Archive for Economics
I’ve received several calls from reporters over the past few weeks to discuss a phenomenon that’s sweeping baseball: the “dynamic pricing” of tickets. In general, this means charging different prices for the same seats for different games. This may involve charging a higher price for rivalry games, weekend games, or the latest fad charging more for game-day purchases. All of these adjustments are designed to generate more revenue for clubs by varying the price of the ticket according to changes in fans’ willingness to pay for games.
I can see how fans might find such policies repugnant. A seat is a seat, the stadium’s in place, the salaries are set, and workers have been hired. What justifies the price increase? Joe Eskenazi expresses his frustration with the Giants dynamic pricing policy
The oft-quoted model for the new, likely soon-to-be-ubiquitous baseball pricing system is airline ticket purchasing. It’s almost certain readers have experienced first-hand the joys of last week’s $300 tickets this week being priced at $410. It’s a strong incentive to buy early before myriad contrived supply-and-demand factors are tossed into the algorithm and you end up paying through the nose. As noted before, inducing people to spend quickly and pinging those who do not is good business sense.
On the other hand, it just seems downright wrong that you should be made to pay more for a baseball game because it’s a “great day for baseball.” It seems exploitative that you should be made to cough up extra dollars when Tim Lincecum is on the mound; will we be given a deep discount when Zito is pitching or Pablo Sandoval takes a day off? Further following the airline model, will we be charged extra for using the restroom? Do clean seats cost more? Do I have to pay extra to stay out of the all-felon, all-drunk, all-jerks talking loudly about work on their iPhone section?
The act of charging different prices for different units of identical items is known to economists as price discrimination. While discrimination has pejorative connotations, in this sense the term merely describes the act of charging different prices according to different willingnesses to pay. There are several conditions that must be met for price discrimination to work, and baseball teams meet them all pretty well. And while successful price discrimination definitely increases profits, it also has the benefit of increasing output. For baseball, this means more, and sometimes cheaper, baseball for fans.
How does charging more for premium games benefit fans? For the fan who was previously able to buy a ticket for $20 who must now pay $25, that fan is certainly worse off. But, if he values attending the game at $25 or greater, then all that has been lost is consumer surplus—the difference between what a consumer is willing to pay and what he/she actually pays. That loss to the consumer is offset by the gains to the seller. If we’re not picking sides, the world has the exact amount of surplus as it used to have; all we’ve observed is a transfer of surplus from one party to another. It’s easy to see yourself as the fan who’s ticket price has gone up and be pissed about it. But, I’m not really all that sympathetic. People are paying a price for a product they value at that price or higher, I’m not seeing a downside. You used to be able to buy something you valued more for less, and now you have to pay a higher price that is still equivalent to or less than what you value the product. And when the product is a baseball game, cry me a river in the name of social justice.
But, that’s not the reason why price discrimination is a good thing. If you want to take sides with the fans paying a higher price, I’m not going to stop you. The blessing of charging different prices for a product is that it allows more units to be sold at a lower price. In a world were a seller chooses only one price for a product, it must be the one where it maximizes the gains from selling a few units at a high price and selling many units at a low price. Where that price occurs it’s going to result in some people paying prices less than they value the product and some having to pay more than they value the product. The former group will continue to purchase the product, but the latter group is priced out of the market—this is very bad.
Son: Hey dad, will you take me to see a baseball game? I’ve never been.
Dad: Sorry son, tickets are expensive and we can’t afford that right now.
Son: I don’t care what team we see. I just want to go to experience the ballpark. I’ll go see the Royals play on a cold night.
Dad: It doesn’t matter what the weather is or who’s playing. Tickets are tickets, and we can’t afford them right now.
But with price discrimination, those marginal fans have the opportunity to go to games when other fans value them less. (What you’re upset that poor people can’t go to big games? When world poverty is eliminated come back to me and we can discuss the moral importance of assuring poor people the right to see important games.) Fluctuating prices don’t just mean higher prices, they result in lower prices as well. The team now has the freedom to charge lower prices without losing revenue from all the fans willing to pay higher prices when games are in high demand.
If dynamic pricing goes away, so do the cheap seats. If you have to choose one price to maximize profits, it’s going to be one that prevents a lot of fans from going to the games, and that is a bigger tragedy in my mind.
Craig Depken links to an interesting article on the economic impact of the Super Bowl at Division of Labour. It’s your usual discussion of economic benefit claims with private consulting firms claiming $100s of millions in benefits, with economists arguing these numbers are grossly exaggerated. Interestingly enough, the consulting firm hired to do one of the studies discussed refused to comment.
