For the small number of people who are not totally sick of #minimumwage threads, let me walk you through some of the @UW team's findings for #Seattle now that they've cleared peer review (conditionally accepted, AEJ:EP).

A lot of them are packed into this one picture.
This isn't the analysis that used Synthetic Seattle and counted up the number of jobs paying under some threshold, except it couldn't count jobs at multi-location firms.

This is a longitudinal analysis of individual workers working for low pay in Washington as of early 2015.
We follow these workers and tally up all their jobs, hours, and earnings. Multiple jobs? They all count. Switch to a multi-location employer? That jobs and its hours and earnings count.

We difference their experiences 2x: from workers elsewhere in WA & earlier cohort.
In studying aggregate trends in Seattle using a hourly wage threshold, we risked overstating negative employment effects due to the "Seattle boom."

With the longitudinal analysis, the bias would go the other way: the Seattle boom should be pushing wages & employment up.
OK, so what do the data show? As the minimum wage rose from $9.47 to as much as $13 (that's 34%) between early 2015 and early 2016:
- Hourly wages went up gradually, ending at +15%
- P(unemployment) for these workers was unaffected.
- Hours declined 3-5%
- Pay went up 5-9%.
Our team benefited from access to statewide administrative data on hours worked. WA is one of only 4 states that collects that information.

Why is the action on the hours margin and not headcount employment? Bear in mind that most of this employment is part-time or seasonal.
From the employer's perspective, headcount = the maximum number of people you can have working at any one time. When you're at your busiest.

A higher minimum wage, rather than change that number, appears to affect how you want to staff your business when it's not busy.
As a 2016 report I wrote for the @SeattleCouncil indicates, employers (at that time) used a wide range of practices to cut staffing back in periods of slack demand. Send people home. Have them be "on call." Have them work split shifts.…
Our results on hours point to effects-for workers employed at baseline-that moderate over time. The pattern suggests a game plan for higher labor costs:
1) Don't lay people off
2) Cut back hours
3) Cut back hiring
4) As workers quit, restore hours for those who remain
(Note that the elasticities in Panel A of the table above may understate employment effects both because of "Seattle boom bias" and because they completely ignore a subset of the workforce: those who didn't have jobs at baseline, before the wage increase. More on them soon.)
We found important differences between more and less experienced workers ("more experienced" here could also mean "with stronger labor force attachment."). The more experienced workers fared better

- hourly wages went up more
- hours cuts tended to be modest
- pay rose more.
Less experienced workers saw greater proportionate cuts in hours and actually saw smaller paychecks for the first 15 months after the first wage increase.
So here's the rub: if you favor a higher minimum wage as a strategy to increase the take-home pay of older workers supporting themselves, the evidence supports your argument. If you’re worried about adverse impacts on teenagers, this evidence supports your argument too.
This is a labor market where people who need income to survive compete against younger workers who need the experience more than the money, and will consequently work for very low pay.

Should such a market be regulated? That's one way to think about the minimum wage.
Now, the longitudinal analysis by necessity leaves out workers who did not yet have a job in early 2015. What inferences can we make about their experiences?

Well, our best guess is that there were fewer of them as the minimum wage increase phased in.
There's a lot more to the @UW Seattle Minimum Wage Study. Beyond the admin data analysis, our team conducted employer surveys, worker interviews, price studies, a non-profit analysis, and much more. You can check all that out here.…
Caveats, of course! This is one policy in one city. Local policies likely have different effects than national policies. We didn't extend the analysis beyond 2016 because our "control group" -- Washington state -- started significant minimum wage increases in 2017.
But the bottom line is this: our evidence shows clear benefits of this policy. It also introduces cautions about costs -- cautions that prior studies have been unable to deliver for lack of reliable data on hours worked. One can spin these results either pro or con.

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More from @JakeVigdor

18 Jan
Hello everyone arguing about the #minimumwage! Someday I'll post a thread about the evidence our @UW team compiled in Seattle.

Today let's talk about hours.

