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Tyler Cowen

NFL player bankruptcy

Posted by Tyler Cowen Dec 14, 2015

Originally posted on September 20, 2009.

 

The ever-excellent Mark Steckbeck offers up a quotation from Yahoo:

The 78 percent number (i.e., 78% of NFL players go bankrupt within two years of retirement) is buoyed by the fact that the average NFL career lasts just three years. So, figure a player gets drafted in 2009, signs for the minimum and lasts three years in the league: He will have earned about $1.2 million in salary. Factor in taxes, cost of living and the misguided belief that there will be more years and bigger paydays down the road, and it becomes a lot easier to see how so many players struggle with money after their careers end.

Originally posted on August 14, 2009.

 

Bruce Bartlett sends me a link to this interesting paper:

How large are the economies of scale of living together? And how do partners share their resources? The first question is usually answered by equivalence scales. Traditional estimation and application of equivalence scales assumes equal sharing of income within the household. This paper uses data on financial satisfaction to simultaneously estimate the sharing rule and the economy of scale parameter in a collective household model. The estimates indicate substantial scale economies of living together, especially for couples who have lived together for some time. On average, wives receive almost 50% of household resources, but there is heterogeneity with respect to the wives’ contribution to household income and the duration of the relationship.

The data are from Switzerland, in case you are wondering, not the United States.

Originally posted on October 13, 2009.

Our estimates imply that every death of a helmetless motorcyclist prevents or delays as many as 0.33 deaths among individuals on organ transplant waiting lists.

 

Here is the paper and I thank Brent Wheeler for the pointer.  So should we mandate or tax the use of such helmets?

Tyler Cowen

Car Alarm Externalities

Posted by Tyler Cowen Dec 14, 2015

Originally posted on December 3, 2009.

 

According to the Census Bureau, New Yorkers are now more bothered by false car anti-theft alarms than by any other feature of city life, including crime or bad public schools. If you ask me, it is the whooping ones that are worst of all, it is hard to stay overnight in Manhattan without hearing one. It also appears that the alarms do not hinder theft. First, most are false alarms and everyone now ignores them. Second, thieves have learned how to disable the alarms quickly.

 

False alarms also can make a community more dangerous, by signaling to everyone either that a) theft is common, or b) no one cares, or both. It becomes common knowledge that the community has poorly defined property rights. Here is the full story on this aspect of the problem.

 

Alternative technologies (see also this paper on positive externalities created by Lojack by Steve Levitt and Ian Ayres) do a much better job of stopping car theft. You can buy a silent pager that communicates the theft only to the car owner. This one works only for tough guys, though, if I knew that my (insured) car was being stolen, I would run the other way. Better is the “silent engine immobilizer,” which simply shuts down the engine when a thief is tampering with the ignition. General Motors and Ford are now using this technology.

Originally posted on August 21, 2009.

 

Brian Skinner writes:

 

Optimizing the performance of a basketball offense may be viewed as a network problem, wherein each play represents a "pathway" through which the ball and players may move from origin (the in-bounds pass) to goal (the basket). Effective field goal percentages from the resulting shot attempts can be used to characterize the efficiency of each pathway. Inspired by recent discussions of the "price of anarchy" in traffic networks, this paper makes a formal analogy between a basketball offense and a simplified traffic network. The analysis suggests that there may be a significant difference between taking the highest-percentage shot each time down the court and playing the most efficient possible game.

 

Here is some additional explanation.  I thank Michelle Dawson for the pointer.

Alex Tabarrok

Teaching PPP

Posted by Alex Tabarrok Dec 11, 2015

Originally posted on March 24, 2012.

 

Nick Rowe has a good method for introducing the concept of PPP:

1. I ask the class for a student volunteer. The student has to come from a country I know next to nothing about, and that has its own currency.

2. I ask the student for the name of the currency used in his home country, and he answers (say) “shillings”.

3. I then try to guess the exchange rate between the shilling and the Canadian dollar. I don’t have a clue. Nor does anyone in the class, except the volunteer. (Any student who is from the same country, or has visited it recently etc., is not allowed to guess).

4. I then ask the volunteer to tell me the price of a dozen eggs (or a cup of coffee, or whatever) in his home country. He tells me.

5. I remind the students that a dozen eggs costs about $2.50 in Canada. We then all have a second attempt to guess the exchange rate. For example, if the student says that a dozen eggs costs 10 shillings back home, I guess that the exchange rate is 4 shillings to one dollar.

