Reflecting on a Job Market – Employee and Employer

To gain significant wealth in the US you have to take significant risk. Usually that means starting your own business or being on the ground floor with someone who is. The individuals who put their neck on the line deserve the spoils of that risk. The last three years proves the fittest survives in business.

We can somewhat reflect now. The once in a generation economy is behind us, so it’s time to see what the new world looks like. It’s lean and flexible. But a chasm is growing between the employer and the employees. Here are some stats from a Mercer survey I read about by Ben Rooney on CNNMoney called  Half of Workers Unhappy in their Jobs:

  • 32% of US workers are seriously considering leaving their job. Up from 23% in 2005.
  • Of the age group 25-34, 40% are seriously considering leaving. Within that number is 44% of employees who are 24 and younger. The cheap labor is ready to bolt.
  • And more alarming, 56% of senior managers are considering leaving. This compares to 34% of managers and 30% of non-managers. The experienced are also looking to jump to other opportunities.
  • A slightly different take, but 21% have disengaged from their employer, meaning they are not looking for a new job, but they are apathetic toward their current one. This could be burn out and it could mean the productivity gains via personnel has reached it’s limit.

Workers are getting disenfranchised by the circumstances of their employment. In addition to that, there are business owners who have moved away from the proper perspective. They’ve had leverage for over three years. Chances are they laid off some people. Those that remain should have a debt of gratitude. It could be worse.

The business owner who has survived is entitled to some fun, but they need to realize no one does it alone. I was out to dinner with a friend in the industrial fabrication and installation field of work. He had an exchange with his boss similar to the one in the movie below. I embellished it for effect, but much of this exchange is true, particularly the part about the water skis

When Ideas Come Together

I sit here and type. Sometimes I have inspiration and sometimes its a slog. I do it because I love when ideas come rushing in. It’s like the end of the Usual Suspects when everything comes together. Its powerful and rewarding.

Good mysteries are fulfilling because we have to hunt for clues. They are rarely obvious and they are as much a study of logic and circumstance as anything else. It’s in our DNA to problem solve this way.

For this reason I’m optimistic about the long term future. The connectedness of the world is bringing figurative detectives together, each with their own clues, to solve problems. Ideas have a better chance to grow. Incremental improvements are good, but we are after leaps forward.

Take this chart from about Cisco’s prediction on the expansion of the internet.

And check out these videos from Steven Johnson about where ideas come from. The first one is a short artistic explanation and the second is a TED Talk. I really enjoy the story at the end.


Defining the Future for the Class of 2011

Congratulations to the class of 2011. You’ve earned the gratification of moving your tassel from one side of the cap to the other.

What does the future hold? What is out there? You’ll be told to find yourself, follow your passion, and chase your goals. And many of you will wonder what those are. There’s debt, perhaps an entry level job, and monthly bills. Before you can commit to your boundless dreams you already have these torments and everything that goes along with it. Chances are you’ll be figuring out credit cards, bad bosses, and hung over early morning meetings. Every day is a new day.

If you learned anything in school, I hope you’ve learned how to think. To ask why? Anyone can follow directions, good thinkers are people who understand why they exist. Great thinkers design the instructions. What is the path? What are the steps? “If this happens, then do this” and the decision tree associated with it. Working through these scenarios develops an ability to cope with complexity. Said another way – for greater enlightenment, do you want a happy meal toy or a jet engine?

And I beg you to create something. The world is a better place when people work to assemble rather than tear apart. If you’re a business person I’d start with thinking about value proposition. Think about a situation and how things work together. What is the context for which a transaction operates? Consider the decisions people are making and the information they are using. What drives them to act and is the desired behavior? Is there a status quo? Have people accepted things as they are?

The world you experience is very different than the world I know. And that is good. There are generally two types of knowledge explicit and tacit.

  • Explicit Knowledge – is knowledge that has been or can be articulated, codified, and stored in certain media. It can be readily transmitted to others. The information contained in encyclopedias (including Wikipedia) are good examples of explicit knowledge.
  • Tacit Knowledge – involves learning and skill but in a way that is difficult to transfer from one person by means of writing it down or verbalizing it. Tacit knowledge can consist of habits and culture that we do not recognize in ourselves. I can tell you how to ride a bicycle, but you won’t know how until you learn to balance.

Graduation means you’ve probably spent close to $1,000 on books over the last few years acquiring explicit knowledge. You’ve studied in your dorm room and memorized facts about amortization. You’ve read what the top of Kilimanjaro looks like.

Now is the time to look yourself. It doesn’t have to be the top of a mountain, a high point works just fine. Experience the edge and feel the risk. Trace the path you’ve taken to this simple point. Once you’ve climbed one high point, you’ll climb another and another.

