Paul Ryan is pressing hard for tax changes to be permanent rather than temporary (see a representative article here). From a traditional economic perspective he is probably right to do so if he wants policy to have maximum impact on the economy, regardless of your preferred performance metric. There exists no shortage of empirical research on this topic and I include a link here to a research note that seems typical (and more importantly is not paywalled).
I think it hardly needs mentioning again, but I guess I will: the legislative process in North Dakota probably makes it even more important that we have some confidence in our revenue forecasts. Our legislators are meeting for three months to determine budgets for the next two years. There is always the possibility of a special session if need arises, but you want that to be the truly exceptional case. Now I am not suggesting that anyone will ever get the numbers spot on, 100% accurate, but we can get closer.
The state released an updated forecast from Moody’s today along with some slides that make little sense (found here). I would go into the details of the forecast but why bother? We have absolutely no insight into the forecast process followed, the assumptions underlying any model relationships, or even a list of variables employed and the time period considered. Seriously, if this were my forecasting class, they would fail.
For the last few days I focused on state level tax data. The release of a new forecast and the general state of the forecast process promises many more posts to come on this topic. I thought for today I would turn toward a more local number. Grand Forks had its largest month for sales tax collections in its recorded history. What does this look like?
Continue reading Some local flavor, Grand Forks Sales Tax Data
I continue to think about the tax situation in North Dakota right now, particularly trying to understand what the data are trying to tell us. Obviously I want to avoid a situation of torturing the data until they confess, but that should not stop us from slicing and dicing the data to find something meaningful.