Monthly Archive: December 2014

Many of my friends and co-workers don’t understand my concern about employment in this country.  The unemployment rate has, after all, fallen from a peak of 10% in October of 2009 to the current value of 5.8% in November of 2014 (data from the Bureau of Labor Statistics).  So, as the argument goes, what’s my problem?

Well, as I try to point out, the unemployment rate really doesn’t tell the whole story.  It is only one measure of the health of the labor market, and not necessarily a good one.  When there is a lot of economic growth, as in the eighties and nineties, the unemployment rate is a reasonable measure of the health of the work force.  When the labor market is experiencing the type of new normal that the country is currently mired in, it is a poor measure indeed.

The reason for this is that the unemployment rate measures the percentage of workers who are in the work force and are actively looking for jobs but unable to be employed to the total population in the work force.  What the unemployment rate doesn’t measure is the size of the work force relative to the population of the country as a whole.

This definition of the unemployment rate appears in both textual and mathematical form in countless articles and texts dealing with employment and the labor market.  So does the caveat about the work force participation.  However, I don’t know of a single place that illustrates these distinctions in an easy to calculate, visual form, and so I decided to supply one of my very own.

For the sake of this illustration, I am going to assume a static population, a snapshot if you will, and I am simply going to calculate percentages based on a visual representation of various components of the population.  Also for simplicity, our population will be divided into four demographics:  minors, college age, working age, and retired.  The definitions for these four demographics are:

Obviously these demographics represent a gross simplification of the actual population.  There are never as cleanly drawn lines in the real case as there are in this ideal case.  But to illustrate the point, these are close enough to make a good model for the economy and an easy model to understand the labor market.  I’ll talk a little more about the complications after the basic model has been developed.

Let’s start with the ideal world shown here

Those members of the population that hold jobs in the economy are colored green while those members who are not in the work force are in gray.  It is worth taking a moment to talk about what it means to be in the work force.  Clearly the minors and retired demographics are not in the work force because they are not allowed to hold a job.  All the members of the working age demographic have a job and so they are obviously in the work force.  The only group that requires some thought is the college age demographic.  In this ideal world this demographic is exactly split in half with two members opting into the work force and two opting to pursue higher education.   With 24 total members of the population and 18 workers in the work force, the work force participation is 18/24*100 = 64.2%.  The unemployment rate is zero since everyone in the work force is working.

Now let’s introduce some economic realism and assume that not everyone who is in the work force can actually find a job.  This more realistic world looks like

where the members of our population who want a job but are unable to find one are now colored red.  In this scenario, two workers from the working age demographic and one worker from the college age demographic are now out of a job.  The number of workers in the work force has remained unchanged at 18, and so too has the work force participation, but the unemployment rate, which is a measure of the percentage of the work force without a job, has risen from zero to 15/18*100 = 16.7%.

Finally imagine that the college age worker has decided enough is enough and he heads back to an institution of higher learning, and that one of the unemployed working age guys decides to stay home and play video games all day long.   These two members of the population have now exited the work force (colored gray with red trim to remind us that they were once in the work force), which shrinks from 18 to 16.

The one remaining unemployed working age guy keeps at it but is still unable to find a job, and the unemployment rate shrinks to 1/16*100 = 6.2%, reflecting only his frustration in not finding a job.  The work force participation now falls to 16/28*100 = 57.1%.

Clearly the population as a whole is no better off with this lower unemployment rate than it was with the earlier case with high unemployment.  In both cases, the number of employed workers in the population remains the same at 15.  These 15 are now charged with producing the goods and services that will be consumed by all 28 members.

So, does this model actually represent reality?  The unfortunate answer is yes.  While the unemployment rate has fallen, so has the work force participation.  Statistics pulled from the Bureau of Labor statistics for both the unemployment and work force participation rates result in

Of course, the US population is not static like the model problem discussed above, so maybe there is some way to reconcile a shrinking unemployment rate with a diminishing work force participation without having to conclude that there is trouble.  Also, what about all this news that says that hundreds of thousands of jobs have been created each month?

The population of the United States was growing annually at about 0.7% in calendar 2013, which is the lowest rate since the Great Depression.  Assume a base population of 300 million, which is a lower bound well below the actual value of approximately 317 million.  The number of new births each month in 2013 was 175,000.  Now assume that somehow this birth rate, in absolute terms, remained the same in perpetuity – that is to say that 2.1 million children would be born each year.  This scenario is clearly unrealistic since the growth rate is measured relative to a growing population, so to achieve this scenario we would have to have ever falling population growth rates.  In fifty years of such a growth model, the population would increase from 300 million to 402.9 million with the growth rate decreasing from 0.70% to 0.52%.  Note that this model also assumes that everyone lives to retirement.  Finally, assume that the country as a whole is content with a 62.5% work force participation rate.

