I’ve had an idea bouncing around my head for a while and haven’t had time to pursue it with any real conviction, so I’m using my blog as a placeholder today.  A place to flush this out a little and hope that I remember to come back to it.  Even though I don’t publish in this area, I’ve always been intellectually interested in how communities respond to economic shocks…particularly the kind of shocks that long-run changes in economic structures provide, like the decline of a key industry.  We’ve seen these types of shocks disrupt communities all across the US for decades: the decline of US manufacturing has made life hard for a lot of people in Rust Belt cities, damage to cod populations in New England hurt waterfront communities from Massachusetts to Maine, and the long-term reorientation of the US economy towards services and away from agricultural production has resulted in the wholesale disappearance of some farming towns.

It seems to me that by looking at how communities have responded to shifts like these and the outcomes of those actions we should be able to learn something about what works and what doesn’t.  In this case, I don’t mean “learn something” in the abstract sense but rather the predictive sense…can we use data to propose coping measures for communities experiencing persistent economic decline?

I’ve been fascinated with the dichotomy of what, in my head, I’ve been calling adaptation versus mutation since I read this Atlantic Monthly piece on Braddock Pennsylvania’s mayor John Fetterman….who Rolling Stone called, “The Mayor of Hell.” I was also really entertained by a talk I watched John Fetterman give at an Urban Growth and Revitalization Symposium where he challenged the conventional focus on high-value added niche businesses for revitalizing decaying industrial communities (think fixed-gear bike makers and craft brewers repurposing old warehouses). He remarked that a huge segment of his population was between 14-25 and 2/3rds of his community lived below the poverty line. They don’t need cupcakes, they need jobs and life skills. He talked about how his focus in his first term as mayor was to attract service industries and how he was proud to have brought a Subway Sandwich franchise to Braddock. Those entry level jobs, he reasoned, would provide his residents, many of whom don’t have an employment history, a chance to earn money and job skills.

In my head I’ve been filing this away as an example of mutation. A formerly thriving steel town in the Monongahela River Valley forced to reinvent itself in order to survive. Mutation.

On the other side of the ledger (rest assured that I am aware that my bifurcated classification is artificial and oversimplified…but let’s run with the simplicity for now) is adaptation. This is my way of appeasing the Anthropologists who will say that economic success of a spatial unit (community, city, whatever) isn’t everything and that identity is important. In the Anthropology literature on “resilience” of a community that term is usually defined as having some connection to whether or not the community retains its identity. In the case of a farm town, can the community adapt to new economic conditions in a way that retains its connection to agriculture?

Fort Bragg is a port city in Mendocino County California about 3.5 hours north of San Francisco. It has traditionally been one of the biggest groundfish and salmon landing ports in California. Decades of landscape modifications (think big damns) and poorly regulated fishing practices have stressed salmon and groundfish populations, which have made it difficult for commercial fishermen to make a living. One of the ways fishermen in Fort Bragg have responded to the tougher business climate for fisherman has been to pursue greater margins through product differentiation. The Fort Bragg Groundfish Association’s website suggests that fishermen in Fort Bragg are experimenting with different gear in an attempt to get premium prices for fish and marketing fish direct to restaurants (going after the retail markup).

This type of behavior is what I’m classifying as adaptation (I know, I probably need a better word but I really like the aesthetics of adaptation v. mutation). The industry that was traditionally important continues to be an economic engine…just in a slightly different way.

Here’s where I’m going with this: I personally don’t care all that much about why one community would choose adaptation over mutation or vice versa. I’m more interested in this: given a path (adapt or mutate) what are some options that will likely lead to good economic outcomes (growth, stability, etc.). With the amount of data we have out there on communities over time I have to think it’s possible make decent prescriptions.  A few more off the cuff examples:

  • We observe that craft brewing companies have been successful in Portland’s warehouse district
  • We observe that marginal farm land in California’s Central Valley has been successfully converted to farms specializing in solar energy production
  • I remember a story from my grad school days where the extension economists were trying to get rice farmers in South Texas to convert their operations to catfish farms because South Texas was a marginal rice growing region but the physical requirements for raising catfish in ponds were very similar to the characteristics of existing rice farms.

My idea (and I haven’t rule out the possibility that it’s total shit) is to organize all the data on community response to economic shocks (decline of manufacturing, fishing, forestry, agriculture, automobile assembly, etc.).  What has worked and what hasn’t.  Once you have that I have to believe there is an intelligent way to evaluate a particular affected community and say, “these strategies are likely to work and the following industries are the best bet for targeting…”

Putting on my empiricist’s hat for a second.  If we observe that community X has been experiencing persistent economic decline due to dependence on a decaying industry.  It shouldn’t be that difficult (once the master database has been assembled) to say, “OK, community X has the following characteristics that make it statistically similar to communities A, B, and C in the data set that have faced a similar decline in a key industry at some point in their history.”

OK, I’m sure there are a ton of holes in this idea, otherwise someone much smarter than me would have done this already.  You all now have my permission to start blasting this idea.

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