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1 Organizational Economics: A behavioral approach

Colin Camerer, Caltech [email protected]

1/9/2018 8:39 上午

Chapter 1: Thinking like a behavioral economist

This book is about basic theories and facts underlying how companies organize workers, structure their companies, and choose business strategies, to be more productive. An important part of this process is how top managers and boards govern those companies to accomplish various goals. The ideas in this book are a new blend of economics and psychology, and a little sociology. The central approach is economic. Conventional economic theories of individual behavior rely on a preferences-beliefs-constraint system. That is, people know what they want (have complete and stable preferences) and make the best choices given their beliefs about uncertainties and constraints on time and money. (In technical language, when we say “preferences are complete”, we mean that each choice can be assigned a numerical “utility” and people are making the best choice, which is mathematically the same as maximizing numerical utility.) Extended to organizations, maximization means that workers pick jobs which provide them the most combination of happiness, financial reward, and other job aspects they value (responsibility, independence, fun, etc.). Managers anticipate what workers will do given an incentive scheme, and sort workers into the jobs they like best and make best use of their skills, and create companies which provide the most economic wealth to shareholders. Sometimes these good organizations are created by design. More typically, they come about through trial-and-error, or imitation-- a good design succeeds and is copied by other firms. Psychology enters as a way of modifying the economic theories to respect the fact that people have natural limits on their abilities to make difficult calculations, to resist temptation, and to act self-interestedly when interacting with friends and enemies. In 2 forming beliefs, people are often optimistic about the future, underestimate how long it will take to solve a hard problem, and are overconfident about their skills and good qualities compared to other people. People also care emotionally about people other than themselves (e.g., they feel envy, guilt, moral obligation, and care about how they are perceived by others), which affects how they behave at work. Judgments about how valuable, numerous, or likely things are, are also constrained by psychophysical properties and the nature of brain mechanisms. Sociology reminds us that people are “socialized”: What we want is often influenced by what others have or want; how we behave is influenced by informal norms of proper behavior, as well as by formal laws and rules; and people are linked to others in social networks which influence what they know and whose opinions and needs they care most about. “Behavioral economics” is a relatively new approach to economics which takes into account these ideas from psychology and sociology to modify economic theories. The idea is to use facts and ideas from these neighboring social sciences to improve economics while respecting the two stylistic principles which made economics successful —namely (i) use mathematics to express ideas clearly and generate insight and surprising predictions that are tested with data, and (ii) to explain phenomena (and give advice) in naturally-occurring situations. This book is intended to fill a gap between economics books which focus solely on the conventional economic model of worker motivation (e.g., Brickley, Smith and Zimmerman, 2001) and psychology books on organizational design which focus on worker motivation and satisfaction, and do not take the economic model very seriously. The goal of behavioral economics is to take the best ideas and methodologies from both approaches, to create a behavioral economics model that is precise and designed to explain patterns, but takes psychological regularity into account in a mathematical and empirical way.

1. Why organizational rules and incentives matter The essence of organization is the idea that if you take the same group of people with the same amount of money, skill, and time, the way their work is organized can make a substantial difference. Organizational economics studies different organizational practices with an eye toward finding what works best. Some of the most dramatic examples come from countries, rather than companies.1 Many studies of economic development suggest that economic and political “institutions”— the rules of how economic activity and politics work— have a huge influence on a country’s economic welfare (CITES). Many poor countries in Africa, for

1 In “Eat the Rich” (1998, Picador Press), P.J O’Rourke summarizes the argument that human resources alone do not determine a country’s wealth. He says: “Why do some places prosper and thrive while others suck? It’s not a matter of brains. No part of the earth (with the possible exception of Brentwood) is dumber than Beverly Hills, and the residents are wading in gravy. In Russia, meanwhile, where chess is a spectator sport, they’re boiling stones for soup” (p. 1). O’Rourke later (pp. 233-234) summarizes his view of the six pillars of good economies: Hard work, education, responsibility, property rights, the rule of law, and democratic government. 3 example, have stores of some of the most valuable natural resources on earth, such as oil and diamonds. (Nigeria produces 3% of the world’s oil and is the 7th largest OPEC producer; Sierra Leone produces 0.3% of diamonds.) The countries are poor, it appears, because the value from selling these natural resources does not trickle down to the poor, because of governmental corruption, and because companies and bureaucracies in those countries are not organized to align worker talents with jobs in a way that motivates them. Inefficient organizations in such countries often lead to “brain drain” in which the most talented and ambitious workers leave for other countries, if they can, where their skills are better used and appreciated. The last couple of decades of thinking suggest that three parts of the organizational design are crucial: Decision rights; incentives, and evaluation. All three have to fit together for the organization to be productive. In economic language, they are complements; in business language, they must produce synergies. Decision rights refer to the assignment of who has the authority to make decisions, when the authority has not been clearly spelled out in advance (often called “residual decision rights”). The authority to make decisions shows up most sharply when there is a dispute— when one side wants A and another wants B, and there is no rule for adjudicating the dispute that was agreed-upon, what actually happens? In many legal and political systems, for example, there is a clear system of decision rights (typically with checks and balances). In the US, for example, the President has the clear authority to nominate Federal judges. But they must be confirmed by the “advise [advice] and consent” of the United States Senate. In companies, there is usually an important distinction between nominal authority and real authority (sometimes called “formal” and “informal”). For example, in many families you would think the parents make decisions. But a screaming toddler often has the real authority because whenever the toddler screams loudly the parents do what she wants. Another example comes from universities. In universities the faculty department chair typically comes and goes with a short term (say, 3 years) while a senior administrator (typically a woman) is in her job for 10 years or more. So in practice, while the faculty department chair has formal authority, the senior administrator knows how things work, how to get things done, and authority is often ceded to her. Generally, we care about informal authority more than formal authority. But the conflicts that arise when the two mismatch are of interest too, as they can contribute to crises and inefficiencies. Incentives are the way in which evaluations determine pay and anything else that workers value— promotions, “voice” in decisions, job titles, larger offices, parking spots budgets, and so forth. Keep in mind that money is a powerful incentive because everybody wants more of it, and it has a crisp numerical measure. But other incentives matter too. Monetary pay may even erode or “crowd out” other kinds of incentives, like pride in a job well done, or the fun of being part of a team. As a result, choosing the right balance of pay and other attributes workers value is tricky. Evaluation refers to the system by which performance is evaluated. In well- functioning firms there is usually a formal component and an informal component. A formal component might be an annual review or numerical measurement of how much a 4 worker produced. The informal component is typically opinions, in the form of written letters or a meeting at which opinions are expressed. Good organizations understand that these three components have to work together. The idea is to give the right to make decisions to people who have an incentive to make good decisions, and evaluate them so that incentives are paid out for good decisions and penalties doled out for bad ones. The last paragraph sounds so bland and obvious that you might wonder why you need a whole book on this topic. The answer is that getting the assignment of decision rights, incentives, and evaluation— three different features-- to all fit together at the same time is not so easy. It is a little like juggling three balls: A juggler has to constantly be passing two balls between her hands rapidly, while the third ball is in the air. Furthermore, changes in personnel, technology, law and cultural norms mean the organization has to constantly adjust the mix of decision rights, incentives and evaluation. The best mixture might also be different for different types of workers and cultures. For example, a typical mistake in balancing decisions, incentives and evaluation occurs in multitasking, when a job requires somebody to perform more than one task. For professors, a typical example is teaching and research; for salespeople, it might be direct selling (for a commission) and training other salespeople. Incentives which reward one activity, but not another, can lead to people overproducing the incentivized task and not doing enough of the task they aren’t paid for, even though the organization would prefer the worker to do some of both. For example, suppose a fast-food restaurant asks people working at the counter, directly serving customers, to clean up when there are no customers to serve. If serving customers is more fun than cleaning up, workers prefer to linger at the counter or appear to be busy. They might implore friends to come and “visit”, essentially posing as customers so they can avoid the unpleasant duty of cleaning up. (Generally, the solution is to link incentives to the task which can be measured and monitor the other for an adequate level of quality.) Another fact about human nature that makes organization difficult is that people are different. Some people like responsibility in their job and like having decision rights. Others get stressed by tough decisions and prefer to let other people decide. Therefore, decision rights and evaluation have to be tailored to individuals to some extent. Different people are also motivated by different incentives. In this book, the default meaning of “incentive” is money. However, do not make the mistake of thinking that money is the only incentive people care about, or is even the strongest. In wartime, people risk their lives for combat pay, but also for national pride, the camaraderie of engaging in a history-making enterprise, out of a principled sense of justice, or to protect the lives of the fellow soldiers they have come to treat like siblings. In some cultures, shame is such a powerful incentive that people commit suicide rather than face shame, or kill their own relatives in “honor killings” if they have brought shame upon the family for actions that other cultures would completely forgive (such as being involuntarily assaulted, or marrying for love). These examples show how powerful emotions can be as motivators. The importance of making workers emotionally happy, not just rich— though riches probably contribute to happiness—is recognized in starting companies. 5

Entrepreneurs often describe their goal as providing a fulfilling place to work, which creates a wonderful product that makes people’s lives better. These entrepreneurs see financial earnings as the market’s way of rewarding them for making a great product, and for taking a large risk to do so. Because people are different, the right combination of decision rights, incentives, and evaluation will also depend on the people who work in the organization. This raises an important challenge of sorting—finding the right people for the right jobs. Organizational meltdowns One way to appreciate why organization matters is to study cases where organizations failed, even when they are composed of talented, energetic people. (We will see many throughout this book, and we will study successes as well.) These stories remind us of the difficulty of coordinating decision rights, incentives, and evaluation, especially when people care about different goals. For example, many adventure books describe how physically fit, mentally tough adventurers band together to accomplish some goal. Many of these stories end badly because of social mayhem, despite the participants enduring incredibly physical hardship. In Shooting the Boh (1992), Tracey Johnston describes a white-water rafting trip to a very difficult river in Borneo which white men had never rafted before. Their expedition endures terrible challenges in the form of tropical jungle diseases and the physical challenge of rafting a steep river surrounded by huge rocks on both sides through many of its swiftest rapids. In Running the Amazon, Joe Kane describes a kayaking trip from the very headwaters of the Amazon river, on a mountaintop in Peru, through the entire Amazon to the Atlantic Ocean. (His book ends with one word —“salt”— which is what he tastes on his fingers as he dips them in the ocean, after paddling in the fresh water river for months.) Both adventures end on bitter notes. (Keep in mind that these are the successful adventures-- everybody survived, and the trips were worth writing books about. Presumably the unsuccessful expeditions faced even bigger organizational challenges.) Johnston’s Borneo group bickers over physical hardship. Stress is caused by the fact that ill-prepared travelers end up borrowing routine items from others, like dry socks, which become extremely valuable in the wet rainforest. The heroic river guide, who single-handedly helps the group overcome many rafting and logistical obstacles (e.g., finding food) ends up romantically involved with a beautiful ex-model who is on the trip. Others come to think that the intrepid guide favors the ex-model at their expense, which causes jealousy and tension. Kane’s Amazon trip includes an Olympic-level paddler, and a businessman who raised money to have part of the adventure filmed. The trip goes slower than planned and the group begins to run out of money. The Olympian is frustrated by the slow paddling pace of the others. The businessman, who is the slowest paddler, holds the group back but feels no guilt because his fundraising (for the planned film) financed much of the trip. Only Kane and one other paddler finish the entire trip. Adventure trips like these are special organizations in two respects: First, they are temporary or “instant” organizations created once, to accomplish a special challenge. 6

This means that people often go into the trip not knowing much about the goals and skills of others. It also means that the threat of not doing business with somebody in the future if they don’t live up to expectations, which is often a strong incentive in long-lived businesses, has little power. Second, the expeditions require people to travel together, and are physically demanding. These trips are like a chain whose strength depends on its “weakest link”. If one person complains a lot, eats too much food, sleeps too late, or paddles too slowly, the entire group is demoralized, hungry, or slowed down. Organizations like these are especially vulnerable to design mistakes. One design mistake reflected in these adventures is pure optimism: The adventurers almost always underestimate how difficult their tasks were. This is common in business and government as well: Large building projects, for example, usually take twice as long as planned and cost twice as much.2 Another design mistake is failing to recognize how differently people are motivated. For example, the Borneo trip’s guide is certainly motivated by pay and pride. But on this particular trip, he was also motivated by personal feelings toward one of the travelers. Either the guide could not put these feelings aside— and no evaluation system was in place to discipline him—or, more likely he felt he could fulfill his obligation to the group while still enjoying his romantic time with one of the people in the group. In simple economic terms, the issue with the river guide was the extent of his working hours—during downtimes, is he still “on the clock” and obligated to deal with the group’s endless suffering, or is he “off the clock” and entitled to rest and relaxation of his own. The group thought they had the decision rights to allocate the guide’s spare time, and the guide thought he had the decision rights. This story also shows how psychology enters: Different people often have perceptions of situations which are “self- serving”, and lead them to blame others rather themselves, so that both sides end up blaming the other. Fraud: Burning down the house A useful way to appreciate the importance of balancing decisions, incentives and evaluation is in studying large-scale frauds and scandals, which often have a huge financial cost to firms (and entire industries, through reputational spillovers) far beyond the gains to the people who perpetuated them. 3 Let’s see a couple of examples and then think about what lessons can be learned from them. Daiwa: How to lose a billion dollars…slowly. Toshihide Iguichi went to work for Daiwa Bank in 1977. Daiwa was a large bank with $200 billion in assets and $8 billion in reserves (as of 1996) and a growing trading operation. For seven years, Iguichi worked in the back office of Daiwa’s growing New York government bond trading

2 [FINISH CITE LOVALLO KAHNEMAN HARVARD BUSINESS REVIEW EXAMPLES]

3The website at http://www.erisk.com/Learning/CaseStudies.asp has some good case studies, two of which are excerpted here. 7 operation, keeping track of the enormous amount of paperwork involved in reconciling accounts of a trading operation. Iguichi became a trader in 1984—authorized to buy and sell government bonds for customers, and for the Bank’s own account—but Iguichi also kept his job supervising the “custody” department (which kept track of who owned the bonds). Daiwa’s custody account was administered via a “sub-custody” account at Bankers Trust. Daiwa and its customers kept track of activity in their accounts through reports from Bankers Trust. The reports were filtered through Iguichi. Early in his trading career, Iguichi lost a few hundred thousand dollars trading bonds. He decided to sell other bonds in the Bankers Trust account to cover his losses, but falsify the reports so nobody who saw the reports realized the bonds had been sold. As he lost more and more money trading, Iguichi falsified more and more Bankers Trust reports to disguise transactions he had made to cover the trading losses. The false reporting persisted because Daiwa’s internal auditors never checked Iguichi’s false reports against Bankers Trust’s own records. Presumably they trusted Iguichi because he had worked in back-office operations for many years before beginning to trade. Iguichi’s fraud lasted not one year, or two…it lasted 11 years. During that time, Iguichi sold $377 million of customers’ securities, and $733 million of Daiwa’s securities —a total of $1.1 billion. To cover these fraudulent trades, he forged 30,000 trading slips, an average of 10 each day. In fact, Iguichi was never actually caught: He confessed. On July 13, 1995, he sent a 30-page letter to the president of Daiwa in Japan explaining what he’d done. He said he confessed because “after 11 years of fruitless efforts to recover losses, my life was simply filled with guilt, fear and deception”.4 He got tired of covering up his bad trades, and of waiting to be found out. The aftermath of Iguichi’s massive, long-lasting fraud is just as remarkable. Daiwa waited a month to tell the Japanese Ministry of Finance about the losses, on August 8. Despite a clear legal requirement to report the fraud to U.S. regulators, Daiwa actually waited another month to tell the U.S. Federal Reserve Board. (The finance minister, Masayoshi Takemura, later denied that his Ministry had failed in its reporting duties, made a veiled apology to the U.S. Treasury Secretary, then later denied apologizing.) During September, Daiwa higher-ups took Iguichi’s advice to keep the losses secret until “appropriate measures” could be taken to ensure stability after the news broke. Iguichi was told to pretend he was on vacation when he was actually hiding out in the apartment of a Daiwa manager in New York trying to reconstruct the history of his long-lasting fraud. Daiwa finally told the Feds what had happened, in mid-September. Unhappy about being left in the dark, FBI agents interviewed Iguichi on September 23. Shortly after that, Iguichi was arrested and the bank itself was indicted on 24 counts including conspiracy, fraud, and failure to disclose federal crimes. It then become apparent that Daiwa’s New York trading operation had a long history of rogue operation which was invisible to Daiwa’s top managers back in Japan. The bank had operated an unauthorized trading area for seven years, from 1986 to 1993; they even disguised as a trading room temporarily as a storage room when regulators 4 http://www.time.com/time/magazine/1997/int/970210/interview.i_didnt_set.html interesting interview 8 stopped by. Iguichi also said that from 1984 to 1987, other New York traders had created major losses which were concealed from regulators by shifting the losses to overseas affiliates. In November, 1995, the Fed ordered Daiwa to quit doing business in the U.S. A month later, it sold its U.S. assets and 15 offices to Sumitomo Bank for $3.3 billion. In February, 1996, Daiwa agreed to a $340 million fine to settle the indicted charges—a record for a criminal case. Many senior executives resigned or took early retirement. Iguichi himself was sentenced in December 1996 to four years in prison and a $2.6 million fine. The final sting was an order from a Japanese court requiring 11 current and former board members to pay the bank $775 million in damages, as compensation to the bank’s shareholders and as a penalty for the managers’ failure to report the incident promptly. An important cost from this kind of fraud is an “externality”-- the loss of reputation of people and institutions associated in people’s minds with Daiwa. Even if the fraud was the work of a single rogue (and unskilled) trader, if the capital markets think it is the tip of an iceberg, and other frauds are going on undetected, that can harm capital market credibility. Indeed, before the scandal, Japanese banks were charged a .1% premium above the London Interbank Offer Rate (LIBOR, an international benchmark for interest rates). Afterwards the premium went up to .25%. Daiwa’s corporate mistake therefore tainted other Japanese corporations and cost them huge sums in extra borrowing costs. One analyst said the problem was that the Japanese Ministry of Finance was “severely compromised” as a regulator, making it “difficult to be reassured that this is not going to happen to someone else.”5 The markets therefore perceived Daiwa’s fraud as something that could be going on in other banks, that would not be sharply regulated by the Finance Ministry, which led them to charge an extra premium just in case. What can we learn from the Daiwa case? The basic one is that Daiwa did not have decision rights, incentives, and evaluation balanced properly. Iguichi had the right to make trades independently, but was also in charge of the back-office reporting of account gains and losses, which provide numbers to evaluate the performance of his trades and those of others. Iguichi was in charge of supervising himself. Keep in mind that people who are in charge of their own evaluations do not always cheat. The sorting process, assigning traders to small offices where they run their own back-office operations, could work fine if you can find enough traders who are both talented traders and honest back-office worker bees. But as one source wrote, “Bankers who hire money hungry geniuses should not always express surprise and amazement when some of them turn around with brilliant, creative, and illegal means of making money”.6 Furthermore, Daiwa’s actions upon learning of Iguichi’s fraud made matters worse, and perhaps reflected a culture of tolerating or covering up scandal which licensed Iguichi to start covering up his own losses in the first place. Daiwa managers, and the

5 http://www.asiaweek.com/asiaweek/95/1027/biz2.html 6 The quotation is from a speech by the financial thriller writer Linda Davies on the Psychology of Risk, Speculation and Fraud, at a conference on EMU in Amsterdam. 9

Finance Minster, seemed incapable of taking quick action or admitting fault, until a lot of diplomatic and economic damage was done. A behavioral economics question is: What motivated Iguichi to commit his crimes? (Understanding his motivation might help us prevent situations like these or know how to sort for employees who are both talented and unlikely to commit fraud.) In a 1997 interview conducted in prison, Iguichi said his early actions did not feel like a crime. “To me,” he said, “it was only a violation of internal rules.” He then explains that his actions were driven by a deep desire to erase trading losses—a pattern psychologists call the “break-even effect”, or in studies of pathological gambling it is called “going on tilt”.7 As Iguichi described it: “I think all traders have a tendency to fall into the same trap. You always have a way of recovering the loss. As long as that possibility is there, you either admit your loss and lose face and your job, or you wait a little – a month or two months, however long it takes”. In fact, most traders do not fall into this trap for as long as Iguichi did. The ability to resist “chasing losses” by taking bigger risks, or covering up the losses with a back- office fraud, and making peace with losses, may be one of the traits that distinguish successful traders from those with perennial losing records, like Iguichi.