None of these assumptions are realistic let alone likely, but by making them, we can calculate a lower limit on the number of new jobs needed each month just to keep the status quo.  That number, of course, is 175,000 new jobs per month to match the birth rate.  Now we can compare the actual job growth seen in the country over the time span from 2007 (the time when the country actually had about 300 million in it) to today against this 175K target.

Over this time span, a net 2.835 million jobs were created according to the BLS job creation numbers.  That’s it! Barely enough jobs were created in 7 years to cover about 1 and 1/3 years of population growth in the anemic model discussed above, and probably not even a full year under realistic population growth.  In order to get back to work force participation levels close to where we were in 2006, the last year where job growth approximately matched population growth, the country as whole would have to produce at least 14.7 million extra jobs in 2015.  That’s over 1.2 million jobs a month – a far cry from the job growth currently hovering around 240,000 a month on average for the first 11 months of 2014.

So the next time you hear news about the rosy picture of the US economy based on the unemployment rate, take a moment, breathe, and think about all those poor souls who have given up looking for a job.

A curious situation came to light over the Thanksgiving holiday this year that offers a microcosm in which to examine the role of government regulation on the free market.

The New York Times, in their Upshot column, published an article entitled ‘Taxi Owners In New York Seek Inquiry on Medallion Prices’ by Josh Barro.  In this article, Mr. Barro reports about a beef that the taxi drivers of New York have with the governmental body that regulates them.

On one side is The Greater New York Taxi Association (GYNTA), which represents the taxi industry.  One the other side is the New York City Taxi and Limousine Commission (TLC) representing the city government.  At the center of the dispute is a report that TLC published on the average price for taxi medallions.  A taxi medallion is a license that allows its owner to legally operate a yellow taxi within the city limits (from TLC website)

TLC regulates the number of medallions that are active in the market, and sets the initial purchase price when new ones become available.  All other transactions occur between businesses, who either buy or sell – much like the exchange of securities on the stock market – based on need.  As the TLC states clearly on the their website

In addition, they track them and record the price as part of their regulatory function, and make the statistics associated with these transactions a matter of public record.

According to Mr. Barro’s article, TLC’s report has listed the price for medallions as holding steady at or near the peak they reached in the spring of 2013 when, during that same period, ‘they were actually falling’.  Depending on the type of medallion being purchased, the price drops range from 17 to 25 percent.   As further evidence that something is not quite correct, Barro states:

The original report has since been pulled from the TLC website, so an independent scrub of the data and the conclusions is not possible. But in an earlier article, Barro attributes the competition from Uber and Lyft as the source for the downward pressure on the medallion prices, which had peaked at over $1,000,000 per medallion, and are now slightly above $870,000.  In addition, he provides this additional nugget on how the TLC generates its statistics:

The rule just listed should disturb anyone with even a rudimentary exposure to statistics.  With a peak asking price at a million dollars, a $10K fluctuation in price is approximately one percent.  To put their statistical approach in more familiar terms, consider what would happen if they were charged with tracking the price of a gallon of gasoline.  The TLC would say that any price drops that were greater than 3 cents ($0.03) would be ignored.

Is there a way to try to understand why the commission does its statistics in this fashion?  To answer this, I started by making a simple survey of the data available on medallion transfers for the calendar year 2014 (Jan-Nov).

During that time span, the TLC lists 69 transfers of individual medallions along with a brief explanatory note on some of them, ostensibly to explain ‘out of family’ prices.  It is accepted practice when statistically analyzing a population to exclude outliers, and in certain months this seems to be needed.

For example, the May 2014 record lists 10 transactions, with 7 of them at approximately $1000K, 1 at $880K and 2 others well below those values.  One of these two was a medallion sale for $525K with an explanatory note of “Partnership Split” and the other, which sold for just under $81K, bore the note “Selling 10%”.

It is reasonable, when trying to appraise the fair market value of the asset, to exclude such outliers where the transaction may have been associated with a desperate situation which may have skewed the asking price from fair market (e.g., an asset had to be liquidated quickly in a “Partnership Split”).  Based on this philosophy, I removed from the data all cases where the transaction price was well out of family.  This left 62 sales distributed unevenly over the 11 months.  The monthly average is shown in the figure following, where a definite downward trend can be seen in the last 4 or 5 months reported (it is important to note that September has only one transaction).

