Not surprisingly, Democrats have taken a dim view of this approach, with the White House budget director, Shaun Donovan, being particularly vocal:
“Dynamic scoring can create a bias favoring tax cuts over investments in infrastructure, education, and other priorities.”
The main objection appears to be a political one, namely that dynamic scoring will make Republican priorities easier to pass at the expense of Democratic ones. To an extent this is true, since dynamic scoring as proposed by the House would only apply to tax changes and not to the effects of changes in infrastructure spending.
Still, the overall effect of this change is likely to be relatively small. Only a few bills each year would qualify for the 0.25% threshold, and those that do wouldn't see a dramatically different scoring. A recent example was the tax reform bill offered last year by Rep. Dave Camp (R., Mich.). With dynamic scoring, the plan was estimated to add only $5 billion to $70 billion per year to federal revenue.
The biggest problem seems to be the uncertainty in the estimates, which depend on various untestable assumptions. In particular, the computer models can't calculate an outcome without assuming that debt to GDP eventually reaches a constant percentage, and guesses about that percentage and how it will be achieved can give a broad range of outcomes, as observed in the Camp plan.
Ultimately, dynamic scoring looks like it could be the right way to go, but it would benefit from a couple of improvements. First, the uncertainty inherent in the estimates should be acknowledged by requiring that the full range of possible outcomes be included in the official scoring. Ideally, this would be presented as a best estimate and uncertainty, as is done in any scientific endeavor. Second, Congress should consider applying dynamic scoring to more than just tax legislation, for example infrastructure spending. At the very least, these improvements would help remove the appearance that dynamic scoring is primarily a tool to gain a political advantage.