I had a grand plan, dear reader. I planned to shed quantitative light on the problem of income inequality. And I planned to do it with a million games of web-scraped Scrabble tournament data.
But, like in Scrabble, where the best-laid plans sometimes leave you holding a rack full of vowels and nowhere to play them, I was left with unanswered questions and a pile of data. Turns out that Scrabble can’t explain a core economic concept. But please bear with me. There’s a data party favor at the end.
Inequality has skyrocketed in the U.S. over the past half-century, documented in great detail by all sorts of folks. One theory for the widening gap between the 1 percent and the 99 percent is technological change: When new technologies emerge, higher-skilled and higher-earning workers can more quickly adapt to and exploit them, the notion goes. They become more effective at work and can earn even more, and the gulf widens.
There has been dramatic “technological” change in Scrabble, too, and it involves dictionaries. Every weekend, in hotel ballrooms, empty offices and fast-food restaurants across the country, tournament Scrabble players take their seats, two to a board, to place the game’s 100 lettered tiles. This is the Scrabble “economy.” Governing the interactions in this economy is a dictionary full of tens of thousands of allowed words. Every so often, the book gets even thicker — Scrabble’s “technology” improves.
About a decade ago, there was an actual technological revolution in the game: Its training tools went digital, allowing players to learn words and strategies more easily. In 2006, an early version of Zyzzyva, a now-indispensable word-study tool, was first publicized, and Quackle, a Scrabble analysis engine and A.I. sparring partner, was publicly released.
The biggest change happened that same year, in March, when a new dictionary, the second edition of the Official Tournament and Club Word List, took effect. This edition christened QI and ZA as valid Scrabble words in North American play, along with FE, KI, OI and an additional 11,000-odd longer words. Two-letter words are the building blocks of Scrabble’s DNA, and the Q and Z are juicy high-point tiles — so the game evolved instantly.1 You can see that in the data set I created by scraping over 1.5 million tournament games covering the years 1973 to 2017 from cross-tables.com, an online clearinghouse for Scrabble tournament results. After the new dictionary hit the scene, the average score grew by about 10 points per player per game overnight. (The average score in the data set is about 374.)2
But what other effects did this technical lexical revolution have? Did better players more effectively exploit these new words, increasing their average score more than their mediocre weekend counterparts? Did the Scrabble rich get Scrabble richer? Did income — ahem, point-scoring — inequality increase?
I, and experts I spoke with, suspected yes. “One of the reasons I love Scrabble is the complex metagame — every time there’s a new development, like a dictionary update, everyone has to adapt, and the game changes,” Evans Clinchy, a top player from Oregon, told me. “I definitely think that stronger players are better-equipped to exploit these changes than weaker ones.”
But when I ran the numbers, trying to uncover inequality-exacerbating distributional effects of new words in the Scrabble economy, there was weak evidence at best. In fact, players of all skill levels seemed to benefit more or less equally from the expanded word lists, and average scoring went up similarly across the board. But maybe some light does shine through this Scrabble keyhole onto larger questions of macroeconomy. Thomas Piketty, a French economist and the author of “Capital in the Twenty-First Century,” for example, is sharply skeptical of the technology explanation for income inequality, even if he probably didn’t have Scrabble in mind. (By my count, the game isn’t mentioned once in his book’s 704 pages.)
FiveThirtyEight believes in showing its work, which is why you read this piece of scratch paper and why we’ve posted the data to GitHub. If you find anything interesting, or end up using this in your economics dissertation, let me know!
Dhrumil Mehta contributed research.
