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 HBR.org.

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
persuasive.

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.

http://www.kaltura.com/index.php/kwidget/wid/_203822/uiconf_id/1898102/entry_id/1_bukfpvkn/

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.

Simple Heuristics that Make Us Smart Cover

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:

Recognition
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.

A
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)

March 2011 Jobs Report and Wages

Here are the job market and compensation numbers for March 2011 (based on the job report):


Net gain
of 216,000 jobs in the month

  • Analysts expected an overall gain of 192,000
  • Private sector payrolls increased by 230,000
    • Private service producing industries added 199,000 (152,000 last month)
    • Goods producing industries gained 31,000 (70,000 last month)


  • February was revised to a gain of 194,000 from an original reading of 192,000
  • January was revised to a gain of 68,000 from a revision of 63,000 and an original reading of a 36,000 gain
  • Payroll processor ADP reported an employment gain of 201,000 jobs
    • 49% of the 208,000 ADP reported gain came from small business (firms with less than 50 employees). It was 46% last month
    • The last four months average an increase of 211,000 jobs. The prior four months saw an average increase of 74,000 jobs


  • 6.1 million people have been jobless for more than 6 months (long term
    unemployed) – up from 6.0 million last month
    and down from 6.5 million in March 2010

    • 45.5% of the unemployed
      are long term unemployed

  • Employers
    announced plans to cut 41,528 jobs in March. It was 50,702 jobs in February and 57,724 in March 2010

Unemployment rate dropped to 8.8%

  • Analysts predicted it would remain at 8.9%
  • Since November 2010 the unemployment rate has dropped 1%
  • The labor force
    participation rate is 64.2% (66.5% is average to good) – unchanged
  • The employment to population ratio is 58.5% – up from 58.4%
  • The
    U-6
    report, which is a broader group to count (workers who are part
    time but want to be full time and discouraged worker), dropped to 15.7% from 15.9% last month and from 16.7% in December 2010
  • PMI,
    a measure of manufacturing pace, is 61.2% and the 22th consecutive
    month of readings over 50 percent. Anything above 50% means the
    machines are running
  • Service
    sector activity dropped to 57.3%, down from 59.7% last month. An unexpected drop when compared to other improvements. It was the
    16th straight month of growth

Specific Segment Job numbers:

  • Manufacturing gained 17,000 jobs
  • Construction lost 1,000 jobs
  • Retailers lost 17,700 jobs
  • Leisure and Hospitality Services gained 37,000 jobs
  • Government sector lost 14,000, all state or local
  • Education and Health Services grew by 45,000 jobs
    • Health Care and Social Assistance grew by 44,500

  • Professional and Business Services grew by 78,000
    • 28,800 jobs gained in Temporary Help

Wage (can be revised):

  • The
    average weekly paycheck (seasonally
    adjusted) is $648.48 –
  • The average hourly earning (seasonally adjusted) is $19.30 – down 2 cents from last month
  • Average
    weekly hours and overtime of production and nonsupervisory employees on
    private nonfarm payrolls by industry sector, seasonally adjusted is
    33.6 hours, up slightly from 33.5 last month and 33.4 in January 2011
  • 6.3% of hourly-paid workers in Pennsylvania earned the minimum wage or less in 2010
  • 9.5% of hourly-paid workers in Texas earned the minimum wage or less in 2010

Bureau of Labor Statistics

The March 2011 Silicon Valley Positive Outlook Update

The focus of my last post was on whether or not I believe Silicon Valley has inflated like a 1999 bubble. I don’t think it has, but the stock valuations are still pretty high. If I were to guess, I’d say the apparent investment upswing is a byproduct of cash sitting on the sidelines. Investors have it and big companies have it. Investors plant the seeds and companies like Google buy the fruit at the first sign of flowering.

Silicon Valley is an interesting place though. Many of the companies there don’t want to be viewed as uncool. Once that happens it means a particular culture has set in. Which of these companies seem more exciting: Facebook or Yahoo? Google or Microsoft? You get the picture.