Raise the wage on most low-paying jobs & workers still don't have enough to live on. Because they can't get enough hours.
The graph above is based on data from WA: one of only 4 states that collects systematic data on hours worked. It shows information for anyone earning under $11/hr in 2014-15, when the minimum wage was no more than $9.47.

The data have some important limitations:
-No information on gig/contractor employment
-No "informal" employment
-No jobs outside WA

However if a worker is holding 2+ job we can "see" all of them and include them all in calculating how many hours they work.

The median low wage worker works ~800 hours per year.
Read 12 tweets
23 Jul 20
Q: Why is rent so high? (median US renter paid 14% of income for rent in 1960, 24% by 2017.)

That’s the topic of my new paper with @evansuw alum Alanna Williams, presented this morning @nberpubs #NBERSI2020.

Want the answer (or at least a partial one)? Follow along! (1/11) Image
First some clues to the mystery: this is more a story of rents rising fast, rather than incomes falling. Renter incomes track inflation well, but sometime around 1970 rents started accelerating ahead of inflation. Had rents just tracked inflation they’d be about 50% lower today. Image
It’s a story of rents for older apartments rising, in particular. Long ago, affordable housing was created through depreciation, not construction. Older units rented at a significant discount – 25% off for a 10-20 year old unit. There’s still a discount, but it is smaller. ImageImage
Read 11 tweets
9 Jan 20
Here, #econtwitter, is a photo (replete with San Diego Marriott poolside lounge chair backdrop) of the AEA budget for FY 2020. I picked it up at the sparsely attended business meeting at #ASSA2020.

Although printed in black, there's some serious red ink. Let's explore.
The AEA operating budget was in the black five years ago. This year, they expect to spend $1.23 for every $1 in operating revenue. What gives?

Revenues (+7% in nominal terms 2015-2020) are not keeping up with expenses (+35.5%).
You know how the AEA is actually doing important stuff, like the RCT registry, @LetoC's ombuds office, workshops, and so forth?

"Program and activity" expenses are up 85% over five years. The association is dipping into its bank account to pay for them.
Read 9 tweets
5 Sep 19
In light of the recent suicides of Alan Krueger and Martin Weitzman, economists & other professionals at risk of aging might find insight in this recent letter penned by Princeton professor emeritus Avinash Dixit.

Thread follows.
Dixit's letter was written in response to @ArthurBrooks' recent @TheAtlantic essay, which is itself a must read for any professional at risk of aging:…
@arthurbrooks @TheAtlantic There's a mental trap to be wary of. We might call it the life=work trap. Not exclusively a risk for economists or academics more broadly, but a significant one.

There's the old joke: "academics are the only people who retire so they can get some work done."
Read 7 tweets
7 Aug 19
ICYMI @NickKristof found "reason for hope" in a groundbreaking experimental study by @OppInsights. Go read if you haven't, then come back here for a contrarian thread: in broader context, this is a desert island of hope in a rising sea of despair.…
@NickKristof @OppInsights The @OppInsights study showed that offering assistance to Seattle-area housing voucher recipients made them about 40 percentage points more likely to use their vouchers to rent an apartment in "high opportunity" neighborhoods.

Link to the study here:…
@NickKristof @OppInsights "High opportunity" in this case is defined as neighborhoods where low-income kids born 36-41 years ago wound up earning more money as adults. This matters critically. I'm going to refer to these as "neighborhoods historically associated with upward mobility" (NHAWUMs).
Read 18 tweets
20 Jun 19
Six weeks ago, I invited the 9 candidates for AEA leadership positions to offer statements on pressing issues in the economics profession, including diversity/inclusion & the publication process.

Response rate, as of today: zero.

There is, of course, an economic explanation...
Economics teaches us that producers profit from imposing scarcity of their product, restricting output and imposing barriers to entry. They gain, would-be competitors and consumers lose.

This is what economists preach. It is also what they practice.
Fourcade, Ollion and Algan (2015) document the peculiar economic practice of restricting professional leadership to the top departments. Our leaders are nominated by a committee, themselves drawn predominantly from top departments. Our peer institutions behave very differently.
Read 10 tweets

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