There are many ways to slice and dice employment and the change in employment in a community. How webs to do it, and whether the approach generates meaningful outcomes, is not always clear. We can look at particular sectors and attribute outsize importance to them and fear job loss is symptomatic of a declining employment base. It could also be the case the local labor market composition changed and the losses in one sector were the gain in another.
Sales tax data is a continuing theme for me these days and so I thought we could look at any seasonal patterns in the data. I am going to hold off on the formal statistical tests for right now and we will go with the graphical approach. If there is seasonality in the data the graph should show common movements in the lines for different years. For example, if June is always a slow month for retail sales the collections should drop for most June observations compared to the May observations. I generate this for 2001 to 2017.
A closer look at recent sales tax data seems a logical follow-up to the long run view from last time. There are two things to note from this graph: 1) the negative trend is still clearly evident, and 2) the reduction in volatility from the twelve month rolling is clear.
I was asked about the path of sales tax collections in Grand Forks recently so I took to my trusty computer and made a graph. This looks at the rolling total tax accumulations by month for each of the last five years. There is a great deal of clustering for these years, but 2017 looks to be on track for one of the lowest years.
JT and I are devoting a portion of my weekly appearance to the Grand Forks Flood of 1997, and the business and economic consequences of the event. The consequences of some events are best understood with the perspective of time, and natural disasters are clearly this type of event. For today’s post I chose to look at unemployment, but in a slightly different way.