Gary Becker and Stephen Dubner discuss organ donor markets, applying market price to immigration, US economy

I’m posting here a link to a discussion between Freakonomics co-author Stephen Dubner and Nobel Prize Winning Economist Gary Becker conducted on ExpertInsight.com last week. It’s a 30 minute discussion, amazingly packed with thought provoking questions and discussions. In particular I found very interesting the first video segment which focuses a lot on a market for organ donation and the second video which discusses opening up immigration to anyone who would pay an immigration fee (of say, $50,000).

As I watched the videos the key thread is applying markets to solve both economic and social problems. Obviously this should come as no surprise who know Becker’s work and also know that he is a University of Chicago economist. The free marketers have taken a beating over the last couple of years due to the financial meltdown, and it would be a shame of the average person or the politicians take their mistrust for markets too far, as some of the ideas Becker has recently proposed free market solutions to make a lot of sense. In the first and third segments of the interview he talks about applying a free market/pricing to the organ donation market. I’m sure this would dramatically increase the supply and also improve matching in the market as Becker notes.

He also proposes that a price be set to immigration, something like $50,000 to immigrate to the US; and that loan programs be put in place to allow immigrants who benefit from the better opportunities to pay some of that back to the US. He notes that this would both reduce objection to immigration from the public (because it will provide tax revenue) as well as making immigration more beneficial by bringing on average higher-skilled immigrants.

He mentions among the greatest achievements of economics the move towards free trade, the notion that price controls are bad, and the realization (though many thought the opposite for a time) that communism would not succeed as an economic system.

One thing I find distinctly absent in the conversation is the concept of equity aside that of economic efficiency. I think the work of economics in general has done a much better job in solving the problem of creating economic efficiency than they have in that of economic equity. The easy response to that is to solve for efficiency and then redistribute, but redistribution tends to be a much harder political task (at least in the US, and more certainly across international borders).

I’ve personally always thought of the current format of US immigration policy (or lack thereof) as a form of wealth distribution from the US to poorer countries, as our formal international aid is so meager (remittances end up being a form of international aid). Trade barriers are a very inefficient way (but still a way) to create a more equitably compensated labor force (though this may not benefit the poor/working class overall if they pay more for the protected goods). I think most economists feel more comfortable discussing economic efficiency than equity is that they can all agree that a bigger pie is better, and how the pie is cut up is a more philosophical discussion. Still – we know that only the very upper echelons of society in the US are better off now then they were 30 years ago, even with tremendous economic growth. If I had my 30 minutes with Gary Becker – I think I’d ask him about what level of equity he thinks is fair, and the best way – from both an economic theory/policy and political standpoint – to achieve it.

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Applying Sports Performance Analytics to CEO Compensation

While many people complain about athletes being overpaid – and certainly the make a lot of money – at least in the case of baseball it would be hard to find too many professions where players’ net value was more carefully scrutinized with data. Given the tremendous amount of performance data and analysis, we would have to imagine that baseball players, at least on a relative basis, are fairly compensated – at least to the extent we can use their past performance to predict future performance.

One of my favorite concepts in baseball is VORP – Value over Replacement Player. The idea behind is it pretty simple and powerful. It asks the question, how much value are we getting from our current starting player compared to a “replacement” level player –e.g. minor leaguer at the same position. The point of the analysis is we should make our compensation decisions on the output gap between two players rather than the overall output of the players.

Ever since I have come across this concept, I have been trying to dream up ways you could apply it outside of sports. What if we could somehow measure the contribution a CEO’s or executive team made to an organization and then could compare that to the contribution of say a mid-sized company (500-1000 employees)? There were 100 CEOs with total annual compensation over $15 million in 2009/10. So my question is, if I took the top-100 managers I could find willing to work for $1 million/year, would I significantly degrade the average performance of these 100 companies? Does the VORP of these CEO’s justify an aggregate compensation of $2.25 billion (these top-100 earned $2.35 billion) over the $100 million I would pay the hundred best qualified “replacement level” managers?

I’m asking this question not because I know the answer, but because I don’t. It’s undoubtedly complex to manage corporations that are global, have billions of dollars of revenue and in some cases 100s of thousands of employees. It’s easy to argue that in a company with billions of dollars of revenue, making a mistake and underpaying a CEO to save money would be foolish and would make saving $15 or $20 million seem insane.

And that, in the end, is exactly why executive salaries are as high as they are. It’s the same principal that firms like Goldman use in the IPO market – who wants to get petty over a few million in a billion dollar transaction when the downside to not going with the best is so large? Many-a-house in the Hamptons were built on this principal, to be sure.

