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. 2022 Dec 11;24(2):563–587. doi: 10.1007/s10902-022-00609-z

Table 5.

Heterogeneity analysis

Dependent variable: Happiness (1–5) (1) (2) (3) (4)
Panel A. Full sample
Age*quake − 0.003** (0.001)
Middle school*quake 0.056 (0.036)
High school*quake 0.112** (0.044)
Pension*quake 0.062 (0.037)
Medical insurance*quake 0.294*** (0.041)
Baseline covariates Y Y Y Y
Year FE Y Y Y Y
Province FE Y Y Y Y
Adjusted R2 0.082 0.082 0.059 0.061
N1 3677 3677 2920 2965
N 32,205 32,205 27,152 27,525
Panel B. Rural subsample
Age*Quake − 0.001 (0.002)
Middle school*quake 0.054 (0.055)
High school*quake 0.006 (0.068)
Pension*quake 0.314*** (0.099)
Medical insurance*quake 0.624*** (0.065)
Baseline covariates Y Y Y Y
Year FE Y Y Y Y
Province FE Y Y Y Y
Adjusted R2 0.085 0.085 0.048 0.050
N1 1986 1986 1321 1350
N 17,047 17,047 12,891 13,102

The dependent variable is self-reported happiness (1–5). The quake indicator is interacted with age, education dummies, a pension dummy, and a medical insurance dummy in columns (1) to (4), respectively. Columns (3) and (4) further control for the pension and medical insurance dummies. All the columns include interactions between each pair of the variables among Sichuan dummy, a post-earthquake dummy, and the characteristics used for the heterogeneity analysis. N and N1 represent sample size and the number of observations from Sichuan province, respectively. Standard errors, clustered at a province level, are shown in parentheses. *** significance at the 1% level, ** significance at the 5% level. FE: Fixed effect