Table 3:

Mortality Components

	Standard Deviation
Mortality Index $\left({\gamma }_j+{\overline{\theta }}_j\right)$	0.099 [0.095, 0.103]
Unadjusted:
Place Effects (γj)	0.077 [0.067, 0.087]
Health Capital $\left({\overline{\theta }}_j\right)$	0.073 [0.057, 0.085]
Correlation of γj and ${\overline{\theta }}_j$	−0.139 [−0.322, 0.281]
Selection Corrected:
Place Effects (γj)	0.054 [0.040, 0.069]
Health Capital $\left({\overline{\theta }}_j\right)$	0.088 [0.071, 0.099]
Correlation of γj and ${\overline{\theta }}_j$	−0.093 [−0.322, 0.413]

Notes: These standard deviations across CZ give equal weight to each CZ and standard deviations for the mortality index, place effects, and health capital use the split-sample approach. 95% confidence intervals are computed using 100 replications of the Bayesian bootstrap. For the “unadjusted” results in the top panel, γ_j is defined as the destination fixed effects ${\widehat{\tau }}_j^{\mathit{\text{dest}}}$ from equation (3), and average health capital ${\overline{\theta }}_j$ is given by the average value of the remaining terms in that equation (excluding the age term a_iβ) within each bootstrap and split-sample. For the “selection corrected” results in the bottom panel, γ_j is defined as the difference ${\widehat{\tau }}_j^{\mathit{\text{dest}}}-{\widehat{\eta }}_j^{\mathit{\text{dest}}}$, where ${\widehat{\tau }}_j^{\mathit{\text{dest}}}$ is the destination fixed effect from equation (3) and the unobservable component ${\widehat{\eta }}_j^{\mathit{\text{dest}}}$ is inferred following the steps broken out in Table 2; average health capital ${\overline{\theta }}_j$ is then calculated using the same approach as in the unadjusted results. Within each bootstrap, the correlation of γ_j and ${\overline{\theta }}_j$ are calculated as $\left[\mathit{\text{Var}}\left({\gamma }_j+{\overline{\theta }}_j\right)-\mathit{\text{Var}}\left({\gamma }_j\right)-\mathit{\text{Var}}\left({\overline{\theta }}_j\right)\right]/\left[2\mathit{\text{StDev}}\left({\gamma }_j\right)\mathit{\text{StDev}}\left({\overline{\theta }}_j\right)\right]$, with each variance and standard deviation calculated using the split-sample approach.