If I’ve learned anything from my Office for Coastal Management colleagues this summer, it’s that all data was created for a particular reason. No data set can do it all or should do it all. And oftentimes, the more specific the data’s intended purpose, the greater the limitations and the opportunities associated with it.

People can play with the data in the dynamic ENOW Explorer (ooooo pretty colors!)
People can play with the data in the dynamic ENOW Explorer (ooooo pretty colors!)

During my time at the Office for Coastal Management, I got to work with this incredibly nifty dataset called Economics: National Ocean Watch (ENOW). ENOW’s intent is to describe the economic impact of six different sectors on the ocean and Great Lakes economy using a nationally consistent suite of indicators that provide information on employment, wages, number of establishments, and GDP from the national down to the county level. The sectors valued range from Tourism and Recreation to Offshore Mineral Extraction, but this summer my work focused on the Living Resources sector. Or, in the words of my 3-year-old cousin, the “fishies stuff” — the commercial fishing, aquaculture, seafood processing, and seafood market industries.

So here’s the idea: the ENOW indicators alone, although very useful, can’t necessarily tell the whole story. For instance, in the Marine Transportation sector, the ENOW data can tell us the number of jobs, wages, and GDP, but it doesn’t give us any insight as to the enormous value of the cargo itself that is being transported and that depends upon the sector employees to arrive at its destination. Seems like a detail that might impact decisions affecting the industry, right? Thus the need for the plus. In most, if not all sectors, other data exists which can be used to supplement the ENOW statistics in an attempt to tell more of the story. We just need to go fishing for it.
Image of shrimping boats at dock.This is of particular importance for the Living Resources Sector. Virtually all fishermen are self-employed, meaning that their jobs are not captured in the ENOW dataset, which is derived from the Bureau of Labor Statistics and the Bureau of Economic Analysis. For information on the self-employed, we have to go to the Census Bureau. Lots of Bureaus to keep straight, I know. Once we pull in the non-employer statistics from Census, we find that for South Carolina (my primary region of study) the self-employed account for over 60% of the sector! That’s a pretty considerable chunk. So for the fishing industry, we need the actual non-employer statistics to accurately tell the jobs story.

Next, we want to dive even deeper and consider datasets unique to commercial fisheries.  Fisheries data itself may not seem very technical in nature, but the collection process is complicated enough to confuse even those most closely connected to the industry. Landings are reported according to different standards and via different protocols depending on the state and the fishery (although there is a national push to homogenize the process as much as possible). The data must be aggregated from literally thousands of fishermen and dealers along the coast. It sometimes passes through a number of hands before ending up at a coastal fishery statistics program (Atlantic Coastal Cooperative Statistics Program for the Atlantic Coast) where it is assembled at a regional level, and ultimately the NOAA Fisheries Statistics Division where it is served up for public viewing via streamlined, user-friendly online queries.

Image of some of the day's catch being weighed.

Despite the complexities of the data collection process, landings data provides another important insight that nicely supplements the ENOW data: it tells us about the value of the resource itself before additional value is added via distribution, processing, and retail. This is particularly important for states like South Carolina that lack seafood processing facilities and resultantly export much of their seafood, relinquishing the economic benefits once it crosses the state line. In these scenarios, landings data allows us to separate the value of the natural resource itself from the value of the overall sector.

Ex-vessel value recorded in landings data also gives us an idea of roughly how much money is getting back to the fishermen (a difficult number to capture since many fishermen don’t claim their fishing income on their taxes).  Of course, the more we respect the intended purpose of the data, the more success we’ll have applying it. An important caveat of landings data is that it is used largely for regulatory purposes, allowing fishery managers to monitor stocks and ensure that annual catch limits are not exceeded. Because of this intended use, there is some inherent inaccuracy that results from underreporting of landed and discarded catch on the part of certain dealers and fishermen who may be frustrated with fishery closures and struggling to make ends meet in an increasingly regulation-ridden industry. Ironically, the data which fishermen may be tempted to underreport is the same data that we need to fairly represent the true value of commercial fisheries to the ocean economy!

The best we can do as data users is acknowledge the limitations of our data, understand how to appropriately use our economic indicators, and accept that there’s always more to the story than the numbers on the page, especially when we’re dealing with people.

“Fishermen never lose their love for the employment. Keep him at home a few days, or set him at other labor, and you shall see that he longs for the toss of the swell on the reef, and the sudden joy of a strong pull on his line” –Un Noin

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