By Matt DeVeau, Project Manager
In the course of facilitating community and economic development planning work around the country, one notices certain themes that are remarkably consistent from place to place. For instance, we commonly hear from executives and human resources professionals that it is difficult to get school-aged children in their community interested in a career in manufacturing. The story tends to the same in places large and small, in communities with a strong “blue collar” history and in service-based economies where manufacturing makes up a relatively small slice of employment.
I was thinking about this topic the other day in the process of developing a strategy and decided I would ask someone who works in manufacturing about how they got their start in the business. Then it hit me…
Wait, do I actually know anyone who works in manufacturing?!?
I thought for a moment and realized that, yes, I do but they are all professional contacts whom I have met through my work in community and economic development. Among my personal contacts, I couldn’t think of a single friend or family member who works in the sector. So next I did a nerdy thing that probably explains why my pool of friends isn’t bigger: I made a spreadsheet for fun.
I pulled up a list of every four-digit NAICS business sector and started placing tick marks next the sectors in which someone in my social circle works. To keep things manageable, I came up with a few conditions:
- I had to be close to 100 percent sure about where someone worked in order to classify them.
- I looked only at the roughly 160 personal contacts stored in my phone. Going through social media profiles of friends may have yielded better results but would have been too cumbersome through user interfaces.
- I assumed that everyone worked in their parent company’s main line of business (not a narrowly focused business unit that might be classified in a different subsector) and I only classified people into “Management of Companies and Enterprises” if I knew their job was in a corporate headquarters operation for a large firm with many locations.
I ended up sorting 85 friends and family members into 32 business sectors. I’ve shown the top 10 sectors in the following table.
Looking at the table, I can clearly see the impact of the social networks I formed through my education. From my time in Western Washington University’s journalism program, I have multiple friends in public relations (5418). From Georgia Tech, I know landscape architects and civil engineers (5413) and people who work for community development nonprofits (8133). (I also know a lot of public school teachers for some reason.)
But, there are no manufacturing sectors represented in that top 10 – or anywhere on my list for that matter. Maybe that shouldn’t come as a surprise. My friends are mostly: 1) people who attended college with me, 2) people who attended college with my wife, or 3) people who are in a relationship with someone from those categories. Most are doing something related to our areas of study, none of which line up well with a career in manufacturing.
This is The Big Sort phenomenon to a certain degree – the idea popularized by Bill Bishop and Robert Cushing in their 2008 book. The idea is that Americans have “sorted themselves geographically, economically, and politically into like-minded communities over the last three decades,” and it now comes up around every big general election.
Beyond electoral politics, I think the “sorting” concept has interesting implications for community and economic development – too many to list here in fact. But, I wonder what would happen if we developed a social network visualization that somehow incorporated data covering the business sector in which each individual worked. I would assume that we would see a correlation between clusters of social connections and business sectors – most people probably have friends from work, at a minimum. But, might we see that certain sectors of the economy are fairly “isolated” from one another in terms of how connected their workers are to one another? Put another way, might data suggest that one of the reasons students are reluctant to consider careers in manufacturing is because their parents don’t know anyone in that field? I don’t know the answer, but it’s the type of thing I hope we can find out as new data analysis tools and techniques are applied to community and economic development.