economic incentives. They think there is a culture of violence that cannot be overcome. They think this even as they see that Northern Ireland is no longer racked by daily explosions and that tens of thousands of former insurgents in Iraq are sticking to their new role as Concerned Local Citizens. Maybe they are right in the case of Palestinians and Israelis, but history does not support their assumption.
Throughout the long history of Muslim domination of the Middle East until roughly the start of the Second World War, Jews lived better and more freely among Muslims than almost anyplace else in the world. When the so-called Moors controlled Spain, Jews enjoyed the tolerance of the Muslim leadership. That tolerance came crashing down when Ferdinand and Isabella unified Spain under Catholic rule. The basis of Palestinian-Israeli conflict resides, at least for many, in economics, not religion. Religion is a politically useful and easy organizing principle that unscrupulous people use to marshal support, but it is not what the fight was or is primarily about. The fight is about land in a locale where, for most, the economy was historically tied to owning property, just as it is in all traditional societies. The economies in the territories lived in and controlled by Israeli and Palestinians still rely significantly on land, but not nearly as much as they did decades ago. Israel has a modern economy in which agriculture plays a much altered role. The Palestinians aspire to a significant degree to have a modern, service-based economy in tourism. These are the conditions that are ripe for a self-enforcing incentive plan.
Naysayers are too quick to equate what they see people do with what they think their core values are. Because terrorist acts seem so extreme, so fanatical, so incomprehensible, many of us are quick to assume that terrorists are a breed apart. They are thought of as people who cannot and will not respond to rational arguments. And yet we already know that even al-Qaeda insurgents in Iraq can be induced to change their ways for just ten dollars a day. Put the history of Jewish-Muslim relations together with the responsiveness of former insurgents to modest economic rewards, and it’s hard to see the downside to trying a new economic approach, especially one that promises virtually no economic downside for one party and huge gains for the other. As the old anti-war song says, “Give peace a chance.”
Even those who absolutely cannot believe that Palestinians or Israelis would value economic incentives over religious principles should want this tourism-incentive plan tried out. Why? Because it has a “hidden hand” benefit alluded to earlier that directly addresses the concern of naysayers. I think we can all agree that there are some hard-liners on the Palestinian side who don’t care about building a strong Palestinian economy, and others on the Israeli side who are certain God did not intend the land to be occupied by anyone other than Jews. These hard- liners will do whatever they can to thwart peace. They will foment violence to prevent tourists from coming. But we should also be able to agree that there are at least some pragmatists on each side as well. The revenue-sharing strategy will ensure that the pragmatists have a strong incentive to identify hard-liners and fight them. The pragmatists will have an incentive that they do not currently have to provide counterterrorism intelligence to their governments in order to ferret out the hard-liners and stop them from interfering with the massive economic improvements promised by this plan. Thus, it should become easier to find and punish the hard-liners, thereby strengthening the hand of pragmatists on both sides. That’s something that should appeal to those who fear the power of the hard-liners.
What I want more than anything to show in this book, and hope that I have done so to a degree so far, is that by thinking hard about the interests involved in a given problem, we have the opportunity to take the
INCENTIVIZING IGNORANCE
Arthur Andersen was driven out of business by an aggressive Justice Department looking for a big fish to fry for Enron’s bankruptcy. Later, on appeal, the Supreme Court unanimously threw out Andersen’s conviction, but it was too late to save the business. Thousands of innocent people lost their jobs, their pensions, and the pride they had in working for a successful, philanthropic, and innovative company. Andersen’s senior management apparently was entirely innocent of real wrongdoing. Unfortunately, they nevertheless helped foster their own demise by not erecting a good monitoring system to protect their business from the misbehavior of their audit clients. In fact, that was and is a problem with every major accounting firm. In Andersen’s case, I know from painful personal experience how needless their sad end was.
Around the year 2000, the head of Andersen’s risk management group asked me if I could develop a game- theory model that would help them anticipate the risk that some of their audit clients might commit fraud (this is where my work related to the Sarbanes-Oxley discussion from a few chapters back began). As I have related, three colleagues and I constructed a model to predict the chances that a company would falsely report its performance to shareholders and the SEC. Our game-theory approach, coupled with publicly available data, makes it possible to predict the likelihood of fraud two years in advance of its commission. We worked out a way to identify a detailed forensic accounting that helps assess the likely cause of fraud—if any—as a function of any publicly traded company’s governance structure.
We grouped companies according to the degree to which our model projected that they were at risk of committing fraud. Of all the firms we examined, 98 percent were predicted to have a near-zero risk of committing fraud. Barely 1 percent of those firms were subsequently alleged to have reported their performance fraudulently. At the other end of our scale, about 1.5 percent of companies were placed in the highest risk category based on the corporate organizational and compensation factors assessed by the model. A whopping 85 percent of that small group of companies were accused by the SEC of committing fraud within the time window investigated by the model. This is a very effective system that produces few false positives—alleging that a company would commit fraud when it apparently did not—and very few false negatives—suggesting that a company would not commit fraud when it subsequently did.
Enron was one of the 1.5 percent of companies that we highlighted as being in the highest risk category. You can see this in the table on page 119, which shows our predictions for a select group of companies that eventually were accused of very big frauds. The table shows our assessment of the risk of fraud for each company each year. The estimates of interest are for 1997-99. These assessments are based on what is called in statistics an out-of- sample test. Let me explain what that means and how it is constructed.
Suppose you want to know how likely it is that a company is in either of two categories: honest or fraudulent. Using game-theory reasoning, you might identify several factors that nudge executives to resort to fraud when their company is in trouble. A few chapters back we talked about some of those factors, such as the size of the group of people whose support executives need to keep their jobs, and we talked about factors that provide early-warning signs of fraud, such as dividend and management compensation packages that are below expectations given the reported performance of the firm and its governance structure.
We know that some conditions, including the amount paid out in dividends, indicate whether fraud is more or less likely; but how important is the magnitude of dividend payments in influencing the risk of fraud compared to, for instance, the percentage of the company owned by large institutional investors? That too is an important indicator of the incentive to hide or reveal poor corporate performance. There are statistical procedures that evaluate the information on many variables (the factors identified in the fraud game devised by my colleagues and me, for example) to work out how well those factors predict the odds that a company is honest or fraudulent (or whatever else it is that is being studied).
There is a family of statistical methods known as maximum likelihood estimation for doing this. We won’t worry here about exactly how these methods work. (For the aficionados, we used logit analysis.) The important thing is that these methods produce unbiased estimates of the relative weight or importance of each of the factors, each of the variables, thought to influence the outcome. By multiplying each factor’s value (the number of directors, for