I also tried to characterize the fluctuations in these data, and I used the population standard deviation of the 62 cases as a measure, which gave a reasonable estimation of approximately the price fluctuations at $55K.  This number is well over 5 times larger than the $10K used by the commission, and so it is easy to see how their rule of excluding from consideration any transaction that was $10K below the previous month’s average basically led to the conclusion that prices were flat.  In effect, the commission’s asymmetric approach introduces a bias into their analysis that simply ignores the statistical data from August to November.

Why might the TLC indulge in such statistical black magic?  Well, obviously to protect their own interests; but what exactly are these?  Let’s explore some of the possibilities.

The commission is seeking to protect the livelihood of the taxi driver.  This seems an unlikely explanation, as propping up the price of a medallion clearly helps the owner maintain his investment while only indirectly helping the driver to maintain his job.  The median salary for a taxi driver in New York is about $38K, which is a far cry from having the capital needed to buy a medallion, so it is unlikely that there is a high percentage of owners/drivers in the market.

The TLC is trying to maintain the regulated market in the face of the intrusion by Uber and Lyft.  There is a segment of the population who believe that preventing Uber and Lyft entry to the marker would have two benefits.  The first is that it would prevent the kind of fare-wars that erode driver pay and would protect their salaries.  The second is that Uber and Lyft are not as committed to passenger safety as they should be.  Both of these explanations also seem to be unlikely.  Uber and Lyft have made huge inroads into the driver-for-hire markets in many US cities with the tacit or explicit permission of the local governance, and so the TLC would be going rogue.  Second, there is no evidence that Uber or Lyft drivers commit felonies at any higher rate than yellow cab drivers.  In this hyper-connected world, bristling with social media, it is actually in the best interest of Uber and Lyft to have trustworthy drivers.  Also, some conventional taxi drivers must be law breakers. else why have a court ruling upholding the commission’s authority to suspend licenses of drivers who break the law.

The TLC is trying to maintain its own influence and position.  This is the likeliest explanation.  Government bureaucracies trade in their own form of wealth and capital, which is measured by the authority they wield and the favors that they curry from the industries they regulate.  GNYTA’s website suggests that their relationship with the TLC is a cozy one when they state

Of course it is in GNYTA’s interest to align itself with its regulators as long as the latter controls the number of medallions, thereby maintaining a barrier to entry for competition.  It is a classic protectionism arrangement where government and big business are partners for each other’s benefit, and the consumer is of secondary interest.  This type of arrangement is sustainable only until an external force upsets the status quo.  This is what seems to be happening with the rapid rise of Uber and Lyft, and the cracks in the relationship between the TLC and GNYTA are starting to appear.

Economics is all about making choices.

Usually, the tendency is to think of economics as being about unemployment, and inflation rates, and the stock market.  But these things are a reflection of the choices we make about who provides what products and services, and how much of each should be made available, and who gets to consume them.

As individuals and as a society, we are constantly making choices because we are limited in what we can do.  There are only so many hours in a day, only so many skills that can be learned, only so many experiences to be sampled.  So, at its core, economics is the science of making choices.

An increasingly fertile approach to understanding how people make choices and what optimal choices can be made is found in the study of game theory.  And one of the more interesting and deceptively complex games is the Prisoner’s Dilemma.

Introduced by Merrill Flood and Melvin Dresher in 1950, with polish and the name added by Albert Tucker in the same year, the Prisoner’s Dilemma (PD) is an example of a game where an individual making choices based on his own rational best-interest may actually hurt himself in the long run.

The narrative behind the game is usually quite simple and a little dull, so what I present here has been dramatized a bit to add flavor.  It is based on the introduction to the PD found in Principles of Economics: Economics and the Economy, Version 2.0 by Timothy Taylor.

Two known felons, call them Al and Bob, have just knocked over a liquor store and have made off with $5000.  They jump in their old beater of a car and rush from the scene of the crime.  As they barrel down the city streets they run a red light, smash into the back of car, and flee on foot with the money.  Owing to their bad luck, a police prowl car was nearby and the officers begin to chase them.  Al and Bob duck into an alley where they toss the money in a sewer drain minutes before they are apprehended by the cops.  As the police are charging these two with hit-and-run and reading them their Miranda rights, the radio starts blasting out an APB describing two men wanted in the liquor store hold-up.  Since Al and Bob match the description, the officers haul them to the police station where each is held separately until they can appear in a line-up.  Unfortunately, the store owner is unable to positively identify either of them and, hoping to loosen their tongues so that they would confess, the police try the following tactic.

