Estimated treatment effects and their 95% CIs. Each estimate was obtained from separate difference-in-differences regressions of a specific outcome, estimated separately for men (blue) and women (red). Treatment effects are interpreted as outcome changes in Melbourne between 2019 and 2020 relative to outcome changes in Sydney between 2019 and 2020, holding constant location fixed effects, location-specific time trends (wave year indicators interacting with Melbourne indicators), individual-specific effects and time-varying observable confounders. Each regression applies sample weights to make the sample representative of the population ( Methods , equation ( 1 )). Human impact is measured across four domains of human life: (1) health (mental health, n = 24,357 for male and 27,854 for female), general health ( n = 24,192 for male and 27,622 for female) and bodily pain ( n = 24,336 for male and 27,825 for female); (2) health behaviours (BMI, n = 23,622 for male and 26,566 for female), frequency of alcohol consumption ( n = 21,731 for male and 23,322 for female) and frequency of physical activity ( n = 24,355 for male and 27,848 for female); (3) social connectedness (feeling safe, n = 27,939 for male and 31,246 for female), feeling lonely ( n = 24,237 for male and 27,699 for female) and feeling part of the local community ( n = 27,907 for male and 31,187 for female); (4) labour supply and income (weekly working hours ( n = 27,964 for male and 31,272 for female), weekly income from all sources ( n = 27,964 for male and 31,272 for female) and weekly government transfers excluding family benefits ( n = 27,964 for male and 31,272 for female) (see Supplementary Table 1 for definitions). Each outcome is standardized to mean = 0 and s.d. = 1. Standard errors are clustered at the household level to account for the fact that all household members (aged 15 or older) were interviewed and that full households were mobility-restricted in the same location. Supplementary Tables 4 and 5 present the full model results.
