A major difference between econ and epi is that econ is more authoritarian while epi is more democratic. This has positive and negative consequences @epiellie
Econ is authoritarian in the sense that well-respected senior economists are the gatekeepers to all the top journals. If you want your paper to be widely read, you need to get it past an editor who is a leading authority in a (usually) related field.
This means that for a methodological innovation to diffuse, only a few top people need to be convinced that it is worthwhile and they will ask for it as a condition of publication in the top journals.
Additionally, papers are written to appeal to the very best people rather than the lowest common denominator. My experience talking to epi people is that many decisions are made to avoid confusing readers who are bad at stats.
This is not because most epi readers are worse at stats, but because epi authors might need to get their paper past referees and editors with a weak stats background. This doesn't happen so much in econ for editors at top journals.
An example is balance tests where the repeated feedback I got from epi people was that it's bad to report balance tests for RCTs not because they are not informative, but because they might lead a paper to be wrongly rejected.

The downside of authoritarianism is that it is harder to get work published that doesn't fit the established mold. In epi, my experience is that journal editors and referees come from a wide variety of backgrounds with different expectations.
In econ, there is a lot of homogeneity in what top journal editors are looking for. Raj Chetty can get great new data and publish a descriptive paper with some new stylized facts, but a grad student who did the same thing would get hammered by referees and editors.
There are real advantages to the authoritarian model --- good papers are relatively rare, and it's good that we have a few journals with high standards enforced by very knowledgeable gatekeepers to help reduce search costs.
A major unknown is the cost of authoritarianism -- how much truly innovative work disappears entirely because it never receives sufficient exposure.
I haven't seen a systematically analysis quantifying this trade-off and obviously it would be difficult to do, but these kinds of organizational issues are understudied as drivers of scientific progress (see, e.g. @carolyn_sms and @RyanReedHill work)

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More from @Jabaluck

27 Sep
Good news everyone! I'm "a prominent economist".

But Twitter folks -- stop critiquing my calculation by pointing out that it makes assumptions and ignores factors.
That is what calculations of this nature do. I thought it was important to get a rough order of magnitude sense of how many people need to wear a mask in public areas to save one life in the US. I did the most reasonable calculation I could think of given the data available.
I would be very pleased to see other calculations -- for example, embedding our results in a structural model which figured out the long-run impact on deaths given a race between vaccines and natural immunity would be extremely interesting.
Read 7 tweets
16 Sep
It does seem a little sinister when I get threats from "a meeting of the Clans", but hey, at least I'll be "part of world history"
Incidentally, we found that masks reduced COVID symptoms for people under the age of 50 but imprecise zeros for serologically-confirmed COVID.
This could mean that masks are most effective at combatting other respiratory diseases for people under 50, or just that we have less precision when we restrict to serologically confirmed COVID.
Read 4 tweets
13 Sep
This is a very important point -- masks prevent COVID and are valuable in places where many people are dying of COVID. Currently, this is many places.

However, that doesn't mean that a cost-benefit analysis suggests, "everyone should wear masks until COVID is eradicated"
The value of masks in places where nearly everyone is vaccinated is clearly lower. Caution is necessary since, in many parts of the world, the vaccines being used have lower efficacy than in the US, meaning that masks likely have value on top of vaccines.
Additionally, masks may prevent breakthrough cases which may eventually spread to unvaccinated people. However, I haven't seen a quantitative calculation of the magnitude of this benefit -- it may be small.
Read 10 tweets
5 Sep
An intuitive way to grasp the effectiveness of masks: extrapolating from our results, every 600 people who wear masks for a year in public areas prevents 1 person from dying of COVID given status quo death rates in the US.
Note that this is *taking into account current vaccination rates in the US*. Despite the availability of vaccines in the US, the weekly death rate is higher than at any point prior to November 2020.
Here is how I arrived at this number. Our study shows that inducing a 30 pp increase in mask-use prevented 35% of COVID cases among the elderly.
Read 15 tweets
5 Sep
Academics need to engage with people spreading dangerous misinformation, especially under the guise of technical jargon.

If you do not respond, no one will and the world will be less informed.

The kind of engagement @jhausdorfer wants is *not possible* on the needed scale.
The idea that we should patiently educate the aggressively ignorant sounds laudable, but it practically means disengagement. How many who liked the above post consistently attempt to do this? They might try once, but they'll give up because it's too time-consuming.
Here is the thread:


Here is my response:


Note that the original post received a fair amount of attention. This is not a case where signal-boosting is a concern.
Read 18 tweets
3 Sep
For those keeping track at home, this is definitely not what a confidence interval is. A 95% CI is a function of the data such that, given the data generating process with an unknown true parameter, the CI constructed in this way will contain the true parameter 95% of the time.
The idea that all values in a 95% CI are equally likely is preposterous. If one were instead constructing Bayesian credibility intervals, you do not need a gaussian prior to rule this out.
In the Bayesian problem, this would represent an absurd corner case where the data was completely uninformative about the underlying parameter within a specified range. I can't imagine how this would be a reasonable model of the situation at hand.
Read 5 tweets

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