6. The student then tells us the exchange rate, and we see how close our second guess is.

It usually works quite well. Because:

1. About half the students figure out PPP by themselves, and can explain it to the other half.

2. They learn that a theory can be false, but still useful. Our second guess using PPP is never exactly right, but it’s a lot better than our wild first guesses.

3. They learn the difference between a conditional forecast (the second guess) and an unconditional forecast (the first guess).

4. If our guess based on PPP is wrong (which it always will be, to some extent) I ask the student (rhetorically) why he doesn’t buy eggs where they are cheap, load up his suitcase with eggs, and sell them where they are dear, whenever he flies between Canada and home. That teaches students both the equilibrating mechanism behind PPP, and the limitations of PPP in a world with transportation costs and other restrictions on trade.

(Last time I tried it, my second guess failed badly. But a couple of other students Googled the price of eggs in the home country, and said it was very close to PPP, and much lower than what the student said. Maybe he rarely bought eggs himself. Maybe I should try another good that students frequently buy. I could use the Big Mac Index, but I don’t think that works as well pedagogically. After all, McDonalds is one company, and maybe they just choose to price that way.)

Originally posted on November 25, 2009.

 

The monetary regime has changed and, as a result, many people are misinterpreting the recent increase in the monetary base.  Paul Krugman, for example, posts the picture:

 

benbase_2.png

 

His interpretation is that the tremendous increase in the base shows that the Fed is trying to expand the money supply like crazy but nothing is happening, i.e. a massive liquidity trap.  (Krugman is not alone in this interpretation, see e.g. this post by Bob Higgs).  Thus, Krugman concludes, Friedman was wrong both about monetary history and monetary theory.

 

Krugman’s interpretation, however, neglects the fact that the monetary regime changed when the Fed began to pay interest on reserves.  Previously, holding reserves was costly to banks so they held as few as possible.  Since Oct 9, 2008, however, the Fed has paid interest on reserves so there is no longer an opportunity cost to holding reserves.  The jump in reserves occurred primarily at this time and is entirely under the Fed’s control.  The jump in reserves does not represent a massive attempt to increase the broader money supply.

 

Here’s a bit more background.  When no interest was paid on reserves banks tried to hold as few as possible.  But during the day the banks needed reserves – of which there were only $40 billion or so – to fund trillions of dollars worth of intraday payments. 

 

As a result, there was typically a daily shortage of reserves which the Fed made up for by extending hundreds of billions of dollars worth of daylight credit.  Thus, in essence, the banks used to inhale credit during the day – puffing up like a bullfrog – only to exhale at night.  (But note that our stats on the monetary base only measured the bullfrog at night.)

 

Today, the banks are no longer in bullfrog mode.  The Fed is paying interest on reserves and they are paying at a rate which is high enough so that the banks have plenty of reserves on hand during the day and they keep those reserves at night.  Thus, all that has really happened – as far as the monetary base statistic is concerned – is that we have replaced daylight credit with excess reserves held around the clock.  The change does not represent a massive injection of liquidity and the increase in reserves should not be interpreted as evidence of a liquidity trap.

 

Addendum: (For the truly wonkish.)  If you want more, see my earlier post on excess reserves, posts by Jim Hamilton, and David Altig, and especially two very useful Fed articles, Keister, Martin, and McAndrews (n.b. the last section) andEnnis and Weinberg.

Originally posted on April 22, 2010.

 

An intuitive illustration of the velocity of (cash) money can be found on the website, Where’s George? Where’s George lets users enter the serial number of a bill and in this way track the bill as it circulates around the country.  Here’s a picture of one bill’s travels.  At the time of posting, this particular bill had traveled 7,293 Miles in 2 Yrs, 85 Days, 2 Hrs, 19 Mins at an average of 8.9 Miles per day. More information on when and how this bill was spent and received can be found here (including some slightly risque but potentially amusing notes from one bill receiver.) In a loose sense, Where’s George lets us see the velocity of cash by tracking how quickly cash moves from one person to another but the picture is incomplete since we only track the bill when someone enters its serial number.

 

billmap.gif

Originally posted on October 5, 2009.

