It's hilarious that a high-end New York City restaurant going plant based has triggered so many right wingers. It's almost like they don't trust the market anymore.
As someone who appreciates a well-regulated and vibrant market for food-away-from-home, I'm looking forward to spending some of my forced savings over the past year at Eleven Madison Park next time I'm in NYC!
Cc: @vegan might want to tweet about it ;-)
Technological advances in plant-based food have been quite amazing TBH.

So getting even a few high profile Michelin-rated restaurants to do what Eleven Madison Park did can help influence attitudes and behavior.

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More from @arindube

4 May
One possible reason why there are complaints about "labor shortage" is that adding 1M workers when U-rate is 6% has no precedence in pre-pandemic period 1990-2019.

1/
Importantly this is about *changes* ... and doesn't mean that we've reached a high *level* of lab mkt tightness. We're likely to get to much lower u-rate in coming months. But the scale of *change* can explain growing pains and complaints about shortages.

2/
From this perspective:

Temporary Shortage = f(Lab Market Tightness, Rate of Change in Employment)

So temp shortages (even if they were real) may not necessarily imply we've reached maximal tightness, if it's happening at a time of unprecedented rate of change in emp.
3/
Read 5 tweets
24 Apr
IIRC the zero K tax result of Chamley Judd has (at least) two really important limitations.

1) It actually suggests potentially confiscatory tax for a long time, and then eventually reduce rate to zero

2) It assumes infinitely elastic supply of K w.r.t. net-of-tax rate
For the first point, see Straub @IvanWerning: aeaweb.org/articles?id=10…
For the second point, Piketty and Saez: nber.org/papers/w17989
Read 4 tweets
11 Apr
Is the $300/week UI supplement holding employment growth back?

Tl'dr: whatever may be happening with vacancies or applications, extending my prior analysis to end of March finds little difference in job growth using x-sectional variation in replacement.
1/
Much of the popular discussion on this seems to have centered around restaurants, which TBH has other things going on: which means "shortages" in restaurants is hardly just about overall labor supply

Here is a thread about some of those possibilities.


2/
However, as @p_ganong points out in this thread, there is an indication that vacancies have risen while job search (proxied by google searches) has fallen - this is a broader point than just restaurants (though would be useful to see broken down).

3/
Read 8 tweets
2 Apr
Rcall makes it fairly straightforward to use packages in R for Stata users. I just wanted to demonstrate with an example of the new Augmented Synthetic Control approach of Ben-Michael, Feller, and
@rothstein_jesse, arxiv.org/abs/1811.04170
Here's the bit of Stata code to run it.
If you're a Stata user, you should download R, install some of the key packages, and get rcall. It'll make your life easier.
Also, here is a useful set of comparisons of R and Stata commands for Stata users. matthieugomez.com/statar/index.h…
Read 5 tweets
29 Mar
A proof of concept for how modifying Jorda "local projection" w/ clean controls (few lines of code) can produce event studies devoid of negative weighting problems

forval k=0/5 {
g dify`k' = F`k'.y - L.y
reg dify`k' treat i.period if ((D.treat==1) | (treat==0 & Fk.treat==0))
}
This modifies LP approach to *only* use controls that are still untreated by event date k. Similar to Sun Abraham. But LP approach also readily allows controlling for pre-treatment outcome dynamics and covariates. For example, see Acemoglu et al. economics.mit.edu/files/16686
Read 9 tweets
27 Mar
Seems to me another simple way of doing event study w/ staggered DiD is using the Jorda local projections approach modified to admit obs that are either untreated, or first period of treatment.

Something like:
reg (Fk.y-L.y) D i.period if ((D==1) | (D==0 & Fk.D==0))

Thoughts?
With this estimator, and example below, I think state 1 will be in the sample (as control) in years 1, 2, and (as treatment) in year 3. State 2 will be in the sample (as control) in year1, and (as treatment) in year2. State 3 will be in the sample (as control) all years.
Inclusion of year fixed effects means we're basically doing a pooled cross section estimation.

Seems to me this provides a consistent event study estimate under HTE with staggered treatment.

But wold like to hear from @borusyak @pedrohcgs @agoodmanbacon and others.
Read 13 tweets

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