by Adam Alter
Of course, there are other differences between fluently and disfluently named companies: service and retail companies might emphasize smoother names more than, say, mining and resources companies, and larger firms might invest more than smaller companies in choosing a catchy name. To rule out the possibility that our effects actually reflected better performance among certain industries or company sizes, we ran a separate study focusing on ticker codes—the brief letter strings that identify each company on the stock market, historically printed on ticker paper alongside stock price updates. To most of us they’re gibberish, but to investment experts, they contain multitudes of information. Mention AAPL and investors ask when Apple will release its next blockbuster product; mention HOG and investors ask when Harley-Davidson will release a new motorbike (or hog as it’s known among enthusiasts). Some ticker codes are transparent (e.g., Google’s ticker code is GOOG) and others are more opaque (United States Steel has the coveted single-letter code X). One way of measuring the fluency of a ticker code is to assess whether you can pronounce it as an English word; GOOG is pronounceable, but RSH (RadioShack’s ticker) isn’t pronounceable according to the rules of spoken English. Sure, you can torture it to sound like “Rish,” but it isn’t readily pronounceable based on the way we combine vowels and consonants in spoken English.
When we compared the performance of stocks with pronounceable (fluent) tickers with that of stocks with unpronounceable (disfluent) tickers, we found the same results as we’d noticed when we focused on stock names: after just one day of trading, stocks with fluent tickers yielded a roughly 15 percent gain across the New York Stock Exchange and the American Stock Exchange, but those with disfluent tickers yielded only a 7 percent gain. If you’re a fledgling company, or a serious investor, an 8 percent bonus makes a very big difference. Predicting stock performance in the short term is notoriously challenging, and financial experts everywhere have long struggled to hit on a solid predictor of early stock performance. This is a powerful result, because it shows that name fluency effects exist even when you eliminate all other information that might be bound up with the fluency of a name. For example, perhaps fluent names like Apple convey more information than disfluent names like Aegon or Aeolus, which tend to be nonsense words or unfamiliar names. This ticker demonstration is striking because fluent and disfluent ticker codes contain basically the same quantity of information (almost none). Moreover, even novice investors can understand the concept of fluency—you don’t need a degree in financial mathematics to know that Belden and GOOG are fluent, but Magyar Tavkozlesi Részvénytársaság and RSH are disfluent. Name fluency, then, has the power to shape not only personal outcomes but also the fortunes of investors and companies in the stock market.
Cuddly Names and Powerful Names: The Role of Phonemes
Some simple spoken sounds, or phonemes, emerge easily, while others emerge with some difficulty, but once they’re spoken aloud, many of them conjure visual images even if they have no meaning at all. In the 1920s, German psychologist Wolfgang Köhler wrote a classic textbook on how we perceive the world. Köhler argued that people share a common idea about how some nonsensical names would look if they were ascribed to a shape. In one thought experiment, readers were asked to consider which of the following shapes was called a maluma and which was called a takete.
If you’re like most people, you’ve never heard the words maluma or takete, but that doesn’t stop you from “knowing” somehow that the smooth, curvy shape on the left is a maluma and the jagged, spiky shape on the right is a takete. Even children who are too young to read are capable of matching rounded shapes to rounded words and hard-edged shapes to hard-edged words. Only a strange, counterintuitive language would assign the labels the other way around, and so it is that many English words just sound “right.” Here’s a quick thought experiment: imagine you define the words stop and meander, or the words haste and dawdle, but refuse to tell a non-English-speaker which definition belongs with each word. Would she be able to connect the words to their correct definitions? Just as maluma seems curvy and takete seems jagged, so meander and dawdle seem soft and slow and squishy, and stop and haste seem sharp and jagged and immediate. It doesn’t make sense, then, to name your sharp, lifesaving pharmaceutical company Baloomba Inc. and your children’s party business Zintec Inc., but the names work quite nicely in reverse. I’d be happy to take a new drug manufactured by Zintec and attend a party run by Baloomba, but Zintec sounds like a hard-nosed party planner and Baloomba seems too whimsical to engage in serious science. Perhaps it’s not surprising, then, that a 1979 study found that thirty-eight of the top two hundred U.S. brand names began with the dominant sounds K or C, and that a whopping ninety-three of them contained the K sound somewhere in their names.
The research I’ve described in this chapter suggests that names are far more important than we might assume based only on intuition. From your name alone, people have some idea of your age, your ethnicity, and whether you’re wealthy or poor. They might decide to hire you if your name’s easy to pronounce and well chosen, or to relegate you to the bottom of the pile if your disfluent name prompts all the wrong associations. Proper names—the labels we give to ourselves and to the companies we promote—are not so different from the linguistic labels we give to the concepts that fill our lives every day. Labels, like names, shape how we view the world, and as the next chapter shows, the people we label as “black,” “white,” “rich,” “poor,” “smart,” and “simple” seem blacker, whiter, richer, poorer, smarter, and simpler merely because we’ve labeled them so.
