Data-based analysis can contribute in meaningful ways to Utah’s political dialogue.
Last Friday, March 4, the Senate passed HB 497, Rep. Stephen Sandstrom’s illegal immigration enforcement bill, by a vote of 22-5 with 2 absent. The bill was almost identical to HB 70, the “Arizona-style” bill, but the Senate required Sandstrom to renumber the bill in hopes that it would shed its “Arizona” stigma.
HB 70 had passed the Utah House two weeks earlier. When it did, I wrote a post here predicting the vote on HB 70 (now HB 497) in the Senate. After posting those predictions, I heard from a few people that I was wrong, and that Senator X or Senator Y would not be voting as I predicted. (Actually, I was told by various people that several of my predictions were wrong.)
But it turns out that my predictions were almost perfect. I don’t bring this up to boast–only to demonstrate that data-based analysis can contribute in meaningful ways to Utah’s political dialogue. Of course, if you didn’t believe that already, you wouldn’t be at this site.
The table below shows my prediction for each Senator and then each Senator’s actual vote. I did not make a prediction for the two freshmen, Senators Reid and Thatcher, since I had no data to base it on. I predicted that HB 70 (now HB 497) would divide the Democrats, with Senators Jones and Morgan on the fence. Sure enough, one of them voted “yes” (Jones) and one voted “no” (Morgan). My only incorrect prediction was for Senator Mayne, who supported the bill. To be fair, though, I also stated in my previous post that I was less certain about my prediction for her than for any other Senate Democrat.
|Senator||My prediction on Feb 22||Actual vote on March 4|
|Jones||On the fence||No|
|Morgan||On the fence||Yes|