Neil Irwin, writing at The Atlantic, excerpted from his book How to Win in a Winner-Take-All World:
And among economists, the evidence keeps building that the concentration of major industries among a handful of superstar firms might be connected to deep economic dysfunctions. When there are fewer employers in an industry, for example, they have more power to depress workers’ wages. Big dominant companies might focus more on defending what they have than on generating the kinds of innovations that drive economy-wide productivity growth. And the rise of superstar firms is likely related to the rise of superstar cities and the hollowing out of many local economies.
This is important and persuasive work—much of which I’ve written about in my day job as an economics writer at The New York Times. But in all the piling on, I fear something really important is missing from the conversation. The rise of superstar firms is rooted in fundamental technological and economic shifts that are mostly desirable. And policy changes aimed at limiting the downsides of corporate concentration—an important goal—wouldn’t restore an economy built on local, artisanal companies. They would instead leave us with a slightly larger variety of very big, technologically advanced companies dominating the corporate landscape.
I’m broadly supportive of using public policy to address market power and industry concentration, but within the political sphere calls for those policies seem, in my view, to ignore the point that Irwin makes above.
This is a great thread from Ben Casselman, economics reporter at The New York Times and formerly FiveThirtyEight, on working with data.
Casselman is careful to note that he’s not an economist and that losing sight of that would lead him to err. But the approach he outlines — humble, integrative, quantitative — is why I suspect analytical journalists can outperform experts in some circumstances in terms of reaching better empirical assessments. See here, here, and here.
A couple pieces that made me a bit hopeful for the internet:
Can “Indie” Social Media Save Us? – New Yorker
Could the IndieWeb movement—or a streamlined, user-friendly version of it to come—succeed in redeeming the promise of social media? If we itemize the woes currently afflicting the major platforms, there’s a strong case to be made that the IndieWeb avoids them. When social-media servers aren’t controlled by a small number of massive public companies, the incentive to exploit users diminishes. The homegrown, community-oriented feel of the IndieWeb is superior to the vibe of anxious narcissism that’s degrading existing services. And, in a sense, decentralization also helps solve the problem of content moderation. One reason Mark Zuckerberg has called for the establishment of a third-party moderation organization is, presumably, that he’s realized how difficult it is to come up with a single set of guidelines capable of satisfying over a billion users; the IndieWeb would allow many different standards to emerge, trusting users to gravitate toward the ones that work for them. Decentralization still provides corners in which dark ideas can fester, but knowing that there’s a neo-Nazi Mastodon instance out there somewhere may be preferable to encountering neo-Nazis in your Twitter mentions. The Internet may work better when it’s spread out, as originally designed.
Will social media die as private networks become more popular? – Metro
Data collected by the Pew Research Centre has showed that social media user growth is plateauing among most age groups. Twitter’s active users actually declined in the US in 2017 whilst teenagers are either leaving Facebook in droves or are becoming ‘Facebook-nevers’ having never signed up to the platform. ‘While the idea that social media could go out of fashion or that popular sites could suddenly disappear may seem unthinkable, it’s possible to discern the beginnings of another radical change bubbling under the surface,’ Alex Warren, author of Technoutopia: How Optimism Ruined The Internet, says.
One final bellwether: I started using Feedly this week (Teams) and for the first time I’m seeing an RSS reader that’s not just a sad clone of Google Reader but actively better. Who knows, maybe blogging will even come back into fashion…
There was a bunch of Twitter discussion this week inspired by a Vox article by Dylan Matthews about Raj Chetty’s introductory economics course at Harvard. TLDR: The course is basically a review of recent, large-scale empirical work on important social problems like inequality. And it has little to no theory. I’ve watched some of the lectures and I’d recommend them. But the discussion on Twitter was about whether this was a good way to introduce students to economics. Some stuck up for traditional Econ 101; some argued it complemented Chetty’s big data course; some plugged the excellent new(ish) CORE introductory text.
My reaction was this:
Whereas academic economists are concerned with the science of economics, including making their models as accurate as possible, most people who take econ 101 need to learn how to use those models in some way in their work and lives. They need to learn to be analysts, not scientists.
And the way to use economic models is to recognize that they are deeply incomplete approximations of the world, that they’ll be more useful in some cases than in others, and that the best results come from combining them with other useful models from other disciplines. I’m all for more people learning economic models other than just perfectly competitive markets, of course. But even more important is for people who do learn about competitive markets to realize that it’s an approximation that is never true but can sometimes be useful.
As good as the Chetty course is, my preferred update would also borrow heavily from, say, Scott Page or Philip Tetlock.
More on economic models here and here.
Are markets more accurate than polls? The surprising informational value of “just asking”
Psychologists typically measure beliefs and preferences using self-reports, whereas economists are much more likely to infer them from behavior. Prediction markets appear to be a victory for the economic approach, having yielded more accurate probability estimates than opinion polls or experts for a wide variety of events, all without ever asking for self-reported beliefs. We conduct the most direct comparison to date of prediction markets to simple self-reports using a within-subject design. Our participants traded on the likelihood of geopolitical events. Each time they placed a trade, they first had to report their belief that the event would occur on a 0–100 scale. When previously validated aggregation algorithms were applied to self-reported beliefs, they were at least as accurate as prediction-market prices in predicting a wide range of geopolitical events. Furthermore, the combination of approaches was significantly more accurate than prediction-market prices alone, indicating that self-reports contained information that the market did not efficiently aggregate. Combining measurement techniques across behavioral and social sciences may have greater benefits than previously thought.