Mike Konczal has an interesting posting about what becomes of people who are unemployed.  I liked most diplomatic observation: “from what I’m seeing the whole idea of dropping out of the labor force from unemployment is completely under-theorized in the literature.”

I naive model might assign each person into one of three categories: employed, unemployed, other.  The first two are the labor pool; and other includes prisoners, children, the retired, the idle rich, etc. etc.  In this game the dice has three sides, in each round of the game people stand some probability of changing category.  So how is the dice weighted?  It’s worth pausing at this point and asking yourself; what would guess?

This charts shows that.

It shows the chance an unemployed person will move into one of the other two states. Each round of the game is 3 months, i.e. a quarter.  For example in 1967, the left most data point, an unemployed person stood a 45% chance of becoming employed, and a 34% chance of dropping out of the labor pool.  The third outcome isn’t shown; the i.e. the 100-45-34 = 21% chance that in the next round of the game he’d still be in the pool.

It’s amazing to me how close these two lines are.  You don’t do much damage to generalize: people exit unemployment out both exits with a 50/50 probability.  If you want a better generalization you have to look at the size of the gap; he has another chart.  You’ll notice those lines cross, but only once.

If your interested in Network Effects this article from the San Francisco chronicle is worth reading.  It outline a few ways you might be able to tackle an incumbent firm that owns the natural monopoly of a strong network effect.

First they might screw up – which we call “bad execution.”  This is the usual story for how Facebook managed to take the territory that other earlier social networks had already colonized.  Friendster and Orkut are both fine examples of failure of execution.  This has other names: lack of vision, lack of follow, etc. etc.    I believe that one subspecies of failed execution is the lack of necessary complements – it is damn hard to build a huge internet business without access to the resources that Silicon Valley has and nobody else does.

Secondly it is possible that the current entrant hasn’t managed to cover the ground; in which case you can grab up real estate and later displace them.  I suspect that for this theory to work out you need to seize higher ground.  Thus Facebook’s happen stance initial market – ivy league schools – turned out to be higher ground that MySpace’s Los Angeles party scene (plus bands & fans).

The third is an old one – vertical specialization.  Right from day one people have presumed eBay would face serious competitive threats from more narrowly focused auction sites: antiques say, or fashion, cars, etc.  While on balance this tactic does not make sense for eBay it might make sense for other networks biz.  And, it goes both ways.  For example is there a social gaming business that can exist outside of Facebook – taking traffic away from them – or on the other hand – are all the massive multiplayer games going to collapse into Facebook?

The fourth segment of the article is mostly about Group-on and it’s based on a bogus strawman.  Nuff said.  And again they don’t seem to understand how Gilt Groupe is a network effect business of a slightly different color.

At one point in the article tries to split hairs about scale effects.  They argue that Google’s search get’s better because they can learn by observing the behaviors of their crowd of users.  I don’t see that.  It is a common delusion – in my experience – that the folks who run these network businesses believe that their fine execution with it’s deep craft knowledge is the primary cause of their success rather than just a necessary part of avoiding the failure of bad execution.

Many wags like to argue the CraigsList is, sooner or later, going to fail due to an or all of these reasons.  Possibly, but lordy that prediction is a perennial.  Anyhow, the article contains this delightful slide enumerating various attempts to seize some of that territory using vertical specialization (click to enlarge).  Of course, the same chart could be made for eBay’s categories.

Gosh that looks like a roll up waiting to happen.  I’m happy that the LinkIn IPO is going to make that a lot harder.

though tacky, this was fun…

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This is an  amazingly thought provoking technique for collecting and presenting survey results.  It is just better.  Imagine, if you will, how the usual newspaper headline would summarize this data.

You could use this to survey employee moral.  Marketing people could use this to survey customers.  I find the comments very interesting; again, imagine the damage the usual newspaper article would do to that diversity of opinion.

There are lots of second order things you could do.  For example, thumbs up/down on the comments.  It would be fascinating to watch the ebb and flow over time.

You could also allow people to select ovals rather than points.

Please, somebody, add this to your survey taking site.

Pretty amazing chart this:

It shows the probability of a prisoner being granted parole depending on when during the day his parole hearing takes place. The dotted lines indicate when the parole board to a break for food.

Ok, now:  Schedule your next meeting.

Steve Waldman raises a point I’ve thought about in the context of peak oil.

Say you know only a few things:  Your king.  You have oil reserves.  The price of oil is rising fast due to peak oil, say 12% a year.    From a financial perspective you have a large asset that is returning 12% a year.  This asset is reasonably liquid, you could pump some and invest in other things.  But your only going to do this if you think you can find better assets to invest in.  These days that seems unlikely.  So you let it sit.

People like point out that as we transition across the top of the peak or peak oil the supply of oil doesn’t suddenly dry up.  It just stops increasing.  I think that misses the message in the above model.  Because what does change is the expected furture value of all oil reserves.  If the supply of oil is growing faster than the demand then prices fall, and the moment that reverse they can be confidently predicted to rise.  And the moment that happens anybody with oil in the ground is better off leaving it there.  That may not be a positive feedback loop, but it certainly a phase change.  To compound the effect; absent macro economic intervention, the phase change triggers a shape rise in prices which triggers a economic downturn which hollows out expected value of alternate investments so that hording is even more attractive.  That last sentence, of course, explains the psychology of recessions – or why all the money hides in a self destructive manner under the mattresses.

Steve at this points out that maybe you know another thing.  Maybe you, the king, know your countrymen hate you.  If you figure there is a 10% chance per year they will throw you out then your best course of action is to convert that oil into something even more liquid.  This is what happen in Russian after privatization, everybody who managed to get their hands of a high value asset thought: “Gosh this situation looks pretty volitile… i think i should try to convert this into something less risky.”  And they stripped the assets to the best of their ability and moved the money out of the country.

Generally political upheaval in oil producing regions drives up oil prices, but in Steve’s model a dose of political unrest creates an incentive for the king to increase supply.