Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study. Replication Data Package.
Description
Background: Policymakers have attempted to mitigate the spread of covid-19 with national and local non-pharmaceutical interventions. Moreover, evidence suggests that some areas are more exposed than others to contagion risk due to heterogeneous local characteristics. We study whether Italy's regional policies, introduced on 4th November 2020, have effectively tackled the local infection risk arising from such heterogeneity. Methods: Italy consists of 20 regions, further divided into 107 provinces. We collect 35 province-specific pre-covid variables related to demographics, geography, economic activity, and work mobility. First, we test whether their within-region variation explains the covid-19 incidence during the Italian second wave. We use the LASSO algorithm to isolate variables with high explanatory power. Then, we test if their explanatory power disappears after the introduction of the regional-level policies. Findings: The within-region variation of seven pre-covid characteristics is statistically significant (F-test p-value $<0\cdotp001$) and explains a further 21\% of the province-level variation of covid-19 incidence, on top of region-specific factors, before regional policies were introduced. Its explanatory power declines to 7\% after the introduction of regional policies, but is still statistically significant (p-value $<0\cdotp001$), even in regions where stricter policies were applied (p-value $=0\cdotp067$). Interpretation: Even within the same region, Italy's provinces differ in exposure to covid-19 infection risk due to local characteristics. Regional policies did not eliminate these differences, but may have dampened them. Our evidence can be relevant for policymakers who need to design non-pharmaceutical interventions; it also provides a methodological suggestion for researchers who attempt to estimate their causal effects.