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Justin Wolfers Profile picture
@JustinWolfers
Oct 29, 2019 • 5 tweets • 4 min read • Read on X
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BIG NEWS: You can use my new introductory econ textbooks -- written with @BetseyStevenson -- for FREE in Spring 2020.

It's a great opportunity for you to see what we've done.

To be clear: FREE for you and FREE FOR YOUR STUDENTS.

Deets ==> forms.gle/u9U6vX2YcUzUjp… #TeachEcon
@BetseyStevenson We have an Introductory Micro book, Macro, or combined Principles of Econ. Choose the book you need.

It'll be free for you (the instructor) & we’ll provide free (digital) copies for all of your students. Plus instructor resources.

Your students will see you as a superhero...
@BetseyStevenson Read the fine print: Obviously, I can’t persuade my publishers to do this forever. The free textbooks are just for Spring 2020. After that, it’ll back to usual retail pricing (though @MacmillanLearn is usually more competitive than its rivals).
@BetseyStevenson @MacmillanLearn Where’s the economics in all this?

It’s a signaling game.

My publisher knows whether the book is high quality. Instructors don’t. This offer loses money unless folks who try the book end up loving it. It's a costly signal worth sending only if you believe the book really works.
@BetseyStevenson @MacmillanLearn If you just want to have a look at our new textbook, we'll hook up any economics instructor or journalist with a free review copy. Just fill in your deets here: forms.gle/jqbdoC1ALFv4bb…

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More from @JustinWolfers

Justin Wolfers Profile picture
May 2
Phew.

Payrolls grew a relatively uninteresting (and positive!) +177k in April, and unemployment was unchanged at 4.2%.

This economy is still humming along.

NOTE: This is a reading largely from the pre-tariff period. Still very foggy about what lies ahead.
Revisions were somewhat worrying: March was revised down -43k to +185k. Feb down -15k to +102k.

Three month average payrolls growth -- a useful indicator of the underlying pace of job growth -- is a healthy +155k. That's a pretty great place to be at this point in the cycle.
Nominal wage growth was 0.2% this month, and are up 3.8% over the year. That's probably enough to keep inflation above the Fed's target (and that's before factoring in the effect of tariffs).

The period of disinflation may be over.
Read 8 tweets
Justin Wolfers Profile picture
Apr 30
Ugh. It's happening. The economy shrank in the first quarter, at an annual rate of -0.3%.

The good news: Consumption and investment remained strong. Think of this as a hard-to-interpret report due to -- **all of this**. Remember, this is the average of Q1, and the real concern is about Q2.Image
Look into the details, and the GDP report really isn't that bad. (We already know from the jobs data that the economy did okay in Q1.)

@jasonfurman suggested focusing on Real final sales to private domestic purchasers (basically C+I, the reliable parts of GDP) which grew +3.0% Image
The sharp rise in investment appears to be almost all due to pre-tariff front-running. Investment contributed 3.6%-pts to Q1 GDP growth.

Of that, inventory accumulation was 2.2%-pts.

And an additional 1.1% came from equipment investment (which is what the China tariffs hit).
Read 6 tweets
Justin Wolfers Profile picture
Apr 9
Biggest mistakes I'm seeing in early reporting:

1. Tariffmageddon isn't over: Lotsa tariffs to account for, but the average tariff rate is only down around one quarter.
2. He's not going to get big wins: Tariffs were low before this mess, and if Trump negotiates competently, they'll be low again. Basically no gain.

You've seen this movie before: It was NAFTA which got relabeled by Trump in 2020, but really barely changed.
3. The rationale for this policy keeps changing. Remember when it was all about bringing manufacturing home? (That was yesterday.) Now it's negotiating deals. Those are fundamentally in tension.

(I'm only going to build a factory in the US if tariffs are likely to persist.)
Read 5 tweets
Justin Wolfers Profile picture
Feb 17
One thing I've learned to do when I have questions about social security number holders who are age 100 or older is to look up the SSA Inspector General audit report, "Numberholders Age 100 or Older Who Did Not Have Death Information on the Numident."

oig.ssa.gov/assets/uploads… x.com/elonmusk/statu…
It's a gripping read. It tells me, for instance, that 98 percent of these folks have received no payments. Image
Why are there dead people on (this one table of) the social security database? They died before the use of electronic death records. Image
Read 4 tweets
Justin Wolfers Profile picture
Nov 4, 2024
This counter-response essentially says that any form of weighting in survey research is herding. If so, I love herding!

He's right about the motivation: All weighting is done to ensure that you don't get crazy results. But that's a feature of a good poll, not a bug!
After all: Is there a principled difference between weighting on age (to ensure that your sample includes youngs and olds) and weighting on past vote (to ensure you get folks from across the political spectrum)?
Both age and past vote are:
- Predetermined (before this poll)
- Non-manipulable
- Though self-reported
- And we have good population estimates to weight them to.

What principle would make one of these a legitimate survey design weight and the other "herding"?
Read 9 tweets
Justin Wolfers Profile picture
Nov 4, 2024
Lately @NateSilver has been arguing that pollsters are "herding" —tweaking or hiding their results to avoid publishing outliers.

I don't know any (reputable) pollsters who do this.

And I think the problem here is Nate making a simple math/stats error 🧵 Image
Let's explore binomial distributions and the standard errors of weighted samples.

TL, DR: Weighting across groups with different voting patterns should change how you calculate confidence intervals.

Fail to do so, and you'll falsely accuse pollsters of herding.
First, what @NateSilver538 does:

In a “vanilla” poll—a simple random sample—the standard error of an estimate of a candidate’s vote share, p, in a two horse race is √[p(1-p)/n].

This is the perspective that animates Nate's analysis of "herding". Image
Read 34 tweets

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