Policy Emulation or Policy Convergence? Potential Ambiguities in the Dyadic Event History Approach to State Policy Emulation

I demonstrate a source of bias in the common implementation of the dyadic event history model as applied to policy diffusion. This bias tends to severely overstate the extent to which policy changes depend on explicit emulation of other states rather than on a state's internal characteristics. This happens because the standard implementation conflates policy emulation and policy adoption: since early adopters are policy leaders, later adopters will appear to emulate them, even if they are acting independently. I demonstrate this ambiguity analytically and through Monte Carlo simulation. I then propose a simple modification of the dyadic emulation model that conditions on the opportunity to emulate and show that it produces much more accurate findings. An examination of state pain management policy illustrates the inferential differences that arise from the appropriately modified dyadic event history model.