2.
LABELS
Labels Make a Complex World Simpler
In 1672, Sir Isaac Newton passed a beam of white light through a clear prism and projected the resulting rainbow against the wall of his laboratory. He perceived five distinct colors within the rainbow, which he labeled red, yellow, green, blue, and violet. These labels pleased him for a while, but he believed that colors and musical notes shared a single structure, and that both fell along seven-step octaves. So he returned to his rainbow and decided that a thin sliver of orange fell between thicker bands of red and yellow, and a subtle strip of indigo fell between the blue and violet bands. The resulting seven-colored rainbow is the one we know today. Newton’s detractors were unimpressed, and they debated the true composition of the rainbow for many years, sometimes claiming that Newton’s prisms were cloudy, dirty, or impure, and sometimes arguing that he had seen in the prism too many colors, too few colors, or the wrong colors altogether. But Newton was no more or less right than his critics, because the colors that form the visible rainbow are part of a continuous spectrum. We see distinct colors in the spectrum, but their boundaries are impossible to measure precisely. Regardless, why should it matter whether we use Newton’s five-color taxonomy, his seven-color taxonomy, or some other variation? The colors don’t change merely because we give them different labels, so why should we see them differently?
As it turns out, Newton’s choice was far from trivial, because colors and their labels are inextricably linked. Without labels, we’re unable to categorize colors—to distinguish between ivory, beige, wheat, and eggshell, and to recognize that broccoli heads and stalks are both green despite differing in tone. To show the importance of color labels, in the mid-2000s, a team of psychologists capitalized on a difference between color terms in the English and Russian languages. In English, we use the word blue to describe both dark and light blues, encompassing shades from pale sky blue to deep navy blue. In contrast, Russians use two different words: goluboy (lighter blue) and siniy (darker blue).
The researchers asked English-speaking and Russian-speaking students to decide which of two blue squares matched a third blue target square on a computer screen. The students performed the same task many times. Sometimes both the squares were light blue, sometimes both were dark blue, and sometimes one of them was light blue and the other was dark blue. When both fell on the
same side of the blue spectrum—either light or dark blue—the English and Russian students were equally quick to determine which of the squares matched the color of the third target square. But the results were quite different when one of the colors was lighter blue (or goluboy according to the Russian students) and the other was darker blue (siniy). On those trials, the Russian students were much quicker to decide which square matched the color of the target square.
The task from the blue-matching experiment. On each trial, Russian and English students attempted to match a target square to two options. When the two options straddled the border between the Russian colors siniy (darker blue) and goluboy (lighter blue), Russian students were faster to match the target square to the correct option.
While the English students probably looked at the target blue square and decided that it was “sort of lightish blue” or “sort of darkish blue,” their labels were never more precise than that. They were forced to decide which of the other blue squares matched that vague description. The Russian students were at a distinct advantage; they looked at the square and decided that it was either goluboy or siniy. Then all they had to do was look at the other squares and decide which one shared the label. Imagine how much easier the task would have been for the English students if they had been looking at one blue square and one green square; as soon as they determined whether the target square was blue or green, the task was trivially easy. In fact, an experiment published one year later showed that Russian students perceive dark blue to be just as different from light blue as the color green is from the color blue to English students. When Russian students located a dark blue square within an array of lighter blue squares, part of the visual field within their brains lit up to signal that they had perceived the odd square. The same brain areas were much less active when English students looked at the same array of squares—except when the odd square was green within an array of blue squares. When the colors had different labels for the English students, their brains responded like the brains of the Russian students. We also know that the Russian students relied on these category names, because their advantage over the English students disappeared altogether when they were asked to remember a string of numbers while they were performing the color discrimination task. Since their resources for processing language were already occupied with the task of repeating the number string, they weren’t able to rehearse the names of the colors. Without the aid of linguistic labels, they were forced to process the colors just like the English-speaking students. This elegant experiment shows that color labels shape how people see the world of color. The Russian and English students had the same mental architecture—the same ability to perceive and process the colors in front of them—but the Russians had the distinct advantage of two labels where the English students had just one. This example is striking, because it shows that even our perception of basic properties of the world, like color, is malleable in the hands of labels.
The notion that labels change how we see the world predates the blue-matching experiment by almost eighty years. In the 1930s, Benjamin Whorf argued that words shape how we see objects, people, and places. According to one apocryphal tale, the Inuit people of the Arctic discern dozens of types of snow because they have a different word for each type. In contrast, the rest of the world has perhaps several words—like snow, slush, sleet, and ice. The story isn’t true (the Inuit describe snow with roughly the same number of words as we do), but it paints a compelling picture: it’s much harder to convey what’s in front of you if you don’t have words to describe it. Young children illustrate this difficulty vividly as they acquire vocabulary—once they learn to call one four-legged creature with a tail a “dog,” every four-legged creature with a tail is a dog. Until they learn otherwise, cats and ponies share the same features, so they seem just as doggish as real dogs.