Nothing worth doing is easy and life isn’t fair. Your experience is unique, always ask why, and focus on creating something, anything. And remember, every day is a new day.

Split Personalities – Tax Breadth and Tax Depth

We seem to have split personalities when it comes to the news and our politics. In the news we hear about natural disasters and the sour economy. In politics we hear about the failings of the President and the deficit. Why are these two voices talking about different subjects?

The truth is they are talking about the same problem, just different ends of it. The US is maturing. A large portion of the population is entering their retirement years. Every day, for the next 19 years, 10,000 baby boomers will turn 65. By 2030, 18% of the U.S. population will be over 65, compared with today’s 13%.

This is important for several reasons, but here are two:

  1. Federal tax collection is based on income. Those that are retired usually don’t make significant income, so the taxes they contribute are very low. A change or decrease of 5% is a huge impact to the revenue of the government. Or said another way, 10,000 people, who have a high average income, can drop out of the tax pool everyday.
  2. The baby boomers have been in leadership positions for two decades. The groups behind them, smaller in numbers, will need to fill the void.

The first reason is why you hear about Medicare and the budget. The second reason is why you hear about stimulus and silicon valley.

– When we talk about the deficit and paying down the debt we are talking about the inevitability of time. Our demographics show an aging population who will not be contributing to tax rolls. Less income means less spending. Tax Breadth.

– When we talk about innovation and stimulus spending we are pushing for investment and hopefully an improvement in future wealth and the standard of living. This would offset the loss of tax income from those no longer in the workforce. Tax Depth.

Both of these are concerns. I tend to be more transfixed with the latter. Many young professionals are either not entering the workforce or they are at compensation levels below the norm of 5 years ago. This lag in pay is not easily overcome and tends to persist for a career. Smaller income means smaller taxes paid. In addition to that, younger professionals are not moving into challenging roles as they would have in the past. Opportunities for learning experiences are reduced. Plus what they’ve been taught in school isn’t applicable e.g. China has changed dramatically since 2007, but the text books didn’t.

The 18% not in the workforce is unavoidable, but what should be asked is what’s to come of the under employed?

There will always be some number of the under employed, but we are currently looking at a devastating mix of long durations and loss of skills. The recession as it began in 2007 was a supply and demand recession, meaning nothing out of the ordinary occurred. But the last two years has led to a structural recession. This means that the skills and knowledge the US worker has isn’t quite matching up with what labor is needed. If this is more than a blip then high unemployment will continue for a few years as education and training requirements sort themselves out.

But I also feel like the 16-24 group, or more broadly the under 30 age group, is pioneering a new track. The way the view the world is much different than their older counterparts. As a consumer group they can influence the creation and offering of products and services. The next 24 months will be telling about the future of this country.

Using Particular Phrases to be More Compelling

I’ve recently been on vacation and I’m catching up on some reading. One of my favorite magazines and websites is the Harvard Business Review or

In the March 2011 issues is an Idea Watch section about the persuasiveness of experts. What the finding suggests is that when experts are less certain about their opinion, the more likely the opinion is going to be interesting and perhaps more intriguing to the audience.

What does this mean? It’s a little nugget for helping when people are scanning through information. If there are themes or patterns people tend to zone a out a bit. Important nuance can be lost. But when those themes are broken the reason for the deviation prompts curiosity.

This can be applied in the workplace. As the labor reports are coming out the economy is slowly picking up steam. There are many people looking for work. If you are writing a job recommendation for someone, its good to pepper in the phrase “high potential” in addition to “high achieving.”

  • High Achieving – Is a reference to the past. It shows capability and success but it isn’t necessarily relevant.
  • High Potential – Is a reference to the future. It latches onto a vision, onto hope, and shows adaptability and flexibility. Its more inspiring.

Here’s a blurb from the article Experts are More Persuasive When They’re Less Certain:

What makes a message compelling?

By “compelling,” I mean relevant to the core argument. In
another study, we had subjects read reviews that also gave four out of
five stars, but their content wasn’t really about the restaurant. They
said things like “My friend and I laughed the whole time. I liked the
way the menu looked and the colors they used.” That’s not compelling.
Even if it were interesting, it’s not what makes a restaurant good or
bad. Whether the reviews were confident or not, people didn’t find them

Where else do you want to take your certainty research?

One thing I’ve started looking into with some other
collaborators, Jayson Jia and Mike Norton, is how people view potential.
Our initial findings seem to show that people value high potential more
than high achievement.

That explains why a rookie quarterback like Sam Bradford makes more money than Super Bowl champ Drew Brees.

Sports are a great example. In one study,
participants read the scouting report on a basketball player. Some read
the actual stats for the player’s first five years in the league; others
read predictions for the first five years’ performance. The numbers
were identical. Then we asked, How much would you pay this player in
year six? On average, people gave the veteran who had performed $4.26
million and the rookie who was projected to perform $5.25 million, over
20% more.