Barings: TBA

Figure: Nick Leeson (seated at right), the man who destroyed Barings Bank, talking to the press after his release from prison in Singapore in July, 1999.

7 E.g., Johnson and Thaler (19??) Management Science. FINISH 10

Most of these frauds have three ingredients: Poorly-designed rules which enable the fraud to go on without being revealed; high stakes which make the fraud worthwhile; and a moral willingness or psychological motive to commit the fraud, and sometimes embarrassment or shame which prevents the fraud from revealing his crime, and leads to desperate attempts to cover it up. Sometimes the guilty party blames others for not discovering it, or just sees the fraud as winning at a game with poorly-designed rules. An interesting question is: Are these frauds common or rare?8 No monitoring, enforcement, or regulatory mechanism is perfect. Assume that the marginal cost of monitoring rises dramatically with the probability p of detecting a fraud; i.e., the marginal cost of raising the probability c’(p) as p 1. (Mathematically, that is the same as saying that is expensive to track down the most wily and clever fraud — then there will be some frauds even if firms are optimizing.)9 An interesting question is how widespread such frauds are and whether firms are taking all the right steps to prevent them. It is difficult to show that firms are systematically misaligning decisions, incentives and evaluation in these case studies without a full model showing the costs and benefits of mistakes. An analogy which can help us think about the optimal amount of fraud is “shrinkage”—the polite term for employee theft and customer shoplifting—in retail stores. A typical shrinkage rate is about .5% of inventory. In some businesses, like groceries, profit margins are only 1% of sales, so shrinkage may do a lot of damage to the bottom line. But reducing shrinkage to zero is just too costly. The lowest rates among retail chains are only on the order of .1%.

2. Building blocks of economic analysis Before proceeding, let’s spend some time on the most basic concepts of economics. These form a foundation for eventually thinking about workers in companies. Marginal analysis In economics, we start with the presumption that people will perform an activity until the marginal benefit of continuing is equal to the marginal cost of continuing. This way of thinking is loosely called “marginal analysis”. Marginal analysis is also important at the market level: If prices are determined by the intersection of supply and demand, then prices are not determined by the person 8 CFC FINISH Joe Jett case http://www.smeal.psu.edu/faculty/huddart/Courses/BA521/Jett/JettNYTimes97.shtml (note starbuck quote) http://money.cnn.com/1999/12/29/investing/century_greed/

9 This is why economists think policies which are intended to reduce the number of violations of some rule to zero, are noneconomic pipe dreams. The rhetoric of zero-tolerance may prove politically useful, or may motivate people to get closer to that goal, but it is not the result of optimization if the marginal cost of detection skyrockets when you try to catch every last user or terrorist. 11 who values a good most, or the seller who can make it most cheaply: Instead, prices are determined by the marginal buyer and seller. A famous example is water and diamonds. Suppose there was a massive shortage of fresh water on the earth. Because water is necessary for life, the price would skyrocket. (In fact, after natural disasters like earthquakes and floods, water and other valuable staples, and useful items like batteries, candles and shovels, often do rise sharply in price.) So why is water so cheap? The answer is that water is plentiful—sometimes you can even collect your own in a bucket for free, when it falls from the sky. Developed economies can purify and distribute clean water on a massive enough scale to spread the fixed costs across a large market. Thus, the marginal cost of supplying one more gallon of water is quite low. Since the water business is usually competitive (and often regulated as a public utility), prices are low too.10 So even though water is necessary for life, it is not very valuable “at the margin”. An opposite case is diamonds. While diamonds may be a girl’s best friend, no girl ever died if she went without wearing them for a few days, as she would if she went without water. So why are diamonds so expensive? Part of the answer is that the supply of diamonds is a classic monopoly. A large portion of the world’s diamonds are controlled by the DeBeers corporation, which carefully controls the supply to keep prices high. Diamonds are also rare and laborious to mine, compared to the cost of pumping and purifying water. Constrained utility maximization The central modeling tools in the economics of consumer behavior are the concept of utility, budget constraint, and (combining the two) maximization of utility subject to constraint. These ideas form the mathematical underpinning of a “demand curve”—a function that tells you the number of units of demand for a product D(P) if the product’s price is P. A product or activity’s “utility” is a number which reflects how much people seem to anticipate liking the product when they make choices. (Some nuance to this definition will be offered below.) There is much confusion about the concept of utility. Here’s how you should think about it: Suppose you sat down and watched a bride and groom and a wedding planner, planning a wedding. Suppose each wedding “menu” has a certain number and type of flowers, a venue where the wedding is held, and a food menu for dinner. As the wedding planner leafs through a thick book listing these (flowers, venue, menu) “bundles”, the bride says “I like this one more than that one” many, many times. We call this list of statements of what she likes a “preference relation”, often written mathematically in the notation A ≥ B (read “A is weakly preferred to B”, or A ~ B if A and B are equally preferred). Suppose that her preferences are “complete”, so that for any pair of (flowers, venue, menu) triples she can decide that she likes one, or that she

10 Keep in mind that prices depend on cost and how competitive a market is. Suppose there was a water monopolist who controlled all the water and could charge whatever they would like. Because water is such a necessity (we say its price elasticity—the percentage change in demand relative to the percentage change in price—is low) the monopoly price would be high. (Generally, when a good is price-inelastic, a monopoly’s profit-maximizing price is high). Thus, water is cheap in developed economies because it can be mass-produced cheaply, and also because the supply side of the market is usually competitive (or regulated to create low prices). 12 is truly indifferent between them. Suppose further, that her preferences are transitive—if she likes A more than B, and B more than C, she also likes A more than C. If the bride’s preference relation is complete and transitive, then we can keep track of what she likes by assigning numbers to each triple of (flowers, venue, menu). Suppose there are N such triples. Just assign N points to the one she likes most, N-1 points to the one she likes second-most, and so forth. These point assignments are “utilities” which are revealed by her expressed preferences. The utility numbers just equate the statement “I would rather have A=(roses, Peninsula Hotel, seafood) than B=(tulips, Malibu beachfront, filet mignon)” with the statement “A has a higher utility number than B”, or “u(A)>u(B)” (where u(x) is the function which assigns a number to x). Notice that we are not insisting that the bride actually consciously has numbers in mind when she expresses preferences (although people may do so in unusual cases). But if she can always make up her mind between two bundles, and does so transitively, she is acting just as if she is would act by consulting a list of numbers, and picking the bundle with the higher number. The important part of this utility-maximization process for economics is the concept of tradeoff: Assuming the bride has a limited budget, she must decide whether she is willing to spend less on flowers in order to have more hors d’ouevres served at the reception. We call the amount of one good which is worth giving up to get more of another the marginal rates of substitution between the two goods. In most choices people actually face, there are two other components which are missing from the simple sketch above: Risk, and time. Usually we are not sure exactly what the consequences will be when we make a choice—the choice is risky. For example, if the wedding is planned for a Malibu beachfront, there is a small chance that it will rain and the wedding must be moved inside. So the bundle (tulips, Malibu beachfront, filet mignon) is really a probabilistic series of different realized bundles. In traditional economics we usually assume that people value these probabilistic “gambles” by taking a probability-weighted sum of the utilities of the possible consequences. That is, if a gamble G has n different possible outcomes, denoted xi, and n each has probability pi then u(G)= i= 1 pi u(xi). This is called the “expected utility” (EU) rule. It is mathematically equivalent to a person’s evaluations of different gambles obeying a series of “axioms” or general principles. The key axioms which imply the EU form are “completeness” of preference and transitivity, and cancellation. The completeness axiom says that people can always decide whether one gamble is better than another, or they are indifferent between two different gambles. The cancellation axiom says that if two gambles have a common component—if they are drawn as a series of branches in a “decision tree”, then a common branch with the same probability of the same outcome in both gambles can be cancelled out when comparing the gambles. An important variant of the EU form is when the probabilities of outcomes are not given by historical evidence (like records of car accidents insurance companies use to establish auto insurance premiums) or a theory which makes a sharp prediction about 13 probabilities. More typically, events have a “subjective” or “personal” probability, such as a person’s perception of the chance they will be promoted, or a company’s guess about whether a rising executive can be a capable CEO someday. Then the EU form is replaced by a subjective probability p(S) over a “state of nature” or random event S. The SEU form for the overall value of a gamble G that yields outcome u(x(S)) in state S is u(G)=S p(S)u(x(S)). Moving from known risks of outcomes xi, in the EU form, to unknown risks over states S, allows us to model differences in opinion and phenomena like optimism or overconfidence (i.e., a worker might think p(Success) is greater for themselves than other people think it is). The EU and SEU forms will play a substantial role in thinking about employment contracts where a worker’s income can be high or low because of random factors out of their control (so that valuing different jobs for their income potential is like choosing a gamble). Most practical choices also yield a stream of goods or activities which unfold over time. This raises a question of how people weight the utility of good and activities they get at different points. The standard theory is called “discounted utility” (DU). The idea is that people weight utilities which they receive one time period from now by a discount factor, δ. Utilities received t periods in the future are weighted by δt. Therefore, the discounted utility of a stream of outcomes received at points t=0, 1, 2,… T in the future is T t u(x0,x1,x2, … xT)= t=0 δ u(xt). This exponential form is logically equivalent to valuing outcome streams in a way that is “dynamically consistent”. Dynamic consistency means that if people value one future outcome stream I which begins at a future time T over another stream J today, then when time T arrives they still prefer I over J (provided there is no new information which might change what they prefer). Behavioral economics: Limits on rationality, temptation and self-interest The simple model of utility maximization above, on which almost all modern economic theories rest, has proved to be a very powerful tool for explaining many features of our economy, for giving people advice, and for engineering new rules and systems. However, because the economic model is a mathematical simplification of a complicated cognitive process, a more careful study of human psychology complicates the simple picture of utility maximization (and the extensions to including risk and time delays). “Behavioral economics” is an approach to economics which tries to use empirical regularities and ideas in psychology (and to some extent other fields) to improve economics mathematically, to make it more empirically accurate and more useful (Camerer, Loewenstein and Rabin, 2004). The main theme in behavioral economics is that some people are limited in computational ability, willpower, and greed, in some economics domains. Highly experienced experts, or unusual types of people, may have lower rationality limits or may have learned to behave in a highly rational way from experience. It is important to keep in mind, therefore, that the rational model is sometimes a useful special case, and that heterogeneity across workers and managers is important in determining outcomes. For example, even if most people do not know how to value very rare risks, like floods or car accidents, highly-trained actuaries can do so. Therefore, an insurance company might set accurate premiums for insurance because 14

actuaries “do the math”, even if their customers cannot (and may even think their premiums are not an accurate guess about their own personal risks). Here I will just give a quick sketch of some of the basic ideas that will be put to use later in this book. Table 1.1 shows a comparison of rational choice principles on which most economic theory is based, and some alternative models which have become common in behavioral economics.11 For example, an alternative to the EU form for valuing risky gambles with known probabilities pi is “prospect theory”. In prospect theory people value outcomes relative to some point of reference, r. Experiments and studies with field data from many different parts of the economy (see Camerer, 2000) suggest that in the vicinity of the reference point, people dislike perceived losses (outcomes below x) roughly twice as much as they like perceive gains (outcomes above x). Furthermore, in prospect theory probabilities are thought to have a decision weight w(p) which is not linear—specifically, low probabilities may be overweighted (w(p)> p for small p) and high probabilities are underweighted (w(p)

r w(pi)u(xi-r). The coefficient λ measures the strength of loss disutility relative to gain utility; it can be used to help understand phenomena like why workers dislike having their wages cut, and why firms are reluctant to do so even in a recession when the demand for labor falls. A commonly-used weighting function is w(p)=1/exp[(ln(1/p)) γ]. Note that when γ=1 the function reduces to linear weighting of probability, w(p)=p.

Table 1.1: Some rational-choice principles and behavioral economics alternatives Rational (or simplifying) Behavioral alternative model Representative citation assumption Complete preferences description-invariance Framing, reference- Kahneman-Tversky 1979 dependence Koszegi-Rabin 2005 procedure-invariance Contingent weighting Slovic-Lichtenstein, 1971, Grether-Plott 1979 context-independence Comparative utility Tversky-Simonson ? separable u(x) Regret, disappointment Loomes-Sugden ?, Gul 1991 Choice over Risk Prospect theory Kahneman-Tversky 1979 Ambiguity Nonadditive decision weight Schmeidler 1989 Time Hyperbolic β-δ discounting Laibson 1997 Self-interest Inequality-aversion, fairness Rabin, 1993, Fehr-Schmidt 1999

11 Note that there is still much healthy debate about which of many alternatives explains data best, and the advantage of different principles for producing theory and new predictions. 15

Bayesian judgment Overconfidence Odean 1998 Encoding bias Rabin-Schrag 1999 Equilibrium Learning Erev-Roth 1998 Camerer-Ho 1999 Quantal response, cognitive McKelvey-Palfrey, 1995, hierarchy Camerer-Ho-Chong 2004

The field of behavioral economics is now so well-developed that reviewing all the details of the ideas compiled in Table 1 will take up too much time at this point. Also, many of the ideas have not been carefully applied in organizational economics. So they will be referred to one by one as they become useful in the discussions later in this book. Some day, our understanding of the limits on rationality which are expressed by the behavioral economics models in Table 1 will be supplemented by a detailed understanding of the brain processes which create human behavior. (The subfield that strives to do this is called “neuroeconomics”.12) Property rights A “property right” is the right to use or sell property. Two components of property rights are “use rights”—the right to use the property— and selling rights (or “alienable” rights). In America, you have a use right to your kidney but not a selling right (it’s illegal for you to sell it, though you can donate it). If you rent an apartment, you have a use right but not a selling right.13 Some property rights are enforced by social convention. For example, in Chicago it snows a lot in the winter. Many people do not have garages and park their cars on the street. As a result, people often spend laborious hours shoveling snow away from their parked car after a big snowfall, so that they can drive the car to work. It is common after digging out your car to put a chair or traffic cone into “your” empty space (sometimes with a homemade “do not park here” sign). These markers are understood by Chicagoans to establish a property right to the empty space. The idea is that when a person arrives home from work, “their” space is still available. The system incentivizes people to dig spaces out. Those people who do not mark their spaces with a chair or cone are “donating” the space to whomever needs it later. The social norm works amazingly well, despite the fact that it is not backed by legal enforcement. If you removed a cone and parked in a space that somebody had claimed for themselves, the police could not issue you a ticket for parking illegally or “parking space theft”. (However, the police would probably berate you for “stealing” the space and encourage you to move your car to another spot.) The system is also enforced by vandalism— if you park in somebody’s marked spot, there is a chance your car would be scratched by a key or even have its tires slashed. In developed economies, clear property rights are taken for granted and are enforced by the “rule of law”-- a system of clearly specified legal rules, policing, access

12 See Camerer, Loewenstein and Prelec (2004a,b). 13 Can you think of an object that has a selling right but not a use right? 16 to legal representation, and fair judges. Poor enforcement of property rights can lead to underinvestment, because firms are afraid their machinery will be confiscated or stolen. Poor enforcement can also lower consumer demands for valuable goods which other people can steal. The damage done to an economy by weak enforcement of property rights can be seen most clearly in less developed economies. In developed economies, enforcement of property rights often lapses temporarily during dramatic occasions such as riots and floods, when police are overwhelmed or busy. Some small-scale societies practice what some anthropologists call “tolerated theft”. Tolerated theft is a norm of sharing community property, in which the property “owner” is not obliged to offer, but the “owner” cannot turn down a request to share. For example, suppose you spend a couple of hours picking a large basket of berries in the heat, and head down the road toward our village. If I see you and grab a handful of berries out of your basket, and you don’t stop me, then you’ve tolerated theft. Tolerated theft means that your private property isn’t just yours. This practice, in turn, reduces the private incentive to gather anything valuable, if it takes work to do so (like hunting, fishing, or gathering). Not surprisingly, small-scale societies with tolerated theft are usually poor. In some of these societies, one of the most valuable goods a man can own is a pair of pants. Pants with large pockets are particularly treasured. Why? In a society with tolerated theft, pants with pockets are like a safe deposit box—they are a place in which goods can be hidden and protected from theft. Pants are an economic institution. Poaching on property rights in a way that is akin to tolerated theft can occur at much higher economic scales too. In 2004, Nigeria was the world’s seventh-largest oil exporter. There, Alhaji Mujahid Dokubo-Asari openly admits that his homegrown militia of up to 2,000 armed volunteers siphon off hundreds of barrels of crude oil from pipelines operated by Royal Dutch Shell, refine it, and sell it cheaply to locals (LATimes, 2004). Dukobo-Asari claims to control 3 local governments. He says he is fighting to “end the disenfranchisement of his Ijaw people in Nigeria” following allegations of ballot fraud in the national election. Dukobo-Asari’s group do not see themselves as stealing somebody else’s oil. They say they are simply reclaiming some of the oil that should belong to the people, but which was given away by a corrupt elite who he accuses of “eating the money” from the oil. “As far as we are concerned,” Dokubo-Asari says, “it is the Nigerian state that is stealing oil”. He says he is being pursued by the state for criticizing the government about electoral ballot fraud. The government says Dokubo-Asari is wanted for gangland killings involving another outlaw, Ateke Tom; but Dokubo-Asari says Tom was hired by the government to kill him. A Shell-commissioned report concluded that thieves annually took $1.5-4 billion of crude oil illegally (though less interested estimates are much lower). When he is asked how much crude oil his militia siphons from the pipelines, Dokubo-Asari says “As much as we can. It’s free.”14

14 Questions to think about: Who is right and who is wrong in this situation? As you think about your answer, suppose you were a judge or a UN Committee chair charged with adjudicating this situation. What information would you need to know to make a judgment? 17

Economic transition in formerly-Communist countries in Eastern Europe, and in the former U.S.S.R., has been erratic and spotty. One problem is that poor property rights enforcement gives firms an incentive to invest heavily in private security (as a substitute for publicly-supplied security in the form of policing and legal enforcement of contract; e.g., Grief and Kandel, 1995). This gives a competitive advantage to firms that are Mafia- connected; as a result, many observers are shocked by the deep influence of various organized crime mafias on legitimate Russian business. Another problem in economic transition is “deprivatization”—when governments change the rules after a privatization. After the collapse of the USSR, about 70,000 state- run companies were privatized (i.e., sold by the state to a private owner or syndicate). Some or the new owners were investors from Europe and the United States. For example, the LBO firm Kohlberg Kravis Roberts teamed up with an enterprise called the US- Russia Investment fund and bought a majority interest in the Lomonosov Porcelain Factory in St. Petersburg in 1998 (Business Week, 1999). But in October 1999, a Russian court stripped the American owners of their majority interest by declaring the company’s initial privatization several years earlier had been illegal. The problem is that judgments of illegality often hang on the tinest of legal threads or judicial whims—like a missing piece of paper in a document file, or a failure to meet a requirement by an obscure deadline. The newly-deprivatized company was rumored to be sold to its former Soviet- era managers who were due to be “downsized” when the Americans took over. Figure 1.1 below shows the powerful effect of property right protection on economic growth during transition (Hoff and Stiglitz, 2004). The y-axis shows the ratio of GDP in the year 2000 to 1989 GDP. The x-axis is the percentage of survey respondents in different countries who disagreed with the statement “I am confident that the legal system will uphold my contract and property rights in business disputes”. A high percentage (right hand side) means a lot of respondents do not think their property rights are protected. (This measure is also correlated with other measures of the “rule of law”, such as a 0-10 index constructed by the Wall Street Journal.) Each circle in Figure 1.1 is a different post-Communist society. In all those countries which grew in GDP (the y-axis ratio is above 1, Poland, Slovenia, Hungary, and Slovakia) less than 40% of respondents were insecure about their property rights. In the countries with the most insecurity (highest disagreement percentage, which is furthest to the right, such as Ukraine, Macedonia, and Russia) GDP actually shrank over the 11 years by half or more. 18

Figure 1.1: Growth and property rights in security in 20 transition economies (Hoff and Stiglitz, 2004)

Complications with property rights can also arise when there are too many layers of property rights which are not clearly prioritized. A nightmare version of this problem was seen after the devastating 1994 earthquake that struck Kobe, Japan. Landlords, landowners, tenants and subtenants all had various legal rights over decisions about property they owned or rented. The Japanese decided that everyone with any rights over property had to agree to a rebuilding place before construction could begin. But the thicket of property rights made the process very slow. One city block had 303 different renters, lessees, and subletters. Two years after the quake, despite a $30 billion rebuilding fund, only 4 of 100 “construction teams” had actually begun doing any new building (Wall Street Journal, 1996).