Cablinasians, Blacks, Whites, the Rich, and the Poor: Categories Resolve Ambiguity
Long before children began confusing domesticated cats and ponies with dogs, humans began labeling and cataloging each other. Eventually, lighter-skinned humans became “whites,” darker-skinned humans became “blacks,” and people with intermediate skin tones became “yellow-,” “red-,” and “brown-skinned.” These labels reflected reality no more faithfully than Newton’s seven colors reflected the reality of rainbows, and if you lined up a thousand randomly selected people from across the earth, none of them would share exactly the same skin tone. You could arrange them from darkest to lightest and there wouldn’t be a single tie. Of course, the continuity of skin tone hasn’t stopped humans from assigning each other to discrete skin-color categories like “black” and “white”—categories that have no basis in biology but nonetheless go on to determine the social, political, and economic well-being of their members.
These racial labels function in part like the color labels that allowed Russian students to sharpen the fuzzy line that separates darker and lighter blues. They impose boundaries and categories on an infinitely complex social world, but once in place, these boundaries are very difficult to dissolve. When emerging golfing prodigy Tiger Woods appeared on The Oprah Winfrey Show in 1997, he claimed that he was not “black” but rather “Cablinasian,” a portmanteau word combining his Caucasian, black, Native American (American Indian), and Asian heritages. In the United States, golf has long been a segregated sport, with white players relying on the expert advice of black caddies. Woods was railing against the idea that he was simply a black player breaking the mold—in his view he was a complex mix of ethnic backgrounds that were irrelevant to his prowess as a golfer.
Unfortunately, just as Russians see dark and light blue distinctly because they have different linguistic labels, people are apt to resolve racial ambiguity by resorting to racial labels. In a study conducted at Stanford University, an experimenter showed white college students the picture of a young man whose facial features made it difficult to determine whether he was white or black. For half the students, the man was labeled “white,” and for the other half he was labeled “black.” The students were asked to draw the image in front of them as accurately as they could, so the next participant would be able to match the drawing to the face they had just seen. To sweeten the deal, the student who created the most accurate drawing was promised a $20 cash prize. Some of the students were identified as more likely to endorse racial stereotypes, and those students showed a striking pattern in their drawings. The students who were told that the man was black tended to exaggerate his “typically black” features, whereas those who were told he was white did the reverse, exaggerating his “typically white” features. Although the students from both groups were looking at exactly the same photograph, they perceived the image through a lens that was tinted with the racial label that the researcher provided earlier in the experiment.
The term “tinted lens” borders on the literal here, as a second experiment showed that people believe the same face is darker when its owner is described as black rather than white. Here are three faces from that experiment—one depicting a black man, one depicting a white man, and the middle face depicting a man who could be plausibly described as either white or black.
Which face looks the darkest? And which looks lightest? Although they’re identical in tone, people perceive and later recall the face belonging to the black man on the left as darker than the face belonging to the white man on the right, with the face belonging to the racially ambiguous man in the center falling somewhere between the two. If you cover up the facial features with your hand and focus only on the foreheads, you’ll be able to see that the faces share an identical skin tone. Racial labels are so powerful that we’re incapable of judging skin tone accurately in their presence.
Unfortunately, we’re also incapable of ignoring social labels when assessing a person’s intelligence. In 2005, then Harvard University president Larry Summers attributed the dearth of female science and engineering professors to a “different ava
ilability of aptitude at the high end.” Three years later, British psychologist Chris McManus made a similar claim about working-class citizens, arguing that the working class lacked the intelligence to be doctors. It’s actually very difficult to judge intelligence objectively, especially when the evidence is mixed or inherently ambiguous. In one classic study, two researchers showed that evaluators use labels as a tiebreaker when interpreting this sort of mixed evidence. In that study, Princeton University students decided whether a young fourth-grader named Hannah was performing above, below, or precisely at the level expected of an average student in the fourth grade. During the first phase of the experiment, the students watched one of two brief videos. In one of the videos, Hannah was shown playing in a landscaped park set in a wealthy neighborhood. A quick sweep of her school suggested that it was modern and sprawling, graced with athletic fields and an impressive playground. While the students watched the video, they read a brief biographical report on Hannah, which mentioned that her parents were both college graduates and, now, professionals. This version of Hannah was associated with a series of very favorable labels: wealth, a good school, and educated parents who were now employed as professionals. The other Princeton students were acquainted with a very different and less fortunate version of Hannah. They watched a video of Hannah playing in a fenced-in schoolyard with high-density brick buildings, set amid a neighborhood of small and rundown family homes. This time, the biographical report described Hannah’s parents as high school (but not college) educated, her father working as a meat packer, and her mother as a seamstress from home. This time the labels were portentous, suggesting that Hannah would need to overcome socioeconomic and educational hurdles before attaining academic success.