Replacement was another idea we talked about in the population analysis class this week. Essentially it is an estimate of the deaths and migratory outflows from the area over a specified period of time and is called replacement because it is what you would need to replace in order for population to remain the same. Not really terribly complicated, but it is a nice complement to the idea of turnover and I generated both the level and the rate for counties in North Dakota.
I am teaching population analysis to the undergraduates this year which is a first for me and them. We talked about the simple calculation of growth rates and how population change is actually more complicated and subtle than an overall population growth rate. This got us to the idea of turnover, that is just measuring the births, deaths, inflows, and outflows that go into the changing population. Basically just seeing how much is changing compared to the overall population level. So I thought I would have some fun and apply this to North Dakota. This could be especially important given that some North Dakota counties experienced high degrees of population change due to economic circumstances.
With all the discussion surrounding migration these last many months I thought it time to revisit the issue with a specific look at North Dakota. There are many ways to evaluate this issue and I will not go through them all here in one post. These are things that need to be evaluated independently. The issue in this post is the thought that North Dakota has a problem attracting in people. As this first map reveals, that is not really the case.
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.
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.