Working on a paper for presentation at a conference this summer and chair work took much of this week away from my blogging. Oh and a revise and resubmit. That is not helping either. I keep getting excited by the paper though. It is a continuation of a master’s thesis I supervised and the former student, now co-author, really likes the topic too. We are looking at fertility and the impact of various employment classifications for women and their partners. We have around 2.2 million observations so if a variable is not significant we know it is NOT significant.
The May release came out earlier this week and was a real whopper. It was quite high and well outside the 95% confidence interval of the forecast from last month. We need another month, or months, of data to determine the impacts of the tax increase though. This number could be high in anticipation of the higher rate, or it could be pent up demand shifted to March due to bad weather in January or February. Easter also occurred in March this year and, as my forecasting class saw, that increased sales tax collections in Grand Forks in the past so it could be that situation again. It could also be related to tax cuts at the federal level though I am a bit skeptical that it would just start showing up in spending data for March. Like I said though we need to see where it is at over the next few months before determining the longer term trajectory. Here is the updated forecast.
I’m researching the impacts of age structure on retail sales and other measures of economic activity at subnational levels. In many cases I find states are too large an area to look at for meaningful insights. However, I hoped for more from a graph than this.
Same project but this time I am looking at the dependency ratio, so I thought a map showing the way this number is spread across the country was in order. It is something we talk about in the summer demography class so I figured at the least the students would find it a bit interesting.
As part of a bigger research project I am looking at county level retail sales and a variety of population and labor measures so I took a quick stab at mapping out how this looked. The map is for sales per establishment. If you do not normalize by something the map is useless, though what the proper normalization is part of the question. What is interesting is the relative degree of uniformity in sales per establishment. This is a consistency, not a perfect uniformity. It is really surprising where there are hot spots and where there is just more of the same. Gray shaded counties have data suppressed due to privacy concerns and white filled ones are not in the economic census for some reason.