I would argue that low wage workers are subject to the same types of data driven scrutiny that baseball players are. The simpler the task, the more easily we can measure performance for it. Data-driven measures (customer satisfaction, absenteeism, productivity) are increasingly used on the low-end of the wage scale in promotion and firing decision, which directly impact compensation (going up upon promotion, going to zero upon firing). To be sure for line working and middle management positions, there is a huge supply of potential “replacement players,” to replace workers who don’t meet performance criteria.

What could be keeping the wages as high as they are at the CEO level is likely viewing the supply of those capable of being a Fortune-500 CEO very narrowly; Boards of Directors are typically very risk averse, and would be quite unlikely to hire a person who is not proven at the highest levels of corporate management. This assessment of the supply of capable executives, may or may not be correct.
What we would need to accurately assess a VORP for CEO’s are two things: an accurate assessment of top CEO’s current “value” to their company and a way to identify and predict the performance of the “replacement level CEO.”

I think to assess a CEO’s value the most broad measure we could construct is some measure of industry adjusted long-term total return to shareholder. This study here (http://hbr.org/2010/01/the-best-performing-ceos-in-the-world/ar/1) does exactly look at this measure of industry adjusted TRS. We could get one clue about market efficiency of compensation just by looking at excess TRS vs. total compensation in a scatter plot. If they seemed to have a strong positive correlation then we would know the market had some efficiency to it. That wouldn’t answer our question totally, but it would be a start. In fact, there have been a multitude of studies looking at CEO performance vs. compensation and there is only weak evidence for positive performance (see here for example http://www.gsb.stanford.edu/news/research/compensation_daines_ceopay.shtml). What I have yet to find is one that tries to calculate the excess pay being wasted – it would likely be an underestimate anyways since we haven’t even considered the wider market for “replacement level” CEOs.

To do a good job of understanding the potential to fill roles with replacement value players we could also find the industry adjusted performance of small company CEO’s (I bet company size and CEO compensation are quite correlated) and then see how much industry adjusted TRS our lower group is contributing vs. their compensation.

Of course then we would need “league adjustment factors” to understand how much performance is degraded by moving up from…

Defending Jalen Rose (Sort of)

There’s been a bit of an uproar and an ensuing series of television interviews and editorials written over the last week about Jalen Rose’s “Uncle Toms” statement in the documentary “The Fab Five.” I’ve been following this topic not only as a basketball fan, but as a person who attended Michigan at the same time as Jalen Rose and the Fab Five. I copied the quote here as they have stated it in a Forbes article:

“I hated everything I felt Duke stood for,” Rose said in the documentary, describing his feelings as a 17-year-old high schooler. “Schools like Duke didn’t recruit players like me. I felt like they only recruited black players that were Uncle Toms.”

Rose’s use of the derogatory phrase Uncle Tom to describe a whole generation of black basketball players at Duke has caused widespread outrage, as it should. Grant Hill, one of the Duke players of the Fab 5 era, blasted back with an editorial of his own, and others such as ESPN’s Michael Wilbon have responded as well. I think these two quotes sum up the response:

“The notion that there is one definition of “Blackness” is insidious and dangerous and too often promotes the notion that athletic achievement is “black” and academic achievement is “white.” – Michael Wilbon

“To hint that those who grew up in a household with a mother and father are somehow less black than those who did not is beyond ridiculous” – Grant Hill

Let me be very clear on my personal opinion here: Jalen Rose was wrong for referring to anyone as an Uncle Tom, this is a loaded and derogatory term that no one should use to refer to anyone. There should also be no doubt in anyone’s mind (and I would guess Jalen Rose feels this way as well) – that looking down on academic achievement is stupid and counter-productive. This topic is not new in the media or in the African American community, and it’s an important dialogue that needs to continue. But there’s an important issue that’s being overlooked in the national discussion and I want to examine it a bit more.

The title of Michael Wilbon’s article is “Grant Hill and Jalen Rose ‘Ain’t all that Different.” The article goes on in the article to tell about the many similarities between the two, and there truly are many. But let me ask this question, if you had asked 100 people in 1991, if Grant Hill or Jalen Rose were more likely to be an executive producer and a TV analyst, how many of them do you think would have answered Jalen Rose? I My point here is that people often jump to conclusions based on superficial information that just are not right. Unfortunately, in life, many important outcomes are often heavily influenced by such superficial judgments.

I think what’s getting lost in the shuffle here is what may be Jalen Rose’s deeper underlying point. Grant Hill would have gone on to a successful life in some field if he weren’t a talented basketball player. He might not be as wealthy or famous as he is now, but his capabilities, intelligence and motivation, along probably with the financial support he would get from his parents if needed, would have landed him somewhere. But what about Jalen Rose? With his single-parent upbringing and Detroit Public Schools education, how would he have fared without basketball? In a way, Duke’s choice of Grant Hill instead of Jalen Rose is important in Jalen Rose’s mind because it’s a metaphor. If Grant Hill – as an African-American – already had an uphill battle to climb, Jalen Rose had a steeper one.

