Last week I argued (here) that the city’s use of the rolling twelve month total was not really about accuracy as much as stability. Essentially the two concepts are conflated in the presentation. I still fail to see the virtues of knowing the total sales over the last twelve months, and how it better informs policy decisions. I present the following graphs to demonstrate my points about stability. Here is the monthly collection amount (recall there is approximately a two month lag between sales tax collections and the reported amount).
The March 2018 release came out and did not provide a ton of good news. The new data represents events of January 2018 and therefore pushes us through the entire holiday season. The March 2018 data was down 4.79% from the March 2017. The first quarter of 2018 declined 6.02% from the first quarter of 2017. The city likes to report a rolling 12 month total. For those that do not know, this means they look at the total for the last twelve months and as a new month is added they drop the oldest month. The city suggests this is more accurate, though I am not clear what the term accuracy implies here. The rolling 12-month total generally crosses multiple fiscal years so it is not really reflective of a specific budget year which would be something like calendar year accumulations. The other issue is this measure is down 19 of the last 20 months.
Grand Forks released retail sales tax collections which are for the month of December 2017. There are various different ways to look at the number. It is a .31% increase over December of 2016. An increase is an increase I suppose, though it should be cause for concern if the general optimism permeating the economy surrounding the tax cut happened to miss Grand Forks. The January and February releases for this year, which represent sales tax collected in November and December of 2017, are around 6.5% less than this time last year. It is also the case that 17 of the last 38 months have been lower than year ago values for sales tax collections.
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.