# New UW Report Finds Seattle’s Minimum Wage Doing Modestly Well For Some Low-Wage Workers

Recently a few friends of mine have linked to this article, which summarizes a new University of Washington study about the Seattle minimum wage increase. The article headline reads, “New UW Report Finds Seattle’s Minimum Wage Is Great for Workers and Businesses.” My friends like to add snarky comments feigning surprise, implying that of course the minimum wage is great for workers and businesses.

SMH.

## 1. The “of course” intuition runs in the opposite direction.

Demand curves almost always slope downward so our naive intuition would be that business will shed low-wage workers because they cost more under a minimum wage regime. It was only after David Card and Alan Krueger’s 1993 paper (and the subsequent papers that utilized a similar difference-in-difference strategy*) that economists had hard evidence that small minimum wage increases might not reduce employment.

*Difference-in-Difference is an easily understood technique in which you compare the starting and ending points of two different rates to see how they performed relative to one anther. My mile time was 11 minutes last year, but now it’s 10 minutes. Your mile time was 11:30, but now it’s 9 minutes. I improved by one minute, but you improved by two minutes and thirty seconds. One minute and thirty seconds is the difference between our individually differenced before and after times, which is where the technique gets its name (difference is just another word for subtraction).

Suppose we’re genetically similar, say, for example, that we’re twins. We now know that the difference in our mile time improvements over the past year was due to our different training regimens and not genetics and — setting aide the fact that we only have a sample size of two — we now have proof that your workout regimen is better (at least for people that share similar genes).

The attractive thing about difference-in-difference experiments is that they don’t use any fancy math: the results are both easy to calculate and easy to understand. If you can find two people — or groups, or cities, or things — that are similar and can track their performance over time all you have to do at the end is subtract a couple of times and you have a statistically valid result.

## 2. The headline and content of the article sorely misrepresents the results of the study.

The article’s author cites only the Seattle increase portion of the difference-in-difference approach. It’s the equivalent of me citing my increased mile time and telling you how great my workout plan is without telling you that my twin did much better with a totally different workout plan. Most of the increase in employment and business success was due to the recent strength of the Seattle economy. An acquaintance that recently started at Amazon told me they just had their largest orientation ever, 600 new employees.

Ultimately the authors conclude with this finding:

The major conclusion one should draw from this analysis is that the Seattle Minimum Wage Ordinance worked as intended by raising the hourly wage rate of low-wage workers, yet the unintended, negative side effects on hours and employment muted the impact on labor earnings.

The authors don’t find that the minimum wage increase was “great” for businesses, but instead mostly a wash. There was a small, 0.7 percentage point, increase in the rate of business closure. The authors also note that:

A higher minimum wage changes the type of business that can succeed profitably in Seattle, and we should thus expect some extra churning. Our results are consistent with those of Aaronson, French, and Sorkin (2016), who conclude that minimum wage laws prompt increases in both entries and exits (particularly in chains), with closures coming from more labor intensive industries and establishments, and more openings occurring in more capital intensive industries.

I think this structural realignment is foreboding for the future of Seattle’s low-wage workers. The minimum wage currently stands at “only” \$11 and we’re a year into the experiment. What happens in seven years and an additional \$7 in hourly pay? There could be serious negative structural adjustments to low-wage industries.

So what about the workers themselves? The best estimate is that the minimum wage decreased low-wage employment by 1 percentage point. Far from “great.” The workers that were still employed did experience modest gains in material well-being:

Seattle’s low-wage workers who kept working were modestly better off as a result of the Minimum Wage Ordinance, having \$13 more per week in earnings and working 15 minutes less per week.

One has to ask themselves if the minimum wage was so great why didn’t low-wage workers flood into the Seattle area? On the contrary the authors find the following:

…we conclude that the Seattle Minimum Wage Ordinance reduced the probability of low-wage workers continuing to work in the Seattle (rather than elsewhere in the state) by 2.8 percentage points.

As director of the Seattle Minimum Wage Study, it is my sad duty to report that this article grossly mischaracterizes the tenor of our report. I encourage readers to refer directly to that report:https://seattle.legistar.com/View.ashx?M=F&ID=4579065&GUID=39743A75-1D9F-4C32-B793-2F699D51B0F7

## 3. An argument against the minimum wage is not an argument to condemn the poor.

The minimum wage is only one poverty reduction strategy. Many economists are in favor of small increases in the minimum wage because there is a lot of evidence that suggests the impact on employment is small or non-existent (or even slightly positive). But many economists are quite nervous or ambivalent about \$15 minimum wage increases. As this Forbes headline notes, “Even Alan Krueger Thinks That A \$15 An Hour Minimum Wage Is Too High.” And Alan Krueger helped pioneer the argument in favor of the minimum wage!

See this poll and this poll for reference.

This echos my thoughts in an early post about things I do and don’t hear in Seattle.

