“Our Economy Thrives on Bad Feelings” by Astra Taylor in The New York Times is an important, passionate piece—one of those long think pieces that I champion and occasionally write.
The tl;dr is “Compare and despair. You’re not alone. Inequality is pervasive and exhausting.” (The ChatGPT summary? “Focusing solely on inequality overlooks the broader issue of manufactured insecurity, where economic systems exploit and perpetuate feelings of vulnerability.”)
The older I get, the more I view self-acceptance as a vital, radical act in a world that profits off of making us feel a constant lack. So it’s important.
The piece contains a data dump that’s so typical today:
A few months ago, I presented at the national conference of ASJA, the American Society of Journalists & Authors, about the allure of the data dump. Writers use it, primarily, because we think that hitting people over the head with all of these numbers demonstrates our mastery of the topic, adding a sense of urgency to the situation. It shows—like someone reaching a new level of post-therapy emotional maturity—that we’ve “done the work.”
The problem with data dumps is their failure to clarify the real meaning.
Instead, they communicate an idea: “I have done lots of research! This is important!!”
Aside: why no one thinks they’re dumping data
If you’ve spent years studying inequality, you’re bleeding numbers and would have no problem writing a textbook; three figures seems like nothing. It’s oversimplifying the situation, you think.
But everyone else in the world is approaching these numbers from a completely different context. For all of us, this is new. Even if we know about inequality, we’re used to kicking around different numbers. We all suffer from the curse of expertise: we don’t realize how much we know, and how foreign our knowledge is to everyone else.
My suggestion? Assume that the other person isn’t interested. That they’re tuning out after a few numbers. That they’re already overwhelmed.
Making the strange familiar and the familiar strange
One suggestion is to take a lesson from cultural anthropology: make the strange familiar and the familiar strange.
Numbers are a foreign language, and numeracy varies just as much as literacy. When we treat numbers like a foreign language, we get:
First, break down the meaning
The NYT paragraph is actually making three separate points:
- Rate/change in wealth inequality over time
- Global wealth inequality
- U.S. wealth inequality
So let’s break down this paragraph.
Since 2020, the richest 1 percent has captured nearly two-thirds of all new wealth globally — almost twice as much money as the rest of the world’s population.
Here, we’re talking about a change in overall wealth since 2020. To look at change over time, we can use many tools:
- Contrast a picture of then and now. 2/3 of the extra pocket money accumulated in the last 3 years has ended up in the pocket of 1 person out of 100.
At the beginning of last year, it was estimated that 10 billionaire men possessed six times as much wealth as the poorest three billion people on Earth.
- Don’t double-up on vocabulary when it has different meanings. “Billion” really doesn’t mean anything—from a linguistic perspective, our brain draws similarities between “billionaire” and “billion people” when they’re so close together. Sometimes, smaller is sharper.
- Don’t make people do math. Here’s the cardinal sin: 6 times as much wealth? Leave this out.
In the United States, the richest 10 percent of households own more than 70 percent of the country’s assets.
If we divided apartments the same way that the U.S. divides wealth, in an apartment of 100 buildings, 10 people would own 70 of those buildings.
Focusing on the U.S., rather than global inequality, paints a clearer picture of the differences. I’d recommend that the author paint a picture of apartments (total wealth inequality) on a global scale.
Instead of bombarding people with numbers, paint a picture of your most representative fact.
We’re more sensitive to relative numbers (instead of absolute quantities). Don’t throw around huge numbers—use small, sharp ratios.