______Disputed property rights and law: The Barry Bonds 73rd Home Run Ball15

15 Ian Krajbich wrote the background material for this section. 19

On October 7th 2001, Barry Bonds [pictured below] hit his 73rd home run of the year into the right field arcade of PacBell Park in San Francisco, setting the Major League Baseball (MLB) record for most home runs in a single season. The MLB single season home run record is arguably the most prized record in sports; as a result, the record breaking ball was highly sought after by sports enthusiasts (and was guessed to be worth a million dollars).

Left: San Francisco Giant Barry Bonds. Right: Patrick Hayashi, photographed by Josh Keppler after picking up the disputed Barry Bonds 73rd home run ball.

As the baseball soared over the right field fence, dozens of fans scrambled and fought for a chance to catch the ball. The first fan to get his glove on the ball was Alex Popov. But Popov was quickly “buried face down on the ground under several layers of people…[and was] grabbed, hit and kicked ”16. Another fan, Patrick Hayashi [pictured], was also “involuntarily forced to the ground… While on the ground he saw the loose ball. He picked it up, rose to his feet, and put it in his pocket”. Hayashi motioned to a photographer, Josh Keppel, to point the camera to him, which Keppel eventually did when someone else in the crowd asked him to. Then Hayashi “held the ball in the air for others to see. Someone made a motion for the ball and Mr. Hayashi put it back in his glove.” Who owned this valuable ball? This case illustrates how property rights can be under dispute, even in a developed economy with clear legal rules. As the Superior Court verdict noted, “While there is a degree of ambiguity built into the term possession, that ambiguity exists for a purpose. Courts are often called upon to resolve conflicting claims of possession in the contexts of commercial disputes…Because each industry has different customs and practices, a single definition of possession cannot be applied to different industries without creating havoc”. Indeed, different legal rules of possession have emerged differently in different businesses. For example, in hunting and fishing, “the hunter acquires possession upon the act of wounding the animal not the eventual capture”. On Oct 24th, Popov sued Hayashi for ownership of the ball, claiming that it had been stolen from him. Popov and Hayashi both thought they were the rightful owners and would prevail in the case. According to the law, prior to the time the ball was hit, it was owned by Major League Baseball. At the time it was hit, the ball became ‘intentionally abandoned property’. The first person who came in possession of the ball owned it.

16 This quote, and all others in this description (unless noted otherwise), is from the December 18, 2000 Superior Court verdict at http://www.courttv.com/trials/baseball/docs/BondsVerdict.PDF . See also http://www.courttv.com/trials/baseball/ballsold_ctv.html 20

But who possessed the ball? Popov was the first to touch the home run ball, but he lost it in the crowd. The Court defined possession like so: “The central tenant [sic17] of Gray’s Rule is that the actor must retain control of the ball after incidental contact with people and things” [note emphasis] and thereby concluded that Popov had not possessed the ball. Hayashi argued that Popov did not possess the ball because he did not have “complete control” of the ball. As a law professor testified, “the first person to pick up a loose ball and secure it becomes its possessor.” That person was Hayashi. But wait. The contact between Popov and the fans nearby was clearly not “incidental”. It is not as if Popov bumped into a fan and dropped the ball, which would then be legally possessed by whomever picked it up. The fans mobbed Popov. A major purpose of law is to protect its citizens and to discourage reckless and dangerous behavior. Denying Popov possession of the ball because of the illegal actions of the fans who mobbed him would set a dangerous precedent by encouraging violent behavior at ballgames. As the Court ruled: “The reason we do not know whether Mr. Popov would have retained control of the ball is not because of incidental contact. It is because he was attacked. His efforts to establish possession were interrupted by the collective assault of a band of wrongdoers …Mr. Popov should have had the opportunity to try to complete his catch unimpeded by unlawful activity. To hold otherwise would be to allow the result in this case to be dictated by violence. That will not happen.” The judge acknowledged that Popov’s chance to control and possess the ball was undermined by the unruly San Francisco mob. At the same time, the judge noted that awarding the ball to Popov would be unfair to Hayashi because Popov could not prove that he would have caught the ball even if the mob weren’t surrounding him. Popov’s fumble established a “pre- possessory interest” in the ball. But, in the court’s words, that interest “does not establish a full right to possession that is protected from a subsequent legitimate claim” (like Hayashi’s). Taking into account both interests—neither should win, but neither should lose--, the judge ruled that both men had equal rights to the ball and should evenly split the proceeds from selling it. When the ball was actually auctioned off, it sold for only $450,000, roughly half of its originally estimated value. After the sale, Hayashi and Popov were both disappointed with the sale price. Popov hinted that his legal debt was in the hundreds of thousands of dollars, because he had paid his lawyer by the hour. (Whoops.18) Hayashi had cut a deal with his lawyers for a contingency fee based on a percentage of the ball’s sale price, so he came out ahead compared to Popov. In 1960 Ronald Coase (later awarded the Nobel Prize in 1991) wrote a remarkable paper suggesting that if two parties can easily bargain to an agreement, which party has a property right doesn’t matter for social efficiency—because the party who values the property right most will bargain to get it.19 The Barry Bonds disputed ball-possession case should be a perfect example of the Coase theorem in action: Hayashi had the ball in his possession. If it really was more valuable for Popov—either he wanted it more, or thought he could sell it for more-- then Popov should have paid Hayashi to turn it over. But if Hayashi wanted it more, or thought it was more valuable, there would be no price Popv could offer. Either way, the person who valued the ball most should have ended up with it— the only question is whether was whether Popov would have to pay. Looking back, neither Hayashi nor Popov valued the ball just to keep it in a glass case. They wanted to sell it. They should have agreed to sell it and split the money. They would have

17 The Court meant “tenet”. 18 Suppose you hire someone to paint your house and you pay by the hour. Your neighbor hires somebody and pays a lump sum for the whole paint job. Whose house do you think gets painted more rapidly? More carefully? 19 Of course, the property right assignment matters for who gets the most from the deal, because if the property right is assigned to the lower-value owner, then that person can get paid by the higher-value party. If the higher-value party gets the property right, then they don’t have to pay. 21 saved themselves enormous lawyer’s fees, which probably chewed up half or more of the ball’s $450,000 value at auction. This case illustrates why the Coase theorem can fail. The crucial assumption in the Coase theorem is that people can cheaply reach an agreement by bargaining. Then it doesn’t matter who has the property right (“ownership”) to begin with; after bargaining, the person who values it most will end up with it. The problem, of course, was that Popov and Hayashi each thought they had the morally rightful claim to the ball, and also thought they would prevail in court and get all the money. The Coase theorem can fail if bargaining fails, because both sides think they are more likely to win than they really are, so they are willing to tolerate a costly delay in bargaining or risk an expensive litigation process. Psychologists call this kind of collectively-inconsistent belief “self- serving bias”. ______

Specialization, gains from trade, comparative advantage People are good at different kinds of work. In an ideal economic system, people specialize in what they are best at and enjoy. (Those two things can be different of course.) Specialization is the engine of economic gain because it is easy to show that if everyone can specialize in what they are best at (as judged by the tastes of consumers), and trade occurs freely, the system makes the largest quantity of products everyone wants. Specialization is essentially the same as a division of labor in which tasks are assigned to those people who are best able to do them. When specialization is allowed to flourish, economic systems benefit when people have diverse skills as workers, and tastes as consumers, because the value of dividing labor is higher. An example that illustrates this principle is the television show, “The Amazing Race”. In the show, pairs of people are required to complete a series of physical and adventure tasks— like riding a board across a 15-foot waterfall, or finding an envelope with a clue in a dark cave filled with bats-- while racing around the world. The teams which complete the tasks fastest win prizes. Some tasks require brainpower, others require brawn, and still others require bravery. Generally, there are two big challenges for the teams: (1) Coordinating their activity, so they don’t waste time bickering or arguing about what to do; and (2) having at least one person in the team who is particularly good at each task. One season’s show featured a pair of identical twins. Is being in a team with your twin a good or bad idea, if you want to win “The Amazing Race”? For coordinating with your teammate, working with your twin is probably good— twins share all their genes, and probably think and act alike on many dimensions. On the other hand, a pair of twins will have less diversity in abilities, and are poorly equipped to have at least one teammate who is a specialist in each type of task. In fact, the twins—two athletic men— placed in about the middle of the competition, perhaps because their skills were too similar so the benefits of a division of labor and specialization were too small. The benefits of specialization can be illustrated by a joke: In Heaven, all the chefs are French, the car mechanics are German, the bankers are Swiss, the police are British, and the lovers are Italian. In Hell, the chefs are British, the car mechanics are French, the bankers are Italian, the police are German, and the lovers are Swiss. 22

The advantage of specialization is driven by comparative advantage. A worker’s absolute advantage is her productive capacity in a certain activity. A worker’s comparative advantage is her relative absolute advantage—that is, her absolute advantage relative to other workers. The point is that even workers who are not good at any activity in absolute terms have some comparative advantage because there is always an activity they are relatively less bad at. For example, a father making sushi is faster than his 10-year old at making proper sushi rice, cutting nori seaweed, buying sushi- grade fish, making sushi rolls, and setting the table for dinner. But the 10-year old is not so bad at setting the table, and is hopeless at everything else. So the 10-year old’s comparative advantage is in setting the table. It is useful to think about comparative advantage in terms of opportunity cost. An activity’s opportunity cost is the value of the opportunity that is lost by doing that activity versus another. If a friend gives you Lakers playoff tickets as a treat, does going to the game cost you anything? At first blush, the answer is “No”—the tickets were free.20 But suppose that you could have sold the tickets for $500 to a ticket agent, or “scalped them” in the active market that assembles before each game just outside Staples Center in Los Angeles. Then by going to the game you gave up the opportunity to have $500 instead. The tickets “cost” you $500 in an economic sense (though not in accounting sense). In behavioral economics, we often find that people underweigh opportunity costs (Thaler, 1985). The ticket example is a classic one, in fact— many people would not pay $500 to go to the game, but would go and forego the opportunity to earn $500. It seems that people don’t see giving up $500 they could easily have as psychologically equivalent to pulling $500 out of an ATM. Thinking about opportunity costs does not seem to be natural: It takes the ability to reason counterfactually, to figure out something that did not happen (but could easily have), and give the counterfactual the same cognitive status as what did happen. An example that illustrates the link between opportunity cost and comparative advantage is the fictional small town in which there is just one lawyer, who is also the fastest typist among the 100 people in town who can type at all. What should the lawyer do with her scarce workday? If she types, the most typing will be produced. But if she only types, the opportunity cost is high because the fast-typing lawyer is the only lawyer; no lawyering will get done. The correct answer is that the lawyer should practice only law and have somebody else type. The best activity for each worker to perform is the one that has the lowest opportunity cost.

Partial and general equilibrium: Unintended consequences

20 You’re thinking like an economist if you immediately thought that the tickets might have been costly because you could owe the friend a favor in the future (i.e., the tickets create a debt which has a future cost), or you got them because you were being repaid from a previous debt (so that accepting the tickets forecloses the opportunity to collect on the debt in another form). 23

A very important concept in economics is “partial” versus “general” equilibrium. A general equilibrium analysis takes account of all of the likely reactions and counterreactions of all agents in the economy. A partial equilibrium analysis assumes— usually incorrectly—that some agents do not react after a change, or are not behaving optimally. Why would you do a partial equilibrium analysis rather than a general one? One answer is, “For simplicity”. Computing what happens in general equilibrium, when all agents are optimizing, is often enormously difficult. And ignoring the actions of some agents is often a reasonable approximation to the result of a perfect analysis that includes actions by all agents (just as ignoring Pluto’s gravitational pull on the Earth is not a bad approximation). For example, suppose you are interested in the effects of a serious earthquake in Los Angeles on the world economy. The most obvious economic effect is commercial and residential property damage in Los Angeles. A partial equilibrium analysis would analyze only the immediate effect on businesses and people if there is an earthquake. But an earthquake might also cause people to relocate away from Los Angeles. A general equilibrium analysis will include this relocation effect and takes into account its impact on the cities Los Angelenos move too. Suppose Los Angelenos move to Las Vegas, which causes a spike in housing prices. Suppose higher property taxes that are collected by the city are spent on infrastructure, like expanding the airport (which is always busy). That lowers the cost of traveling to Vegas to gamble, which lowers demand for Native American casinos in northern California. So one effect of an earthquake in Los Angeles could be a recession in Native American casinos which are hundreds of miles away. General equilibrium analysis is like the zany machines designed by Rube Goldberg (see Figure 1.2 below). At each step of the analysis you ask “And then what?”—given the temporary result of the analysis, do any agents have an incentive to change what they will do? The analysis ends when no agents have an incentive to change; the economy is then said to be “in equilibrium”. 24

Figure 1.2: A Rube Goldberg machine

The distinction between partial and general equilibrium is important because a general equilibrium analysis will often reveal surprising effects or “unintended consequences” of a policy which a partial equilibrium analysis misses. (We will see some of these unintended consequences below in studying the effects of changes in incentive systems— sometimes they create cheating, or produce too much of one kind of worker effort at the expense of another.) A common unintended consequence is that a change in the economy which is bad for many people actually benefits some. For example, the end of the Japanese stock market bubble in the late 1990’s led to a severe recession which led to a rise in burglaries in big cities Tokyo and Osaka. Where’s the silver lining in this gloomy cloud? Ask Makoto Iida, the founder of a leading security systems company Secom. Iida said “I feel very good now” because sales of his $80/month intruder-detector systems rose 20% a year through the mid-90’s as the crime rate rose. Incidence of taxes is another example of how the general equilibrium effects of an economic change might be quite different than they appear at first blush. When a tax is applied, who ends up actually paying the tax depends on supply, demand and competitiveness. For example, suppose you impose a profit tax on corporations. Suppose profits were determined by competitive forces before the profit tax, so that shareholders who supplied capital were earning a fair rate of return on investing in corporations. Then after the tax is imposed the net (post-tax) corporate profit will be lower; and by inference, it will be less than shareholders will accept because the new profit rate will be lower than the higher pre-tax rate. To make up the effect of the tax to shareholders, firms will raise price (or cut costs). Raising prices will usually cut demand, to an extent which depends on the price elasticity. If profit taxes are imposed across the board (on all firms), then prices will generally increase. So consumers may end up paying higher prices because of a tax on corporate profit. A few years ago, a luxury tax was imposed on the sales of fancy cars and boats that only wealthy people buy. The tax was 10% of the price of cars and boats above some threshold (which implicitly defined the threshold of “luxury”). A small political debate ensued after people realized that the tax would reduce demand for boats, which were often built in small shipbuilding towns like New Bedford, Massachusetts which were struggling economically even before the proposed luxury tax. So what appears to be a tax on the rich had an unintended consequence of raising unemployment of some un-rich craftsmen.

Unintended consequences I: Gang members in Iraq?

Under heavy pressure to produce enough soldiers to fight the war in Iraq, in 2004, the Pentagon created a “Moral Waiver” program whose stated goal was to “better define relationships between pre-Service behaviors and subsequent Service success”. In practice, the changes appear to have relaxed previous restrictions on backgrounds of people recruited into the Armed Forces. The Baltimore Sun reported (February 2006) that 25 from 2004 to 2005 the number of recruits with “serious criminal misconduct” in their background (including aggravated assault, vehicular manslaughter and—ironically— making terrorist threats) rose by half. Even worse, the Chicago Sun-Time reported in 2006 that there was some evidence of gang members being recruited.

At Camp Cedar II, about 185 miles southeast of Baghdad, a guard shack was recently defaced with "GDN" for Gangster Disciple Nation [a Chicago gang], along with the gang's six-pointed star and the word "Chitown," a soldier who photographed it said.

...A law enforcement source in Chicago said police see some evidence of soldiers working with gangs here. Police recently stopped a vehicle and found 10 military flak jackets inside. A gang member in the vehicle told investigators his brother was a Marine and sent the jackets home, the source said. (from Sun-Times)

"We're lowering our standards," [Defense Department gang detective Scott] Barfield said. "A friend of mine is a recruiter," he said. "They are being told less than five tattoos is not an issue. More than five, you do a waiver saying it's not gang- related. You'll see soldiers with a six-pointed star with GD [Gangster Disciples] on the right forearm."[....]

(Picture from Sun-Times article).

Why would gang members be eager to join the Army? Take a guess. As the Chicago Tribune reported (“FBI Probes Military Gangs”May 3 2006):

Of particular concern are reports that the Folk Nation, consisting of more than a dozen gangs in the Chicago area, is placing young members in the military in an 26

effort to gather information about weapons and tactics, said FBI Special Agent Andrea Simmons, who is based in El Paso, Texas.