Rookie talent in general, not just in sports, seems vastly overweighted.

Exactly. If you present people with letters of
recommendation for one job candidate described as “high potential” and
another described as “high achieving,” they’ll find the letter for the
high potential candidate more interesting and possibly more persuasive.

How can people be so thick?

Proven achievement is very certain. It’s less surprising
and less interesting to think about. Potential is uncertain and kind of
exciting. You can imagine many outcomes. Maybe they’ll do better than
you expect!

OK, I have to ask: How certain are you about the validity of your research?

I think our findings tell us something important. But you
never know what other variables could be in play here. The more we
research this, the better we’ll understand it.

I’ll buy that.

You see? It works.

Using Data as a Predictor of Sports Success

There’s a huge celebration going on this week – a celebration of decision making. You see the NFL Draft starts Thursday (4/28/11) and runs through Saturday (4/30/11) and fans tune in to see who their team selects. No games are played, just people’s names being called.

Why do we care? The simple answer is hope. We’ve entrusted the future of our favorite teams to a room full of guys with spreadsheets. We want to believe they have the magic formula for selecting the players who succeed in the NFL. They’ve studied film, measured height, weight, speed, interviewed the candidates, and surveyed other experts. They’ve quantified all these inputs and ranked the candidates. Most of the time they tier them for purposes of trading up or down. Teams win Super Bowls because of these three days.

It’s a lot of data and yet every year mistakes are made. As a General Manager, the person ultimately making the decision, you need the hits to be proportionally more successful than your misses. And you need to learn from your data year over year to see which inputs pan out and which ones do not. From there you can use heuristics to simplify the ranking order and reduce the risk of missing on a selection.

Below are two videos. One is from the Sloan Sports Conference and it features Peter Tingling. I’m a fan of Mr. Tingling and his company, Octothorpe Software (this is not a paid endorsement). Peter provides a presentation about how how successful NHL drafts are.

The second video is from the most famous sixth round pick ever – Tom Brady. He is your classic case of not using the data correctly.

Simple Heuristics That Make Us Smart – A Book Review

Quick Take: Simple Heuristics That Make Us Smart is a collection of academia based essays proving the comparative value of decision making based on good enough information. The examples and anecdotes are good, but there is complex math to wade through. It isn’t a leisure read. However, each section can be consumed on it’s own. If you’re a student of decision making, whether it’s group dynamics or individual situations, then this book is a good heuristics reference.

Detail Review: Many of us have a comfortable chair which serves as our place to relax.
Its great for 40 winks. But why do we relish peacefully falling asleep
in a chair? Most of the time it’s because we are mentally exhausted. Everyday we are faced with an ever changing list of choices to make and each has a list of known variables and all kinds of factors which are unknown. We try to streamline choices that have worked so we don’t need to concentrate on it. I take the same route to work everyday even though there are probably another ten ways to get there, for instance.

I wish I had a computer in my head to compute all the different inputs into making a decision. I could continually collect data and analyze it practically to a 100% decision certainty. But I don’t have a computer or unlimited time, instead I rely on heuristics. Heuristics are simple methods for using particular cues and constraints to make a choice. Gerd Gigerenzer, Peter M. Todd, and The ABC Research Group authored this tome as a study of how accurate specific heuristics are.

Here are a few heuristics covered in the book:

Definition – If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion.
Example – If I ask 100 Americans which city in Germany is more populated Berlin or Saarburg? The results will be close to 100% correct – Berlin is more populated. Of the 100 people few, if any, will recognize Saarburg as a city, but practically all of them will have heard of Berlin. Because of that recognition they will answer Berlin even though they know little about the actual number of people who live in either city.

Take the Best
Definition – When making a judgment based on multiple cues, the criterion are tried one at a time according to their cue validity, and a decision is made based on the first criterion which discriminates between the alternatives.
Example – Suppose we ask the question about population again, but instead of Saarburg we use Frankfurt. Berlin and Frankfurt are both recognizable so we must use other reasons to discriminate population. We pose a list of usual indicators of large populations – historical relevance, it’s a capital, tourism, sports teams, and so on. From that list we rank the list based on which ones usually are more of an indication of population and try to separate the two. We compare Frankfurt and Berlin for tourism and realize that Berlin is much more of a destination than Frankfurt is. We stop there and don’t review the other reasons. We take the best separator – tourism – and decide to invest no more time in evaluating. Berlin is the answer.

