by Dan Ariely
Nitin nodded, and Ramesh picked the Simon game at random. In this game, one of the four colored buttons lights up and plays a single musical tone. Nitin was supposed to press the lighted button. Then the device would light the same button followed by another one; Nitin would press those two buttons in succession; and so on through an increasing number of buttons. As long as Nitin remembered the sequence and didn’t make any mistakes, the game kept going and the length of the sequence increased. But once Nitin got a sequence wrong, the game would end and Nitin’s score would be equal to his largest correct sequence. In total, Nitin was allowed ten tries to reach the desired score.
“Now let me tell you what good and very good mean in this game,” Ramesh continued. “If you manage to correctly repeat a sequence of six steps on at least one of the ten times you play, that’s a good level of performance and will earn you twenty rupees. If you correctly repeat a sequence of eight steps, that’s a very good level of performance and you will get forty rupees. After ten attempts, we will begin the next game. Is everything clear about the game and the rules for payment?”
Nitin was quite excited about the prospect of earning so much money. “Let’s start,” he said, and so they did.
The blue button was the first to light up, and Nitin pressed it. Next came the yellow button, and Nitin pressed the blue and yellow buttons in turn. Not so hard. He did fine when the green button lit up next but unfortunately failed on the fourth button. In the next game, he did not do much better. In the fifth game, however, he remembered a sequence of seven, and in the sixth game he managed to get a sequence of eight. Overall, the game was a success, and he was now 40 rupees richer.
The next game was Packing Quarters, followed by Recall Last Three Numbers, Labyrinth, Dart Ball, and finally Roll-up. By the end of the hour, Nitin had reached a very good performance level on two of the games and a good performance level on two others. But he failed to reach the good level of performance for two of the games. In total, he made 120 rupees—a little more than a week’s pay—so he walked out of the community center a delighted man.
The next participant was Apurve, an athletic and slightly balding man in his thirties and the proud father of twins. Apurve rolled the die and it landed on 1, a number that, according to our randomization process, placed Apurve in the low-level bonus condition. This meant that the total bonus he could make from all six games was 24 rupees, or about one day of pay.
The first game Apurve played was Recall Last Three Numbers, followed by Roll-up, Packing Quarters, Labyrinth, and Simon, and ending with Dart Ball. Overall, he did rather well. He reached a good performance level in three of the games and a very good performance level in one. This put him on more or less the same performance level as Nitin, but, thanks to the unlucky roll of the die, he made only 10 rupees. Still, he was happy to receive that amount for an hour of playing games.
When Ramesh rolled the die for the third participant, Anoopum, it landed on 5. According to our randomization process, this placed him in the highest-level bonus condition. Ramesh explained to Anoopum that for each game in which he reached the good level of performance he would be paid 200 rupees and that he would receive 400 rupees for each game in which he reached the very good score. Anoopum made a quick calculation: six games multiplied by 400 rupees equaled 2,400 rupees—a veritable fortune, roughly equivalent to five months’ pay. Anoopum couldn’t believe his good luck.
The first randomly selected game for Anoopum was Labyrinth.* Anoopum was instructed to place a small steel ball at the start position and then use the two knobs to advance the small ball through the maze while helping it avoid the trap holes. “We’ll play this game ten times,” Ramesh said. “If you manage to advance the ball past the seventh hole, we’ll call this a good level of performance, for which you will be paid two hundred rupees. If you manage to advance the ball past the ninth hole, we’ll call that a very good level of performance, and you will get four hundred rupees. When we’ve finished with this game, we’ll go on to the next. Everything clear?”
Anoopum nodded eagerly. He grabbed the two knobs that controlled the tilt of the maze surface and stared at the steel ball in its “start” position as if it were prey. “This is very, very important,” he mumbled. “I must succeed.”
He set the ball rolling; almost immediately, it fell into the first trap. “Nine more chances,” he said aloud to encourage himself. But he was under the gun, and his hands were now trembling. Unable to control the fine movements of his hands, he failed time after time. Having flubbed Labyrinth, he saw the wonderful images of what he would do with his small fortune slowly dissolve.
The next game was Dart Ball. Standing twenty feet away, Anoopum tried to hit the Velcro center of the target. He hurled one ball after another, throwing one from below like a softball pitch, another from above as in cricket, and even from the side. Some of the balls came very close to the target, but none of his twenty throws stuck to the center.
The Packing Quarters game was sheer frustration. In a minuscule two minutes, Anoopum had to fit the nine pieces into the puzzle in order to earn 400 rupees (if he took four minutes, he could earn 200 rupees). As the clock ticked, Ramesh read out the remaining time every thirty seconds: “Ninety seconds! Sixty seconds! Thirty seconds!” Poor Anoopum tried to work faster and faster, applying more and more force to fit all nine of the wedges into the square, but to no avail.
At the end of the four minutes, the Packing Quarters game was abandoned. Ramesh and Anoopum moved on to the Simon game. Anoopum felt somewhat frustrated, but he braced himself and tried his utmost to focus on the task at hand.