"Our understanding is that they find members without a criminal history so that they can join, and once they get out, they will have a new set of skills that they can apply to criminal enterprises," Simmons said. "This could be a concern for any law enforcement agency that has to deal with gangs on a daily basis."

Amazingly, Army investigators deny that there are many real gang members. According to the Tribune, “nearly every one of the cases that we have looked into, it is a young man or woman who thought that the symbol looked cool," said Christopher Grey, spokesman for the Army's Criminal Investigation Command. "We have found some people even get gang tattoos not really knowing what they are, or at least that they have not had any gang affiliation the past."

______THINK ABOUT THIS: Who do you believe about this threat, the FBI’s Andrea Simmons, or the Army’s Christopher Grey? Why? ______

Unintended consequences are particularly important because of a phenomenon called a “Lorenz effect”. Lorenz was interested in weather systems. These systems are often chaotic, which means that even deterministic equations produce behavior that looks random. Chaotic systems often exhibit an important property called “extreme sensitivity to initial conditions” or “path-dependence”. This means that a small initial effect can have a large long-run consequence. Extreme sensitivity to initial conditions is an important idea for two different reasons. First, it means that if you are trying to create a system that evolves to a good long-run solution, starting in the right initial condition can be crucial. Second, it means that it is often fruitless to search for a “big” reason why two systems would be vastly different, because the reason might be a small difference in initial conditions. (The “continental divide” game described below in section 4 is a good example.) Here is an actual example. The Vietnamese dominate the nail salon business in California. By one count reported in 2005, about 6500 of almost 8000 salons were owned by people with Vietnamese last names (Central City Extra, 2005). Why? Nobody is quite sure of the starting point. One story is that the film actress Tippie Hedren, star of “The Birds”, visited a resettlement center in the 1970’s for Vietnamese refugees and had a pleasant manicure. To help the Vietnamese she offered courses and urged the state to give the test in Vietnamese. Nobody is sure if the story is true or whether Hedren played a large, small, or no role. A more likely mixture of ingredients is mentioned by Philip Nguyen, CEO of Southeast Asian Community Center, says: Most if not all Vietnamese are refugees and they want to work desperately for their living, to get themselves out of public assistance, and to support their loved ones still in Vietnam. Only a very small percentage can speak fluent English. They will jump into any businesses which do not require much English, have 27

short training and or training in Vietnamese and low capital for opening the business: Nail salons fit in very well with their expectation. Starting in the 1996, the written test manicurists must pass for state licensing was offered in Vietnamese. Poor English skills become a barrier to entry in other service professions, and a huge draw for the nail salon business. A complementary input is capital. Vietnamese have a practice called “hui”, known more generally as a “ROSCA” or rotating credit and savings association. In a ROSCA a group of people agree to meet regularly, typically every week or month. Each person brings a fixed sum of cash to each meeting. At the meeting, exactly one person collects everyone’s cash in that meeting (after collecting, the winner cannot collect again until everyone has gotten cash). This is an informal forced-savings mechanism which is equivalent to making a regular payment into a bank and then withdrawing a large sum to start a business.21 An initial start in nail salons can lead to “increasing returns” and industry-wide spillovers, which favor the Vietnamese salons over others, in a variety of ways; for example, Vietnamese people may be able to convey their knowledge to one another by sharing tips about business, because they all speak the same language or are more inclined to share tips because of ethnic ties. An important force that may cement one group’s presence in an industry is learning-by-doing. In almost every human activity, people get better and better at the activity as they practice. In fact, the “learning curve” is so regular that learning often follows a simple power law— the time it takes to perform an activity is t(q)=qa, where q is the accumulated output.22 So if the Vietnamese women have a headstart, their costs can be lower, and quality higher, because they have more experience (higher q) than later entrants. Put simply, they are further down the learning curve. Supply and demand A central principle in price theory is that supply and demand adjust to “clear” the market— to find a quantity of trade and a price at which everybody who wants to buy and sell at that price can do so. An important feature of this adjustment process is that when the price of something rises, supply will often appear in creative ways; and when prices fall, supply shrinks (businesses fail and people switch jobs). Economists have great faith in the ability of markets to create supply, perhaps rapidly, when prices rise to make supplying more goods profitable. A surprising example of the emergence of supply is in massively multiplayer online role playing games

21 FINISH http://www.santersghostwriting.com/tippi.htm http://www.billingsgazette.com/index.php? id=1&display=rednews/2002/06/16/build/business/14-nailsalon.inc more on ethnic specialization… http://www.thejournalnews.com/apps/pbcs.dll/article? AID=/20050930/BUSINESS01/509300305/1066/BUSINESS01 22 FINISH GET SOME MORE ON THIS. E.G. CIGAR ROLLING DATA FROM PSYCH BOOK 28

(MMORPGs), a new genre of computer games where a player assumes the role of a particular character and interacts with other online players, either attacking them to steal in-game items and money or working with them to complete quests or defeat computer- controlled enemies.23 The main goal in most of these games is to build up “experience points” (gained from completing quests and killing enemies) which then make your character more powerful and give you access to new moves, items, quests and enemies. Many of these games are addictive and time-consuming. A major motivation is to build up one’s character within the game in the hopes of eventually rising to elite status. Since building up a character’s resources is so time-consuming, some players have sought to economize by paying real money for “pre-played” characters that have been significantly built up by someone else. Responding to this demand for “pre-played” characters, a videogames “sweatshop” industry has emerged. Companies hire foreign workers, whose time is literally cheaper, to play online videogames and advance characters to a point where they can be sold to gamers for a handsome profit. The first known example of this type of business was a company named BlackSnow. “BlackSnow hired workers in Tijuana to earn gold by ‘farming” in Ultima Online. [BlackSnow] sold that in-game tender online for a handsome real-world profit while only paying [their] employees pennies on the dollar.” Industry insiders have estimated that this new industry has grossed roughly $500 million. Innovators in the industry have created scripts which do most of the work, so that all the workers have to do is “fend off the occasional player itching for a fight or game master who’s hunting for these automated farming programs.” Several game makers have tried to fight this new industry. The creators of Lineage II did so when they banned certain Chinese IP addresses from playing the game. The experience-for-hire industry has responded by becoming trickier about how they go about their business. For example, the best firms in the business are reputed to play on every game server available to spread out their activities. They also use multiple accounts with different IPs, credit cards and computers, to hide the accumulation of experience points. Another example comes from a story about how many “extra” unmarried males are expected to exist in China, because of a longstanding preference for male children over females (perhaps combined with one-child policy instituted to fight population growth). One estimate is that by 2020 there will be 30 million extra males. The Chinese government has reacted by trying to both limit the ability to abort girl babies, and incentives for having more girls (such as a 100 yuan/month girl bonus in rural areas plus free educational fees). As ABC News reports24:

Today, as urban areas become more economically prosperous, parents can afford to find out the sex of the fetus and can opt for sex-selection abortions. So the government in China's largest province has banned the use of ultrasound 23 The discussion of outsourcing MMORPG’s was contributed by Ian Krajbich. FINISH CITE FROM LA TIMES 24 ABC News: China fears lopsided sex ratio could spark crisis, Jan 12, 2007. http://abcnews.go.com/International/story?id=2790469&page=1 29

machines unless warranted by medical reasons. And the ban on abortion drugs goes into effect this year.

But Chinese couples determined to have a son can easily get around the new laws. A black market has sprung up of people with ultrasound machines in the trunks of cars who will reveal the sex of the fetus for a price.

Managing markets and firms: Centralized and decentralized planning Questions about ideal organizational design within a firm often revolve around the same questions which are posed more generally in economics, about whether centralized government planning or decentralized planning are better methods of allocating capital and resources. To make the argument simple, let’s talk about bosses and workers. Roughly speaking, centralization is better if bosses have information that workers do not have, if workers do not value control and autonomy too much, and if centralization enables bosses to coordinate activity so that workers are all doing the same thing (like an army) or if the bosses can allocate different workers to tasks better than the workers could do themselves. Decentralization is preferable when the opposite factors hold, if workers know things the bosses do not, if workers like autonomy, and if workers can coordinate and self-organize without the boss’s interference. Generally, economists are skeptical about the virtues of centralized planning. Below are five stories about centralization and decentralization. One highly stylized example illustrates the disadvantage of centralization and two historical examples illustrate big disadvantages of centralization leading to death and poverty. The fourth example illustrates a beautiful system which is mostly decentralized. The fifth example illustrates a disadvantage of centralization. Bad centralization of decisions: In communist Russia people could always buy bread. State-owned factories were ordered to produce a certain amount of bread and sell it at a low price. As an economist you should think: If bread is cheap, who determines who gets the bread? Do people get the bread they most want? The answer to how bread is allocated, which is common when goods are deliberately underpriced, is lining up (queueing) and favoritism. Russians would line up for hours to buy cheap bread. Relatives of bakers would have bread put aside they could get after hours or without queuing. Economists hate queues because people waste time standing in line, and their valuable time spent lining up could be better used as labor to make something. Two disadvantages of this system are also that the quality of bread might be uniform and poor. People who like fancy breads, and will pay more for them, cannot buy them. Typically, state-owned agencies have no direct incentive to produce good bread. The advantage of this system (perhaps its only social advantage) is that there is a steady supply of bread. Mao-tse Tung spoke of the “iron rice bowl”, his way of saying that in Maoist China people could always be guaranteed (hence “iron”) of getting a bowl of rice. Supplying a staple regularly at a low price is often a hallmark of a centralized regime, but also an indicator that people who want to buy a wider variety of goods are not likely to get them from a system focused on such a simple goal. 30

Compare this system to a decentralized one, in which the government allows anybody to bake bread and charge whatever price they like. In theory, firms which make good bread can demand higher prices; their higher prices also signal other firms about how to compete. If people switch from bread to a substitute, like pasta, then bread makers go out of business and pasta companies spring up. Bad centralization of expeditions: A beautiful example of the difference between centralized planning and decentralized market activity is a study by Jonathan M. Karpoff (2001) on expeditions to the North Pole and the Northwest Passage, between 1818 and 1909. There were three major goals of these expeditions: (1) discovery and navigation of the Northwest Passage (2) discovery of the lost Franklin expedition (3) discovery of the North Pole. Some of these expeditions were motivated by profit, as there were rewards out for some of these discoveries: For example, the British government posted a 15,000 pound award for the discovery of a Northwest Passage, since it would have provided a potential alternate trade route to the Orient that would not be exposed to attacks from the Spanish and Portugese. Some expeditions were organized by governments and funded by public funds. Others were privately financed. While it is harder to determine which expeditions were more successful (because scientific discovery can be hard to quanity), it is easier to measure components of failure. Table 1.2 compares three measures of failure: The frequency of deaths of crew members (as a percentage of the total crew); the number of ships on the expedition, and the number that were lost; and the percentage or crew members who came down with scurvy (a debilitating and ultimately fatal disease due to a vitamin C deficiency). In all cases the public expeditions did worse: More crew members died, a higher number and percentage of ships were lost, and almost half got scurvy (ouch). Why did the public expeditions do worse? Karpoff suggests there are two main reasons. First, the public expeditions often had worse leaders. One can imagine that a political process influenced the choice of leaders, and those who emerged the victors were not necessarily the most brave or best-qualified. Second, the public expedition leaders adapted slowly to new information and were reluctant to change course.25 Their slow responses might have been due to a desire to save political face (if changing plans is seen as admitting a mistake) or just an inability to process new information in an objective way.

Table 1.2: Failure rates in public and private expeditions to the North Pole and Northwest Passage (Karpoff, 2001) Public (n=35) Private (n=56)

25 The modern equivalent is somebody like Michael Brown, the head of FEMA who was widely blamed for reacting slowly to Hurricane Katrina, which devastated New Orleans in 2005. Brown was a political appointee whose previous job had been legal counsel to the Arabian Horse Association. He had no background in emergency management, a field in which experience surely counts. Among many well- publicized mistakes, “Brownie” (as President George Bush called him) said he was not worried about the hurricane because he got up in the morning and read in the papers that New Orleans ‘dodged a bullet’; but no newspaper headlines used that phrase, and many said the opposite. 31

Crew deaths (%) 8.0 6.2 Number of ships (number 1.63 (.53) 1.15 (.24) lost) % with scurvy 46.7% 13.2%

Bad centralization in Communist China: The Great Leap ‘Backward’: In 1958, China’s Communist government launched the Great Leap Forward (GLF) movement. The goal of the movement was to use collectivization and central planning to make agriculture more efficient, and then divert resources towards surpassing Great Britain and the United States in industrial production. The Leap proved to be backward, not forward. National grain output plummeted by 15% in 1959 and another 16% by 1961, causing a horrible national famine. Estimates of premature deaths during the GLF famine range from 16.5 to 30 million, making it the worst famine in recorded world history. Not surprisingly, given the Chinese government’s penchant for secrecy and making far- fetched claims to save face, China’s officially blamed the famine on bad weather. However, recent research in economic history has shown that the famine can more likely be attributed to poor organization and decision making. The Great Leap Forward was expected to be successful based on the “false premise ingrained in the dominant Soviet economic ideology that collectivization would transform Chinese agriculture from small household farming into large-scale mechanized production, achieving a great leap in productivity” (Li and Yang, 2005). With increased agricultural productivity, the government could then divert more resources to the accelerated industrialization campaign, in the form of surplus workers and increased tax revenue, and “impose excessive burdens of grain procurement on the rural population” (i.e., force the farmers to sell more grain to industrial workers and consume less themselves). The main organizational change of the GLF was in combining small cooperatives into 26,500 “people’s communes”, each one comprised of thousands of households. Political propaganda about the benefits of collectivization led to wild, falsified estimates of grain yield, such as “Grain output in 1958 was forecasted to grow to 525 million metric tons from just 195 in 1957!” The actual 1958 output was around 200 million metric tons, only a tiny improvement, and output then fell from 1958 to 1959. Accepting the wishful thinking behind these baseless agricultural predictions, China’s government reduced the agricultural labor force by 38 million peasants between 1957 and 1958, and reduced the amount of cropland by 13%. Table 1.3: How to cripple an agricultural economy: Statistics during China’s “Great Leap Forward” Start (1957) End (1959) Forecasted output 195 MM tons 525 MM tons (1958) Grain output 195 MM tons 170 MM tons Grain imports 2.1 MM tons 3.9 MM tons 32

Grain procured from 46 MM tons 64 MM tons communes Grain kept by rural areas 273 kg/capita 193 kg/capita Rural per-capita calorie 2100 calories/day 1500 calories/day (1960) consumption % of provinces where 20% (1955) 60% (1957) workers can not exit communes

Table 1.3 summarizes agricultural statistics between 1957 and 1959. Total grain output actually fell, presumably due to inefficiencies in the large-scale commune system. Such communes create a classic “free riding” problem or prisoner’s dilemma— why should a farmer work hard when he or she cannot enjoy the additional output, as they would from a home garden? Smaller-scale cooperatives appear to overcome the free riding problem by scolding slow workers, kicking them out of the cooperative, and relying on family and ethnic ties to promote cooperation. While grain output fell, imports grew only a tiny amount. Most importantly, the amount of grain procured from the communes to feed industrial workers and others (perhaps Communist party officials) rose from 46 to 64 million tons. As a result, there was a sharp drop in grain retained to feed farmers. Per-day caloric consumption fell from 2100 calories to 1500. (Healthy typical Americans consume about 2200 calories for men and 1500 for women.) This drop in calorie intake has been shown to adversely affect a human’s physical capacity to do manual labor, and therefore must have adversely affected labor productivity. Why did central planning fail? “Diversion of resources reduces agricultural output directly. Excessive procurement, when combined with an actual reduction in productivity caused by collectivization, significantly reduces food available for consumption in rural areas, leading to a severe nutritional deficiency among rural workers. The resulting reduction in physiological capacity to carry out manual labor would in turn reduce the quality of labor input in growing next year’s crops, leading to an additional decline in production.” In plainer English, as farm productivity fell, and more grain was moved from communes to industrial areas, hungry workers became too weak to produce more food, so productivity fell further, in a literal death spiral. Using an econometric model, Li and Yang found that the most important determining factor was the diversion of resources from agriculture, accounting for 33% of the decline in output from 1958 to 1961. “Excessive procurement of grain, which decimated the physical strength of the peasantry” was responsible for 28% of the collapse in output. Bad weather played a role too, but accounted for only 13% of the decline. Another contributing factor to the famine is the “deprivation of peasants’ rights to exit from the commune.” Some researchers have argued that “the threat of withdrawal from an agricultural collective by harder-working members helps discipline would-be 33 shirkers. The removal of exit rights destroyed this self-enforcing discipline, reduced work incentives, and hence contributed to the fall in grain output.” The fraction of provinces with no exit rights increased from 20% in 1955 to 60% in 1957. Imagine what would have happened if a system of property rights for workers’ output, labor mobility, prices, and free trade were suddenly installed in 1959. The most productive workers would exit the communes to keep profits from their grain and would produce more. High prices would signal to other countries that they can profit from selling grain to China, producing a flood of imports (assuming trade is free). Instead of this scenario, the ironically-named Great Leap Forward was a perfect storm of bad organizational design. Wild optimism led leaders to believe productivity could soar, so more grain could be taken from the communes. The best workers could not leave the communes; discouraged and hungry, their private incentives to work hard were muted. (Suppose you were pretty hungry and spent all day peeling potatoes, only to see them shipped away to someone else. Wouldn’t you be hungry and angry?) Furthermore, the centralized structure of the Communist party meant that information about the disaster was slow to reach the top party officials, or was withheld along the way for fear of disapproval. As Li and Yang note, “the GLF crisis could have been far less devastating had local officials not faced strong political incentives to implement apparently poorly conceived policies and to conceal unfavorable information on local economic performance.” Good decentralization: Claiming races for horses: The public likes competitive races (bet more when odds are close). How to arrange them? Obvious, not-so-great idea: Somebody at the racetrack knows all the horses and asks around, and puts them in races together. Good idea: Announce a race for $20,000 purse and $20,000 claiming price. If horses are entered they can be claimed (bought) by anybody for $20k. Horses worth more than $20k won't enter. Bad ones won't either (too expensive to race in a hopeless race).