But Craig highlights a quote that really sticks out to me.
Advocates of the Super Bowl as an economic engine dismiss its academic skeptics as using complicated formulas to obscure the obvious. And they note that the reports bashing NFL figures bring the professors coveted media coverage as the big game approaches.
“It’s dangerous to say these games don’t generate economic impact,” said Robert Canton, a director in PricewaterhouseCoopers’ Hospitality and Leisure practice who focuses on sports and tourism.
“It’s not logical,” he added.
So, let me get this straight. Professional teams that earn billions of dollars of public subsidies based on the notion that the sports events and venues generate otherwise uncapturable benefits are honest brokers of the truth, while academic economists can’t be trusted because they’re glory hounds. That’s the kind of logic that brings us multipliers of five.
Yesterday, I received a call from a reporter working on a story regarding the economic impact of a minor league baseball team. These calls have become rather routine, but yesterday’s call was a bit more interesting.
The reporter, Louis Llovio, was checking into a claim made by Richmond Flying Squirrels executives that the team would generate as much as $40 million a year in economic impact to the community. I’m accustomed to exaggerated claims of non-existent benefits, but $40 million is outrageous. To put this in perspective, that’s more than double the $15 million that Gwinnett County officials claimed the Gwinnett Braves would generate, and I’ve been quite clear about that number being a farce.
Here is my response.
“That is so laughable I don’t know what to do,” he said of the prediction of $40 million in annual economic impact.
Bradbury said economic-impact studies are based on faulty assumptions.
Those projections “assume people are spending money they wouldn’t otherwise be spending” on movies, dinner or other forms of entertainment, he said.
“What the fans are doing is relocating money from other entertainment” to the baseball team, he said.
That means the money is already in the local economy and would have stayed there regardless of a sports team.
He also said that most minor-league players don’t live in the area and their salaries are relatively low, so they don’t pump much into a local economy.
No sweat. Almost as if it came from a can, time to say good-byes and move on. But, I couldn’t hang up without asking how the executives justified this estimate, and I heard the following.
That figure, he said, is based on a Minor League Baseball formula that takes the amount of revenue generated by the organization and multiplies it by five.
At which point I literally bent over double laughing. Count gross spending as net new spending and multiply it by five! I asked for clarification, “Did you say ‘five?’” No wonder. $8 million is ridiculous enough without a multiplier of five. I’ve seen a lot of PR studies that use multipliers greater than one (for which there is no empirical justification) and most are less than two—even Gwinnett used 1.7—but five? Wow! As the kids would say, ROTFLMAO.
If the Flying Squirrels are angling for a new publicly-funded stadium, such claims aren’t going to build much trust with the public. They wouldn’t stand for it with the Braves, and I don’t expect them to start now.
One interesting feature of the database is that it includes some salaries from far back in baseball’s history. The database is not comprehensive (nor should it be expected to be) but the data that is there provides an opportunity to analyze salaries at a key times in baseball history.
For example, in 1914, the Federal League began play as a competing major league. It lasted for two seasons until reaching an agreement with the American and National Leagues. During its two years of operation it raided AL and NL rosters for its players, ignoring the reserve clause that was keeping salaries below the competitive level. Most of the jumpers left in 1914 (80 players), with 16 additional players joining the FL in 1915.
The agreement between the AL and NL created a monopsony for baseball talent, because each team represented a single buyer for its player’s services. The FL added competition for baseball talent, and even if players didn’t jump, their wages were likely to raise from the threat of jumping. While I don’t think anyone would disagree with this in theory, it would be nice to observe this effect. Someone may have examined this before, but I haven’t seen it.
Though the salary data is not comprehensive, it’s possible to track players who play over consecutive seasons to see how their salaries changed from the previous year. Salaries are affected by many factors; however, by tracking percentage-changes for individuals, player quality is approximately constant. I looked at a ten-year sample from 1910–1919, tracking an average of 16 players per year (ranging from 9 to 28 players). The figure below maps the average annual changes over the sample.
Before the Federal League became a major league, the AL and NL showed healthy salary growth, which is consistent with their average annual attendance growth of 4.5% from 1901–1913. During the FL’s inaugural season in 1914, there was a drastic spike in salary growth. 1915 also showed a 27% rise in salaries, which is the third-greatest change in the sample. In 1916, after the league disbanded, salaries rose only a paltry 7%; and in 1917, salaries fell by 3%—the only negative year in the sample. In 1918 and 1919, salary growth was 16% and 11%, muted compared to what it was before and after the entrance of the FL.
