Moody and Marvell (MM) have now replied to our comment (Ayres and Donohue 2009) on their initial 2008 publication, “The Debate on Shall-Issue Laws.” MM begin their latest effort—“The Debate on Shall-Issue Laws, Continued”—by declaring that they “are not advocates” of laws granting citizens the right to carry concealed handguns (RTC laws). In support of this claim, MM note that they have published papers finding that RTC laws both reduce crime (two papers) and do not reduce crime (three papers). Given the fact that neither MM (2008) nor their latest comment tries to reconcile the internal conflict in their own writings, let alone the larger literature, it is puzzling that a tweak on our 2003 paper would lead them to conclude their 2008 paper by stating, “In our judgment, the weight of the evidence … indicates that shall issue laws reduce crime.” To the extent that they have now backed away from that claim, progress will have been made.

Nonetheless, MM claimed to find support for the crime-reduction hypothesis by extrapolating some trends estimated in Ayres and Donohue (2003). Indeed, MM criticized us for only evaluating the trend for five years, noting that 10 of the 24 states on which we generated our trend estimates contained data for at least six years. We would have thought, though, that one would want to be very cautious in extending trends beyond five years when 14 of the 24 states have no post-passage data beyond three years. Indeed, trying to draw inferences about what is happening to crime in 14 states five years after passage when one only has data for three years is the essence of extrapolating beyond the range of one’s data, which is always a perilous exercise. We are thus puzzled by their latest assertion that perhaps we “did not believe the implications of [our] model, since [it] contained trend variables that continue indefinitely” (Moody and Marvell 2009). Of course, every linear model may be thought to continue indefinitely, but we certainly wouldn’t encourage extrapolation beyond the range of the data over which the linear trend is estimated.