His first attempt with Simon resulted in a two-light sequence—not very promising. But, on the second try, he managed to recall a sequence of six. He beamed, because he knew that he had finally made at least 200 rupees, and he had eight more chances to make it to 400. Feeling as though he was finally able to do something well, he tried to increase his concentration, willing his memory to a higher plane of performance. In the next eight attempts, he was able to remember sequences of six and seven, but he never made it to eight.
With two more games to go, Anoopum decided to take a short break. He went through calming breathing exercises, exhaling a long “Om” with each breath. After several minutes, he felt ready for the Roll-up game. Unfortunately, he failed both the Roll-up game and the Recall Last Three Numbers task. As he left the community center, he comforted himself with the thought of the 200 rupees he had earned—a nice sum for a few games—but his frustration at not having gotten the larger sum was evident on his furrowed brow.
The Results: Drumroll, Please . . .
After a few weeks, Ramesh and the other four graduate students finished the data collection in a number of villages and mailed me the performance records. I was very eager to take a first look at the results. Was our Indian experiment worth the time and effort? Would the different levels of bonuses tally with the levels of performance? Would those who could receive the highest bonuses perform better? Worse?
For me, taking a first peek into a data set is one of the most exciting experiences in research. Though it’s not quite as thrilling as, say, catching a first glimpse of one’s child on an ultrasound, it’s easily more wonderful than opening a birthday present. In fact, for me there’s a ceremonial aspect to viewing a first set of statistical analysis. Early on in my research career, after having spent weeks or months of collecting data, I would enter all the numbers into a data set and format it for statistical analysis. Weeks and months of work would bring me to the point of discovery, and I wanted to be sure to celebrate the moment. I would take a break and pour myself a glass of wine or make a cup of tea. Only then would I sit down to celebrate the magical moment when the solution to the experimental puzzle I had been working on was finally revealed.
That magical moment is infrequent for me these days. Now that I’m no longer a student, my calendar is filled with commitments and I no longer have time to analyze experimental data myself.
So, under normal circumstances, my students or collaborators take the first pass at the data analysis and experience the rewarding moment themselves. But when the data from India arrived, I was itching to have this experience once again. So I persuaded Nina to give me the data set and made her promise that she would not look at the data while I worked on it. Nina promised, and I reinstated my data analysis ritual, wine and all.
BEFORE I TELL you the results, how well do you think the participants in the three groups did? Would you guess that those who could earn a medium-level bonus did better than those who were faced with the small one? Do you think those hoping for a very large bonus did better than those who could achieve a medium-level one? We found that those who could earn a small bonus (equivalent to one day of pay) and the medium-level bonus (equivalent to two weeks’ worth of work) did not differ much from each other. We concluded that since even our small payment was worth a substantial amount to our participants, it probably already maximized their motivation. But how did they perform when the very large bonus (the amount equivalent to five months of their regular pay rate) was on the line? As you can tell from the figure above, the data from our experiment showed that people, at least in this regard, are very much like rats. Those who stood to earn the most demonstrated the lowest level of performance. Relative to those in the low- or medium-bonus conditions, they achieved good or very good performance less than a third of the time. The experience was so stressful to those in the very-large-bonus condition that they choked under the pressure, much like the rats in the Yerkes and Dodson experiment.
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The graph below summarizes the results for the three bonus conditions across the six games. The “very good” line represents the percentage of people in each condition who achieved this level of performance. The “earnings” line represents the percentage of total payoff that people in each condition earned.
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Supersizing the Incentive
I should probably tell you now that we didn’t start out running our experiments in the way I just described. Initially, we set about to place some extra stress on our participants. Given our limited research budget, we wanted to create the strongest incentive we could with the fixed amount of money we had. We chose to do this by adding the force of loss aversion to the mix.* Loss aversion is the simple idea that the misery produced by losing something that we feel is ours—say, money—outweighs the happiness of gaining the same amount of money. For example, think about how happy you would be if one day you discovered that due to a very lucky investment, your portfolio had increased by 5 percent. Contrast that fortunate feeling to the misery that you would feel if, on another day, you discovered that due to a very unlucky investment, your portfolio had decreased by 5 percent. If your unhappiness with the loss would be higher than the happiness with the gain, you are susceptible to loss aversion. (Don’t worry; most of us are.)
To introduce loss aversion into our experiment, we prepaid participants in the small-bonus condition 24 rupees (6 times 4). Participants in the medium-bonus condition received 240 rupees (6 times 40), and participants in the very-large-bonus condition were prepaid 2,400 rupees (6 times 400). We told them that if they got to the very good level of performance, we would let them keep all of the payment for that game; if they got to the good level of performance, we would take back half of the amount per game; and if they did not even reach the good level of performance, we would take back the entire amount per game. We thought that our participants would feel more motivated to avoid losing the money than they would by just trying to earn it.