Earlier this week there was an article on cnn.com in the Tech section about the hiring on Silicon Valley. Here are some quotes and stats from the article “Silicon Valley experiencing new hiring boom” by Dan Simon:

  • Silicon Valley: 10.6% unemployment rate
  • Last month’s (March 2011) national average was 8.8%
  • Silicon Valley produced 1,200 jobs last month and expected to add thousands more in 2011.
  • According to SimplyHired.com, a search engine for job listings:
    • nearly 40% of 130,000 open positions in Silicon Valley are for software engineers
    • Since July of 2009 there’s been a 245% increase in openings that have “Facebook” as a keyword
    • Over the same time period, a 421% increase in “Twitter” job postings
  • Innovations in social media, mobile and cloud computing are driving the growth, said Dion Lim, SimplyHired’s president.
  • LinkedIn, the social-networking site for professionals, hired nearly 500 workers last year — almost doubling its workforce.
  • “As we grow the company, we’re always on the lookout for top talent,” said Jeff Weiner, LinkedIn’s CEO.

Differentiating Using Strategy and Technology

The Academy Awards were a few weeks back and the popular movie The Social Network was nominated for Best Picture. It didn’t win the award, but it did elevate Facebook into a cultural phenomenon. It’s no longer another website – it’s Facebook. People care about it like their Nike running shoes, Apple iPod, and Starbucks coffee.

Each of these brands has used slight advantages in their products to become the dominate company in the space. How or why does this happen? Well, first I’ll mention luck. It always plays a role. In addition to luck, it’s the people.

Individuals and teams within these companies differentiate their offerings. They do so within a cost structure that maintains competitiveness and they do so with an eye toward value. Most people think of value as what Wal-Mart offers. One product 10 cents cheaper than a competitor and that is true in a commodities evaluation. Paper towels are paper towels. Value becomes much more abstract when the offering – product or service – has an association related to it. Starbucks originally pulled people in because the coffee was stronger. The association was that it woke up better than other options. And Apple combats technophobia because they create electronic devices that are easy to use.

This value is marginal at first, but then it snow balls. Getting it to snow ball is the key and then building on that is paramount. Facebook used exclusivity as the differentiator and then opened up the site to ride the network effect. Now it can exploit it’s pure numbers for monetary gain.

Earlier this year Goldman Sachs in a backroom deal valued Facebook at $50 billion dollars. Valuations like this have some to speculate that there is another tech bubble. Groupon, Google, Facebook, and others are the poster children.

In the world of the internet, small differences in your products can be the difference in sinking or swimming. Because of that Silicon Valley is leading the way in an escalating war for tech talent. Google is offering $20,000 more than average to the people they’ve targeted. Some firms are teaching their employees how to be entrepreneurs. In Silicon Valley it’s an inevitability, might as well make it a perk.

Do I think its a new tech bubble? I don’t. How engineers are using the internet now is very different than 15 years ago. Now it’s used to implement strategies that were inconceivable just three years ago. New approaches can separate and new technology can accelerate. What goes into the making of a Best Picture? It’s more than just film, it’s artistry.

Teacher Pay and Motivation: What is Fair?

There are a lot of people hurting as this recession drags on. At least six million people have been without a job for more than six months. There’s anger.

And there’s resentment. Currently teachers are the target and it means a review of their total compensation. Pay, health benefits, pension, time off, and tenure are all seen as unfair in the face of the constant rhetoric of how US students are falling behind on international test scores. Logic says: poor test scores = poor performance = a loss of jobs. If you’re one of the six million people without a job that’s the bitter pill you’ve swallowed.

Although the focus is often on teacher pay, I don’t think that’s the case. The real angst is for tenure. And teaching is one profession that I don’t feel like pay is as important as it is in other industries. The sparkle in a kid’s eye as they figure out multiplication is the seminal motivator of most good teacher.

I’ve stated in other posts that I feel tests are over utilized as a measurement for teachers. Tests should be used to reinforce weak areas for kids development, but it should be coupled with something that kids produce. Creating something involves many more levels of learning, whether it’s creativity and problem solving or math and engineering, and that should be used to judge teachers.