Bad decentralization in a nonprofit foundation: The William and Melinda Gates Foundation (founded by Microsoft mega-billionaire Bill Gates) has $66 billion, about half supplied by famous investor Warren Buffet. Their assets represent about 10% of all the private foundation funds in the US (Los Angeles Times Jan 8 2007, “Money clashes with mission”) and is twice as large as the next-largest foundation. The Gates Foundation has awarded $1.2 million in grants to the nonprofit Solid Ground, a Seattle Foundation that helps victims of “predatory lending”. Predatory lenders are firms that encourage poor-credit borrowers to apply for loans and, allegedly, sometimes falsify loan applications or encourage deception to get loans for borrowers they would not otherwise qualify for. These borrowers are then burdened by large payments they did not expect and some have had their lives turned upside down and net worth drained. 34

One lender accused of these practices, Ameriquest, is owned by ACC Capital Holdings Corp., which in January 2006 settled a class-action suit with 49 states and DC for $295 million (without admitting wrongdoing). The company’s CEO Aseem Mital said, "Doing the right thing for the people we serve has always been one of our core values. We regret those occasions when our associates have not met this ideal." Since the Gates Foundation seems committed to helping borrowers avoid these challenges (their “preferences are revealed” by their grants to the Solid Ground nonprofit), you would think they have no interest in profiting from so-called predatory lenders. But at the end of 2006, the Gates Foundation had more than $2 million invested in Ameriquest. Why is the Gates Foundation investing in nonprofits who help people avoid the headaches caused by companies like Ameriquest, and investing in Ameriquest at the same time? According to the LA Times: The Gates Foundation did not respond to written questions about specific investments and whether it planned to change its investment policies. It maintains a strict firewall between those who invest its endowment and those who make its grants. The “firewall” is a deliberate communication gap between the division of the Gates Foundation that maximizes its endowment through stock and bond investment, and the division that spends money. This is a deliberate decentralization. It is designed presumably to prevent conflicts of interest (which are carefully avoided by nonprofits) like making grants to a nonprofit to promote awareness of AIDS, while also investing in a textbook publisher that profits from AIDS-related publications.26 The deliberate decentralization means that the organization is helping avoid bad practice B while the organization also profits from bad practice B. Other philanthropists decry this practice. The LA Times reported: [Senior vice president of the Rockefeller Philanthropy advisors, a nonprofit foundation group that counsels foundation, Douglas] Bauer said he had this message for the Gates Foundation and others that invest with a blind eye to the consequences: Good returns from bad companies can cancel out the beneficial effects of grants.

"In the extreme example, it would be a wash," he said. "Trustees should say to themselves: 'As we think about the work that we are trying to do with this foundation, which ultimately should contribute to the public good, we should be making sure that everything we do is aligned to the mission, including the investments we make.' "

26 To be fair, nonprofits are carefully scrutinized for conflicts of interest which the Gates Foundation’s “firewall” is designed to prevent. Since nonprofits are not taxed, one way to avoid taxes is to set up a nonprofit which coordinates giving grants to activity A while holding stock in firms that benefit when more activity A is done. A good way to avoid the appearance of conflict is to clearly separate the grant-making and investment divisions of the nonprofit. The problem, of course, is that you may get the opposite result: While the IRS is worried about the two parts of the nonprofit working together, it might be that the two parts cancel out each other’s humanitarian efforts. 35

Another nonprofit foundation had an interesting reason for avoiding centralization of grants and investment. The LA Times reported: If the Hewlett Foundation created a large staff to monitor the social impact of its investments, said Eric Brown, the foundation's communications director, "The L.A. Times might call us and ask why we have so much overhead."

Prediction markets: TO BE ADDED

Orange juice Roll study

3. Models of human nature TBA A crucial ingredient for organizing people is a working hypothesis about human nature. Do people only care about themselves? Or do they care about doing right, or helping their friends, or something beyond narrow self-interest? Are there different types of people in these categories; and if so, how can we identify them and match them with the right kinds of jobs? Job satisfaction: One model of human nature is that a happy worker is a productive worker. Unfortunately, many studies measuring both job satisfaction and productivity suggest the correlation between these two variables is zero. To an economist, this makes sense in one way, and makes no sense in another. On the one hand, if being paid more than you are worth produces satisfaction, then a satisfied worker is an overpaid worker, which is bad for the firm. Thus, maximizing job satisfaction should not be the goal of managers. On the other hand, if wages reflecting compensating differentials—or psychic income from jobs that are fun or awful—then if workers are happy at their work, you can pay them less (since they earn “psychic income” from enjoying their work). So creating satisfying jobs reduces the firm’s total wages paid (“the wage bill” in labor economics terms). Perhaps the reason the correlations between satisfaction and productivity are zero is that these forces conflict. An autoworker on an assembly may not be happy but is productive. A happy worker at a retail store, on the other hand, might be spending too much time schmoozing and isn’t productive. In any case, the job satisfaction model is not very helpful unless the job satisfaction leads to productivity, to low turnover of employees, or to a willingness to accept less wage and benefits (compensating differentials).

Organizational citizenship: Group psychology. Identity. Essentialism. Nike tattoos. Gangs etc. 36

Social preferences and heterogeneity: Economists tend to use pure self-interest as a default assumption, and it is often a good approximation. The belief in self-interest stems from a skepticism that even when people talk a lot about fairness, helping others, doing what is right, and so on, when push comes to shove their own self-interest will win out—or worse, the rhetorical arguments about fairness are a way to cloak their self-interest in high-minded garb. As Stigler (1981) wrote, cynically, “when self-interest and ethical values with wide verbal allegiance are in conflict, much of the time, most if the time in fact, self-interest theory…will win.”27 Just below we will consider models in which some players are purely self-interested but others have social preferences (perhaps accompanied by emotional feelings like envy, guilt, shame and so forth). Despite skepticism like Stigler’s, there is a long history of models that attempt to formalize when people trade off their own payoffs for payoffs of others (e.g., Edgeworth, 1881; “equity theory” in social psychology; and Loewenstein, Bazerman and Thompson, 1989). Sensible models of this type face a difficult challenge: Sometimes people sacrifice to increase payoffs of others, and sometimes they sacrifice to lower the payoffs of others. The challenge is to endogenize when the weights placed on payoffs of others switch from positive to negative. A breakthrough paper is Rabin’s (1993), based on psychological game theory, which includes beliefs as a source of utility. In Rabin’s approach, players form a judgment of kindness or meanness of another player, based on whether the other player’s action gives the belief-forming player less or more than a reference point (which can depend on history, culture, etc.). Players prefer to reciprocate in opposite directions, acting kindly toward others who are kind, and acting meanly toward others who are mean. As a result, in a coordination game like “chicken”, there is an equilibrium in which both players expect to treat each other well, and they actually do (since doing so gives higher utility, but less money). But there is another equilibrium in which players expect each other to act meanly, and they also do. Rabin’s model shows the thin line between love and hate. Falk and Fischbacher (2005) and Dufwenberg and Kirchsteiger (2004) extend it to extensive-form games, which is conceptually challenging. A different approach is to assume that players have an unobserved type (depending on their social preferences), and their utilities depend on their types and how types are perceived (e.g., Levine, 1998 and Rotemberg, 2004). These models are more technically challenging but can explain some stylized facts. Simpler models put aside judgments of kindness based on intentions, and just assume that people care about both money and inequity, either measured by absolute payoff deviations (Fehr and Schmidt, 1999) or by the deviation between earnings shares and equal shares (Bolton and Ockenfels, 2000). Charness and Rabin (2002) introduce a “Rawlsitarian” model in which people care about their own payoff, the minimum payoff (Rawlsian) and the total payoff (utilitarian). In all these models, self- interest emerges as a special case when the weight on one’s own payoff swamps the weights on other terms. These models are not an attempt to invent a special utility function for each game. They are precisely the opposite. The challenge is to show that the same general utility function, up to parameter values, can explain a wide variety of data that vary across games and institutional changes (e.g., Fischbacher, Fong and Fehr, 2003). Experimental evidence suggests two important modifications to the view that self-interest is reliable and basic: 27 Stigler, G. (1981). Economics or ethics? Tanner Lectures on Human Values. S. McMurrin. Cambridge, Cambridge University Press. 37

First, not everybody is self-interested all the time. In fact, in experiments only a minority of people will act purely self-interestedly when they know it will harm somebody else, even when they can get away with it. Second, people are different (or at least, in any given situation people will not all act in the same way). Heterogeneity matters because it can interact with economic structures in interesting ways. The existence of limits on greed, and heterogeneity, is nicely illustrated by a story in Dawes and Thaler (1988):

Several models have emerged of “social utility”. These models try to add variables that represent emotions or other forces to the basic model of self-interested used in game theory and labor economics. The idea is to calibrate parameters which represent weights on the variables and use them to see whether the same basic model of social utility can explain patterns in many different games. One model was created by Fehr and Schmidt (1999). Call the vector of payoffs created in a game X≡(x1, x2,…xn ), where xi is the payoff to player i (and there are n players). They assume the utility of player i for the payoff vector X is

n n (1) ui(X)=xi – α/(n-1)k=1 x [xk–xi]0 –β/(n-1)k=1 [xi –xk]0 where the function [x] 0 denotes the maximum of x and 0 (i.e., if x<0 then the function is set to zero). The first term (after the own-payoff xi) represents “envy”, how a player’s utility is reduced by knowing that other players are getting more than she is. The second term represents “guilt”, how much a player’s utility is reduced by knowing that other players are getting less than she is. The parameters α and β represent the “strength” of envy and guilt relative to self-interest. Experimental evidence from various types of games suggests that α and β are both positive and α > β (i.e., envy is stronger than guilt; in fact β=0 is often a good approximation and reduces the theory to one with one free parameter). In fact, some observations suggest that people like being ahead of others, so that β<0 and people get pleasure from having higher income status than others. One problem with the Fehr-Schmidt formulation in (1) is that it does not incorporate a conception of intention… 38

FINISH MORE FROM BOOK ADD ROBERTO

TO FINISH suggests there are three types of people: Conditional cooperators; purely self-interested agents; and “saints” who always cooperate, regardless of whether others do. But rationalization…point is that there are differences and possibly structural influences.

“Trust, but verify”- Sam Walton.

“Traits or states”: Good people, or good times? “Love thy neighbor but keep your guard up/You can’t trust nobody when they hard up”— hip hop lyric.28 Attribution theory is a branch of social psychology that deals with how people make natural attributions of cause and effect. There are two main findings which will be important later as we think about performance evaluation: 1. Attributions are often self-serving; 2. Attributions overattribute cause to traits of people rather than situational influences (in Western populations). Self-serving attribution means that if something goes wrong, you blame bad luck, and if something goes right, you take credit. One study of corporate annual reports showed exactly this type of difference in the way companies described their annual performance (Bettman and Weitz? Admin Sci Q). There is some evidence [check this] that men are more likely to make self-serving attributions than women. The tendency to blame or credit a person, rather than a situation, was called the “fundamental attribution error” by Nisbett and Ross. In formal terms, you can think of a person’s unobserved “type” and a situation’s “type” as combining in an equation to determine performance. For example, some jobs are hard and some are easy; some workers are skilled and some are not. The overattribution to personal types means that people in easy jobs will be thought to be more skilled than they are, and people in hard jobs will be thought to be less skilled. It is as if the brain runs a regression with performance on the left hand side, and variables for personal skill and situational difficulty on the right hand side, and derives too high of a coefficient on personal skill.

28 When I was Googling to find information on this topic I typed in “love thy neighbor but keep your guard”. The top-ranked link contained the phrase “Love thy neighbor but keep your eye on ‘em”. Clicking on that link led to a page with a link for “Potent Love Psychic $10”. Clicking through to that link got you to an “insight guide”, such as “Mystic Phaedre” who could tell you “Is it true love? Are they ready to commit? Will you change careers?” for only $1 per minute. Isn’t this empirical confirmation of the need to keep your guard up? 39

Of course, it is difficult to establish whether a behavior is truly caused by a person or by some situational factor that cannot be controlled. For example, the overattribution to personal skill predicts that after bad seasons, sports coaches will often be fired, on the grounds that they caused the poor season. But of course, a bad season probably is some evidence that a coach is unskilled, so it is difficult to tell whether coaches are being fired unfairly. Because it is difficult to measure attribution mistakes in naturally-occurring settings, the best evidence comes from experiments in which situational factors can be manipulated, and see whether subjects appreciate the importance of the situational change. A classic experiment exhibits what I call the “Anchorman effect”. In a group of students, two students are picked at random. One is told to ask some questions and the other student has to answer them. All the others watch. After the questions-and-answers, the students rate the person who asked, and the person who answered, on various personality traits, including intelligence. Typically, the person who answered questions is rated as less intelligent. Why? The answering person probably did not know some of the answers and made mistakes. The person asking the questions, however, is in a situation where he or she cannot possibly make the same mistakes. The students rating them should adjust for this difference in the roles they have, of course, but typically do not. I call this the anchorman effect because news reporters on TV are comfortable on camera, and usually ask questions (typically written down for them, or on a teleprompter). The interview subjects are less practiced on camera, often nervous, and don’t know the questions in advance. Recently, Nisbett and colleagues have been studying whether overattribution to individual traits is universal across cultures. It appears not to be; subjects from Asian cultures often are quicker to blame a situation for success or failure rather than a person, so the overattribution to people seems to be not “fundamental” at all. One study of oil company executives showed self-serving overattribution to personal skill that illustrates these patterns perfectly. Bertrand and Mullainathan (FINISH CITE) reasoned that oil prices are largely exogeneous—or situational, in the psychology jargon— because any single oil company CEO has little to do with raising or lowering oil prices (which are largely set by worldwide demand, OPEC decisions and supply shocks). But if the board of directors, who sets the CEO’s compensation, do not realize the influence of the oil price variable on profits, they will give the CEO large bonuses when oil prices go up (and profits go up too). That is exactly what happens. But what happens when oil prices go down? Then self-serving attribution kicks in, and the CEO’s compensation does not suffer. The scandals at Abu Ghraib (in which American soldiers were photographed humiliating prisoners in various ways) seems to be an example. Any social psychologist would have warned that taking lightly-trained soldiers (many from the National Guard, who never expected to serve overseas), putting them in a hot, uncomfortable foreign country, and having them guard people who are culturally different is likely to lead to at least some trouble. But the Army’s response, to a large extent, was to treat the problem as if it was caused by a few rogue soldiers. 40

FINISH Example of Asian vs white student attributions of skill and effort in educational achievement (New York Times)

TO FINISH Money as symbolic (exchange of $ with Mrs. Wong, rejecting ultimatum offers, “fine is a price” etc). Class notes from insurance Data from Ian on auction, coin auction, etc TO FINISH

4. Game theory and incentives A “game” is a term social scientists use for a mathematical description of a strategic interaction. The elements of a game are: Players, strategies, information, the order of moves (moving early or late is sometimes a strategy), outcomes, and utilities. Outcomes are determined by the strategies players choose, and the information they have. While we will often talk about financial outcomes as a canonical case (especially in thinking about companies), an outcome is usually a physical event in the world— getting promoted, bankruptcy, earning profits, an apology and money in a legal settlement, getting custody of children, and so forth. We assume that people have numerical utilities over outcomes. Usually we only need to assume that people can rank which outcomes they like best (i.e., utility is “ordinal”—1st, 2nd, 3rd, and so on). Game theory is a promising mathematical language to partly unify natural and social sciences because games can be played at many levels of analysis—from genes to nations. Here are some examples: Tennis players deciding whether to serve to the left or right side of the court; the only bakery in town offering a discounted price on pastries just before it closes; employees deciding how hard to work when the boss is away; an Arab rug seller deciding how quickly to lower his price when haggling with a tourist; rival drug firms investing in a race to reach patent; an e-commerce auction company learning which features to add to its website by trial-and-error; real estate developers guessing when a downtrodden urban neighborhood will spring back to life; San Francisco commuters deciding which way to work will be quickest when the Bay Bridge is closed; Lamelara men in Indonesia deciding whether to join the day's whale hunt, and how to divide the whale if they catch one; airline workers hustling to get a plane away from the gate on time; MBA's deciding what their degree will signal to prospective employers (and whether quitting after the first year of their two-year program to join a dot-com startup 41 signals guts or stupidity); a man framing a memento from when he first met his wife, as a gift on their first official date a year later (they're happily married now!); and people bidding for art or oil leases, or for knickknacks on eBay. There are many books on game theory. The goal in the next few pages is to give the briefest sketch, and some notation, to equip you to grasp the essentials of game theory as it can help us understand organizational life. If you do not have some other background in game theory, and are serious about deeply understanding the theories and examples which will be described later, you should definitely read other books.29 First, some notation. Usually we talk about a finite number (n) of players, indexed by i. Player i’s strategy is denoted si. A vector of strategies, one for each player is denoted S={s1, s2, ….sn}. The part of this vector which removes player i's strategy (i.e., contains every other player's strategy) is denoted s-i. The utility of player i's payoff from playing si when others are playing s-i is denoted ui(si,s-i).

An example: The “p beauty contest” game

Let’s start with an example. It is a simple game that has been played in many experiments for money, so we can compare different theories with some data. In this game each of N (>2) players in a group chooses any number in the interval [0,100] at the same time, without talking beforehand. The numbers are collected and averaged. A target number is defined, which is p times the average. Suppose p=2/3 for concreteness. (Shortly we’ll see what might happen for different values of p.) The player whose number choice is closest to 2/3 of the average, in absolute value, wins a fixed prize of $20.30 This game is sometimes called the “p-beauty contest”, because of a famous passage in John Maynard Keynes’s book The General Theory of Employment, Interest and Money. Keynes wrote:

Professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those which, to the best of one's judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees. 29 A good introductory book (low on math) is Dixit and Skeath (1999). More mathematical books include Rasmusen (1994) and Osborne and Rubinstein (1995). Gintis (1999) includes fresh material on evolutionary theory and experimental data, and tons of problems. The heavy tomes which are used in graduate classes at places like Caltech include Fudenberg and Tirole (1991). 30 Ties are broken randomly, or the players can share the $20; in theory it does not matter which tie rule is used. 42

Think of numbers in the game as the time at which you decide to sell in a rising stock market. If you sell too early (e.g., at time 0) then you miss the chance to profit as stocks are rising. But if you sell too late, by choosing a selling date number higher than other people, then you’ll be too late—if everybody else sold before you, the price will have crashed and you’re selling too low. So the trick is to sell just before the tide turns and everyone else sells. This is equivalent to choosing a number which is below what other people pick, but not too far below. And of course, they are trying to do the same thing.