# Things I Do and Don’t Hear (in Seattle)

In the category of “signs of mood affiliation” here is a short list of things I do and don’t hear in Seattle:

Do hear: “Who funded that study showing GMOs were safe?”
Don’t hear: “Who funded that study showing GMOs weren’t safe?”

Do hear: “We should be believe in climate change because nearly every major scientific organization (and many individual studies) have shown it’s real and a serious problem.”
Don’t hear: “We should believe in the efficacy of GMOs because nearly every major scientific organization (and many individual studies) have shown both their safety and benefits.”

Do hear: “Are you aware of the methodological and measurement problems of GDP?”
Don’t hear: “Are you aware of the methodological and measurement problems of the inequality data?”
Don’t hear: GDP isn’t perfect, but it correlates with nearly everything else we do care about and so as a single measure it’s not bad.

Do hear: “That scathing op-ed in the New York Times by the ex-Wall Street executive really hit the nail on the head.”
Don’t hear: “That scathing op-ed in the New York Times by the ex-Wall Street executive was an ‘n’ of 1. Hundreds of thousands of people work on Wall Street, surely some of them find the work challenging and rewarding.”
Do hear: “That interview with the Iraqi teenager who supported America’s invasion was an ‘n’ of 1. Surely, there are many other contrary opinions among Iraqis.”

Do hear: “You can’t trust the corporate billionaire’s position on tax reform, she’s totally biased!”
Don’t hear:
“You can’t trust the social justice activist who’s entire identity is wrapped up in opposing corporations writ large, she’s totally biased.”

Do hear: “We need to learn from and honor the traditional practices of the Tanzanian farmer.”
Don’t hear: “We need to learn from and honor the scientific techniques of the modern American farmer.”

Do hear: “Justice Antonin Scalia was a horrible person. His interpretation of the law is crazy. He’s allowing corporations to take over elections because he subscribes to corporate personhood.”
Don’t hear: “I’ve actually read Citizen’s United or any other judicial opinion/concurrence/dissent from Scalia.”
Also hear: “Why are you even quoting the bible if you haven’t read the whole thing cover to cover? Why don’t you read something before forming an opinion.”

Do hear: “Justice Antonin Scalia was a horrible person. His interpretation of the law is crazy. He’s allowing corporations to take over elections because he subscribes to corporate personhood.”
Rarely hear: “I have any knowledge at all of the law or legal history.”
Don’t hear: “I’m aware that most of the econometric work on elections shows that spending has little effect on election outcomes and since the Citizen’s United decision campaign spending has actually gone down.”

Do hear: “Supreme court justices on the right are obviously biased about gay marriage and should recuse themselves.”
Don’t hear: “Ruth Bader Ginsburg presided over a same-sex wedding and should recuse herself in gay marriage cases.”

Will probably hear: The person that wrote this post is obviously a republican and pro corporation/Monsanto/a bunch of other stuff and anti same-sex marriage/equality/a bunch of other stuff.
Will probably not hear: The person that wrote this post has views that do not comport with either political party, but as he lives in Seattle he tends to hear more liberal hypocrisy than conservative hypocrisy, of which there is undoubtedly mounds.

# How delicate are relationships?

Recently I have been thinking of dating analytically as a stream of dates where one must build up “relationship capital” before certain pieces of information are revealed or certain negative events occur. The stream must more or less occur in a particular order. That is, if you examined a series of weekly dates over the course of a year and scrambled that order – maybe the 37th date came first, the 5th date came 9th, etc. – the outcome wouldn’t necessarily be the same.

Many of us believe that there are instances when a piece of personal information is revealed “too soon.” For example, if on a first date your companion tells you that he or she recently filed for bankruptcy it may be a “deal breaker” as you assume this reflects negatively on their level of responsibility (as well as being an “overshare”). However, if the same piece of information is revealed after you’ve been dating, say, three months you can weigh the strength of that assumption against the person you’ve come to know. Likewise, some intense external shocks can prematurely end a relationship if they occur too close to the beginning of a relationship whereas  if that same event occurred after sufficient time had passed both partners have would have built up enough “relationship capital” to weather the storm.

I decided to expand on this idea by creating a simple model (which I plan on elaborating over time) by assuming a couple goes on a date once a week for a year (or 52 dates over whatever time period you like). Three pieces of information or events must be reveled only after enough capital has accrued. Event 1, is low level and can only occur after Date 5, Event 2 must occur after Date 20, and Event 3, the most serious, must occur only after Date 40.

What if we kept the concept of “deal breakers” and randomly scrambled the order or the dates. How many relationships would still last a year or more (by chance) simply because events happened after enough capital was built up? It turns out that scrambling the order of the stream of dates results in a failed relationship about 88% of the time.

Of course, this is a theoretical exercise, not just because it’s impossible to scramble the order of dates, but because in practice it is us who decide if we want to be with a person despite difficult times or questionable personal sharing.