Below is an interesting 60 Minutes report that highlights pay as a performance element for teachers:

http://cnettv.cnet.com/av/video/cbsnews/atlantis2/cbsnews_player_embed.swf

February 2011 Jobs Report and Wages

Here are the job market and compensation numbers for February 2011 (based on the job report):


Net gain
of 192,000 jobs in the month
(Revised in March to a gain of 194,000)

  • Analysts expected an overall gain of 190,000
  • Private sector payrolls increased by 220,000
    • Private service producing industries added 152,000
    • Goods producing industries gained 70,000


  • December was revised to a gain of 152,000 from a revision of 121,000 and an original reading of 103,000
  • January was revised to a gain of 68,000 from a revision of 63,000 and an original reading of 36,000 gain
  • Payroll processor ADP reported an employment gain of 217,000 jobs
    • 46% of the 217,000 came from small business (firms with less than 50 employees)


  • 6.0 million people have been jobless for more than 6 months (long term
    unemployed) – down from 6.2 million last month
    and 6.4 two months ago

    • 43.9% of the unemployed are long term unemployed – up from 43.8% last month (the overall population count has changed resulting in one number improving positively, but another appearing to be negative compared to last month)
  • Employers
    announced plans to cut 50,702 jobs in February, a subdued number but a year over year increase (42,090 in Feb 2010)

Unemployment rate dropped to 8.9%

  • Analysts predicted it would rise to 9.1%
  • The unemployment rate dipped below 9.0% for the first time 21 months
  • Last month there was an oddity of a low increase in jobs but a large drop in unemployment rate. After further inspection this is the result of an unusual squeeze of the components to this equation Number of people in the workforce (civilian labor force) – number of people with jobs (employed) = number of unemployed people.
    • The civilian labor force shrunk a little more than a normal drop with people dropping out of the labor force and the number of people with jobs increased a touch resulting in a significant drop in the unemployment rate
    • The “Not in Labor Force” number rose by 2.4 million people from February 2010 to February 2011

  • The labor force
    participation rate is 64.2% (66.5% is average to good) – unchanged
  • The employment to population ratio is 58.4% – unchanged
  • The
    U-6
    report, which is a broader group to count (workers who are part
    time but want to be full time and discouraged worker), dropped to 15.9% from 16.1%.
  • PMI,
    a measure of manufacturing pace, is 61.4% and the 21th consecutive
    month of readings over 50 percent. Anything above 50% means the
    machines are running
  • Service
    sector activity rose to 59.7%, up from 59.4% last month. It was the
    15th straight month of growth

Specific Segment Job numbers:

  • Manufacturing gained 33,000 jobs
  • Construction gained 33,000 jobs (lost 32,000 so an even start to the year)
  • Retailers lost 8,100 jobs
  • Leisure and Hospitality Services gained 21,000 jobs
  • Government sector lost 30,000, all state or local
  • Education and Health Services grew by 40,000 jobs
    • Health Care and Social Assistance grew by 36,200

  • Professional and Business Services grew by 47,000
    • 15,500 jobs gained in Temporary Help (lost jobs last month after several months of gains)

Wage (can be revised):

  • The average weekly paycheck (seasonally adjusted) is $647.56 – an increase of $1.94 and a $3.35 positive change from December, 2010. $19.08 gain in the last year (there’s been low inflation so this is good)
  • The average hourly earning (seasonally adjusted) is $19.33 – flat from last month
  • Average
    weekly hours and overtime of production and nonsupervisory employees on
    private nonfarm payrolls by industry sector, seasonally adjusted is
    33.5 hours, up slightly from 33.4

Bureau of Labor Statistics

An Economic Transition – Negotiate It

    Jurgis talked lightly about work, because he was young. They told him stories about the breaking down of men, there in the stockyards of Chicago, and of what had happened to them afterwwards – stories to make your flesh creep, but Jurgis would only laugh. He had only been there four months, and he was young, and a giant besides. There was too much health in him. He could not even imagine how it would feel to be beaten. “That is well enough for men like you,” he would say, “silpnas, puny fellows – but my back is broad.”
    Jurgis was like a boy, a boy from the country. He was the sort of man the bosses like to get hold of, the sort they make it a grievance they cannot get hold of. When he was told to go to a certain place, he would go there on the run. When he had nothing to do for the moment, he would stand round fidgeting, dancing, with the overflow of energy that was in him. If he were working in a line of men, the line always moved too slowly for him, and you could pick him out by his impatience and restlessness. That was why he had been picked out on one important occasion; for Jurgis had stood outside of Brown and Company’s “Central Time Station” not more than half an hour, the second day of his arrival in Chicago, before he had been beckoned by on of the bosses. Of this he was very proud, and it made him more disposed than ever to laugh at the pessimists. In vain would they all tell him that there were men in that crowd from which he had been chosen who had stood there a month – yes, many months – and not been chosen yet. “Yes,” he would say, “but what sort of men? Broken-down tramps and good-for-nothings, fellows who have spent all their money drinking, and want to get more for it. Do you want me to believe that with these arms” – and he would clench his fists and hold them up in the air, so that you might see the rolling muscles – “that with these arms people will ever let me starve?”