Take the Last
Definition – When making a judgment based on multiple cues, the criterion are sorted according to what worked last time. It uses memory of prior problem solving instances and works from what was successful before.
Example – I’m now comparing Frankfurt and Munich in population. I’ve heard of both so I can’t use Recognition. I use Tourism as the candidate since it worked with Berlin and Frankfurt. This time I go with Munich because they’ve hosted an Olympics and is more of a destination than Frankfurt. This answer is correct and time and energy was saved because I didn’t need to sort through all the other criteria.

In addition to those there are:

  • Franklin’s Rule – calculates for each alternative the sum of the cue values multiplied by the corresponding cue weights (validaties) and selects the alternative with the highest score.
  • Dawes’s Rule – calculates for each alternative the sum of the cue values (multiplied by a unit weight of 1) and selects the alternative with the highest score.
  • Good Features (Alba & Marmorstein, 1987) selects the alternative with the highest number of good features. A good feature is a cue value that exceeds a specified cutoff.
  • Weighted Pros (Huber, 1979) selects the alternative with the highest sum of weighted “pros.” A cue that has a higher value for one alternative than for the others is considered a pro for this alternative. The weight of each pro is defined by the validity of the particular cue.
  • LEX or lexicographic (Fishburn, 1974) selects the alternative with the highest cue value on the cue with the highest validity. If more than one alternative has the same highest cue value, then for these alternatives the cue with the second highest validity is considered, and so on. Lex is a generalization of Take the Best
  • EBA or Elimination by Aspects (Tsersky, 1972) eliminates all alternatives that do not exceed a specified value on the first cue examined. If more than one alternative remains, another cue is selected. This procedure is repeated until only one alternative is left. Each cue is selected with a probability proportional to its weight. In contrast to this probabilistic selection, in the present chapter the order in which EBA examines cues to determine by their validity, so that in every case the cue with the highest validity is used first.
  • Multiple Regression is a statistically analysis of how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. This is beyond the capacity of a normal human and usually requires a resources like a computer.

The book uses the city example to run a test against a few heuristics and Regression testing (computing intensive). The results are startling when you consider the number of cues needed to reach the decision (a low number for Take the Best and Take the Last and a high number for the other three).

Here’s a chart showing relative performance for this particular case study:

As you can see, Take the Best and Regression Analysis are very similar in performance. This means if you pick the right Heuristic to use for the situation you can save time and resources and still get the performance that is comparable for the trade off (time and energy).

So what does this mean? Sometimes it’s the difference between life and death.

man is rushed to a hospital in the throes of a heart attack. The doctor
needs to decide quickly whether the victim should be treated as a
low-risk or a high-risk patient. He is at high risk if his life is
truly threatened, and should receive the most expensive and detailed
care. Although this decision can save or a cost a life, the doctor does
not have the luxury of extensive deliberation: She or he must decide
under time pressure  using only the available cues, each of which is,
at best, merely an uncertain predictor of the patient’s risk level. For
instance, at the University of California, San Diego Medical Center, as
many as 19 such cues, including blood pressure and age, are measured as
soon as a heart attack patient is admitted. Common sense dictates that
the best way to make the decision is to look at the results of each of
those measurements, rank them according to their importance, and
combine them somehow in to a final conclusion, preferable using some
fancy statistical software package.

Consider in contrast the simple decision tree below, which was designed
by Breiman and colleagues to classify heart attack patients according
to risk using only a maximum of three variables. A patient who has  a
systolic blood pressure of less than 91 is immediately classified as
high risk – no further information is needed. Otherwise, the decision
is left to the second cue, age. A patient under 62.5 years old is
classified as low risk; if he or she is older, the one more cue (sinus
tachycardia) is needed to classify the patient as high or low risk.
Thus, the tree requires the doctor to answer a maximum of three yes/no
questions to reach a decision rather than to measure and consider 19
predicators, letting life-saving treatment proceed sooner.

To wrap up, the book has many interesting essays as chapters, ranging from bicycle races, hindsight bias, ants, mate selection, and bargaining. It’s a solid 365 pages with small font. The math and the science can be dense, but the applicability of the results are real. It doesn’t sugar coat what goes into making heuristics worthwhile – a lot of up front analysis. It does however show how powerful those paths or decision trees can be once they are implemented.

Gerd Gigerenzer has other books that are probably more digestible for the heuristically curious (Gut Feelings: The Intelligence of the Unconscious and Calculated Risks: How to Know When Numbers Deceive You) but if you’re into behavior and why particular decision paths are more economical than others, then this book is a good educational read.

Other Reviews:
The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices – A Book Review / How Pleasure Works – A Book Review / Why We Make Mistakes – A Book Review / Drive: The Surprising Truth About What Motivates US – A Book Review / Rules of Thumb – A Review / I Hate People – A Review / The Job Coach for Young Professionals – A Review / A Review of The Fearless Fish Out of Water: How to Succeed When You’re the Only One Like You / A Quick Review of Johnny Bunko (a manga story)