Ramesh carried out this version of the experiment in a different village with two participants. But he went no further because this approach presented us with a unique experimental challenge. When the first participant stepped into the community center, we gave him all the money he could conceivably make from the experiment—2,400 rupees, equivalent to about five months’ salary—in advance. He didn’t manage to do any task well, and, unfortunately for him, he had to return all the money. At that point we looked forward to seeing if the rest of the participants would exhibit a similar pattern. Lo and behold, the next participant couldn’t manage any of the tasks either. The poor fellow was so nervous that he shook the whole time and couldn’t concentrate. But this guy did not play according to our rules, and at the end of the session he ran away with all of our money. Ramesh didn’t have the heart to chase him. After all, who could blame the poor guy? This incident made us realize that including loss aversion might not work in this experiment, so we switched to paying people at the end.
There was another reason why we wanted to prepay participants: we wanted to try to capture the psychological reality of bonuses in the marketplace. We thought that paying up front was analogous to the way many professionals think about their expected bonuses every year. They come to think of the bonuses as largely given and as a standard part of their compensation. They often even make plans for spending it. Perhaps they eye a new house with a mortgage that would otherwise be out of reach or plan a trip around the world. Once they start making such plans, I suspect that they might be in the same loss aversion mind-set as the prepaid participants.
Thinking versus Doing
We were certain that there would be some limits to the negative effect of high reward on performance—after all, it seemed unlikely that a significant bonus would reduce performance in all situations. And it seemed natural to expect that one limiting factor (what psychologists call a “moderator”) would depend on the level of mental effort the task required. The more cognitive skill involved, we thought, the more likely that very high incentives would backfire. We also thought that higher rewards would more likely lead to higher performance when it came to noncognitive, mechanical tasks. For example, what if I were to pay you for every time you jump in the next twenty-four hours? Wouldn’t you jump a lot, and wouldn’t you jump more if the payment were higher? Would you reduce your jumping speed or stop while you still had the ability to keep going if the amount were very large? Unlikely. In cases where the tasks are very simple and mechanical, it’s hard to imagine that very high motivation would backfire.
This reasoning is why we included a wide range of tasks in the experiment and why we were somewhat surprised that the very high reward level resulted in lower performance on all our tasks. We had certainly expected this to be the case for the more cognitive tasks such as the Simon and Recall Last Three Numbers games, but we hadn’t expected the effect to be just as pronounced for the tasks that were more mechanical in nature, such as the Dart Ball and Roll-up games. How could this be? One possibility was that our intuition about mechanical tasks was wrong and that, even for those kinds of tasks, very high incentives can be counterproductive. Another possibility was that the tasks that we considered as having a low cognitive component (Dart Ball and Roll-up) still required some mental skill and we needed to include purely mechanical tasks in the experiment.
With these questions in mind, we next set out to see what would happen if we took one task that required some cognitive skills (in the form of simple math problems) and compared it to a task that was based on pure effort (quickly clicking on two keyboard keys). Working with MIT students, we wanted to examine the relationship between bonus size and performance when the task was purely mechanical, as opposed to a task that required some mental ability. Given my limited research budget, we could not offer the students the same range of bonuses we had offered in India. So we waited until the end of the semester, when the students were relatively broke, and offered them a bonus of $660—enough money to host a few parties—for a task that would take about twenty minutes.
Our experimental design had four parts, and each participant took part in all four of them (this setup is what social scientists call a within-participant design). We asked the students to perform the cognitive task (simple math problems) twice: once with the promise of a low bonus and once with the promise of a high bonus. We also asked them to perform the mechanical task (
clicking on a keyboard) twice: once with the promise of a low bonus and once with the promise of a high bonus.
What did this experiment teach us? As you might expect, we saw a difference between the effects of large incentives on the two types of tasks. When the job at hand involved only clicking two keys on a keyboard, higher bonuses led to higher performance. However, once the task required even some rudimentary cognitive skills (in the form of simple math problems), the higher incentives led to a negative effect on performance, just as we had seen in the experiment in India.
The conclusion was clear: paying people high bonuses can result in high performance when it comes to simple mechanical tasks, but the opposite can happen when you ask them to use their brains—which is usually what companies try to do when they pay executives very high bonuses. If senior vice presidents were paid to lay bricks, motivating them through high bonuses would make sense. But people who receive bonus-based incentives for thinking about mergers and acquisitions or coming up with complicated financial instruments could be far less effective than we tend to think—and there may even be negative consequences to really large bonuses.
To summarize, using money to motivate people can be a double-edged sword. For tasks that require cognitive ability, low to moderate performance-based incentives can help. But when the incentive level is very high, it can command too much attention and thereby distract the person’s mind with thoughts about the reward. This can create stress and ultimately reduce the level of performance.
AT THIS POINT, a rational economist might argue that the experimental results don’t really apply to executive compensation. He might say something like “Well, in the real world, overpaying would never be an issue because employers and compensation boards would take lowered performance into account and never offer bonuses that could make motivation inefficient. After all,” the rational economist might claim, “employers are perfectly rational. They know which incentives help employees perform better and which incentives don’t.”*