In our notation, their number choices are 0

I will describe two different analyses of this game. You can judge which might be more plausible as a guess about how people behave, and what the different types of analyses are good for. One analysis, called a “cognitive hierarchy theory”, works like so.31 Different players perform different numbers of steps (k) of strategic thinking. Players doing more steps have figured out what the lower-step players will do. The overall behavior of a group depends on what each of the k-step players does, and what percentages of such players there are (call those percentages f(k)). To start the process off, assume some people aren’t really sure what to do. They don’t spend much time wondering what others will do and choose a lucky number or a random number. Suppose, to make the theory precise, they are equally likely to choose any number in the [0,100] interval. We call these folks “0-step thinkers”. Other “1-step” players think they are playing against people who are doing 0- steps of thinking. (Alternatively, you can think of them as just having no clue what others will do, sometimes called a “diffuse prior” or “ignorance prior belief” in Bayesian decision theory.) The 1-step players use their belief to calculate expected values for different strategies and pick the strategy with the highest expected value. We call this “best-responding”. Why stop there? Some players are probably thinking even more strategically. We can imagine that there are 2-step players who believe they are playing against 0- and 1- step thinkers. The 2-step players have to make a guess about what percentage of players

31 The form of this theory described here is from Camerer, Ho and Chong (2004). The basic idea was proposed earlier by Nagel (1995), for the “p-beauty contest” game, and by Stahl and Wilson (1995). See also Crawford, Costa-Gomes and Broseta (2001) for an elegant study of decision rules using information about what payoffs subjects are looking at on a computer screen (cf. Camerer et al, 1993; Johnson et al, 2002). 43 are doing 0-steps and what percentage are doing 1-step. In our CH specification, we assume that they somehow guess the relative percentages correctly, but ignore the fact that other players are doing two steps of thinking (or more). Continuing on, 3-step players believe they are playing 0-, 1-, and 2-step players. They figure out what those players do, guess the relative proportions correctly, and choose a strategic best response given their guesses. Now let’s translate this model into notation. First define the probability that strategy si) is chosen by a player doing k steps of thinking as Pk(si). To make things simple assume that only integer choices are allowed so the strategies are 0, 1,… 100. (There are 101 such strategies.) The notation is also written out as if there are just two players; when there are many players the notation becomes a bit more complicated. The 0-step players are simple. They have Pk(si)=1/101 (all numbers are equally likely to be picked). For higher-step players we need to compute their beliefs about the distribution of others’ steps, the expected payoffs that result, and their strategy probabilities. These k- k-1 step players form the belief about the percentage of h-step types of gk(h)=f(h)/∑n=0 f(n). For example, the 1-step player forms beliefs g1(0)=f(0)/f(0)=1 (i.e., the 1-step player believes everyone is doing 0 steps of thinking. The 2-step player forms beliefs g2(0)=f(0)/ (f(0)+f(1)) and g2(1)=f(1)/(f(0)+f(1)). (The beliefs g2(n)=0 for n>2.) A 3-step player forms beliefs about the percentage of 0-, 1- and 2-step players g3(0)=f(0)/(f(0)+f(1)+f(2)), g3(1)=f(1)/(f(0)+f(1)+f(2)), and g3(2)=f(2)/(f(0)+f(1)+f(2)). Given these beliefs, the k-step player (for k>1) computes an expected payoff for strategy si of 100 k-1 Ek(si)= ∑j=0 π(si, sj) (∑n=0 gk(n)Pn(sj)). k-1 The term ∑n=0 gk(n)Pn(sj) is the average probability that the strategy sj will be chosen by the other players, weighted by the fraction of players of different steps. The Ek(si) summation is then taken over all the possible strategies sj. Finally, the k-step player chooses the strategy which has the highest expected payoff. That is, Pk(si)= 1 if si= argmaxs Ek(s)

To make the theory precise, we need to figure out what the distribution f(k) is. Because the thinking steps are thought to be discrete, we need a distribution that only gives percentage for integer values of k (or we need to take a familiar distribution, like a Gaussian bell-curve, and discretize it somehow). It is also useful, for doing statistical estimation and for proving theorems, to have a distribution f(k) whose shape can be fully characterized by only one parameter (or two at most). Finally, doing more and more thinking steps is hard cognitive work. A 3-step player has to figure out what 0-step players do, what 1-step players think 0-step players do (to deduce what 1-step players will do), guess the relative percentage of 0- and 1-step players, and then combine all these pieces to choose a best response. So it is plausible that the relative percentage of players who do more and more steps of thinking goes down as k goes up. For example, suppose that the fraction of players who stop at k-1 steps, rather than going on to do just 44 one more step, f(k-1)/f(k), is proportional to k. That is, as k goes up more and more players stop at k-1 steps rather than doing one more step. It turns out the proportionality assumption f(k-1)/f(k)  k implies a unique distribution, the Poisson distribution f(k|)=e-(k)/k! The Poisson distribution is cool! It has a French name that is fun to pronounce (“poisson” means fish in French), drops off very sharply as k goes up (because of the proportionality assumption that is mathematically equivalent), and has the constant e in it. Its shape is completely determined once you specify the parameter , which is the mean and the variance of the Poisson distribution. But what’s ? Our approach is to let data tell us.32 That is, we collected a lot of experimental data sets, derive predicted distributions of strategies for different values of , and see which value of  gives the best fit to the data. Generally we find that =1.5 usually gives a good fit. Figure ? shows what the CH theory predicts when =1.5 (see Camerer and Fehr, Science 06). The theory predicts that every number has a chance of being chosen (because of the 0-step players randomizing), but that most people will pick numbers from about 20-33.

32 You could try to derive it mathematically from an analysis in which players compare the costs of doing more thinking with the benefits of making a better guess, but that is hard to do without knowing more about what cognitive “costs” really mean. 45

Standard game theory takes a very different view of the p-beauty contest game. In standard theory the central concept is the idea of an “equilibrium” pattern of play. When players are “in equilibrium”, they have either figured out by introspection, or learned from experience or some other mechanism, to guess accurately what other players will do, and they choose best responses. That is, if players are in equilibrium they will not be surprised when the choices of other players are revealed. Here is a mathematical definition of equilibrium. The strategies (si*, s-i*) are equilibrium strategies if ui(si*,s-i*)>ui(si,s-i*) for all players i, and all opponent strategies s- i. In plain English, if a strategy si* gives the highest utility (or equally-high utility—note 46

that the inequality is weak, not strict) compared to all other strategies si, assuming the other players are choosing their equilibrium strategies s-i*, then all the strategies si* are in equilibrium. The idea of an equilibrium as I’ve defined it was first proposed by John Nash. He won the Nobel Prize in 1994 and his amazing life was portrayed in the Oscar-winning movie “A Beautiful Mind”. (The movie was based on a book by Sylvia Nasar, which goes into much more interesting detail about game theory than the movie did.) I think the right way to think of an equilibrium is as the ending point of some adaptive process. Indeed, in his thesis Nash talked about an equilibrium arising after “mass action” by a population of players adjusted by trial and error, until the players could not profit by changing their moves. But if an equilibrium comes about from adaptation (or possibly communication, imitation, or evolution of a cultural or biological type). In any case, the concept of equilibrium is valuable for at least two reasons. One is that in finite games, an equilibrium always exists. That means that there is always some kind of prediction a modeller can make. (Indeed, as we will see below there are often multiple equilibria, so then the question is which equilibrium is more likely to occur.) Second, in experiments and naturally-occurring processes most kinds of learning and adjustment from experience will move people in the direction of the equilibrium. So even if people are not in equilibrium right away, they will tend to move in that direction, sometimes quite rapidly. What does Nash equilibrium predict in the p-beauty contest game for p=2/3? The answer is simple, but surprising. If other players are expected to choose numbers with an average of A, then a player should choose si =pA. But if this condition holds, si

Basics of game theory

Now that we have a game in mind, and two kinds of theories, based on limited strategic thinking and equilibrium, we can back up and define some of the key building blocks of game theory. Let’s start with the principle(s) of dominance. A strategy di is a strictly dominant strategy if it is a strict best response to any feasible strategy that the others might play. That is, ui(di,s-i)> ui(si,s-i) for all si ≠ di and for all s-i. (Sometimes a strategy di only dominates one or more other strategies si; then the phrase “for all si ≠ di” in the last sentence is replace by “for some si ≠ di”.) Sometimes one strategy is better than another for some choices by other players, but is not always strictly better and is never worse. Such a strategy di is called weakly dominant. Strategy di weakly dominates si if ui(di,s-i)> ui(si,s-i) for some s-i and ui(di,s-i)> ui(si,s-i) for all s-i. Strict dominance is a useful idea because there is no reason why a player should not choose a strictly dominant strategy, if the utilities fully reflect what the player most wants.33 Choosing a (weakly) dominated strategy is not necessarily a mistake (as choosing a strictly dominated strategy is), but it could be a mistake, depending on what other players do. Consider the simple normal-form game below. In a normal-form (a/k/a strategic- form, or matrix) game players are presumed to move simultaneously so there is no need to express the order of their moves in a graphical tree (or extensive-form). Each cell shows a pair of payoffs. The left payoff is dollars for the row player and the right is for the column player. The payoffs are utilities for consequences. That is, in the original game the consequences may be money, pride, reproduction by genes, territory in wars, company profits, pleasure or pain. A key assumption is that players can express their satisfaction with these outcomes on a numerical utility scale. The scale must at least be ordinal-- i.e., they would rather have an outcome with utility 2 than with utility 1-- and when expected utility calculations are made the scale must be cardinal (i.e., getting 2 is as good as a coin flip between 3 and 1).

Table ?: A dominance-solvable game

33 Later we will talk about what happens when the payoffs are in some observable amounts, like dollars, and players care about how large the other player’s payoff is, because of psychological forces like envy or altruism. Then we need a theory of “social utility” that specifies how different dollar payoffs to all players create utility. 48

L R T 30, 20 10, 18 B 20, 20 20, 18

Suppose you were the row player, deciding between T and B. What would you choose? If you choose B you earn 20 for sure. If you choose T, you are basically gambling that the other player is more likely to choose L. To figure that out, you should think about what the other player’s payoffs are. For the column player, strategy R is strictly dominated by L (because L always gives 20 and R always gives 18). “Deleting” strategy R—a mathematical way of betting heavily that a rational column player will never choose R—then makes the effective payoff from T higher than B, which means T strictly dominates B after R is “deleted” (or assumed to be impossible). So if you are willing to bet that the column player is rational and wants to earn the most money, you should choose T. The unique Nash equilibrium is therefore (T,L). What does the cognitive hierarchy theory predict? If =1.5, then 11% of the players are 0-step column players who choose R half the time. All other k-step players (with k>0) choose L. For row players, 0- and 1-step thinkers randomize equally between T and B. Row players doing 2 or more steps of thinking will figure out that half the 0- step column players pick R and the 1-step column players all pick L. Their belief about what will happen is therefore a (.5)(.22/.55)+(1)(.33/.55)=.80 chance that L will be played. (The fraction .22/.55 is the percentage of 0 players if there are only 0 and 1-step player and =1.5.) So if they are neutral toward risk (i.e., they maximize expected value), T will give an expected value of (.80)(30)+(.20)(10)=26, which is better than 20. The overall percentage of players predicted to choose different strategies by the Nash equilibrium and the CH model are shown Table ? below, along with some data from my class in the winter of 2005. In this case, the Nash equilibrium is not far off, because most column subjects guessed correctly that hardly anybody would play R. But even in this sophisticated group—about one quarter of them had taken a game theory course—the CH model fits a little better than Nash overall.

Table ?: A dominance-solvable game with predictions and data

L R Nash CH Data Data 06 07 T 30, 20 10, 18 1.00 .73 .81 .86 B 20, 20 20, 18 .00 .27 .19 .14 Nash 1.00 .00 CH .89 .11 Data 06 .95 .05 Data 07 .95 .05

Mixed Strategies 49

A mixed strategy for player i is a probability distribution over all the strategies si, Consider the game below, a variation of “matching pennies” (or rock-paper-scissors). In this game, the row player wants the strategy pair to “match” on the diagonal (either (T,L) or (B,R)) and the column player wants to mismatch (either (B,L) or (T,R)). If X=1 the game is symmetric. Notice that there are no equilibria in which players choose a “pure” strategy (i.e., play one strategy with probability one, or play it every time if they are playing repeatedly). How can there be an equilibrium then? The answer is that strategies are mixtures of “pure” strategies. The easiest way to solve for equilibrium mixtures is to just write down a system of utilities and maximize them. Suppose the row player’s probabilities of playing T and B are p and 1-p and the column player’s probabilities are q and 1-q (as shown in the table). Denote these strategy mixtures by p and q, respectively. Dropping out the zero terms, the row player’s expected utility is urow(p,q)=X(pq)+1(1-p)(1-q)=(X+1)pq+1-p-q. The column player’s expected utility is ucolumn(q,p)=1(p)(1-q)+1(1-p)(q)=p+q-2pq. The row player wants to choose a value of p (her strategy mixture) to maximize her expected utility. We can find that optimal value by differentiating urow(p,q) with respect to p and setting the derivative equal to zero. Doing so gives (X+1)q*-1=0 or q*=1/(X+1). Differentiating ucolumn(q,p) with respect to q gives 1-2p*=0 or p*=1/2. This is odd, isn’t it? This is an equilibrium because both players are best- responding given their beliefs. If the column player’s mixture of L and R uses probabilities (1/X+1) and X/(X+1), then the row player can expect to get X with probability 1/(X+1) if she plays T, and 1 with probability X/(X+1) if she plays B. Since these are the same, she is indifferent to whether she plays T or B. Because she is indifferent, she should tolerate choosing any probabilistic mixture of the two. Similarly, the column player’s expected payoffs for L and R are the same, so she is willing to mix between them since neither strategy yields a higher expected payoff than the other. The weird part is that the row player is supposed to mix between T and B equally, for any value of X, but X affects the column player’s mixtures. Intuitively, as X goes up the column player is more and more likely to choose R, as if she wants to keep the row player from getting the high X payoff. But if she is more likely to choose R, the row player wants to choose B. The only pair of mixtures that are mutual best responses are (1/2,1/2) for row and (1/(X+1), X/(X+1)) for column.

Table ?: Asymmetric matching pennies (x>1)

L (q) R (1-q) T (p) X,0 0,1 B (1-p) 0,1 1,0

Mixed-strategy equilibrium is a curious concept. Introducing mixed strategies makes the space of payoffs convex (i.e., for any two points in the space, all points in between are in the space too), which is necessary to guarantee existence of a Nash equilibrium (in finite games). Guaranteed existence is a beautiful thing and is part of what makes game theory productive: For any (finite) game you write down, you can be 50 sure to find an equilibrium. This means that a policy analyst or scientist trying to predict what will happen will always have something concrete to say. However, the behavioral interpretation of mixing strategies is dubious. By definition, a player only desires to mix when she is indifferent among pure strategies, which means she does not (strictly) desire to mix with particular probabilities; she just doesn't care what she does. Furthermore, one player's equilibrium mixture probabilities depend only on the player's payoffs which is odd. A modern interpretation of mixed- strategy equilibrium (called ``purification") is that one player might appear to be mixing but is actually choosing a pure strategy conditional on some hunch variable they privately observe. Mathematically, this works the same way-- as long as each player's belief about the other players' choice matches the predicted probabilities, the mixed equilibrium is a mutual best-response point. Mixing makes more intuitive sense when there is hidden information (as we’ll discuss further below). For example, a liquour store in the South that I heard about had a sign prominently displayed: “A vicious dog guards this store three nights a week. Try to guess which ones.” If a would-be burglar couldn’t outguess the store owner, and a 3/7 chance of being attacked by a vicious dog is enough to deter a burglar, then the clever owner is able to save the costs of having a watchdog on hand while deterring the burglar. Mixing also makes some sense when a game is played over and over and the player’s histories are visible to other players. An example is serving to the left or right side of the court in tennis or kicking left or right in a soccer overtime shootout (where a single kicker faces a goalie and no other players are involved). Some field studies of these sports have shown that it appears that players do randomize fairly well by not exhibiting any predictable patterns (Walker and Wooders, etc., Levitt, Palacio-Huertas).

Prisoners' dilemma

The table below shows a famous game called the prisoners’ dilemma (PD) in a specific numerical form. In this game D (for “defect”) is a dominant strategy. But if both players choose D they each earn 1. If they could both commit to cooperate (C) they would each earn 2. The dilemma is that if players behave individually irrationally (and care only about their own payoffs) then their collective payoff is Pareto-inferior (i.e., they earn (1,1) together rather than (2,2)).

C D C 2,2 0,3 D 3,0 1,1

In the general form, the PD looks like this: The payoffs are often labeled “t” for temptation and “s” for sucker. A PD is defined by the algebraic constraints c>d (both players would be better off cooperating if they could); t>c, d>s (so that D dominates C); and (t+s)/2 < c (so that alternating between (C,D) and (D,C) in a repeated PD is not a better strategy than choosing (C,C)).

C D 51

C c,c s, t D t, s d,d

Empirically, when the PD is played for money payoffs, there is a high rate of cooperation (typically 50%) even if the game is only played once. The rate of cooperation does respond to the variables you would think it responds to—if you raise the relative payoffs t-c and d-s, there is more defection; if you raise the gains from cooperation, c-d, there is more cooperation. However, by far the strongest variable influencing cooperation rates is preplay communication about the game. If players talk and agree to cooperate, they appear to feel obliged to actually cooperate as they promised to.

Coordination Many interesting games have multiple equilibria which have different payoff consequences. These games capture the problem of “coordination”. An important game in this category is “battle of the sexes” (BOS), shown in Table? Two people, Pat and Chris can choose a big fish or a little fish. (This version is modeled on anthropological situation in which two people in a small-scale society are trying to decide how to divide two differently-valued goods.) Both prefers to divide the two fish between themselves, so that one gets the big fish and one gets the little fish, but each prefers to get the big fish. If the players mismatch, and both choose the same-sized fish, they get zero. (If the payoffs from both sides grabbing for the little fish are between 1 and 3, then this a game of “chicken”.34). The BOS game is not dominance-solvable. Neither strategy is dominant (or dominated) for either player because there is no one strategy that is always best. Put differently, each strategy might be best depending on what you think the other person will do. The BOS is a perfect game to illustrate “mixed motives”: Both players want to agree on one of the two divisions on the diagonal of the table (since either division if preferred to getting zero) so they have a common interest in choosing one of the diagonals. But each prefers a different division, so even if they can agree to choose one diagonal—either by talking, or through historical precedent or some other mechanism— they directly disagree about which of the two diagonal outcomes is better (for them).

Table: Battle of the sexes (BOS) game Pat’s choice Little fish Big fish Chris’s choice Big fish 3,1 0,0 Little fish 0,0 1,3

Another interesting coordination game is called “stag hunt”, or the “assurance game” (see Table ?). In this game players can play it safe and earn 1 for sure, by hunting for rabbit. Or they can join a stag hunt, and earn a larger amount x (with x>1) if the other

34 MORE HERE 52 person also hunts stag. However, hunting stag is risky because if the other players hunts rabbit the stag-hunter gets 0. So hunting stag is a bet that the other player is likely to hunt stag too. It is called an “assurance game” because hunting stag requires an assurance that another player will help out. Below is a picture of a “whale hunt” which is a naturally-occurring analogue to stag hunt. Whale hunters in Indonesia, for example, go out in a large boat to hunt for sperm whale35 and other “big game” (mostly manta and devil rays). The alternative is fishing with a line and net in a one- or two-person boat. Large boats (average crew size 11) were able to go out when whale kills were common, coordinating activity, but boat owners had trouble fielding sailors to join them when whale kills declined.

Figure: A naturally-occurring whale hunt game in Indonesia.

The stag hunt game is arguably a better model of organizational decisions than the prisoners’ dilemma. A PDAuthority in organizations often gives bosses the power to demand that players choose “stag” (i.e., go along with a risky corporate plan) or be fired. MORE HERE

35 Michael Alvard and David Nolin, “Rousseau’s whale hunt: Coordination among big-game hunters” Current Anthropology, 43, August-Oct 2002, 533-549 http://anthropology.tamu.edu/faculty/alvard/downloads/Whale_paper.pdf#search='whale%20gintis'). 53

Table: Stag hunt game (x>1) Pat’s choice Stag rabbit Chris’s choice stag x,x 0,1 rabbit 1,1 1,1

Betting games: DETAILS TBA 1 and 2 are each privately informed about a "set of states" 1 will know "It's A or B" (A B) or "It's C or D". 2 will know "It's A" or "It's B or C" or "It's D". A B C D 1's payoffs +32 -28 +20 -16 2's payoffs -32 +28 -20 +16 e.g. A="earnings will be great" B= "earnings will be good" C= "earnings will be bad" D="earnings will be terrible" 1 knows whether earnings will be (good or great) or (bad or terrible) 2 knows whether earnings will be surprising-- either great, (good or bad), or terrible.