This issue of racial relations and fairness is something that has been in the background of my life since I was a kid. I grew up part of my childhood in Benton Harbor, MI and part in St. Joseph, the two towns featured in Alex Kotlowitz’s “The Other Side of the River,” a book that is mostly about race relations between one town that is almost all white and affluent (St. Joseph) and another which is almost entirely Black and poor (Benton Harbor). Today I live in Oak Park, IL, probably one of the most racially integrated places in the entire country; but my house is just 1 block from Chicago’s Austin neighborhood, which is almost entirely African American, with high rates of unemployment and poverty. As I drive through Austin to work, I’m reminded on a daily basis of this uphill battle that kids who come from places like Jalen Rose face.

The fact that Grant Hill and Jalen Rose ARE so similar today should be a very important lesson to us all: you can’t judge a book by its cover. The intrinsic capabilities, motivation and intelligence of a person are not just about how many parents they have and how much money they had growing up. If Duke only recruits African-American players with a certain profile (I will let the reader judge this), then Jalen Rose certainly has every right to call them out on it – not because Duke is placing too much value on the profile it does recruit but too little on the profile it doesn’t. More importantly, whether the…

Why you shouldn’t read the NY Times for oil market information

Earlier this week, one of the lead economic correspondents from the New York Times tweeted a story from the NYT with the headline “US Economy is Better Prepared for Rising Gas Costs.” (http://www.nytimes.com/2011/03/09/business/economy/09gasoline.html). On its face, it seems reasonable enough – we’ve been facing high oil prices during much of the last decade, so to a certain extent we should have adjusted behavior.

The article cites a few facts and anecdotes, some of which counteract this point and some where the factual information has little basis. First, the authors note that many drivers have “given up their gas guzzling sport utility vehicles. Automakers, which are selling more fuel efficient cars than five years ago, reported higher sales in February even as gas prices rose.” The writers even go on to cite cash-for-clunkers as a factor later in the story. The problem with this logic should be obvious: there are over 250 million passenger vehicles in the US, and cash-for-clunkers retired a whopping 690,000 of those or, about 0.2%. The truth is that cars stay on the road for around 15 years, so the increase in fuel efficiency of new cars over the last couple of years has barely made a dent in the average stock efficiency.

Since the stock efficiency has not changed enough to make a difference – consumers have three choices in response to higher prices: drive less, reallocate income towards oil (and away from other goods), or dis-save. Next you get a few anecdotes from the NYT, telling “precisely” how consumers have changed their behavior: Tival Williams, has apparently dumped his SUV for a Mazda CX-9 and is saving $30 a week, we are told. But we don’t hear about the person who bought Tival Williams SUV; apparently he was happy about the bargain he got on it, and is using the money he saved on the deeply discounted SUV to pay for the extra gas it burns.

Next in the NYT anecdote arsenal is Ronnie Undeberg, who has started “planning errands” – this is known in the energy demand world as trip chaining, and is a strategy to reduce gasoline consumption. Trip-chaining, along with car-pooling and reducing discretionary travel are ways that consumers always respond to oil price increases in the short-run. Without doubt the last decade of oil price run-ups must have made us collectively better at changing our behavior in response to high oil prices than we used to be. But we should not equate this to the US economy as a whole being less susceptible to rising gasoline costs, as the article title implies. As a whole, consumers have historically only been capable of changing driving behavior enough to absorb at most 20% of the price increase in the short-run. So that along with the couple percentage points of increased stock efficiency through cash-for-clunkers and other inefficient vehicle retirement still leaves a heavy price burden that consumers have to accommodate somehow.

The mechanism for absorbing the remaining 80% of the price increase burden is through dis-saving and expenditure shifts, and the same Ronnie Undeberg tells us that is exactly his next step, stating that he’d scale back his cable television and cut his cellphone use if gas went up to $4 per gallon.

Dis-saving is not an option for a current portion of Americans right now, so like Ronnie, many people will be reallocating expenditures. Because of this, more money will be reallocated from cellphone and cable television and likely many other goods and services with a significant domestic component. This reallocation of expenditures to oil must slow down the recovery (there are some positive secondary impacts due to petro-dollar cycling, particularly into the capital account, but these also can’t entirely counter-act the expenditure reallocation. See more here: http://www.newyorkfed.org/research/current_issues/ci12-9/ci12-9.html)

There’s an additional factor that I think may worsen the impact of the current price increase on our economic recovery. The unemployment rate is currently still over 9%, and for the unemployed a higher % of their driving has become discretionary and thus the reduction in travel component must be a factor in holding down current oil demand. I ran a quick regression and found that unemployment rates over 6% combined with increasing oil prices serve to reduce passenger vehicle travel (VMT) by 50% more than high prices alone. So during this recession where prices are rising, US consumers have likely curtailed demand more than they would have if unemployment had been lower. As hiring picks up over the next year or so, the newly employed will have to reactivate their gasoline purchases; thus the virtuous cycle between increased employment and increased consumer spending may be dampened by the high gasoline prices. The recovery will be more sluggish.

