Table 4:

Life Expectancy Decompositions

Cross-CZ standard deviation of:
(1) Age 65 Life Expectancy (Lj)	0.79 [0.76, 0.83]
(2) Treatment Effects $\left(L_j^{\ast }-\overline{L}\right)$	0.44 [0.32, 0.55]
(3) Health Capital Effects	0.73 [0.60, 0.83]
(4) Correlation of Treatment and Health Capital Effects	−0.04 [−0.15, 0.09]
Share variance would be reduced if:
(5) Place Effects were Made Equal	0.15 [−0.10, 0.46]
(6) Health Capital was Made Equal	0.69 [0.53, 0.83]

Notes: All objects are computed at the CZ level using the split-sample approach described in Section II.B and give equal weight to each CZ; 95% confidence intervals are computed via 100 replications of the Bayesian bootstrap. In row (2), we compute the standard deviation of life expectancy if health capital were held constant; specifically, for each CZ j, we compute the counterfactual age 65 life expectancy if each CZ had its own γ_j but the nationally representative health capital $\overline{\theta }$ as defined in the text. In row (3), we compute the standard deviation in life expectancy if the place effects were held constant; specifically, we define the nationally representative place effect as the median of γ_j among non-movers, and for each CZ j, compute the counterfactual age 65 life expectancy where the CZ has its own ${\overline{\theta }}_j$, but a nationally representative place effect. Row (4) reports the correlation between the health capital component of life expectancy (whose standard deviation is shown in row 3) and the place component of life expectancy (whose standard deviation is shown in row 2). This is computed by calculating the correlation between the treatment effects in one split-sample and the health capital effects in the other split-sample, and then averaging the resulting correlations from each pair. In row (5) we show the share of the variance that would be reduced if place effects were made equal; this is computed by calculating the variance of life expectancy with place effects held constant (i.e. the square of row 3) and the variance in life expectancy (i.e. the square of row 1), and taking 1 minus the ratio of these numbers. Row (6) is computed in an analogous fashion. Confidence intervals for rows (5) and (6) are computed by using this procedure within each bootstrap.