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Below results based on the criteria 'health policy'
Total number of records returned: 2
Potential Ambiguities in a Directed Dyad Approach to State Policy Emulation
In this paper I discuss circumstances under which the dyadic model of policy diffusion can produce misleading estimates in favor of policy emulation. These circumstances arise in the context of state pain management policy, and correspond generally to policies that states are uniformly expanding. When this happens, dyadic models of policy diffusion conflate policy emulation and policy adoption: since early adopters are policy leaders, later adopters will appear to emulate them, even if they are merely stragglers acting on their own accord. I demonstrate the possibility of this ambiguity analytically and through Monte Carlo simulation. Both start with the assumption that the data are generated according to a standard, monadic model of policy adoption and then converted to a dyadic model, which can incorrectly produce evidence of emulation. I propose a simple modification of the dyadic emulation model --- conditioning on the opportunity to emulate --- and show that it is much less likely to produce inaccurate findings. I then return to the study of pain management policy and find substantial differences between the dyadic emulation model and the conditional emulation model.
The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation
community intervention trials
A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals -- such as households, communities, firms, medical practices, schools, or classrooms -- even when the individual is the unit of interest. To recoup some of the resulting efficiency loss, many studies pair similar clusters and randomize treatment within pairs. Other studies (including almost all published political science field experiments) avoid pairing, in part because some prominent methodological articles claim to have identified serious problems with this 'matched-pair cluster-randomized' design. We prove that all such claims about problems with this design are unfounded. We then show that the estimator for matched-pair designs favored in the literature is appropriate only in situations where matching is not needed. To address this problem without modeling assumptions, we generalize Neyman's (1923) approach and propose a simple new estimator with much improved statistical properties. We also introduce methods to cope with individual-level noncompliance, which most existing approaches incorrectly assume away. We show that from the perspective of, among other things, bias, efficiency, or power, pairing should be used in cluster-randomized experiments whenever feasible; failing to do so is equivalent to discarding a considerable fraction of one's data. We develop these techniques in the context of a randomized evaluation we are conducting of the Mexican Universal Health Insurance Program.