 

No. Real business cycle theory is alive and kicking.  If we write Y=a*F(K,L) and call "a" technology then an RBC theory is mostly about how fluctuations in "a" change output.  Amusingly, Brad DeLong calls this the great forgetting theory of recessions and indeed it is hard to see how we could forget about technology, thus reducing output in some periods.  But this view takes the term technology too literally.

 

I am in my office every day (L=1), my computer is here every day (K=1) but my output and thus my productivity fluctuates.  Why?  It's not that I forget how to use STATA.  Some days, however, a reporter calls and distracts, another day I need to tidy my office, on other days creativity just doesn't strike.  In short, everyone recognizes that at a micro-economic level productivity fluctuates a lot so why should macro productivity follow a smooth process?

 

In fact, there is a standard answer to that question which is the law of large numbers--spread idiosyncratic productivity shocks across many firms and in the aggregate volatility will be low.  In an important paper, The Granular Origins of Aggregate Fluctuations, Xavier Gabaix takes on this answer with a simple but important point: large firms matter.

 

In the United States, for example, sales of the top 100 firms account for about 30% of GDP.  (The share is even larger in most other developed economies.)  In fact, we know from my GMU colleague, Robert Axtell, that firm size follows Zipf's law.  As a result, large firms get larger in larger economies so that firm-level productivity shocks do not disappear in the aggregate even in large economies.

 

Gabaix shows theoretically that combining idiosyncratic shocks and Zipf's law for firm size can produce significant fluctuations in GDP.  Empirically the difficulty is to distinguish aggregate shocks from firm-specific or sectoral shocks.  Using one plausible, but no doubt debatable decomposition, Gabaix shows that idiosyncratic shocks to the top 100 firms can explain about one-third of aggregate volatility.

 

The bottom line is that Gabaix has opened the way for a much richer real business cycle theory in which real shocks can be identified and tied to specific firms and through transmission mechanisms these real shocks can affect the aggregate economy.

Originally posted on October 30, 2009.

 

As we went to press with Modern Principles: Macro we kept having to add zeroes to Zimbabwe’s peak hyperinflation rate and move it up the table of world leaders.  In our final revision, Zimbabwe’s inflation rate had hit 79,600,000,000% per month putting Zimbabwe in second place.  We wondered whether in our  second edition Zimbabwe would overtake the all time hyperinflater, Hungary (1945-1946) at 41,900,000,000,000,000% per month, but it was not to be.  As it turned out, we went to press just as the hyperinflation peaked and Zimbabwe’s currency ceased to exist as a medium of exchange.  Steve Hanke at Cato has the end of the story:

Ashes are all that is left of the Zimbabwe dollar — a remnant of paper money. During Zimbabwe’s hyperinflation, foreign currencies replaced the Zimbabwe dollar in a rapid and spontaneous manner. This “dollarization” process was legalized in late January 2009. Even though the Zimbabwe paper money remnant circulates alongside foreign currencies, its real value is tiny, its use is limited, and its value against the U.S. dollar is cut in half every two days.

Zimbabwe failed to break Hungary’s 1946 world record for hyperinflation. That said, Zimbabwe did race past Yugoslavia in October 2008. In consequence, Zimbabwe can now lay claim to second place in the world hyperinflation record books.

 

Final Postscript: In 2009, Zimbabwe’s central banker, Gideon Gono, was awarded the Ig Nobel prize, not, as expected, in economics but in mathematics for, in the prize committee’s words, “giving people a simple, everyday way to cope with a wide range of numbers — from very small to very big — by having his bank print bank notes with denominations ranging from one cent ($.01) to one hundred trillion dollars ($100,000,000,000,000).”

Originally posted on November 24, 2009.

 

A famous paper in economics showed how cigarettes became a medium of exchange in a POW camp (even leading to booms and slumps depending on Red Cross deliveries).  For a long time cigarettes were the money of choice in American prisons as well but today, according to a great piece in the WSJ, the preferred medium of exchange is mackerel.

There’s been a mackerel economy in federal prisons since about 2004, former inmates and some prison consultants say. That’s when federal prisons prohibited smoking and, by default, the cigarette pack, which
was the earlier gold standard. Prisoners need a proxy for the dollar because they’re not allowed to possess cash. Money they get from prison jobs (which pay a maximum of 40 cents an hour, according to the Federal Bureau of Prisons) or family members goes into commissary accounts that let them buy things such as food and toiletries. After the smokes disappeared, inmates turned to other items on the commissary menu to use as currency…in much of the federal prison system mackerel has become the currency of choice.

