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If it Were Easy, Everybody Would Do it: The Challenge of Creating Jobs (and Counting Them)

The Huffington Post The Huffington Post 28/10/2015 Mark Newberg
ECONOMICS CLASS © Marco Vacca via Getty Images ECONOMICS CLASS

Early this baseball season, when the days were growing longer and hope sprang eternal (for Red Sox fans, at least), I had an idea: that jobs matter tremendously, and that baseball could teach us something valuable about creating them. Specifically, that new-school sabermetrics could be combined with traditional economic development, to create a new approach called Jobs Above Replacement (JAR).

At the time, it was a challenge simply to articulate what JAR might mean, or measure [1]. And it was even more challenging to define what would go into a formula that made it easier to measure good jobs, incentivize them, and avoid spending lots of money on projects that don't produce the right results (insert profligate free agent signing analogy here).
As the season wore on, and the emails rolled in, I found myself facing three general questions:
1.What was I thinking when I came up with this crazy idea?
2.For whom is this actually applicable? And
3.Fine, it's theoretically possible. So, theoretically, how would it work?
What was I thinking? Three things. First, Jobs matter. And not only do they matter, they matter a lot. Jobs (particularly good jobs), form the basis of wealth creation and stable communities, and have a significant effect on health outcomes, educational achievement, and quality of life. So, if jobs matter this much, wouldn't it be great if there were some standardized way to measure (and value) the jobs we're trying to create? [2]
Second, there are companies that have built businesses around the notion that top shelf (and cool) products can be created (and sold) by a workforce around which the company is built. [3] We're talking about employees who are fairly compensated, have training and advancement opportunities, and believe in the mission and management of the company for which they work.
In particular, I was inspired by a couple visits to Shinola's factory in Detroit. Think about it: you've got a company built from scratch, assembling watches at scale in America, in an economically distressed area, focused on creating stable jobs in its community, which keeps expanding (and is now making leather goods on a new production line), and is co-located in a design school, which affords numerous educational opportunities up and down the career ladder. That's the description of something most of us wish most businesses could be. And, for good measure, in the first three years, Shinola has grown to 400+ employees, the vast majority of them full time. [4]

Third, a number of "public" infrastructure projects were being proposed. All of them had some kind of jobs figures attached. All of them included rosy economic projections. And I found myself wondering how, exactly, the projections made it from the ether to paper (or, even, ESPN.com).
Prime example: Even as Los Angeles and the NFL go back and forth (and round and round) over the return of one (or two, or three) teams to LA, another stadium proposal appeared. [5] This time, for the other football (soccer, not Canadian). Now, I don't know whether the project will create the 1,800 permanent jobs it promises, but I also don't know the breakdown of those jobs. Are they full-time or part-time, are they good jobs (with benefits and training and advancement opportunities) or are they minimum wage? Will they appear in two years or ten years? And how much public money would have to be spent in order to see the project through? It seemed to me that there ought to be some way to standardize (with regional adjustments) all the economic claims, and condense them into a simple, meaningful number. Which brings me to...
For whom is this actually applicable?

Making the leap from theory to practicality isn't for the faint-hearted. Or for those who want to sleep much while they try to figure it out. Because an idea (or concept) that's attractive on paper or in conversation becomes increasingly less so if you can't figure out who would use the thing. [6]
What I settled on is this: Those who are most incentivized, as a class, to create good jobs are not employers. It's economic developers and other government officials. The more jobs an economy creates (specifically, the more good jobs) the more discretionary income is available in the community. This, in turn, leads to an upward spiral of economic development. [7] And it's government that is ultimately the keeper (for better or worse) of economic development.
Could private companies use JAR? Sure. They could. One might even argue that measuring the "quality" and "velocity" of jobs within the enterprise should be an important component of sound management practices. Particularly Environmental, Social, and Governance factors. [8] But it's probably not going to take off in the private sector until (or unless) there's a clear link to the bottom line. And that's not a bad thing.
So, theoretically, how does JAR work?

After a lot of time scribbling quasi-mathematical equations in a notebook, there's finally a draft formula to test. The goal is to make it something that can be used across regions, to help inform economic development decisions, track the progress being made toward achieving project-oriented goals, and adjust in real time. Think of it the way Billy Beane, immortalized in Moneyball as General Manager of the Oakland A's, does about a baseball season: you spend the first third of the season evaluating your team and figuring out what you are, the middle third addressing needs through trades, and the final third finding the next gear. [9]
And so, after all of that work, after reminding myself that we're trying to measure the value of jobs to be created (not the valuation of companies, not shareholder wealth, not revenue, but jobs), it boils down to this:
JAR = The Expected Value of Jobs Created With an Intervention / The Expected Value of Jobs Created Absent an Intervention (which is also the "replacement value").
Therefore, the absolute simplest way to express the really complicated formula behind this basic notion of valuing jobs is:
JAR = Ei/Eab
Or
JAR= Ei /R
There it is. After an entire baseball season, more hours spent on algebra than any time since studying for the SAT, and a lot of conversations with some really smart people...a draft approach to measuring that which we know matters: Jobs. Good jobs.
And I promise, the draft formula behind the draft formula is even more complicated than this article has been!
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[1] : Quick primer: The criteria are (Jobs Created, Good Jobs Adjustment, Jobs Saved, Velocity, Multiplier Effect, Cost Per Job, The Baseline)
[2]: To be clear, this is about measuring jobs. Not revenue. Not sales. Not valuation. Jobs. The other stuff is important, but we're only focusing on jobs here.
[3]: An (extraordinarily) incomplete list: Shinola, Solar City, Wash Cycle Laundry. These are just a few I had in mind.
[4]: I've been lucky enough to do a little bit of work for Shinola, but I was a fan before that. I could go on about the model, but I'll stop with this: There's something meaningful about creating tangible goods and revitalizing manufacturing in hard-hit regions. There's a pride to it. And when employees are prideful, and feel respected, everyone (community included) benefits.
[5]: This article appeared after my original story was published, but it's one that really caught my attention.
[6]: This might also be considered sage advice for would-be entrepreneurs. Just because you think it's cool doesn't mean anyone will actually use (or buy) it.
[7]: Here's an interesting take on the Multiplier Effect of tech jobs, written by Brookings' Mark Muro. And a basic primer, from the University of Arkansas, on what a Multiplier is used for.
[8]: Known as ESG, it's the practice of incorporating Environmental, Social, and Governance factors into corporate (and investment) decision-making. And it's growing.
[9]: http://www.newbergreport.com/article.asp?articleid=2258 (It's a different Newberg, to whom I don't think I'm related)

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