Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study. Replication Data Package.

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.

研究背景：政策制定者尝试通过国家及地方层面的非药物干预措施（non-pharmaceutical interventions）遏制新冠病毒（COVID-19）的传播。此外，现有证据表明，由于本地特征存在异质性，不同区域的新冠感染暴露风险存在差异。本研究旨在探究意大利于2020年11月4日推出的区域级政策，是否有效应对了由这类异质性引发的本地感染风险。
研究方法：意大利全国共设20个大区，进一步划分为107个省。我们收集了35项与人口统计、地理特征、经济活动及工作流动相关的新冠疫情前省级特异性变量。首先，我们检验这些变量的区域内差异是否能够解释意大利第二波疫情期间的新冠感染发病率。本研究采用LASSO算法筛选出具有高解释力的变量。随后，我们检验在区域级政策推出后，这些变量的解释力是否消失。
研究结果：在区域级政策实施前，7项新冠疫情前特征的区域内差异具有统计学显著性（F检验p值<0.001），且在区域特异性因素之外，额外解释了21%的省级新冠感染发病率差异。区域级政策实施后，该类变量的解释力降至7%，但仍具有统计学显著性（p值<0.001）；即便在实施了更严格政策的大区中，该结果依然成立（p值=0.067）。
研究启示：即便处于同一大区，意大利各省因本地特征差异，其新冠感染暴露风险仍存在显著区别。区域级政策并未消除这类差异，但可能起到了抑制作用。本研究结果可为需要制定非药物干预措施的政策制定者提供参考，同时也为尝试评估政策因果效应的研究者提供了方法论建议。