Workaholics and redistribution: Some people like working more than others

Via Reihan Salam, here’s a bit from Greg Mankiw:

one reason that people differ in their incomes is that some people care more about having a high income than others…

Bryan [Caplan] goes on to suggest that to the extent this is true, it weakens the case for income redistribution.

He is absolutely right.  Most of the literature on optimal taxation and redistribution, following Mirrlees, assumes homogeneous preferences.  But Matthew Weinzierl has a recent paper on preference heterogeneity, which shows “ to the extent that variation in income is due to preference differences rather than productivity differences, the optimal extent of redistribution is lower, and the neglect of preference heterogeneity biases the results of conventional optimal tax analyses in favor of redistribution of income.”

Sure, but what about the flip side? Call it the striver phenomenon. Preferences for working aren’t homogeneous either. To the extent that some people get more utility out of working at a given task than others that should recommend relatively more redistribution, no?

Algorithms writing news stories? How unambitious

Are algorithms the future of news writing? Wired had an interesting article on that topic last week, focusing on a company called Narrative Science that is already doing it. Here’s the excerpt Wired provides from a Narrative Science story:

Friona fell 10-8 to Boys Ranch in five innings on Monday at Friona despite racking up seven hits and eight runs. Friona was led by a flawless day at the dish by Hunter Sundre, who went 2-2 against Boys Ranch pitching. Sundre singled in the third inning and tripled in the fourth inning … Friona piled up the steals, swiping eight bags in all …

I know as a writer I’m expected to either cower in fear or boast that no algorithm can ever spin prose like mine. But I had a totally different reaction. At the point where algorithms are handling the news, why are we still using news stories?

The news story is, from an informational perspective, pretty unsophisticated. It’s a block of text, a headline, some tags. There’s barely any structure or metadata.

But for an algorithm to be able to report the news it would seem that you pretty much have to impose this kind of structure on the information, and it’s clear from Wired that that’s what Narrative Science does:

Narrative Science’s writing engine requires several steps. First, it must amass high-quality data. That’s why finance and sports are such natural subjects…

…So Narrative Science’s engineers program a set of rules that govern each subject, be it corporate earnings or a sporting event. But how to turn that analysis into prose? The company has hired a team of “meta-writers,” trained journalists who have built a set of templates. They work with the engineers to coach the computers to identify various “angles” from the data…

…Then comes the structure. Most news stories, particularly about subjects like sports or finance, hew to a pretty predictable formula, and so it’s a relatively simple matter for the meta-writers to create a framework for the articles. To construct sentences, the algorithms use vocabulary compiled by the meta-writers.

My question is this: Why, when you’ve imposed all this structure on the information do you package it in such a “dumb” format? Yeah, I get that people are accustomed to reading news articles, and if you experiment with some new information format you risk users not understanding or embracing it.

But is there any reason to think that the news story is the ideal way to take in information? Yes, humans like narrative. But they’ll get that in magazine journalism. The basic news item – who won a game, what happened to a stock – doesn’t need to be digested as a story.

That’s why we have headlines, that’s why we use bullet points and bold stuff.

As Google’s Richard Gingras recently said:

“Do we not deserve to rethink the architecture of what a ‘story’ is, the form of presentation and narrative to meet the needs of people who are consuming, not just by articles?”

If you have an algorithm smart enough to parse events happening in the world and translate it into structured data you ought to be dreaming a little bigger about how to present it to your audience. The upside down pyramid format worked when turning news into data would have been another step. Now that’s already done as a necessity of algorithmic news.

Como se dice… bias?

One of the coolest paper abstracts I’ve read, via MR, presented without comment:

Would you make the same decisions in a foreign language as you would in your native tongue? It may be intuitive that people would make the same choices regardless of the language they are using, or that the difficulty of using a foreign language would make decisions less systematic. We discovered, however, that the opposite is true: Using a foreign language reduces decision-making biases. Four experiments show that the framing effect disappears when choices are presented in a foreign tongue. Whereas people were risk averse for gains and risk seeking for losses when choices were presented in their native tongue, they were not influenced by this framing manipulation in a foreign language. Two additional experiments show that using a foreign language reduces loss aversion, increasing the acceptance of both hypothetical and real bets with positive expected value. We propose that these effects arise because a foreign language provides greater cognitive and emotional distance than a native tongue does.

Where’s the 400 word version of NYT’s Walmart bribery story?

It’s been just a couple months since the media world debated whether Forbes writer Kashmir Hill went overboard in her curation of an NYT story on Target’s predictive analytics. Basically, Hill slapped a way better headline on the piece, pulled in a bunch of quotes, added some of her own prose, and got a ton of traffic.

Oh, and her piece was much shorter.

What did NYT learn? Apparently nothing. They have an awesome long form piece up today about Walmart’s consistent record of bribery in Mexico. It’s a damning piece executed to perfection. It’s also really, really long.

So why doesn’t NYT have a 400 word version published alongside it?

I love longform journalism, but even if every other reader did too, we all tend to read longform stuff about the issues we care most about. This is an important news item that falls outside of my core interests, and I want to know the basics. No doubt there are a lot of other readers who feel the same way.

In my case I read about 60% and skimmed the rest, but plenty of other readers are going to end up on the HuffPo version that captures the story in a few hundred words.

Why doesn’t the NYT beat them to the punch? The longform narrative obsessives and those with deep interest in Mexico, corruption, corporate citizenship, etc. will still read the long version.

But to not offer the bite size option is to cede an opportunity. You’ve already done all the hard reporting. Take even just a few minutes to think about the product.

Facebook does NOT make us lonely

I have a response to The Atlantic‘s cover story Does Facebook Make Us Lonely? up at BostInno. Here’s a snippet:

Is Facebook making us lonely? That’s the question posed by The Atlantic‘s new cover story, and if you’re interested in learning more about the nature of loneliness and its prevalance in American life, I recommend you give it a read. But if you’re looking for proof that Facebook and the internet more broadly are making us lonely, be prepared for disappointment.

There’s a lot of good information about the psychology of loneliness in the piece, but while author Stephen Marche isn’t quite so unequivocal in his conclusion, the article safely answers the question posed by its title: No, Facebook isn’t making us lonely.

Go give the rest a read and let me know what you think.