When should you bet?

Game theory in action:

The point of the Groucho Marx theorem is to humble you. If a deal looks too good, you should always ask— Am I missing something here? Similarly, if you have created a plan that you think can fool somebody else, who is experienced and on the lookout for trickery, you should think very hard about whether you are smarter than the other guy or gal. A gruesome example of this is the sorry tale of University of Pennsylvania economist Rafael Robb. Robb, 56, lived in a home with his wife legally separated for 10 years (she had filed for divorce in 1993 but the suit was dropped in 2004). He came home one afternoon in December, 2006 and, he told police, found his wife brutally murdered, the presumed victim of a burglary attempt (since a window was smashed in). Police were immediately suspicious because in cases like these, the spouse is always the initial and primary suspect. Glass from the window did not appear to be have been stepped on and crushed, as it would if a murdered came through the window from the outside. It later came out that Robb’s wife was planning to divorce him after their long separation. Robb was arrested on January 8, 2007. 54

Figure ?: Professor Rafael Robb: Guilty of hubris and murder or neither?

A possibly ironic touch comes at the very end of the AP story (“Penn Professor Charged in Wife’s Slaying”, jan 8, 2007):

Penn officials said earlier that they had arranged for someone else to teach Robb's graduate seminar in game theory this semester.

Rafael Robb was a game theorist! He almost surely teaches the “Groucho Marx Theorem” in his class, but, perhaps, did not believe it in practice— if he did commit the crime, he thought he could outwit seasoned homicide cops who have seen hundreds of crime scenes.

Game theory and organizational contracting

Before shaking hands: Hidden information

Until the 1970’s or so, game theory was only capable of addressing strategic thinking when both players have the same information, which is quite unrealistic in most organizational contexts. Workers generally know how hard they have been working and usually have an incentive to hide what they know from employers. Other times the opposite is true—employers may know something the employees don’t because they have more experience, like how dangerous high-rise construction work is or how frequently biotech projects lead to valuable products. As is usually true in scientific modeling, game theorists were well aware that people often have different information—and people with less infomraiton may know that there is something they don’t know, but don’t know what that something is-- but they didn’t know how to model such situations. Then in 1967 John Harsanyi showed how, for which he shared the Noble Prize in 1994. Harsanyi suggested studying these situations by assuming that each player has some private information, summarized by their “type”. The key step which makes the modeling workable is that the chance that each player is of a particular type is “commonly known”—which means both players know the chances, both know that others know the chances, and so on. This, too, is unrealistic but Harsanyi’s framework provided a useful start. 55

The ability to mathematically model situations with differences in information rapidly drew attention to the influence of two different kinds of informational asymmetries in organizational life. One is called “adverse selection” or “hidden information”. The term “adverse selection” comes from the insurance business. Consider car insurance. Insurance companies can ask a few basic questions before deciding which drivers to insure—such as age, gender, accident history, zip code, how many miles the driver usually drives, and the type of car the driver owns. But these variables do not perfectly predict whether a person is a safe driver who is likely to get in accidents and file insurance claims. Remember that in a competitive market, insurance companies will offer insurance at a price which equals the expected cost of accidents (plus a premium to pay for administrative overhead, and profit margin). Drivers will choose the policies with the lowest prices. Now suppose the drivers themselves know something about how safe a driver they are, which the insurance company doesn’t know. Then, in theory, the drivers who realize they are unsafe drivers are more likely to buy insurance, if the price is less than what they know the true expected cost to be.36 Thus, the self-selection of buyers who buy insurance goes against the company’s interest—we say they are being “adversely selected against”. The way in which the kinds of people who select to undertake an action depends on their hidden information happens throughout life and is central to modern economic thinking. (Figure ? below shows how it might apply to savvy fish.) In labor markets, selection is sometimes called “sorting” or “assortative matching”. For example, when companies switch to a system where pay is more closely linked to measured output or performance, performance often goes up. Part of the effect is probably due to the fact that people work harder because they now earn more because of their extra effort. But part of the effect could also be due to the fact that the higher bonus draws very hard-working employees into the firm. (At the national level, this is familiar in the stereotype of hard- working immigrants who often literally risk their lives to migrate to a country where hard work can earn them more money.)

36 We are sneaking in an important assumption here—that drivers who are reckless and those who are quite safe have the same “risk tastes” in evaluating bad outcomes like accidents. If safe drivers really dislike risk, and drivers don’t mind, then the safest drivers might buy insurance which solves the adverse selection problem for insurance companies. This is called “propitious selection” or— a term I prefer and just made up—beneficial selection. This consideration complicates the analysis a lot because it means we must consider a driver’s “type” to have more than one dimension, and types who are high on one dimension (e.g. reckless) might want insurance while those are high on another (e.g., risk-aversion) might want insurance too. If the types are negatively correlated (reckless drivers are gamblers) then it is not clear what equilibrium outcomes of insurance markets will result. 56

Of course, companies are often well aware of the adverse selection problem. There are basically two techniques which help companies minimize adverse selection by helping companies: Measure, or screen. Measurement means trying to uncover the hidden information which potential buyers have about themselves. Insurance companies use “experience rating”, which means charging more for people who have had more accidents, or refusing to sell insurance at any price for very high-risk groups. A related technique is to offer lower rates to people who are members of a group, since the insurance company can gather reliable statistics if the group is large, and simply being a member of a group may be correlated with driving safety. For example, young drivers are notoriously accident-prone and insurance companies dislike insuring them. Student with good grades in school can often get discounts on their insurance rates, because insurance companies think (probably based on large samples of accident rates) that students who are conscientious and sharp enough to get good grades are also likely to be safer drivers than average. Screening refers to creating a set of different insurance policies so that drivers with different self-perceived ability choose different contracts. That way, the contracts “screen out” the bad drivers from low-cost policies (which are priced to make profits only if expected accident costs are low). A good example is deductibles. A deductible is the amount that is deducted from the accident cost before the insurance company pays the claim; or put differently, it is the amount the driver “copays” if there is an accident. Drivers who believe they are really safe drivers will not mind a policy with a large deductible ($1000 is typical for a large deductible) since they will not expect to pay that amount very often. Bad drivers will prefer a lower deductible-- or no deductible at all —because they think there is a high chance they will have an accident and have to pay the deductible. The difference in insurance premiums for different deductible policies is enormous. No-deductible policies are wildly expensive. [IK put some numbers here]. 57

This is consistent with the theory that deductibles are helping the insurance company screen drivers by ability: The safe ones accept high deductibles and pay much lower premiums; the riskier ones are happy with lower deductibles even though their premiums are much higher. A famous adverse selection problem was introduced by George Akerlof and then studied by Max Bazerman and some other psychologists in experiments. Imagine a company that is worth some amount V, uniformly distributed between [0,100] where the value represents the per-share stock price. The current owners of the company know its value. You are trying to acquire the company. From the point of view of economic efficiency, suppose you can manage the company better (or there is some synergy between this company and your own), so that whatever the value of V is, it is worth 1.5V to you. To make it simple, suppose you must make a take-it-or-leave-it offer P. The owners will accept it if your offer P is above the value of the company, and turn it down otherwise. Suppose your objective is to maximize the expected difference between the price you pay and the value of the company to you. What should you offer? Think carefully and write down an offer. [Pause for thinking] If you offer P then there is a P/100 chance that your offer will be accepted, because it is accepted if P40) and two thirds of the time you lose money. Furthermore, even though people sometimes bid less after they lose, by bidding less they typically have their offers rejected so the rate of useful feedback slows down. So natural learning forces take a long time to teach people, from trial-and-error, that bidding a positive sum is a losing strategy in the long run.

Winner’s curse MORE HERE

Insurance and market failure: A canonical example of hidden information is in health insurance. Insurers routinely adjust rates or often refuse insurance at any price to people in various professions or who prescribe to various medication (from LA Times, 58

January 8, 2007, “Health insurers deny policies in some jobs”). To an economist, it is surprising that people with various observable jobs or prescription drug use would be denied coverage at any price. Why? One explanation of this practice comes from a simple model. Suppose people have an observable condition that correlates with risk p of filing a claim of size X in a year. Suppose, to simplify the example, the risks across people are uniformly distributed from 0 to 1 and people are risk neutral (i.e., they will buy coverage if their expected cost is above the price). The expected cost to an insurer of such a person is .5X per year. If the company offers coverage at the price .5X, then only people with risks above .5 will buy the insurance. Since risks are distributed from 0 to 1, the expected cost will be the cost of those people who take the policy, which is those with risks from .5 to 1, or .75X. Then the insurer must price at .75X to break even. But then people with risks less than .75 will prefer to forego coverage, and only people with risks above .75 will buy insurance. But the average risks of such people is (.75+1)/2=.875. So the insurer who offers a price of . 75 is losing money. For any price, the company is attracting only the highest-risk people and cannot make money. This stylized example is often used as an illustration of why insurance markets can “fail”— i.e., why people who want to buy insurance at a fair price given their risks cannot do so. But the model leaves out many details. For example, can’t insurance companies figure out something about a person’s risk from their occupation and health? In fact, companies try to do so but their measures seem draconian. For example, insurers routinely deny coverage at any price to people in certain jobs or who are taking certain prescription medications (see Table ? below). Some are very dangerous but others are not.

Here are some occupations that could render workers ineligible for health insurance under the policies of some insurance companies. (LA Times Jan 8 2007)

Air traffic control Building, moving Chemical/rubber manufacturing Circus or carnival work Concrete or asphalt work Crop dusting Firefighting Furniture and fixtures manufacturing Lumber work, including wood chopping, timber cutting and working in a sawmill Migrant labor Oil well or refinery work Police work Roofing Sandblasting Sports, semi-pro or professional Stockyard work, with or without butchering Stables, all employees Stunt work 59

Telecom installation Transportation and aviation Tree climbing Tunnel work War reporting Window work at heights exceeding three stories Source: Health plans' broker guidelines

Eight of the 20 top-selling prescription drugs, ranked by their 2005 U.S. sales (which are often grounds for uninsurability): Lipitor (cholesterol) Zocor (cholesterol) Nexium (heartburn, ulcers) Prevacid (heartburn, ulcers) Advair (asthma) Zoloft (depression) Singulair (asthma) Protonix (heartburn, ulcers)

ALSO ON THE LIST Accutane (acne) Allegra (allergies) Celebrex (arthritis) Celexa (depression) Concerta (attention deficit) Famvir (herpes) Imdur (angina) Imitrex (headaches, migraines) Lamisil (fungal infections) Parolodel (menstrual disorders) Prozac (depression) Ritalin (attention deficit) Tagamet (heartburn, ulcers) Tapazole (hyperthyroid) Topamax (epilepsy, migraines) Source: Health plans' broker guidelines

After shaking hands: Hidden actions Another problem of limited information is called “hidden action” or “moral hazard”. Hidden action means that one party has an incentive to take an action to benefit themselves at the expense of the other. An extreme form is flat-out fraud and cheating. Oliver Williamson uses the term “opportunism”, which he defines as “self-interest seeking with guile”. Hidden action often shows up in ordinary transactions. In many African countries, bags of rice are sold by weight. Stones are sometimes added to the bottom to increase the 60 weight of the bag, and covered up with rice so the stones are not visible. Savvy buyers know to dig their hands into the rice bag looking for stones before they buy. Hidden action is common in one-time transactions, especially if one side has paid in advance for a product or service. Once I hired a contractor to paint my house. We signed a contract and I paid a deposit. The verbal agreement was for 2-4 painters to come the next day and finish the job over a couple of days. The next morning only one painter came; the job took a total of 5 days. In situations like this, the seller of the service has the buyer over the barrel— once the house is partly painted, the buyer typically does not want to cancel the contract and fire the sellers. In professional service firms (such as law) where clients pay for billable hours of service performed by professionals, overbilling can be common. In fact, it is possible that an equilibrium emerges in which clients expect to be overbilled, but also expect to haggle for a reduction in their total legal bill when a case or project is finished. If so, the firm may tolerate overbilling because if they billed honestly the client might still expect a reduction.

Cardboard

Sometimes markets flourish in one place, rather than another, because geographical differences or other forces influence the extent of hidden action. For example, a lot of used cardboard boxes are collected and sold in Los Angeles, to be used again or recycled. Why Los Angeles? Cardboard is not more plentiful in Los Angeles than in another large city, like Houston or New York. The reason why the Los Angeles market flourishes has to do with the nuances of how the market operates. It takes too much time to count the sheets of cardboard in a large stack; it is easier to sell cardboard by weight. But selling cardboard by weight creates the potential for hidden action. Cardboard is porous and becomes much heavier when it absorbs water. This property of cardboard makes it hard for buyers to tell whether they are overpaying because the cardboard they are buying is wet, or because sellers watered the cardboard to increase its weight and sell it for more money. But Los Angeles is basically a desert city; it only rains about 10 days a year. The used cardboard market flourishes in Los Angeles because the air is dry so that variations in cardboard weight from water are low. This fact reassures buyers who are paying by the pound and makes it relatively easy to see if cardboard has been artificially watered to increase its weight.

Pizza barter

Pizza stores that have delivery to your home or office have Pizza delivery Pizza delivery guy http://tipthepizzaguy.com/stories/story17.htm wild stories!! He would take pizzas that couldn’t be delivered (which as a matter of policy the drivers were allowed to return, and eat or give away) and keep them in his car. Occasinally somebody would flag him down and ask if he had a spare pizza; he’d sell them the old undelivered pizza and pocket the cash. 61

Some case studies:

The same driver would offer to pick up anything else somebody wanted (“beer, cigarettes[sic], McDonalds fries, tampons, whatever…”) for a 100% delivery surcharge.

Moral hazard: Employee bartered cheap pizza (due to employee discount) for services.

 I used to pay for my oil changes with pizza. I think we paid 25% of retail for pizzas, so an XL one topper carryout was like $3. Equivalant to the oil change guy, $20. Price of an oil change, $20. I got it for $3.  I attempted to pay for car repair wit pensive to pay for with pizzas. Pony up the $. ;-)

h pizzas, but found myself being this guy's "pizza $@&#!" for two months. Car repair is too ex

The same driver would offer to pick up anything else somebody wanted (“beer, cigarettes[sic], McDonalds fries, tampons, whatever…”) for a 100% delivery surcharge.

Moral hazard: Employee bartered cheap pizza (due to employee discount) for services.

 I used to pay for my oil changes with pizza. I think we paid 25% of retail for pizzas, so an XL one topper carryout was like $3. Equivalant to the oil change guy, $20. Price of an oil change, $20. I got it for $3.  I attempted to pay for car repair with pizzas, but found myself being this guy's "pizza $@&#!" for two months. Car repair is too expensive to pay for with pizzas. Pony up the $. ;-)

(Near-) death from above: Air traffic control

One of the most remarkable studies of moral hazard is a study of air traffic controllers, by Staten and Umbeck (1982). Air traffic controllers work under great stress, to guide planes safely into major airports. Because of job burn-out, the Federal Aviation Administration (FAA) has an elaborate set of rules for detecting worker disability and remedying it. Like all government employees, controllers are covered by the Department of Labor’s Office of Workers’ Compensastion Programs (OWCP), starting in 1974. The Federal Employees’ Compensation Act (FECA) provides disabled workers with either a fixed award or a 62 percentage of salary paid for the duration of an injury. If the OWCP verifies that an injury is disabling, and job related, controllers with dependents can earn 75% of their base salary tax-free (which can higher than their after-tax pay for highly-paid controllers with many years on the job). Starting in September 1974, several amendments to the FECA act increased the incentive for burned-out controllers to collect disability. One amendment was to allow testimony of clinical psychologists, rather than M.D.’s, as primary medical evidence of a disability. Another amendment allowed controllers to pick the private doctor of their choice to supply medical testimony, rather than being examined by their airport’s flight surgeon. These two amendments allowed controllers to “shop” for a doctor willing to attest to their disability. These amendments came at a time when the OWCP, coincidentally, abolished its investigative staff (who previously scrutinized dubious claims) and assigned investigation to busy claims examiners with heavy caseloads, without increasing the number of examiners proportionally to the increased caseload. Furthermore, in May 1972 Congress authorized funds for a “Second Career Training Program”, to provide up to two years of pay for job training, as well as paying 100% of the base rate of pay, for controllers who had worked for more than five years. A controller who files for disability with the intention of being deemed medically unfit to continue is said to “punch out” of the job. The 1974 changes in the OWCP, the reduction in claims investigation, and the Second Career incentive created a “perfecdt storm” situation that is ripe for moral hazard by controllers who want to punch out and get fully paid for two years while training for a new job.

Table ? Disorder Pre-Second Career Post-Second Career Percentage change Program Change Program Change incidence incidence Respiratory 1.9 1.5 -21 Muscles .5 .4 -20 Ear,nose,throat 6.7 8.0 +19 Abdominal 16.7 20.4 +22 Eye 5.6 7.4 +32 Bones and joints 2.3 4.6 +39 Cardiovascular 22.1 32.5 +47 Neuropsychiatric 10.9 27.2 +150

Table ? shows the increase in medical incidence (per thousand person years of service) between the periods before and after the Second Career provision. Notice that reports in all categories rose (except for rare muscle and respiratory cases). The increase was by far the largest in the “neuropsychiatric” category, which almost tripled. The number of controllers who were disqualified from duty for medical reasons, who were not otherwise eligible for early retirement (older than 50, or with 25 or more years on the job) also rose sharply during the mid-1970’s, from 143 in 1973 (before the OWCP changes) to about 400 a year after 1975. This increase, and in the medical incidence increases shown in Table ?, are rough clues that at least some controllers were 63 appealing for medical disability in higher numbers when the incentive to do so rose, because it was easier to shop for a doctor to testify to disability, and because of the generous Second Career provision (two years of full pay during retraining). The most striking evidence, however, comes from specific incidents. In evaluating claims of psychological burnout (as opposed to medical claims, which are easier to verify), OWCP claims examiners “have been instructed to look for specific stressful incidents on the job that were symptoms of or contributed to a disability”. In fact, there are two prominent categories of “specific stressful incidents” the FAA carefully tracks—“system errors”, and the ominously-named “near mid-air collisions” (NAMCs). At air terminals, the minimum physical separation between incoming planes is three horizontal miles, or 1,000 vertical feet. (Keep in mind that jets going 600 miles an hour cover three miles of separation in 18 seconds, and descend before landing at about 1,000 feet in about a minute.) A NMAC occurs when aircraft are forced to take evasive action, or come less than 500 feet apart (a distance they can cover in half a second). Staten and Umback did a statistical regression of the number of system errors in each quarter of the year, for each of 10 FAA regions, from 1973 to 1976, a period that covers the time before and after the OWCP amendments. The biggest determinant of the number of system errors is the amount of air traffic—there were .012 errors per aircraft, or about one for every 80 planes. Amazingly, the number of quarterly errors per region went up by 3.12 after the OWCP amendments (a number which is quite significantly different than you would expect by chance). It appears that some controllers were deliberately letting planes get close together, creating system errors that could be verified by OWCP claims examiners, in order to get out of their jobs and get two years of full pay and job retraining! The controllers were not too reckless, however. A regression of the number of near-miss NAMCs did not show an increase after the OWCP amendments. Furthermore, before the OWCP amendments, about 40% of the system errors were reported by the controllers themselves, and a third of those were also reported as NAMC’s. After the amendments, the percentage of system errors the controllers reported themselves rose to 58%, but only a sixth were also reported as NAMCs. The data suggest the controllers were deliberately letting planes get close together, but not close enough to create dangerous near-misses. (If anything, they were more careful to avoid NAMC’s while creating more system errors.) As with any statistical analysis, it is important to disentangle causality from correlation. It is possible that the OWCP amendments were passed because air traffic control was getting more difficult, and more system errors would have happened anyway. (The analysis tries to control for this by including the amount of traffic, however.) A final piece of evidence suggests, again, that the increase in system errors was a deliberate response to the increased incentive to get medical disability. Remember that controllers needed five years on the job to become eligible for the generous Second Career full-pay retraining benefits. In 1974, before the changes, 20% of the controllers had less than five years experience, but accounted for 40% of the controllers with reported system errors (a ratio of about 2); the corresponding ratio of error-prone controllers with 5-10 years of experience, compared to the number of controllers with that much experience, was about .8. These data suggest, not surprisingly, that inexperienced controllers were creating more errors. But by 1976, the ratio of error-prone controllers among 64 inexperienced controllers—those ineligible for the Second Career benefit—was about 1.2, and the ratio for eligible controllers (those with 5-10 years experience) rose to 1.8: That is, the more experienced controllers were making more than their share of errors. This amazing story shows how how hard it might be to detect a terrible response to a change in incentives. System errors are relatively rare. The pattern detailed above only came to light when two clever economists, armed with a theory that led them to be suspicious that maybe increased incentives would change behavior, put together a lot of data and saw the pattern. Furthermore, this true-life tale shows how carefully incentive systems must be balanced. Changing the OWCP rules may have been a good policy to combat air traffic controller burnout, but if the OWCP had anticipated an increase in dubious claims, and had increased the number of investigators when the doctor-shopping amendments and Second Career benefits were passed, the number of system errors might not have risen so sharply, or at all.

