that target ‘cause they know that things change. What seems like a good idea in July, might not look so good in August.

Now, it’s important to recognize that the set point is a target. Like any target, you’re trying to hit it, but you don’t really expect to hit it exactly. You really expect that the value you get for your performance measurement will differ from your set point by some amount – by some error or what metrologists prefer to call “deviation.” We prefer deviation to the word error because it has less pejorative connotations. It’s a fact of life, not a bad thing.

When we add in the concept of time, we also introduce the concept of feedback. That is what control theorists call it when you take the results of your measurement and feed it back to your decision of what to do next.

What you do next to control whatever you’re trying to control depends, first, on the sign (positive or negative) of the deviation, and, in more sophisticated controls, it’s value or magnitude. In the case of the thermostat, if the deviation is positive (meaning the room is hotter than you want) you want to do something to cool it down. In the case of the economy, if inflation is too high you want to do something to reduce economic activity so you don’t get an economic bubble that’ll soon burst.

What confuses some presidents is the idea that rising economic activity isn’t always good. Presidents like boom times ‘cause they make people feel good – like a sugar high. Populist presidents typically fail to recognize (or care about the fact) that booms are invariably bubbles that burst disastrously. Just ask the people of Venezuela who watched their economy’s inflation rate suddenly shoot up to about a million(!) percent per annum.

Booms turn to busts in a heartbeat!

This is where we want to abandon the analogy with a thermostat and get a little more sophisticated.

A thermostat is a blunt instrument. What the thermostat automatically does next is like using a club. At best, a thermostat has two clubs to choose from: it can either fire up the furnace (to raise the room temperature in the event of a negative deviation) or kick in the air conditioner (in the event that the deviation is positive – too hot). That’s known as a binary digital control. It’s gives you a digital choice: up or down.

We leave the thermostat analogy because the Fed’s main tool for controlling the economy (the Fed-funds interest rate) is a lot more sophisticated. It’s what mathematicians call analog. That is, instead of providing a binary choice (to use the club or not), it lets you choose how much pressure you want to apply up or down.

Quantitative easing similarly provides analog control, so what I’m going to say below also applies to it.

Okay, the Fed’s control lever (Fed funds interest rate) is more like a brake pedal than a club. In a car, the harder you press the brake pedal, the more pressure you apply to make the car slow down. A little pressure makes the car slow down a little. A lot of pressure makes the car slow down a lot.

So, you can see why authoritarians like low interest rates. Autthoritarians generally have high-D personalities. As Personality Insights says: “They tend to know 2 speeds in life – zero and full throttle… mostly full throttle.”

They generally don’t have much use for brakes!

By the way, the thing governments have that corresponds to a gas pedal is deficit spending, but the correspondence isn’t exact and the Fed can’t control it, anyway. Since this article is about the Fed, we aren’t going to talk about it now.

When inflation’s moving too fast (above the set point) by a little, the Fed governors – being the feedback controller – decide to raise the Fed funds rate, which is analogous to pushing the brake pedal, by a little. If that doesn’t work, they push it a little harder. If inflation seems to be out of control, as it did in the 1970s, they push it as hard as they can, boosting interest rates way up and pulling way back on the economy.

Populist dictators, who generally don’t know what they’re doing, try to prevent their central banks (you can’t have an economy without having a central bank, even if you don’t know you have it) from raising interest rates soon enough or high enough to get inflation under control, which is why populist dictatorships generally end up with hyperinflation leading to economic collapse.

Populist Dictators Need Not Apply

This is why we don’t want the U.S. Federal Reserve Bank under political control. Politicians are not elected for their economic savvy, so we want Fed governors, who are supposed to have economic savvy, to make smart decisions based on their understanding of economic causes and effects, rather than dumb decisions based on political expediency.

Economists are mathematically sophisticated people. They may (or may not) be steeped in the theory of automated control systems, but they’re quite capable of understanding these basics and how they apply to controlling an economy.

Economics, of course, has been around as long as civilization. Hesiod (ca. 750 BCE) is sometimes considered “the first economist.” Contemporary economics traces back to the eighteenth century with Adam Smith. Control theory, on the other hand, has only been elucidated since the early 1950s. So, you don’t really need control theory to understand economics. It just makes it easier to see how the controls work.

To a veteran test and measurement maven like myself, the idea of thinking in terms of average inflation, instead of the observed inflation at some point in time – like right now – makes perfect sense. We know that every time you make a measurement of anything, you’re almost guaranteed to get a different value than you got the last time you measured it. That’s why we (scientists and engineers) always measure whatever we care about multiple times and pay attention to the average of the measurements instead of each measurement individually.

So, Fed governors starting to pay attention to average inflation strikes us as a duh! What else would you look at?

Similarly, using words like “transient” and “symmetric” make perfect sense because “transient” expresses the idea that things change faster than you can measure them and “symmetric” expresses the idea that measurement variations can be positive or negative – symmetric each side of the average.

These ideas all come from the mathematics of statistics. You’ve heard of “statistical significance” associated with polling data, or two polling results being within “statistical error.” The variations I’m talking about are the same thing. Variations between two values (like the average inflation and the target inflation) are statistically significant if they’re sufficiently outside the statistical error.

I’m not going to go into how you calculate a value for statistical error because it takes hours of yammering to teach it in statistics classes, and I just don’t have the space here. You wouldn’t want to read it right now, anyway. Suffice it to say that it’s a well-defined concept relating to how much variation you can expect in a given data set.

While the control theory I’ve been talking about applies especially to automated control systems, it applies equally to Federal Reserve System control of economic performance – if you put the Federal Open Market Committee (FOMC) in place of the control computer that makes decisions for the automated control system.

So,” you ask, “why not put the Fed-funds rate under computer control?”.

The reason it would be unreasonable to fully automate the Fed’s actions is that we can’t duplicate the thinking process of the Fed governors in a computer program. The state of the art of economic models is just not good enough, yet. We still need the gut feelings of seasoned economists to make enough sense out of what goes on in the economy to figure out what to do next.

That, by the way, is why we don’t leave the decisions up to some hyperintelligent pandimensional being (named Trump). We need a panel of economists with diverse backgrounds and experiences – the FOMC – to have some hope of getting it right!