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If I understand what you saying (apologies if not, it has been a while since I did modelling), that would only give a better fit if the level of segregation/cri

If I understand what you saying (apologies if not, it has been a while since I did modelling), that would only give a better fit if the level of segregation/crime was consistent between counties and not already taken into account by their other variables (like inequality). Or the paradox segments were at a county level instead of locally. If be happier if they just got the police records for all 721 cases, and did a more local analysis. Let me know if I'm missing something (I probably am).

apathy 10y ago
If you fit a multilevel mixed model, either the intercept or the slopes can vary, but the model fit at each level (and accuracy of predictions on held-out data) will improve substantially if the terms matter and you include them. In this case I would only believe the result if it led to across the board improvement. Contrarily, if a stratified fit's cross-county predictions were dramatically shittier than within county, I might believe it, but by pooling information in a Bayesian framework these authors appear to have aimed at a happy medium. The multilevel aspect is important (country -> county -> locale) because of the shrinkage you get as a side effect of propagating each estimate. If time permits I will look at their code and data (available in the supplement, the author appears to have used Rstan for the fit, although a Python person could probably get a similar result with PyMC or PyStan) and try fiddling some of the dials. The write up is a little wordy, but the model and data is all there for anyone genuinely interested in sensitivity analysis or alterntive fits. They're not hiding anything that I can see.