Thus, the available evidence is consistent with economic theory. New competition raised player salaries, and after the competition went away (buying off the owners most likely to start a new league) salary growth was depressed.
Thanks to Maury Brown and the other folks over at The Biz of Baseball Network for making this tool available.
(Source for the historical info: Quirk and Fort, Pay Dirt, pp. 313–319)
It seems that I have upset a few people with one of my interview answers at Chop-n-Change, involving a paper by Jahn Hakes and Skip Sauer. Here’s a brief response that covers the criticism the paper has received (cross-posted in the comments).
1) The goal of Hakes and Sauer was to test the Moneyball hypothesis that OBP was undervalued relative to SLG; hence, the title of the paper.
2) This test must include OBP and SLG in the model. The concept can be broken down and testing further, which they did, but what is interesting is if this central tenet of Moneyball is true. The exercise is not about designing the perfect model for predicting salaries. I vividly recall discussing this fact with the authors at the time the paper was written when I asked them about alternate specifications of the model. They responded that they had done this and this analysis would be a part of another paper, which it was, but were focused on Moneyball itself for this exercise. This then creates the problem of adjusting for playing time. This could be controlled for in ways other than plate appearances (e.g., interaction terms), but the authors ultimately decided the parsimony of their specification made it the right choice. Adding in the impact of all sectors of the labor market is another tough issue. Ideally, you would like to separate the labor classifications, but they are trying to estimate the market price for the entire labor market—reserved and arbitration-eligible players are a part of that market. So, they include dummies to act as a control. Again, interaction terms or some other correction could have been used, but they felt that their final specification was best. And they were able to convince many other economists (colleagues, editors, and referees) at different levels of review that what they produced was the best choice.
3) The goal of the study was to identify if the market was out of whack at the time the book was written. The findings indicate the pre-Moneyball models don’t predict as well as the post-Moneyball sample based on what we would expect them to be. That is a point in favor of the paper, not an objection. Furthermore, in 2001 the labor market was especially out of whack, and I find it odd that it was the specification chosen for close examination. The regression equation was designed to pick up information from real-world data, the values are not something presupposed by the authors. The coefficient on OBP is negative—higher OBP lowers your salary. You don’t need to plug in any values to see that this is counter-intuitive. Part of the reason why the salaries remain so stable when Tangotiger adjusts the inputs is that the higher value for OBP cuts into the impact of SLG. As Hakes and Sauer acknowledge in the text, the coefficients on OBP are not even statistically significant—the market appeared to be ignoring the relevance of OBP at the time. That’s their argument.
4) So, the Hakes and Sauer papers may be imperfect, joining the ranks of every other empirical study ever written. If you think you can do better, here is a solution. Take the freely available data and run alternate specifications. As it stands, the critique is that the perfect is the enemy of the good. If further testing reveals the labor market was not out of whack, then we have an argument.
Economist Michael Davis has some interesting thoughts on the recent Olympic vote (my thoughts on the vote here). He demonstrates that with some fairly general assumptions about voters’ preferences that Chicago may have been barely defeated. In fact, just making it to the second round might have been able to propel it to victory in a head-to-head contest with Rio.
The key to all of the above possibilities is that they are consistent with the revealed preferences of the voters (with the exception of the three voters whose votes are changed from Tokyo to Chicago). It is also consistent with the geographical solidarity that the voters seem to exhibit.
The scenarios where Chicago ultimately succeeds are probably not the most likely, as there seems to have been a lot of sentimental support for Rio. Scenario 4 seems the most likely to me, but a variation of scenario where Chicago barely beats Rio in the final round certainly seem plausible to me.
The problems with voting and cycling of outcomes in democracy have been studied for some time by economists. If you are interested in some of the analysis, I suggest starting with the work of Kenneth Arrow and Duncan Black.
Why were the odds so awry on the 2016 hosting city? I had assumed Rio would win in a walk, and yet, as shown in the following figure, Chicago was the favorite among oddsmakers.
I normally wouldn’t have much to say here, but I just happened to be following the prediction market on Friday morning quite closely. I was scheduled to go on CNBC to talk about the economic impact of the Olympics if Chicago was elected. It takes me about an hour to get to the studio, so I wanted to know the likelihood of making the trip, and I became fixed on the market for the morning.
Around 9 am, a friend sent me this picture of the InTrade betting market on who would get the games.
The odds show Chicago to be the favorite with a 53% chance of winning, closely followed by Rio at 46%, Tokyo at 3%, and Madrid at 2%. Like all the pundits following the selection were saying, it was a race between Chicago and Rio, but was very close to call. These odds also show something else, Chicago was trending down and Rio was trending up. The trend would continue for the next few hours. And I happened to record these changes on my Twitter/Facebook pages.