Keeping Al and Bob in separate rooms, unable to communicate with each other, the police send in one of their toughest cops, Detective Taylor.  Taylor confronts Al first and lets him know that the police have him dead-to-rights on the liquor store caper and on the hit-and-run and that, all told, Al is facing 8 years of hard time.  Al spits back that all the police have on him is the hit-and-run and he can do the 2-year stint in the county jail (seems Al has been down this road before, if you’ll forgive the pun).  Smiling, Taylor tells Al that Bob is making a deal and, in return for him naming Al as the mastermind behind the robbery, Bob’s going to get out in 1 year while Al does the full 8-years.  Taylor urges Al to not be a sap and to confess.  He tells Al that if he owns up to his crime and implicates Bob as his accomplice, the DA will cut his prison time and send both him and Bob to jail for 5 years.  Taylor says he’ll give Al twenty minutes to think it over and he leaves the room, ostensibly to let Al sweat but really to make the same speech to Bob.

Having been in and out of police stations, court rooms and jails most of his adult life, Al recognizes that the only hard evidence the police have is that Al and Bob were involved in a hit-and-run.  The police are probably not even sure who was driving.  So if he keeps his mouth shut they can’t touch him on the liquor store robbery.  However, he also recognizes that if Bob bites on the deal, there will be enough evidence to convict him of both crimes.  He also knows that Bob is reasoning the same way.

Okay, what should Al or Bob do?  They choices are summarized in the following table.

Clearly, if Al is looking out for his own interests, then the best deal he can make is if he betrays his partner, confesses his involvement, and Bob stays quiet, through either a sense of loyalty or a gamble that Al is staying quiet too.  However, Bob will be pursuing his own interests and he is also likely to confess, betraying Al in the same fashion.

Here is the Prisoner’s Dilemma in all its glory.  It is an example of a social dilemma where one-sided betrayal gives the best results for the betrayer but in which cooperation gives the best results for the group as a whole (cooperation gives 4 years total compared with 9 and 10 years for the one-sided and two-sided betrayal scenarios, respectively).

Of course, the story of Al and Bob is contrived, but it contains key elements of real social dilemmas that affect us all.  And, in fact, the PD has been widely studied and applied in diverse areas such as biology, psychology, sports, and diplomacy.  Any situation where the payoff for cooperation is less than the payoff for one-side betrayal usually presents this situation.  Mathematically, this amounts to any situation where the table above can be translated into

where  the largest payoff  (L) > cooperative payoff (C) > mutually-betrayed payoff (M) > sap payoff (S).

One real world example of this is the OPEC oil cartel.  Each member of the cartel has entered into an agreement with the others to hold production to a particular quota so that each can reap sizable profits.  Each nation also knows that, without warning, one member could either raise production or lower price to boost its earnings – thus betraying the others.  Also, each nation knows that, if two or more of them engage in this behavior, then they all suffer.  Each of us is involved in this game, since oil prices affect prices for all the goods and services we consume.

Another, and perhaps more important, example of the Prisoner’s Dilemma is the situation of the Free Rider problem discussed last week in the context of the Plymouth Colony.  As a citizen in Plymouth Colony, my cooperative payoff (C) is an ample ration of food that the Colony provides, which is made up of some of the food I diligently grew on my farm along with some of the food my neighbors also diligently grew on theirs.  My sap payoff (S) is a smaller ration of food that results when some of my neighbors shirk their responsibility and grow little or no food and my contribution has to be stretched over more mouths. My largest payoff (L) is a ration of the same size as I got for (S) but this time, since I am the one who grew nothing, I am effectively getting free food.  Since I didn’t have to work for it and, indeed, I may not be working at all, I can eat well on the smaller ration as long as other people are the saps. Finally, my mutual-betrayal payoff (M) is a tiny ration, not enough for me to avoid chronic hunger.  It results from most or all of the Plymouth citizens basically betraying each other.

During the years 1621-22, the Plymouth Colony actually engaged (without knowing it explicitly) in a real life Prisoner’s Dilemma with real life consequences.  Many of them died and all went at least a year and a half barely surviving.  They resolved the Prisoner’s Dilemma by eliminating the cooperate box and making all property private.  This eliminated the Free Rider problem and resulted in a bountiful feast in the autumn of 1623.