This study addresses limitations in pooled event history analysis (PEHA) by relaxing the assumption of policy homogeneity.

Context & Problem:

Standard PEHA assumes constant effects across policies, potentially masking important variations.

Methodology:

The authors employ Monte Carlo simulations to compare how different modeling strategies perform under varying levels of heterogeneity. ※ They systematically test these approaches with increasing variance in data.

Key Findings:※

Multilevel models with random coefficients emerge as the superior approach for handling policy differences, offering significantly better estimates than alternative methods.

This methodology provides a more nuanced understanding of how variables impact policies differently across contexts.

Practical Application:※

The paper demonstrates these techniques using a unique dataset tracking 29 anti-abortion policies over time. ※ Researchers can now better explore theoretical implications related to policy diffusion and design variation.