A bit of a wonky discussion on the radio today with the guest host. Policy stances for both fiscal and monetary policy were the major topics, mostly with relations to stock price movements. I could talk policy all day, as I did for most of the hour on the radio.
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.
The January sales tax collections data came out recently and I want to take a longer view of the data here. This typically means messier graphs because the time scale gets compressed. I will put up a post with a shorter timeline tomorrow. Why the longer timeline? I think it is necessary if we are going to try and interpret or understand the numbers we are currently seeing.