Wild Economics

Over at Cafe Hayek and Carpe Diem, Don Boudreaux and Mark Perry have been discussing a recent interview of Sally Smith, the CEO of Buffalo Wild Wings.  The relevant aspect of the interview is this quote:

WSJ: How are minimum-wage increases affecting the way you make business decisions?

MS. SMITH: You look at where you can afford to open restaurants. We have one restaurant in Seattle, and we probably won’t be expanding there. That’s true of San Francisco and Los Angeles, too. One of the unintended consequences of rising minimum wages is youth unemployment. Almost 40% of our team members are under age 21. When you start paying $15 an hour, are you going to take a chance on a 17-year-old who’s never had a job before when you can find someone with more experience?

I’m not going to rehash their arguments.  I recommend you check it out for yourself.  Rather, I want to discuss something that empirical discussions need to be aware of.

Notice how M. Smith says she isn’t closing down her store, but rather not expanding operations. From a purely empirical viewpoint, this might look like there is no negative effects on employment. The same number of workers are still there.

But what is unseen is the jobs that were going to be there, but are not now. Say BWW would have opened another store with 20 MW workers. Now, with the new hike, those 20 potential jobs are gone. Even if the empirical work shows there were no job losses, the economic cost of the hike is still 20 jobs!  These costs are unseen, but very real.

8 thoughts on “Wild Economics

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  3. Researchers imply that their empirical studies are the gold standard for understanding the world. These are measurements from the field, the raw data for determining which theories (models) are true.

    As pointed out here, such studies cannot measure the whole impact of minimum wage policy because that policy causes things to not happen and which are not measured by the study.

    Minimum wage studies are also glaringly deficient by measuring changes in employment around the day an increase goes into effect. But, businesses plan 6 to 12 months ahead and have mostly adjusted to the wage change by the day of effect. Few people are fired on that day. They were fired or removed by attrition during the previous year.

    Why would studies ignore this obvious fact? Because, (1) the result “Higher minimum wages have little effect on employmment” is the desired one by lefty researchers and is easy to manufacture, and (2) It is hard to measure what businesses are gradually doing in the prior months.

    Business owners report their strategy, but this is dismissed as anecdotal, not the hard evidence of measuring the change of employment from one month to the next.

    Researchers add that they do account for these unseen and unmeasured effects by adding in estimates(!). An estimate is the result of their model relating the minimum wage to employment.

    So, the study is supposedly trustworthy because it is empirical, but a large part of the result, enough to invalidate the study, is filled in by a model of what the researchers propose is happening. This is false advertising. What is measured does not decide the question, but adds to the aura of the research.

    Here is an eye-opening chart of teenage unemployment compared to changes in the minimum wage.

    Excess Teen Unemployment
    http://www.aei-ideas.org/2013/02/lets-review-the-adverse-effects-
    of-raising-the-minimum-wage-on-teenagers-when-it-increased-41-between-2007-and-2009/
    02/16/2013 – AEI Ideas by Mark Perry [edited]
    === ===
    The minimum wage rose 41% in three stages between 2007 and 2009. This had a disastrous effect on teenagers. The jobless rate for ages 16-19 increased from about 16% to more than 26% (10 percentage points). The overall US jobless rate also increased from about 5% to 10%.

    The graph attempts to isolate the effect on teenagers by plotting “excess teen unemployment” the difference between the teenage and overall jobless rates.
    === ===

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    • And their models are undoubtedly biased in their outcomes by the researchers’ bias; conscious or not. Model output as data is a scourge upon the scientific world. Exhibit A: Climate “Science”

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