Justin Wolfers Profile picture
Jan 3, 2019 12 tweets 3 min read Read on X
Here’s a piece that David Romer and I just wrote for the latest CSWEP newsletter, which we think contains some useful insights for those seeking to make economics a more diverse place.

Begins on p.7, here: aeaweb.org/content/file?i…

Lemme expand a bit on what we found...
When we took over as editors of the Brookings Papers, we made a conscious decision to try to include more women economists on the program. You can think of this as something of an experiment, and so our note is our attempt to report on the results.
By one measure, we succeeded. The number of women who published in the @BrookingsEcon Papers more than doubled, to a share that somewhat exceeds the share of women in “top twenty” economics departments.
I know you love diff-in-diffs, so Everyone loves diffs-in-diffs, so the it’s worth comparing the share of women writing for #BPEA both before and after our experiment, and relative to comparable conference and journals.

A chart is worth a thousand words: Image
The most important thing that helped, I think, was just trying to be gender aware. Trying to avoid conscious discrimination under the presumption that implicit bias only afflicts the judgment of others is a recipe for gross gender imbalance.
Let’s be concrete. In our experience, the following practices helped:
- We gave ourselves quantitative guidelines
- We interpreted these as a floor, not a target
- We pushed each other to do better
- We broadened the set of fields we would publish
- We had institutional support
Other well-intentioned changes didn’t move the needle:
- Our more formally structured call for papers didn’t lead to a bunch more submissions from women
- Recruiting the top women in the profession simply supported work that was already going to get published
But the bigger picture is that we failed. The relevant question is whether *at the margin* the marginal female participant contributed more than the marginal man. In our judgment, the answer was a resounding yes. Image
“We had thought we were tilting the scale toward women; the reality is, we had simply put a thumb rather than a fist on the scale for men.”

That was a helluva lesson for us to learn. And we’re sure that it has implications for other efforts to improve gender equity.
We wish that we could report on our efforts to improve the underrepresentation of African-Americans and other marginalized voices. But the underrepresentation of some groups is so severe that in reality, we would be reporting anecdotes disguised as data.
What I learned: Even when you think you’re working as hard as you can to redress imbalances, it’s quite possible that you’re still not doing enough. [/fin]
Also, my friend Karen Pence (not that Karen Pence, the economist, Karen Pence...) has a wonderful article with Daniel Covitz, outlining the Fed’s efforts to create a more diverse and inclusive workforce.

See p.4, here: aeaweb.org/content/file?i…

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More from @JustinWolfers

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
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
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
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
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
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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