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This paper investigates the economic impact of the three tiers risk framework implemented in Italy against the COVID-19 pandemic during the Autumn of 2020. Exploiting a large-scale dataset encompassing daily credit card transactions mediated by a large Italian bank, we estimate a set of panel event study models to disentangle the impact of restrictions with low, medium and high stringency levels in terms of consumption reduction. We show that space-time differentiated policies tend to produce stronger welfare losses for progressively more stringent restrictions in specific sectors targeted by these policies such as Retail and Restaurants. However, when we compare provinces implementing the same level of policy stringency, we show that territories with higher income per capita and larger concentration of manufacturing and service activities experience both significantly worse economic and epidemiological performances. Overall, our results suggest that policy makers should properly account for local socio-economic characteristics when designing tailored restrictions entailing an equal and homogeneous impact across territories.

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.

This paper aims to understand whether and to what extent firm-level data can “precisely” support the definition of the Local Content (LC) targets for international oil and gas companies in resource-rich developing countries. The paper addresses this topic with reference to a specific, publicly available dataset developed by the World Bank Enterprises Survey (ES) Unit and uses the case of Mozambique to demonstrate the informative potential of firm-level data for policy-making. Specifically, we compare the LC estimates obtained using firm-level data with those obtained using sector-level data (input-output tables). Based on this comparison, we demonstrate that firm-level data provide a more accurate representation of the capability of the domestic supply chain to provide inputs to the oil and gas industry, while input-output tables lead to a significant overestimation of LC. Overall, our findings suggest that firm-level data can provide a more precise basis for supporting the design of LC policies and the Enterprise Survey for Mozambique could represent a reference model for data collection to be extended to other resource-rich developing countries to support target definition.

During the on-going COVID-19 pandemic, online social media have been extensively used by policy makers and health authorities to quickly disseminate useful information and respond to public concerns in a timely fashion. Notwithstanding the huge amount of literature on analyzing positive and negative emotions conveyed by social media users, researchers have not widely investigated the main determinants of online sentiment during crises. To fill this gap, in this paper we analyse a large-scale dataset of over 1.7 M tweets in order to understand whether online feelings, expressed by Italian individuals on Twitter during the pandemic, have been affected by socio-economic and epidemiological variables. Leveraging both panel models and cross-section regressions at different geographical levels, we find that more pessimistic feelings are communicated by users located in areas where the virus hit more severely, with a higher mortality rate and a larger fraction of infected individuals with respect to the local population. Finally, we show that administrative units exhibiting the most positive emotions are those characterized by lower income per capita and larger socio-economic deprivation, suggesting that sentiments in online conversations could be driven by epidemiological factors and by the fear of economic backlashes in wealthier areas of Italy.