What does behavioral economics have to say about the frequency of people exploiting their hidden action? The evidence described in section ? on human nature suggests that while self-interest is common, reciprocity springing from moral obligation is common too. The effects observed in experiments make it hard to believe that self- interest seeking “with guile” is so prevalent in the business world.

Hidden action and hidden information are thought to be the central issues in businesses like insurance. In principle, insurance is a simple business because no physical product is produced: The insurance company simply creates a contract spelling out the situations in which it will or will not pay an insurance claim. The goal of the contract is simply to shift risk from the insured to the insurer, while maintaining the insured party’s incentive to take care, and protecting the insurer from the risks of too many insured parties with “bad” hidden information buying its insurance. In theory, insurance markets “fail”— that is, people want insurance but can’t buy it, or cannot buy as much as they want—because of hidden information and hidden action. At least one example suggests, however, that these two forces may not be the whole story underlying underprovision of insurance. The example is the market for insurance of jockeys who ride thoroughbred racehorses. Jockeys usually weigh about 100 pounds, and horses weigh 1500 pounds, racing in close quarters at speeds of around 40 miles per hour. (You can imagine the danger by imagining what it is like to cling to the top of a small Mini-Cooper car, in a race around turns at 40 miles an hour with other cars only a foot or two away from you, and sometimes bumping into you.) Riding horses is extremely dangerous—even the best riders are seriously injured a couple of times in their careers, and some are killed or disabled. (Ron Turcotte, most famous for riding Secretariat, ended up in a wheelchair after a terrible spill during a race.) Yet jockeys complain that they can rarely get as much insurance as they would like. For example, in YEAR, 15 jockeys protested the low ceiling on their accident insurance, $1000 per incident, by refusing to ride for a couple of days at Churchill Downs (the home of the Kentucky Derby). The track, incidentally, took the side of the jockeys by banning the 15 boycotters from riding at their track for the rest of the racing season that year (and also found another carrier who would extend the cap to $500,000 for a modest additional premium of $100-200). CITE 65

What makes this example important is that, in theory, hidden information and hidden action which makes underwriting insurance hazardous for insurance companies can be largely avoided by “experience rating”—having a large sample of clear evidence of how accident-prone an insured person is— and monitoring how carefully they take steps to ensure their own safety when they are covered by insurance. But in horse racing, every single time a jockey rides in a race, the entire race is filmed and available for scrutiny. So except for some other activities that might a jockey unsafe (such as losing too much weight, or a drinking or drug problem), there really is no hidden information or hidden action in insuring jockeys for accidents during a race. In theory, insurance companies can see for themselves whether a jockey rides recklessly, and can judge from a film when they are riding even more recklessly if they are heavily insured. So it is a puzzle, from the point of view of standard theory, why insurers are unwilling to provide more insurance to jockeys who say they are willing to pay for it.

IAN KK Post it: “Why does insurance market fail? Adverse selection and moral hazard.” Article title: They Have the Run of Churchill Downs Article gist: 15 jockeys were barred from racing at Churchill Downs for the rest of the season after they tried to protest their accident-insurance by sitting out a couple days. The insurance was a group program that has a ceiling of $100,000 per incident. Churchill has supposedly found a carrier that will write a $500,000 individual supplementary policy for a $100-200 premium, and “several riders have signed up for the coverage.” ______

Preventing damage from hidden information and action

The most important force that reins in cheating is reputation from repeated interactions. If one person cheats another, the cheated party usually finds out and quits trading with the cheater. Sometimes the cheated party can sue for fraud or publicize what happened. The online auction site EBay developed a novel system of “testimonials”: Buyers who bought goods from a seller can post online comments about the quality of service from that seller. New buyers can see the comments; if they’re bad that hurts the seller’s business a lot. [give specific example here FINISH] If you think people are usually guileless in trying to cheat others, you’d be surprised how well the EBay system works. People routinely send checks in the mail and receive goods from people far away who they’ve never met, and may never buy from again. At the same time, the system does not work perfectly, and the amount of fraud from online internet transactions has increased dramatically in recent years. EBay eventually introduced an escrow system in which the buyer can deposit money with EBay, which is transferred to the seller only after the buyer receives their goods and reports their satisfaction.

Holdup A special kind of hidden action is called the "holdup problem". The potential for holdup occurs when one party makes a large investment which is only valuable in a trading relationship with another party. After the investment is made, the investing party 66 is at risk of being “held up” by the other party renegotiating the deal or, more subtly, being stingy in negotiations as quality and price are adjusted over time. To the extent that the investment is irreversible (or “sunk”), and specific to the relationship, a profit- maximizing investing firm has no choice but to accept the unpleasant result of new negotiations. For example, it is common after getting married for men to gain a little weight, and for women to cut their hair shorter or spend less time on their personal appearance. (These kinds of changes don’t appear to be permanent changes in tastes due to marriage, since after divorcing, it is common for men and women to go back to their single ways.) The most charitable way to interpret these common changes is that people work harder at their appearance before they get married, and slack off a little afterwards. Marriage raises the marginal costs of exiting the relationship, and newlyweds take advantage of this fact by investing less than they did earlier. A common economic example is suppliers building a plant near a large customer, to economize on shipping costs. A famous example in economic history is Fisher Body, who were asked to build a plant near General Motors (GM) factories, to build automobile bodies for their large customer GM that would be cheaper to ship to their factories. As the story goes, Fisher Body balked at making this investment, which led GM to vertically integrate by simply buying Fisher Body, to eliminate the post-investment haggling. Another example comes from journalism. Magazines which are produced every week or month usually do not own the printing presses that physically produce their magazine. [check this] . But daily newspapers usually do own their own printing presses. Why are these ownership structures different? The difference is probably not just due to the fact that the daily papers use the printing press more often. Magazine publishers could own their own presses as well, and simply rent them out when they are not being used to print the magazine. The holdup problem suggests a possible explanation. Once a daily newspaper’s content is delivered to the printing press (or “put to bed”), the newspaper is extremely vulnerable. If the paper’s publication is delayed, or a printing mistake is made, it is a small disaster since the paper is expected to come out daily, and on time. If a magazine is published late, or a printing error is made, the damage is lower for a magazine because the news they report is less timely. So if a newspaper owns the printing press facility, they can get more control (e.g. higher priority, and the ability to fix a mistake rapidly). A magazine, in contrast, doesn’t benefit as much from added control over last-minute slips. Assuming there is some cost to integrating—typically, that the employees are less motivated because they are paid a fixed wage, rather than having their earnings depend on market competition— the benefits to a newspaper to owning their presses can be higher than the costs, but the benefits to a magazine are not worth the added costs. It is an open question how common holdup is in business relationships, and why it occurs. There are not many empirical studies. FINISH here.

Other mechanisms for limiting hidden action FINISH

Monitoring (e.g. "spying" on retail employees, LA times article)

Baker et al paper on tracking truckers with GPS). F{TBA} 67

Reputational incentives

Word of mouth Lawsuits (e.g., class actions) Escrow "Bonding" or collateral (e.g., maid services are bonded) long-lived institutions (e.g., family names, Krazy Karl's Karpet Barn, political parties)

Stock market value after airplane crashes, product recalls etc.

But…markets may under/overreact (e.g., all Arthur Anderson employees get tainted) Bad workers can get good recommendations to "outplace" (academic hiring-- phone vs letter)

"Capital T" endgame/lame duck problem (e.g., President Clinton final-hour pardons) FINISH

______Monitoring versus Honor37

Versus

Virtually all corporations older than a month or two have some sort of corporate culture. What about academic institutions? Most do – they range from the emphasis on sports at the University of North Carolina, to the pledge not to drink, have sex or even dance that students at Wheaton College in Illinois make, to the cherished liberalism and activism at the University of California in Berkeley. The California Institute of Technology, a small university in Southern California with a focus on math and science, has at the core of its culture an honor code. This code simply states:

37 This material was drafted by Galen Loram. 68

“No member of the Caltech community shall take unfair advantage of any other member of the Caltech community.” According to Rutgers University Professor Donald McCabe with the Center for Academic Integrity, the recognized expert on the topic, 75% of college students admit to cheating at some point during their college years. But at Caltech students, faculty and staff estimate that only 2-4% cheat. What is responsible for the 20-fold discrepancy between Caltech and other universities? Many schools have some sort of honor code but most are taken lightly. Students often do not know about the existence of a code, or its details or “teeth” (penalties for violating it). The Caltech honor code seems to work extraordinarily well. So what differentiates an effective honor code from one that hardly makes it into the guide for incoming freshman? One reason is that the honor code is visible in practice at many places, and makes life easier. Students and professors leave their doors open, and often unlocked. Virtually all exams are take-home exams (sometimes “closed book”), typically with a maximum time limit that students can spend taking the exam stated. It is also assumed that people will read, understand and follow detailed policies on exam rules and homework collaboration– or clarify misunderstandings. A recent study by the Center for Academic Integrity found that 82% of its members – colleges nationwide – felt that if a school did not already have an effective honor code, it was either ‘very difficult’ or ‘not possible’ to create one. This suggests a strong founder effect: when the university was founded tens or hundreds of years ago, what the founders decided to implement in the way of an honor code has a marked impact on the community years later. Cultures do not arise overnight, and one which requires a vast amount of trust on the part of all concerned and rests on peer enforcement is fragile at best; thus while in the process of attempting to create such a culture a few ‘bad apples’ could ruin the barrel. A key feature of the Caltech honor code is peer enforcement in two forms: First, if a student sees an honor system violation it is his or her responsibility to report it. Failing to report an honor system violation is, in and of itself, an honor system violation. This meta-norm works best if there is a culture in which new members of the community are indoctrinated into the system, under the assumption that the code generally works so that violations are rare and will be noticed. (If people thought the code was not followed, then violations would congest the system and violators would not worry as much about being caught and brought to justice.) Perception is incredibly important: if students, faculty or staff do not believe that the code is followed – whether or not it actually is - they will not feel obligated to follow it themselves. Thus a strong sense of community—upholding the code-- is necessary to maintain the code. Second, adjudication of honor code violations is conducted entirely by students, with minimal faculty oversight.This saves faculty time, and also means the students know they are being judged by peers. For the students who participate in the Board of Control (BoC) process, it provides a way to contribute to campus life and learn to cope with emotional, nuanced issues of morality which helps prepare them for tackling these questions later in their scientific, corporate or public life. While the benefits of the honor code come in a variety of forms, one of the most important is reduced monitoring costs. Unproctored exams liberate professors to focus on their research instead of wasting time futility looking for cheaters. Conversely, students benefit from the chance to take exams in a situation of their own choosing – blasting Nine Inch Nails on their stereo, sitting next to a tranquil stream, or sitting a desk in the classroom where they learned the material. Another benefit comes in the form of gift exchange. Students feel that they are trusted and thus feel obliged to return the favor in the form of not cheating. Graduate TAs are freed from deeply probing for similarities, instead only looking at something if it catches their eye. The honor code has another huge advantage at Caltech: Most students at Caltech study science and engineering, and many will pursue lives working in universities or corporations. 69

These students will work in environments where they have enormous personal responsibility and are relatively unsupervised (e.g., managing a large R&D lab). The pressure to cut corners, take unfair credit, downplay results which discredit a pet theory, or even fabricate data can be severe. The honor code enables scientists-to-be to “practice” and exercise their ability to do the right thing in a relatively benign environment, as a warm-up to the outside scientific world where the moral issues are often subtler and the stakes higher. Surprisingly, the vast majority of the honor system violations are blatant and occur when someone is under extreme stress and “cracks” (perhaps a close family member is diagnosed with terminal cancer or a similarly grave tragedy occurs). The other main source of violations is ignorance. For example, a student from a foreign country may not understand the idea of plagiarism and might submit a paper that heavily relied on a couple of sources and failed to cite them. The increased atmosphere of trust also allows for other benefits, people can accidentally leave their bag around campus with hundreds of dollars inside and come back hours later to find it still sitting there unperturbed. The unspoken threat of social ostracism of a cheater tends to be enough to keep those who would still consider cheating in line. There are drawbacks too, of course. Doubtless, without the close scrutiny a higher percentage of those who do cheat manage to ‘slip through the cracks’ and not get caught. For those 15-20 cases of suspecting cheating each year that do get reported the chair and secretary determine whether or not the issue at hand is an honor code issue, and whether enough evidence is present to warrant the attention of the Board of Control. Examples of something that would not warrant the attention of the Board would be someone being rude to someone else or a case of suspected copying when the two exams had nothing in common. The BoC then views the evidence, interviews any potential defendants and witnesses and then makes three decisions – whether or not an honor system violation has occurred, how to nullify any unfair advantage gained, and how to protect the community from such a thing happening again. These are recommendations to the deans of students, and are accepted about 95% of the time. Because there are so few cases each case is treated meticulously, in a time consuming process. An average case of cheating or “overcollaboration” that goes to the BoC takes about 60 person-hours to resolve. The fact that cases are taken so seriously also upholds faith in the system—a student who loses a BoC case can at least take consolation in the fact that the system worked hard at adjudicating the case, even if the outcome is unpleasant for him or her. ______

5. Summary The central idea in organizational economics is the triangle of (residual) decision rights, incentives, and evaluation. Incentives refer to the contract and motivations of workers. Evaluation is a system that values performance to award incentives. Decision right refer to the latitude workers have to decide what to do when their contract does no clearly specify what they should do. A good organization balances incentives, and evaluation, so that workers with residual decision rights will make the right decisions. One way to see the importance of balancing these three elements is to study when organizations or countries melt down and create horrible outcomes. One example is China’s “Great Leap Forward” from 1957-61. The Chinese government collectivized farms from small units into large ones (1000+ workers) and falsely forecasted a big boost in productivity. Their optimistic forecast led to a central plan in which farms would 70 contribute a lot of their grain to industrial workers in cities (“excessive procurement”). In fact, output gradually fell because the large scale of the communes reduced the workers’ incentives to work hard. Imports rose only slightly. As a result, workers got to keep less of the grain they produced and consumed fewer calories per worker. Poorly fed workers produced less, creating a death spiral of output. To keep the system from failing, the percentage of workers who could voluntarily leave the communes was ratcheted up sharply. The result was a massive famine, the largest in recorded history. The Great Leap Backward is a perfect storm of how centralized planning (combined with poor feedback to central planners, or a reluctance to admit a mistake) can lead to disaster. Some basic economic concepts are useful in understanding organizations. A property right is a combination of the right to use, and to sell, a piece of property. (The property might be a physical good, like a car or machine, or an intangible good like a slice of time—such as a landing slot at an airport—or a band of radio spectrum.) Many studies of economic development across countries show that enforcement of basic property right is highly correlates with economic prosperity and growth. Property rights are the broader equivalent of “decision rights” in firms. A “partial equilibrium” analysis in economics holds behavior of some agents fixed, while changing behavior of other agents is analyzed. When all agents are assumed to respond to change the analysis is a “general equilibrium” one. Partial equilibrium reasoning either assumes that the agents whose behavior is fixed are unimportant, or the economic analyst simply cannot solve mathematically what will happen when all agents change their behavior. A byproduct of a bad partial equilibrium analysis is “unintended consequences”. There are many examples of such behavior. ONE HERE General equilibrium analysis often goes under the useful name of “supply and demand”. MORE Models of human nature

Basics of Game theory Hidden information and hidden action 71

Partial References Wall Street Journal (1996). Building blocked, A1. December 12 (byline J. Sapsford). Business Week. (1999). That Russian company you bought? Maybe you didn’t. p. 70, December 13 (byline M. Coker). Los Angeles Times (2004). Militiamen ‘reclaim’ oil for Nigerians. July 19, p. C3 (byline Michael Peel). Hoff, Karla and Joseph Stiglitz. (2004). After the big bang? Obstacles to the emergence of the rule of law in post-Communist societies. American Economic Review, June, 94, 753-763. Staten, Michael E. and John Umbeck. 1982. American Economic Review, 72(5), 1023- 1037. Dawes, Robyn M. and Richard Thaler. Anomalies: Cooperation. Journal of Economic Perspectives, Summer 1988, vol 2, 187-197. Jonathan M. Karpoff (“Public versus Private Initiative in the Arctic Exploration” Journal of Political Economy, 109(1):38-78, 2001)

From Fisman and Miguel, 72

Joseph Jett and Kidder Peabody

"If I am operating under their system to generate profits, with their approval," Jett asked, "and they turn around and change the rules so the reasons you had for doing this no longer exist -- and, by the way, we are taking all the money back we paid you -- is that fair?"

Lynch, however, argues that exploiting a dysfunctional system can indeed constitute fraud. "It is like receiving a hundred paychecks each week when you should get one," he said. "It's not a defense to say, 'I assumed the company meant to pay me a hundred times.' "

That defense, however, did sway many of the 500 graduate business students at New York University who gathered one evening last fall to hear Jett unabashedly discuss his case. So, was his story an object lesson to a future generation of financiers eager to avoid the same fate?

Not really. William Starbuck, the professor who invited Jett, said "the overwhelming majority" felt that Kidder and GE "had implicitly defined a game by setting up their accounting system."

In the students' view, Starbuck said, "it is the job of employees to play the game, not to decide whether they are good games or bad games."

Afterword 73

New York Times April 6, 1997. “Joseph Jett: Scapegoat or scoundrel?”

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