10:50am: “Intrade Olympics futures market has Rio rising and Chicago falling to a dead heat.”
11:17 am: “Intrade futures market: Chicago shares selling for 30, Rio 53.”
About 10 minutes later, Chicago was out. Looks like useful information was leaking out from knowledgeable parties just before the vote. This is evidence for, not against, the strong-form of efficient markets hypothesis.
But, then we have the question: if Chicago had such high odds, how did it get knocked out of the running first?
[Update 8/3/2009] A few people have asked why I think the odds were all that awry, when they implied that Chicago was only a slight favorite over Rio. True enough, but Chicago went out in the first round, Tokyo in the second, and Madrid in the third, implying that Chicago was the fourth choice of the IOC among the finalist cities, not a slight favorite for first.
This can be answered by the voting mechanism used and the coalitions favoring each region. Here are the vote tallies by round.
City Rd. 1 Rd. 2 Rd. 3 Rio 26 46 66 Madrid 28 29 32 Tokyo 22 20 Chicago 18 Total 94 95 98
There is clearly a Chicago/Rio coalition here, where a group of IOC members favored these two cities over the other two cities. In the first round, Madrid had the most votes; but once Chicago was eliminated, Rio took a commanding lead. The rise in 20 votes corresponds to receiving all the 18 Chicago votes, plus the addition of the two US delegates who were eligible to vote once Chicago was out. This coalition is a natural fit because both Chicago and Rio would both serve prime-time television markets in the US to maximize sponsorship revenue. From this perspective, it’s obvious that the Madrid and Tokyo never had a shot, and that it only took a few members of the tightly split coalition to be swayed one way or the other. If five members had changes their votes, it’s Rio who’s ousted in the first round, not Chicago. It seems that the swing delegates held their cards close to their chests (or hadn’t decided yet), but something that morning—maybe it was a tie color, a handshake with key people, or a whisper—tipped off investors trading in the InTrade market. I see this as a win for prediction markets, not a failure.
Berry College economist Frank Stephenson has a nice Op-Ed on Rome’s bid to keep the NAIA football championship in Rome, Georgia.
Although there are other tourism benefits beyond lodging revenues (e.g., dining revenues), it’s hard to imagine that these effects from hosting the NAIA Championship are large when the lodging benefits are apparently miniscule. Spending more than $3 million on Astroturf and other renovations in pursuit of economic benefits from hosting the football would be a dubious proposition in the best of times. Doing so now, with many families struggling and local unemployment exceeding 11 percent, is unconscionable.
Adam LaRoche career indicates an odd performance pattern in one area. The table below reports his seasonal tOPS+, which measures how well LaRoche did in each half relative to his performance that year.
tOPS+ Year 1st Half 2nd Half 2004 85 113 2005 109 71 2006 77 126 2007 90 113 2008 84 128 2009 83 126
In five of the six years that he has played in the majors, he’s hit better in the first half than he did in the second half. This fact has not gone unnoticed by broadcasters.
I do not think that LaRoche can be counted on to repeat this pattern. In Curve Ball, statisticians Albert and Bennett look at the ability of players to repeat half-season splits and they conclude: “Players don’t generally hit any better or worse in the last half of the season than the first half of the season.” Now, this doesn’t exclude the possibility of a few players having this ability, but I think it is unlikely. I suspect that Adam’s performance represents a run in a small sample that is likely noise.
But, what if Adam is a second-half player, and a team wants him to play more like second-half Adam in the first half? How might a team structure a contract to give LaRoche the incentive to do the things he needs to do (e.g., get in shape, practice, take his medication regularly, etc.) to generate higher production. I have a simple solution: offer a big All-Star bonus. Players with strong first halves have an advantage at making the All-Star team over second-half players. Many players have All-Star bonuses in their contracts in small amounts, a few thousands dollars or so. If a full-year of second-half LaRoche is worth an additional $2 million, offer him a $2 million bonus for making the team. If he can fix the problem, it will likely be fixed. If not, you get the same LaRoche as always without having to pay the bonus.
Excellent advice from Greg Mankiw:
Suppose that an 18-year-old student comes into a science classroom and says, “My grandmother is ill with a serious, rare, and hard-to-diagnose disease. I want to become a doctor to help figure out a cure.” What should the student study? Probably not this specific disease, at least at first. The place to start is with basic biology, chemistry, and so on. Only after these fundamentals have been mastered can the student go to medical school, become a doctor, and be in a position to study the illness that motivated him in the first place. Much the same is true with the study of economics.