I have two simple examples: suppose I work for a firm for 20 years. That equates to 20 job years, the kind of number alluded to in the consultants' report. But I only count as one jobholder working for 20 years. That's how the government counts me, that's how the IRS counts me, and that is how a competent social scientist would count me. Now let's assume that I made an average of $50,000 a year over all of those years. That would sum to $1 million, but I certainly am not a millionaire. In my example I was one job making an average of $50,000 a year. That is the true statement. I was not 20 job years making $1 million. That would be a distortion.

Now let’s go back to the first problem, the one of properly specifying the model.  This study assumes that a substantial fraction of the critical durable goods into the project — the pipes and pumps, for example — will be manufactured within the study region. They provide no justification for that assertion other than the default steel pipe manufacturing values that are contained within their modeling system.  But the probability the pipes and compressors are in fact produced in the region are zero or close to zero. One would have only needed to do a little research to demonstrate that. For one, there are no oil pipeline manufacturers in the region traversed the by the project according to all of the searches that I did,but which I doubt they did.  There are pipe and tube manufacturers of all kinds, but not pipeline manufacturers.  And secondly, and to my amusement, the documentation for the much more notorious Keystone XL pipeline, upon which the methods for the Dakota Access study were based, stated that they would purchase only half of their pipe from a U.S. supplier, and that supplier was in Arkansas.  I contend that the same situation also applies to pump manufacturers.  By assuming a default linkage to regional pipe and pump manufacturing, the study authors inflated the secondary economic impacts substantially.

Finally, the study assumes there is a native pipeline construction industry that will ramp up to accommodate the new demand. In 2013, Iowa had 34 establishments and 245 payroll employees doing that work. It is really hard to believe these, on average, very small firms will be the successful bidders on the pipeline project and able to ramp up in employment, machinery, and other operating scales to accomplish the task.  It is much more likely, and my research on the Keystone Phase 1 pipeline confirms this, that state pipeline construction employment may increase from between 150 jobs to about 650 jobs annually, but that a very healthy fraction of the construction jobs will go to firms from out of state who bring their most skilled workers with them on the project (and who, of course, will send their weekly paychecks back home).  

There are many, many more quibbles that I have with the Dakota Access study, the Keystone XL analysis, and the Keystone Gulf study that was released last year. I believe all engage in substantial double counting of activities and didn't go through the hard work of properly specifying the pipeline construction industry within their models.  I will write more about this in the coming weeks.  All certainly suffer from the two problems that I described, all overstate or mis-measure the likely impacts, and all, I contend, were conducted in a manner so as to paint the rosiest, albeit distorting, job picture possible to policy makers and regulators who are easily wowed by job growth declarations.

Everyone does this. Corn ethanol did this during the last decade and succeeded famously,if not notoriously. Industry sponsored job impact claims for Iowa were in the neighborhood of nine-times greater than I could honestly report.  I have also critically reviewed bloated wind energy and solar economic impact claims.  Some were naïve misstatements, and the proponents were open to corrections. But others were not, and in the case of the Dakota Access study I know this for certain: the authors would not fare well in presenting that study before a panel of regional scientists.  However, in light of most private consulting studies purporting economic impacts, it is a state of the art study.

And now you know what it takes to be state of the art in private sector economic impact forecasting. 

Dave Swenson, Ames, Iowa