The NYT article seems to understand this, quoting an economist who says “high oil prices always hurt our economy” and making the point that “higher fuel costs reduce consumers’ discretionary income, which is often spent on such niceties as dining out or the latest electronic devices.” Somehow, these points become not central to the main thesis of the article.

So is the US economy better prepared for high oil prices? From what I can see, the economic impact of high prices is going to be just as bad, it may just happen more quickly since consumers have practice in reacting to high prices, and a…

Moneysoccer: coming to a pitch near you (hopefully)

Tomorrow and Saturday, I’ll be attending the Sloan Sports Analytics Conference in Boston – for those of you haven’t heard of it, it’s a conference where stat geeks all get together to mingle and give presentations on the recent advances in sports statistics. I’m going to post updates on the more interesting presentations at the conference over the next couple of days.

The area with the most buzz right now seems to be basketball – with 8 out of the 20 research presentations/posters accepted being about basketball (this may also have something to do with the fact that the conference is the brainchild of Daryl Morey – the stats-centric GM of the Houston Rockets).

The most popular sport in the world – soccer has only one paper accepted. Its topic is a bit flimsy by advanced statistical analysis standards, it’s on the impact of high altitude on match outcomes. Compare this to the state of play in baseball, and it’s safe to say soccer is light years behind.

It’s easy to understand why – baseball is so simple compared to other sports with most confrontations being one-on-one (batter vs. pitcher, fielder vs. batted ball, etc). From this standpoint, baseball statistics are easy to collect, even in realtime, and any stat geek can basically gain access to reams of data, free-of charge. The biggest advances of late have been in fielding statistics, which require another layer of data collection, but still relatively straightforward.

Soccer – on the other hand – is faster moving and flowing, with so many interdependencies. Many people have written about the difficulties of applying advanced statistical analysis to soccer, there’s an interesting post on this subject at the soccer blog Run of Play here. (http://www.runofplay.com/2009/01/12/two-kinds-of-faulty-statistics-in-football/)

After reading Moneyball and a few Sabermetrics tomes, I, like many people, started thinking about how advanced statistical techniques could be applied to sports beyond baseball, like soccer and American football. I, of course came to the same conclusion as everyone else: the data wasn’t there. But in some ways, it didn’t really make sense. With all the money in sports, how could the data NOT be there? Why wouldn’t someone did whatever it took to collect it? The costs must outweigh the benefits, I was sure.

Coincidentally, I am on the board of a social enterprise called Digital Divide Data, which has the mission of helping the world’s poorest by creating sustainable IT jobs. DDD has been looking for ways to grow its impact by creating more jobs for the underprivileged in Southeast Asia. Lightbulb! What if we could train people to watch soccer matches and collect unbelievably detailed statistics? As soon, as the idea was born, I couldn’t turn back.

StatDNA, the startup I now run, started officially in early 2010. We’ve trained 30 people and have had them analyzing in incredible detail the entire 2010 Brazilian Serie A and B leagues, with each game taking around 25 hours of analysis. We’ve also moved on to the English Premier League more recently and have over 20 million pieces of data in our database. Other companies do collect advanced soccer statistics, but no one can hold a candle to the amount of information we are collecting. We’ve collected data on things that matter like defensive pressure, shot power and passing trajectories, just to mention a few, that have never been collected before.

We know our data’s not perfect – soccer is a difficult game to capture- but it’s definitely a step in the right direction. And we view this just as the first step. We’re just about to finish our first set of advanced analytics, and we’ve developed some pretty interesting concepts like “GoalEx,” the equivalent of RunEx from baseball. Goal expectancy is statistically modeled on every touch of the ball and players are given credit for their contribution to goal scoring.

We have our own blog at blog.statdna.com and twitter statDNA, where we will post our findings over the coming months. We’re also hoping to light a fire under the soccer analytics world. While all advanced soccer statistics are currently proprietary, we are providing free of charge access to over 300 games of data for soccer researchers, and are awarding a StatDNA research prize this fall. Let’s see if next year, SSAC has a few more soccer papers.

As I go to SSAC with high hopes, I’m reminded of one of the central tenets of Moneyball: how long it took for sabermetrics to become mainstream in baseball. Hopefully, baseball has removed some barriers for the later adopting sports.
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