 

I loved this point which raised the possibility of significant mack seignorage.

…Mr. Muntz says he sold more than $1 million of mackerel for federal prison commissaries last year. It accounted for about half his commissary sales, he says, outstripping the canned tuna, crab, chicken and oysters he offers. Unlike those more expensive delicacies, former prisoners say, the mack is a good stand-in for the greenback because each can (or pouch) costs about $1 and few — other than weight-lifters craving protein – want to eat it.

 

Thanks to Brandon Fuller for the link.

Originally posted September 29, 2010.

 

Unemployment in South Africa is now running at 24% overall with significantly higher rates for blacks. A shift away from low-skill labor combined with minimum wages and strong trade unions, however, has meant that it is very difficult to lower wages and reduce unemployment. From a very good piece in the NYTimes:

 

The sheriff arrived at the factory here to shut it down, part of a national enforcement drive against clothing manufacturers who violate the minimum wage. But women working on the factory floor — the supposed beneficiaries of the crackdown — clambered atop cutting tables and ironing boards to raise anguished cries against it…

 

Further complicating matters, just as poorly educated blacks surged into the labor force, the economy was shifting to more skills-intensive sectors like retail and financial services, while agriculture and mining, which had historically offered opportunities for common laborers, were in decline.

 

The country’s leaders invested heavily in schools, hoping the next generation would overcome the country’s racist legacy, but the failures of the post-apartheid education system have left many poor blacks unable to compete in an economy where accountants, engineers and managers are in high demand….

 

Last year, as South Africa’s economy contracted amid the global financial crisis, unions negotiated wage increases that averaged 9.3 percent [inflation is 5.1%, AT]….

Eight months ago, Mr. Zuma proposed a wage subsidy to encourage the hiring of young, inexperienced workers. But it ran into vociferous opposition from Cosatu, the two-million-member trade union federation that is part of the governing alliance [insiders v. outsiders, AT], which contended that it would displace established workers.

 

Hat tip: Brandon Fuller.

Originally posted on July 14, 2010.

 

In ples Tyler and I explain the Solow model of economic growth and show how the model can easily be run using Excel. I have also written a fun Mathematica demonstration of the Solow model.

 

You can see a quick animation of what the demonstration does by clicking “watch web preview” at the link above but anyone can also run the demo interactively by downloading a free copy of Mathematica Player. The Player is actually a stripped down version of Mathematica so what you see in the demo is not an animation but a computation of the equilibrium on the fly.

 

Many of the other demonstrations in science, math, economics and other fields are also of interest.

Originally posted on April 27, 2011.

 

The trickiest part of the Solow model is probably explaining how and why we get to the steady state where Investment=Depreciation. Scott Baier teaches at Clemson University using Modern Principles and he first teaches the model by assuming that saving is fixed at say 50. By removing one “moving part” it’s easy to show how we reach the steady state using a table. Scott’s Powerpoints make this clear. The extra step is not for everyone but it’s a nice addition to the toolbox.

 

By the way, if you have an iPad Scott also recommends OmniGraphSketcher. It’s the coolest, quickest way to draw graphs on the fly that I have seen (I mean aside from chalk!).

Alex Tabarrok

The Solow Model Videos

Posted by Alex Tabarrok Dec 11, 2015

Originally posted on November 9, 2012.

 

At Marginal Revolution University, our online education platform, Tyler and I have created a course on Development Economics. It’s a complete course and it is open to the world for free. A number of the videos from that course are also useful for teaching micro or macro principles.

 

We have four videos on the Solow model, for example. The first video introduces the model exactly as it is taught in Modern Principles. In the second video we demonstrate some of the comparative statics of that model. The first half of the second video uses only the model from MP, in the second half we introduce population growth which requires just a slight change in notation and interpretation.

 

In the third video we look at the model and data and the fourth looks at productivity. For teaching the model in Modern Principles the first two videos and the beginning (up to 2:46) of the third video will be very useful. For more advanced students, of course, all of the videos may be appropriate. Feel free to use these videos in any way that you find useful, e.g. you could assign them for homework or use the videos as a tutorial.

 

Here are the first two videos. You can find the third and fourth Solow model videos at MRUniversity in the Development Economic Course under the section Economic Growth 2.