This is the beginning of Chapter 2 of Upton Sinclair’s The Jungle. Jurgis Rudkus, an immigrant looking for the American Dream – opportunity, is confident in his physical strength. He has an ability for stockyard work.

The Jungle is often cited as the catalyst for work reform in the US. It was published in 1905 and the industrial revolution was picking up steam; the transition from an agricultural capitalism into a manufacturing one was well underway.

Here we are a little over a 100 years later and another transition is under way. The economy is moving from being goods producing to services and intellectual based. And we are experiencing a fundamental change in the relationship between employer and employee. For instance unions were an off shoot of what Sinclair set in motion. Unions, or collective bargaining, raised the standards for total compensation for all workers. Health care, vacation, and pay levels all improved.

But in today’s age, unions have a dramatically smaller participation rate and there seems to be a general animosity towards them. I can reason for the low participation rate: they’ve served their purpose and are not seen as needed. But the animosity is sort of bewildering to me. I suppose it’s because of the handling of terminations. There’s a notion that someone in a union can’t be fired. For the most part that isn’t true. But I understand it portrays an unfair situation. We, as Americans, believe the best should be rewarded. And the opposite is true too: those that don’t perform are let go.

As I mentioned before, we are transitioning to a different nature of our economy. In a goods producing economy, unions play an important role because the difference in work performed is small. But in a intellectual economy the difference between someone who designs a new microprocessor chip and someone who monitors the ripeness of apples at the grocery store is vast. Should the two jobs only be differentiated by pay grades? Are health care, vacation, and other benefits a given? At one point they were, but with competition being so tough, they are all up for review.

In the long run, it’s tough to review cuts to benefits without the inclusion of the employee. The job structure of the economy assumes certain consistencies. Skills are acquired based on those consistencies – Wall Street pays well, so Harvard graduates go to work there and teaching doesn’t pay well, but it affords flexibility and continued learning opportunities.

From a business perspective, it’s always better to negotiate. Whether it’s with your suppliers or your  workforce. It’s the American Dream.

Your Greatest Weakness

I’m the type of person who relies on metaphors and analogies. It’s just the way I absorb information. So as the sun shone on my face this past weekend, I couldn’t resist comparing the first warm up of the season to the optimism of a reborn employment market. Just like Chance the gardener said in Being ThereIn the garden, growth has it seasons. First comes spring and summer, but then we have fall and winter. And then we get spring and summer again.”

With hiring thawing out, the inevitable uptick in interviews will commence and we’ll see more media stories about the topic. For instance, over to HBR.org Priscilla Claman has a great blog entry called The Worst Interview Question (and How to Answer It). The focus of the writing is on the question:

 “What is your greatest weakness?”

The question from an interviewer standpoint is intended to show how the interviewee handles uncomfortable interactions. If an interviewee has prepared well, then it’s hard to gauge whether the interviewee can perform when unknown circumstances come up, which is bound to happen in the workplace. This type of awkwardness can paint the picture of how this person would react.

But as noted in the blog article, there’s downsides to the question. The first is that it can be embarrassing. And starting off a relationship with embarrassment is not usually a good idea. There’s lots of movies like this. The second is that strengths and weaknesses change depending on the culture and function the person is involved with. For instance, I love analogies is that a weakness? It depends. Because of this grey area interviewees create work around answers like “I’m a workaholic” so they don’t paint themselves into a corner.

However, as the blog states, there are a few good ways to reply. Check
out the cheesy xtranormal video I created this weekend while messing
around for an example.

 

 

http://www.xtranormal.com/site_media/players/jw_player_v54/player.swf

Working Thoughts 2/15/09
NatGeo Has Me Hooked Lately

Working Thoughts 2/15/08
Teachers Who Have the Creative Freedom to Teach